WO2022172198A1 - Deep generative models for downlink channel estimation in fdd massive mimo systems - Google Patents

Deep generative models for downlink channel estimation in fdd massive mimo systems Download PDF

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WO2022172198A1
WO2022172198A1 PCT/IB2022/051213 IB2022051213W WO2022172198A1 WO 2022172198 A1 WO2022172198 A1 WO 2022172198A1 IB 2022051213 W IB2022051213 W IB 2022051213W WO 2022172198 A1 WO2022172198 A1 WO 2022172198A1
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parameters
channel
estimated
network
estimating
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WO2022172198A9 (en
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Javad MIRZAEI
Shahram SHAHBAZPANAHI
Raviraj ADVE
Amr El-Keyi
Hatem ABOU-ZEID
Navaneetha GOPAL
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Telefonaktiebolaget Lm Ericsson (Publ)
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0254—Channel estimation channel estimation algorithms using neural network algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0204—Channel estimation of multiple channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • H04L25/0226—Channel estimation using sounding signals sounding signals per se

Definitions

  • the present disclosure is related to channel estimation in wireless communication systems and is more particularly related to techniques for estimating channel conditions for the downlink in a frequency-division duplexing (FDD) wireless communications utilizing many downlink antenna elements.
  • FDD frequency-division duplexing
  • Figure 1 illustrates a simplified wireless communication system, with a user equipment (UE) 102 that communicates with one or multiple access nodes 103, 104, which in turn are connected to a network node 106.
  • the access nodes 103, 104 tire part of the radio access network (RAN) 100.
  • the network node 106 may be, for example, part of a core network.
  • the access nodes 103, 104 correspond typically to base stations referred to in 3GPP specifications as Evolved NodeBs (eNBs), while the network node 106 corresponds typically to either a Mobility Management Entity (MME) and/or a Serving Gateway (SGW).
  • MME Mobility Management Entity
  • SGW Serving Gateway
  • the eNB is part of the RAN 100, which in this case is the E-UTRAN (Evolved Universal Terrestrial Radio Access Network), while the MME and SGW are both part of the EPC (Evolved Packet Core network).
  • the eNBs are inter-connected via the X2 interface, and connected to EPC via the SI interface, more specifically via SI -C to the MME and Sl-U to the SGW.
  • the access nodes 103, 104 correspond typically to base stations referred to as 5G NodeBs, or gNBs, while the network node 106 corresponds typically to either an Access and Mobility Management Function (AMF) and/or a User Plane Function (UPF).
  • AMF Access and Mobility Management Function
  • UPF User Plane Function
  • the gNB is part of the RAN 100, which in this case is the NG-RAN (Next Generation Radio Access Network), while the AMF and UPF are both part of the 5G Core Network (5GC).
  • the gNBs are inter-connected via the Xu interface, and connected to 5GC via the NG interface, more specifically via NG-C to the AMF and NG-U to the UPF.
  • Massive multiple-input multiple-output is a key technology in helping to meet the demands of next generation wireless technologies.
  • This technology can be deployed in time- division duplex (TDD) mode, where the uplink and downlink occur in the same frequency band but at different time slots, as well as in frequency-division duplex (FDD) mode, where the uplink and downlink operate simultaneously on different frequency bands.
  • TDD time- division duplex
  • FDD frequency-division duplex
  • CSI channel state information between the base station (BS) and the user equipment (UE) is required, so that the scheduling BS can take the fullest advantage of opportunities for spatial multiplexing.
  • CSI acquisition can rely on the assumption of channel reciprocity between the uplink and downlink. Even in such systems, however, due to calibration errors between the uplink and downlink radio frequency (RF) chains, such channel reciprocity may not hold.
  • RF radio frequency
  • the downlink channel is not the same as the uplink channel, nor can it be inferred from the uplink channel without any downlink training.
  • the UE In FDD systems, the UE traditionally estimates its own downlink channel from received pilot symbols, or reference symbols, transmitted from the BS. For the BS to use this information in scheduling and beamforming downlink transmissions to the UE, the UE must signal this estimated CSI to the BS. Transmitting the downlink CSI estimated by the UE from the UE to the base station is feasible when only a few antennas are used at the BS. In this case, orthogonal pilots may be provided for each of the relatively small number of antennas, and the transmitting of the estimated CSI to the BS incurs only a small feedback overhead.
  • the UE may feed a quantized, and possibly wideband, version of the estimated downlink channel information back to the BS to be used for subsequent signal transmission and resource allocation.
  • This wideband quantized CSI information may be utilized by the BS in downlink transmission precoding and link adaptation.
  • this wideband quantized CSI information necessarily lacks frequency- dependent detail, the resulting downlink throughput is inferior to that achieved in TDD systems, where detailed downlink CSI information can be acquired at the BS through channel reciprocity.
  • the channel matrix over each subcarrier is a function of a smaller set of parameters, namely, tire number of propagation paths, the path gains, phases, and delays, as well as the angie-of-arrival (AoA) and angle-of-departure (AoD) for the signals transmitted between the base station and a user equipment (UE).
  • these parameters are estimated, instead of a channel matrix.
  • these underlying channel parameters may be estimated using a short training signal, over a much smaller set of antennas and subcarriers.
  • various embodiments of the techniques described in detail below utilize the following steps to estimate fire downlink channel: 1) estimating the frequency-independent underlying channel parameters, namely, the magnitudes of path gains, delays, AoAs and AoDs during the uplink training; and 2) estimating the frequency-specific underlying channel parameters, i.e., the phase of each propagation path, via downlink training.
  • the burden in FDD downlink channel estimation is shifted to the BS, which is already responsible for uplink channel estimation.
  • the least squares (LS) estimation approach may be used to estimate those parameters that are frequency- independent, or at least very nearly so.
  • the optimization problem in this step is difficult to solve analytically, mainly due to non-linear and non-convex structure of its objective function.
  • DGMs deep generative models
  • the parameters estimated in the first step may then be used to estimate the remaining frequency-specific parameters, via an LS technique.
  • An example method for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element comprises the steps of estimating a first set of one or more parameters for the channel, based on training symbols transmitted from tire second device to tire first device, and estimating a second set of one or more parameters for tire channel, based on training symbols or reference signals transmitted from the first device to the second device and based on the first set of one or more parameters.
  • the first set of one or more parameters may include (but are not limited to) a path gain magnitude corresponding to each radio channel path between the second device and the first device, a delay corresponding to each radio channel path between the second device and the first device, and an angle of arrival for each radio channel path between the second device and the first device, estimating the first set of one or more parameters for the channel comprises estimating a distribution function for the first set of one or more parameters using a deep generative model and observations of the training symbols transmitted from the second device to the first device, as received at the first device, and using a least-squares estimation approach to jointly estimate, based on the estimated distribution function and the observations of the training symbols, the first set of one or more parameters and a phase change parameter for each radio channel path between the second device find the first device, in the direction toward the first device from the estimated distribution.
  • the second set of one or more parameters may include a phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device.
  • Estimating the second set of one or more parameters may comprise using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on observations of training symbols transmitted from the first device to the second device, as received at the first device.
  • An example apparatus for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element comprises a processing circuit configured to estimate a first set of one or more parameters for the channel, based on training symbols transmited from the second device to the first device, and estimate a second set of one or more parameters for the channel, based on training symbols or reference signals transmited from the first device to the second device and based on the first set of one or more parameters.
  • Various embodiments of these apparatuses may be configured to carry out any of the several variations described below.
  • the solutions described herein rely on the fact that the channel matrix over each subcarrier is a function of a smaller set of parameters, namely, the number of propagation paths, the path gains, phases, delays, as well as AoA and AoD. These are the parameters that are estimated, instead of the channel matrix directly. These parameters depend on the physical properties of the propagation environment and on the operating frequencies and, importantly, they are independent of the number of antennas at the BS as well as the number of subcarriers. This allows the channel parameters to be estimated with a much lower pilot overhead.
  • the described solutions exploit the partial reciprocity between uplink and downlink channels, meaning that the frequency-independent downlink channel parameters are first estimated in the uplink.
  • the frequency-specific underlying channel parameters i.e., the phases of the individual propagation paths, are estimated via downlink training. This strategy ensures that the burden in FDD downlink channel estimation is shifted from the UE to the BS, which is, in any case, responsible for uplink channel estimation.
  • the techniques described herein need not assume any sparsity in the underlying channel parameters, instead, this constraint is relaxed, so that it can be considered that the channel parameters have a particular structure that is not necessarily sparse. This structure depends on the physical properties of the environment that the signal is propagating in to.
  • a DGM is used to capture this structure and then incorporate it as a prior into the channel estimation process to improve the accuracy and reduce the pilot overhead.
  • the immediate benefit of the proposed DGM-based channel estimation is that the channel estimation becomes much simpler, where even the least squares technique can provide a significant performance, compared to conventional benchmarks.
  • incorporating the underlying distribution of channel parameters provides resilience to the noise level, i.e., a significant performance improvement is achieved even at very low SNR.
  • Figure 1 is a simplified illustration of a wireless communication system.
  • Figure 2 is a block diagram illustrating an example downlink channel estimation algorithm.
  • Figure 3 illustrates a generative adversarial network (GAN) structure.
  • Figure 4 illustrates the Reg-GAN training procedure.
  • Figure 5, Figure 6, and Figure 7 illustrate performance results for a simulation of an implementation of the techniques described herein.
  • Figure 8 is a flow diagram illustrating an exemplary method according to the techniques described herein.
  • Figure 9 is a block diagram illustrating an example network node.
  • Figure 10 is a block diagram illustrating an example UE.
  • Figure 11 is a block diagram of an exemplary wireless network configurable according to various exemplary embodiments of the present disclosure.
  • FIG 12 is a block diagram of an exemplary user equipment (UE) configurable according to various exemplary embodiments of the present disclosure.
  • UE user equipment
  • Figure 13 is a block diagram of illustrating a virtualization environment that can facilitate virtualization of various functions implemented according to various exemplary embodiments of die present disclosure.
  • Figures 14-15 are block diagrams of exemplary communication systems configurable according to various exemplary embodiments of the present disclosure.
  • Figures 16, 17, 18, and 19 are flow diagrams illustrating various exemplary methods and/or procedures implemented in a communication system, according to various exemplary embodiments of the present disclosure.
  • references to the “downlink” refer to transmissions from the base station to die UE
  • references to the “uplink” refer to transmissions from the UE to the base station.
  • the description below also describes these techniques in the context of massive-MIMO, where the base station employs a relatively large number (perhaps tens or even hundreds) of antenna elements for transmitting to and receiving from the UEs it serves. In such systems, the UEs may have only one or a relatively small number of antennas.
  • the present techniques are not limited to estimating downlink channels in FDD systems, nor are they limited to use in massive MIMO systems. Accordingly, these techniques should be understood as more generally applicable to estimating a channel between first and second devices, where at least one of the devices (and possibly both) uses multiple (and perhaps many ) antenna elements for transmitting to and receiving from the other.
  • CS compressive sensing
  • CS-based techniques require strong channel sparsity in a Discrete Fourier Transform (DFT)-basis, which is not strictly held in some cases.
  • DFT Discrete Fourier Transform
  • CS-based techniques also require a large number of pilots, and these techniques tire often iterative and computationally intensive during decoding, which may lead to long delays.
  • some proposed techniques for downlink CS1 estimation rely on spatial reciprocity between uplink and downlink channels operating on close- by carrier frequencies. Given that the uplink and downlink communication occur in the same propagation environment, the uplink channel estimates can be used in the estimation of downlink channel.
  • Uplink/downlink reciprocity can be considered in two ways. In one way, one can assume full reciprocity, where the multi-path components of the channel (including phase, amplitude, delay, angle of arrival, departure, etc.) are the same for both uplink and downlink. Based on this assumption, it has been proposed to eliminate downlink training and feedback in LTE systems, e.g., in D. Vasisht, S. Kumar, H. Rahul and D. Katabi, "Eliminating Channel Feedback in Next- Generation Cellular Networks," in Proc. ACM SIGCOMM Conf., 2016.
  • the duplex gap between uplink and downlink transmission bands is large.
  • the uplink band is 1710-1755 MHz while the downlink band is 21 J O- 2155 MHz, which means that the duplex gap is 400 MHz. This is much too large to assume full reciprocity in the channel.
  • the techniques described herein utilize the fact that the channel matrix at a given frequency, e.g., at a given subcarrier of a signal transmitted using multicarrier modulation techniques like Orthogonal Frequency Division Multiplexing (OFDM) or Discrete Fourier Transform-spread OFDM (DFT-OFDM), is a function of a smaller set of parameters, namely, the number of propagation paths by which the radio signal travels from one device to the other, the gains, phases, and delays for these paths, as well as the AoA and AoD for each path. According to the presently disclosed techniques, these parameters are estimated, rather than directly estimating the channel matrix. Unlike conventional techniques, where a long training sequence is transmitted over all antennas and over all subcarriers, the techniques described here estimate these underlying channel parameters using a short training signal, over a much smaller set of antennas and subcarriers.
  • OFDM Orthogonal Frequency Division Multiplexing
  • DFT-OFDM Discrete Fourier Transform-spread
  • various embodiments of the techniques described in detail herein utilize the following steps to estimate the downlink channel: 1) estimating the frequency-independent underlying channel parameters, namely, the magnitudes of path gains, delays, AoAs and AoDs during the uplink training; and 2) estimating the frequency -specific underlying channel parameters, i.e., the phase of each propagation path, via downlink training.
  • the burden in FDD downlink channel estimation is shifted to the BS, which is already responsible for uplink channel estimation.
  • the least squares (LS) estimation approach may be used to estimate the frequency-independent parameters.
  • the optimization problem in this step is difficult to solve analytically, mainly due to non-linear and non-convex structure of its objective function.
  • DGMs deep generative models
  • the frequency-independent parameters estimated in the first step may then be used to estimate the frequency-specific parameters, via an LS technique.
  • the optimization problem may be carried out numerically using the gradient descent algorithm.
  • the techniques described herein exploit the fact that the channel matrix over each subcarrier is a function of a smaller set of parameters, namely, the number of propagation paths, the path gains, phases, and delays, as well as the AoA and AoD, find estimate these parameters instead of estimating the channel matrix directly.
  • These parameters depend on the physical properties of the propagation environment and on the operating frequencies, and, importantly, they are independent of the number of antennas at the BS as well as the number of subcarriers.
  • these underlying channel parameters may be estimated using a short training signal over a much smaller set of antennas and subcarriers.
  • the techniques described herein use a DGM framework that can learn the underlying distribution of the channel parameters as a function of the propagation environment, which is complex and difficult to obtain analytically.
  • the generative adversarial network (GAN) structure may be utilized to find a deterministic mapping function (i.e., a generator) that is capable of drawing samples from tire underlying distribution of channel parameters, by feeding it with samples from a low- dimensional standard Gaussian distribution.
  • GAN deterministic mapping function
  • the techniques described herein reduce the pilot and feedback overhead for downlink channel estimation.
  • the sparsity assumption in channel parameters is relaxed.
  • the learned structure of channel parameters is incorporated as a prior into the channel estimation procedure. Consequently, the optimization problem operates in a low-dimensional subspace, with a dimensionality defined by the generator and, importantly, is independent of the number of received pilots. Therefore, a significant reduction in computational complexity as well as CSI feedback overhead may be achieved.
  • SNR signal-to- noise ratio
  • a single-cell single-user communication system is considered.
  • the base station (BS) in this model system is equipped with a uniform linear array (ULA) with M » 1 antenna elements.
  • ULA uniform linear array
  • M » 1 antenna elements For the sake of simplicity of presentation, it is assumed that the UE is single antenna.
  • the proposed algorithm can be directly extended to the case where the UE is equipped with multiple antennas, either by assuming a common set of propagation parameters, i.e., path gains, delays, and AoA and AoD, for all the channels to different UE antennas, or by assuming a distinct set of propagation parameters for each UE antenna, or a combination thereof.
  • the communication between the BS and UE is performed in FDD mode.
  • the UE communicates with the BS at frequency ⁇ up
  • the BS communicates with the UE at frequency ⁇ dl .
  • Both uplink and downlink frequency bands are of bandwidth B.
  • orthogonal frequency division duplex (OFDM) technology is used in both uplink and downlink commutation with K subcarriers.
  • 7i u denote the set of subcarrier indices used for uplink training.
  • the UE transmit training symbol s k, k ⁇ K U , where
  • s fe j 2 P T , and P T is the transmit power.
  • the received signal at BS over the k-th subcarrier is given by: where tif is an M X 1 uplink channel vector between the BS and the UE and is an M X 1 noise vector at the k-tb subcarrier that is drawn independently and identically from a complex Gaussian distribution with zero mean and variance ,
  • the received signal at the UE over the k-th subcarrier at the f-th time slot is given by and the symbols transmitted at the i-th time slot over the k-th subcarrier across all antenna elements in the set M d . It is assumed that denotes the noise term at the i-th time slot over the k-th subcarrier, drawn independently and identically from a complex Gaussian distribution with zero mean and variance s 2 . Note that, in general, £ M. For the case when !.M d j ⁇ M, it is assumed that the antenna elements in the set M d do not transmit during the downlink training, where M is the compliment of the set M d .
  • the received signal can be written as: where Sf is a p x ⁇ M d j matrix of downlink training symbols with on its i-th row,
  • the following geometric channel model is considered. It is assumed that the propagation channel between the BS and the UE in the uplink consists of paths. Through the 1-th path, the signal travels the distance d ⁇ between the UE and the BS. Also, let [0,2 ⁇ ], and denote the random path gain, the random phase change, the random azimuth angle of the signal received, and the random delay corresponding to the i-th path in the uplink, respectively.
  • the channel response between the UE and the BS at the k-th subcarrier can be given by: where is the wavelength of the K-th subcarrier in the uplink, and c is the speed of light . Since kB/K is very small compared to /f p , the subcarrier index in the array response a( ⁇ ⁇ , l) can he ignored.
  • the downlink communication channel at the fcth subcarrier is given by: where L dl is number of path in the downlink, is the wavelength of the downlink carrier frequency, respectively denote the random path gain, the random phase change, the random azimuth angle of the received signal and the random delay corresponding to the ith path in the downlink.
  • b (0 j , ⁇ ) is a subvector of a( ⁇ ⁇ , ⁇ ) where its ith entry with M d being the ith smallest member of set M d .
  • uplink and downlink channel parameters are frequency independent or at least largely so. Specifically, since the signal of each propagation path travels the same distance at the same speed in both uplink and downlink communication link, the delay of each propagation path is the same in both uplink and downlink, i.e., Furthermore, it is shown via both measurement and ray tracing simulations, that the directional and the power gain of each communication path are the same in both uplink and downlink, i.e., ⁇ ; and On the other hand, the existing measurements show that This implies that the channel translation requires downlink training.
  • FIG. 1 shows a block diagram of an example downlink channel estimation algorithm using the techniques described herein. The algorithm utilizes received uplink pilots to perform uplink training to estimate the common uplink/downlink parameters q as well as the uplink-only parameters l u .
  • the terms “pilots,” “training symbols,” “training signals,” “reference signals,” and “reference symbols” are generally used interchangeably, to refer to symbols of a transmitted signal having values that are known to the receiver.
  • the terms “training symbols” or “training signals” may suggest a series of such symbols transmitted expressly for the purpose of training an algorithm used for channel estimation purposes, but reference symbols transmitted for other purposes may be repurposed as training symbols/signals.
  • the common parameters from the estimated uplink parameters are chosen and utilized in estimating the downlink parameters ⁇ d , along with information obtained from downlink pilots.
  • the downlink channel is e mated, or reconstructed, by plugging the estimated common and downlink parameters in the above expression for .
  • Uplink Training the aim is to estimate q up using the uplink training. To do so, observations of the uplink pilots across all subcarriers are stacked well as n up are defined. The co llected received signal in the uplink can then be expresse d as: where A(x up ) is a non-lin ear function of x up .
  • A(x up ) is a non-lin ear function of x up .
  • mapping G(- ) is needed. How this mapping can be determined using the GAN architecture is described later, below.
  • ⁇ d frequency- independent channel parameters (i.e., (a, r, 8)) as estimated during the uplink training are used to estimate ⁇ d , based on downlink training but using fewer training symbols in downlink.
  • ⁇ d the LS technique is used, similar to what was described above for the uplink training. Given is defined, as are Now, stacking the observations across all subcarriers in the set , and defining in the ownlink can be given by: Defining the following LS problem may be solved: e above optimization problem, the constraint is ignored, because the object periodic function of for integer ⁇ .
  • the above optimization problem is an unconstrained least-squares roblem and can be solved using the gradient descent algorithm.
  • quantized downlink CSI feedback from he UE can be used to estimate ⁇ d .
  • the UE can periodically ransmit CSI to the BS, with that CSI including a channel quality indicator (CQI), rank indicator RI), and precoding matrix indicator (PMI).
  • CQI channel quality indicator
  • RI rank indicator
  • PMI precoding matrix indicator
  • DGMs are briefly ntroduced.
  • the core idea of DGMs is to represent a high-dimensional and complex distribution of ata q (in this case (a, ⁇ , ⁇ ) using a deterministic mapping over a low-dimensional random ector ⁇ which has a well-behaved probability density function (e.g., uniform or Gaussian).
  • a DGM is a function ⁇ " ⁇ that maps a low-dimensional random vector z ⁇ Rd ypically drawn independently from a Gaussian or uniform distribution, to a high-dimensional vector where n ⁇ 8 (in practice, due to the structure of q, % can be much greater than d).
  • the mapping G( ⁇ ) is determined such that the distribution of x g , generated by G( ⁇ ), matches the distribution of the real-world data vector x. in other words, using the transformation G(-) from a simple and low-dimensional distribution, samples that belong to the same manifold as x does are generated. This implies that any generated sample from G(-) already satisfies the constraints in the original downlink training optimization problem.
  • the function G ( ⁇ ) is parameterized by a deep neural network, which is trained in an unsupervised way as explained below.
  • One well-known example of DGM is the family of GANs. The GAN architecture may he used to find G( ⁇ ), as described below.
  • GANs are among the most powerful DGMs that are used to capture the distribution of data.
  • the basic structure of a GAN is illustrated in Figure 3.
  • a GAN consists of two fully-connected feed-forward neural networks, namely a generator network a discriminator network D Wd (x): R n ⁇ [0,1] where W g and W d represent, respecti vely, the sets of weights of the generator and discriminator networks.
  • the generator network G w (z) maps the input random vector z ⁇ P z (z), into the data space x g ⁇
  • P g (x) P z (z)
  • P z (z) N(0,I a )
  • P a (x) represents the pdf of the generated samples.
  • the discriminator network, D w ( ⁇ ) receives the two sets of inputs: one set consists of the samples x g generated by Gw a i z ) an d the other set consists of the true samples x.
  • the discriminator network D wd ( ⁇ ) is meant to correctly distinguish between the fake samples x q and the true samples x. Effectively, the goal of the generator network G wg ( ⁇ ) is to generate fake samples, such that the discriminator network D Wd (x) cannot distinguish them from the true samples.
  • the goal of the discriminator network ⁇ m> ( ⁇ ) is to correctly distinguish between x g and x.
  • W d is chosen such that log D W j (x) + log 1 - , is maximized.
  • G w (z) P r (x).
  • Mode collapse an issue that can hinder the training of GANs, refers to a collapsing of large volumes of probability mass into a few modes. This means that although the generator produces meaningful samples, these samples belong to only few modes of the data distribution; therefore, the samples produced by the generator do not fully represent the underlying distribution of real data.
  • a regularized GAN (Reg-GAN) may be used.
  • a Reg-GAN uses a regularization term that is meant to penalize the missing modes.
  • an encoder network E w (x) : x ⁇ z is trained to help the generator avoid the missing modes, where W e is the set of weights of the encoder network.
  • E x [iogDwd ( Gwg , (E We (x)) )j is used to encourage G Wr ⁇ E We ( .) ) to generate realistic samples such that D w ( ⁇ ) assigns, to these samples, a high probability of being a true sample. Therefore, a fair probability distribution is achieved across different modes.
  • the regularized loss functions for the generator, the encoder and discriminator are respectively given by: where A* and l 2 are the regularizer's coefficients.
  • tire aim is to find W d , W ⁇ and W e . This can be done by jointly and iteratively maximizing T D , and minimizing T G and T E.
  • W d , W ⁇ and W e are randomly initialized.
  • a mini-batch of size m is sampled from each of the training set and noise samples j.
  • W d is updated by ascending in the direction of the gradient of T D .
  • Wg is updated by descending in the opposite direction of the gradient of T G .
  • W e is updated by descending in the opposite direction of the gradient of T E for fixed l3 ⁇ 4 andl3 ⁇ 4.
  • the Reg-GAN training procedure is summarized in Figure 4. Note that the gradient- based update can be implemented using any standard gradient-based update. In the numerical simulations, to speed up the convergence, a momentum-based gradient update may be used.
  • an indoor massive MIMO scenario is considered.
  • An example of such scenario is the "11 " scenario provided by DeepMIMO dataset.
  • the “11" scenario comprises a 10 X 10 X 2.5 meters room with 2 tables inside the room.
  • M 64 antennas mounted on the ceiling.
  • the users are spread inside the room across the x-y plane with each of them being 1 meter above the floor.
  • the communication between the base station antennas and each user is in FDD mode and uses X OFDM subcarriers.
  • the uplink and downlink operating frequencies are respectively 2.4 GHz and 2.5 GHz.
  • DL-MMSE In this scenario, the MMSE-based channel estimation is used for downlink training without using any information from the uplink channel training.
  • the constrained LS optimizat ion problem for the uplink is first solved using a similar technique provided in D. Vasisht, S. Kumar, H. Rahul and D. Katabi, "Eliminating Channel Feedback in Next-Generation Cellular Networks," in Proc. ACM SIGCOMM Conf., 2016. Then, using the so obtained ": ⁇ A ⁇ @ ⁇ , the downlink LS optimization problem is solved to find Throughout the simulations, this technique is referred to as DL- Modified-R2F2. In this scenario, it is assumed that . To implement DL-Modified-R2F2, the uplink LS optimization problem is solved using coordinate descent.
  • This approach involves the division of the parameters of the optimization problem into smaller sets for which the constraints are separable. The optimization is then carried out over each of these sets iteratively while treating the variables in the other sets to be constants, thereby reducing the computation complexity.
  • the algorithm iterati vely converges to a minimum by taking strides along directions parallel to the parameter-set axes.
  • the separability of constraints is obtained by taking a,t, Q, and ⁇ d as four parameter sets all of which have box constraints. Since the objective function is non- convex, the global optimality of this technique is not guaranteed. To avoid local minima, the optimization is initiated from 10 randomly chosen initial points and choose the solution with the least value of the objective function.
  • Figure 5 plots the rate per subcarrier versus SNR, for
  • kdj
  • the UP-GAN outperforms the UP-MMSE technique in practical range of SNR. This is due to a better estimate of channel matrix in this range of SNR. This, by itself, is attributed to the rich prior stored in tire weights of network. For the same reason, the DL-GAN yields a much better rate performance compared to what DL-Full-Reciprocity and DL-Modified-R2F2 do. Note that the saturation in rate at high SNR is related to the error floor in channel estimation, which comes from the limits in representation capability of G Wq (z) .
  • Figure 6 shows the rate performance of the DGM-based technique versus p , with jX d
  • p,
  • Figure 7 shows the rate performance of the DGM-based technique versus and SNR- 20 (dB).
  • p is the number of training symbols used;
  • Mdj is the number of antenna elements.
  • a, t, Q the frequency-independent features
  • improved channel estimation can be obtained by learning the distribution of channel parameters.
  • the unknown underlying distribution of the channel parameters is a function of the propagation environment and is complex and difficult to obtain analytically.
  • DGMs may he used to learn this function.
  • a GAN structure can be used to find a deterministic mapping function (i.e., a generator) that is capable of drawing samples from the underlying distribution of channel parameters, by feeding it with samples from a low-dimensional standard Gaussian distribution.
  • the sparsity assumption in channel parameters is relaxed. Instead, by using a generator obtained from GAN, a learned structure of channel parameters may he incorporated into the channel estimation procedure as a prior. The result of this is that the optimization problem operates in a low-dimensional subspace, with a dimensionality defined by the generator. Importantly, the optimization problem is independent of number of received pilots. Therefore, a significant reduction in computational complexity as well as CSI feedback is achieved, while maintaining a simple channel estimation.
  • the process flow diagram shown in Figure 8 illustrates an example method for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element.
  • the first device may be a base station, while the second device may be a UE, for example.
  • the term “antenna element” may refer to a single discrete antenna, or to a combination of antenna structures that are operated as a unit.
  • the second device need only have one antenna element, it may have several (or many), in some embodiments of the illustrated technique.
  • the method includes a step of estimating a first set of one or more parameters for the channel, based on training symbols transmitted from the second device to the first device, i.e., training symbols transmitted in a first direction, such as in the uplink direction .
  • the method further comprises estimating a second set of one or more parameters for the channel, based on training symbols or reference signals transmitted from the first device to the second device, i.e., in a second direction, such as the downlink direction, and based on the already estimated first set of one or more parameters.
  • the first set of one or more parameters may include (but are not limited to) a path gain magnitude corresponding to each radio channel path between the second device and the first device, a delay corresponding to each radio channel path between the second device and the first device, and an angle of arrival for each radio channel path between the second device and the first device. These parameters are generally (although not necessarily strictly) frequency- independent, and thus can be considered to be applicable to both directions.
  • the estimation shown at block 810 may also produce phase change parameters for each radio channel path between the second device and the first device (the first direction), hut these phase change parameters are frequency -dependent, and are not subsequently used to estimate the second set of parameters, for the second direction.
  • the second set of one or more parameters estimated in the step shown at block 820 may include a phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device.
  • This is a frequency-dependent parameter, and such a parameter may be obtained for each of several frequencies, e.g., for each of multiple subcarrier frequencies.
  • these parameters are used for constructing the channel estimate for the second direction, i.e., from the first device towards the first device.
  • estimating the first set of one or more parameters for the channel comprises estimating a distribution function for the first set of one or more parameters using a deep generative model and observations of the training symbols transmitted from the second device to the first device, as received at the first device.
  • estimating the first set of one or more parameters for the channel further comprises using a least-squares estimation approach to jointly estimate, based on the estimated distribution function and the observations of the training symbols, the first set of one or more parameters and a phase change parameter for each radio channel path between the second device and the first device, in the direction toward the first device from the estimated distribution.
  • the deep generative model may be based on a generative adversarial network (GAN), for example.
  • GAN generative adversarial network
  • estimating the second set of one or more parameters may comprise using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on observations of training symbols transmitted from the first device to the second device, as received at the first device.
  • estimating the first set of one or more parameters and estimating the second set of one or more parameters are both carried out by the first device, and estimating the second set of one or more parameters comprises using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on quantized channel-state information feedback received from the second device, where die quantized channel-state information feedback being based on the reference signals.
  • the quantized channel-state information feedback may be used to estimate a downlink channel model, which in turn may be used to generate “virtual” training signal vectors, for use in training the deep generative model algorithm.
  • the method may comprise determining an estimated channel response for the channel in the direction from the first device to the second device, based on the estimated first set of one or more parameters, the estimated second set of one or more parameters, and an antenna array response that depends on the physical arrangement of the antenna elements of the first device.
  • the method may further comprise transmitting a signal to the second device from the first device, using antenna weights determined from the estimated channel response for the channel in the direction from the first device to the second device.
  • Network node 30 may correspond to any of the devices described herein.
  • network node 30 may be a base station configured to carry out all or parts of the techniques described herein.
  • Network node 30 may be an evolved Node B (eNodeB), Node B or gNB, for example.
  • Network node may represent a radio network node such as base station, radio base station, base transceiver station, base station controller, network controller, NR BS, Multi-cell/multicast Coordination Entity (MCE), relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH), or a multi-standard BS (MSR BS).
  • MCE Multi-cell/multicast Coordination Entity
  • RRU Remote Radio Unit
  • RRH Remote Radio Head
  • MSR BS multi-standard BS
  • network node 30 is described as being configured to operate as a cellular network access node in an LTE network or NR network, but network node 30 may also correspond to similar access nodes in other types of network.
  • network node 30 may be adapted to carry out one or more of the methods described herein, e.g,, through the modification of and/or addition of appropriate program instructions for execution by processing circuits 32.
  • Network node 30 facilitates communication between wireless terminals (e.g., UEs), other network access nodes and/or the core network.
  • Network node 30 may include communication interface circuitry 38 that includes circuitry for communicating with other nodes in the core network, radio nodes, and/or other types of nodes in the network for the purposes of providing data and/or cellular communication services.
  • Network node 30 communicates with wireless devices using antennas 34 and transceiver circuitry 36.
  • Transceiver circuitry 36 may include transmitter circuits, receiver circuits, and associated control circuits that are collectively configured to transmit and receive signals according to a radio access technology, for the purposes of providing cellular communication services.
  • Network node 30 also includes one or more processing circuits 32 that are operatively associated with the transceiver circuitry 36 and, in some cases, the communication interface circuitry 38.
  • Processing circuitry 32 comprises one or more digital processors 42, e.g., one or more microprocessors, microcontrollers, Digital Signal Processors (DSPs), Field Programmable Gate Arrays (FPGAs), Complex Programmable Logic Devices (CPLDs), Application Specific Integrated Circuits (ASICs), or any mix thereof. More generally, processing circuitry 32 may comprise fixed circuitry, or programmable circuitry that is specially configured via the execution of program instructions implementing the functionality taught herein, or some mix of fixed and programmed circuitry.
  • Processor 42 may be multi-core, i.e., having two or more processor cores utilized for enhanced performance, reduced power consumption, and more efficient simultaneous processing of multiple tasks.
  • Processing circuitry 32 also includes a memory 44.
  • Memory 44 stores one or more computer programs 46 and, optionally, configuration data 48.
  • Memory 44 provides non -transitory storage for the computer program 46 and it may comprise one or more types of computer-readable media, such as disk storage, solid-state memory storage, or any mix thereof.
  • “non-transitory” means permanent, semi-permanent, or at least temporarily persistent storage and encompasses both long-term storage in non-volatile memory and storage in working memory, e.g., for program execution.
  • memory 44 comprises any one or more of SRAM, DRAM, EEPROM, and FLASH memory, which may be in processing circuitry 32 and/or separate from processing circuitry 32.
  • Memory 44 may also store any configuration data 48 used by the network access node 30.
  • Processing circuitry 32 may be configured, e.g., through the use of appropriate program code stored in memory 44, to carry out all or parts of one or more of the methods detailed hereinafter.
  • Processing circuitry 32 of the network node 30 is thus configured, according to some embodiments, to perform all or parts of the techniques described herein as carried out by a base station or a “first device,” for example.
  • FIG. 10 illustrates a diagram of a user equipment 50 configured to carry out the techniques described above, according to some embodiments.
  • User equipment 50 may be considered to represent any wireless devices or terminals that may operate in a network, such as a UE in a cellular network.
  • Other examples may include a communication device, target device, MTC device, loT device, device to device (D2D) UE, machine type UE or UE capable of machine ⁇ to- machine communication (M2M), a sensor equipped with UE, PDA (personal digital assistant), tablet, IPAD tablet, mobile terminal, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), LTSB dongles, Customer Premises Equipment (CPE), etc.
  • D2D device to device
  • M2M machine type UE or UE capable of machine ⁇ to- machine communication
  • PDA personal digital assistant
  • tablet IPAD tablet
  • mobile terminal smart phone
  • LME laptop embedded equipped
  • LME laptop mounted equipment
  • CPE Customer Premises Equipment
  • Transceiver circuitry 56 may include transmitter circuits, receiver circuits, and associated control circuits that are collectively configured to transmit and receive signals according to a radio access technology, for the purposes of using cellular communication services.
  • the radio access technology can be NR or LTE, for the purposes of this discussion.
  • User equipment 50 also includes one or more processing circuits 52 that are operatively associated with the radio transceiver circuitry 56.
  • Processing circuitry 52 comprises one or more digital processing circuits, e.g., one or more microprocessors, microcontrollers, DSPs, FPGAs, CPLDs, ASICs, or any mix thereof. More generally, processing circuitry 52 may comprise fixed circuitry, or programmable circuitry that is specially adapted via tire execution of program instructions implementing the functionality taught herein, or may comprise some mix of fixed and programmed circuitry. Processing circuitry 52 may be multi-core.
  • Processing circuitry 52 also includes a memory 64.
  • Memory 64 stores one or more computer programs 66 and, optionally, configuration data 68.
  • Memory 64 provides non -transitory storage for computer program 66 and it may comprise one or more types of computer-readable media, such as disk storage, solid-state memory storage, or any mix thereof.
  • memory 64 comprises any one or more of SRAM, DRAM, EEPROM, and FLASH memory, which may be in processing circuitry 52 and/or separate from processing circuitry 52.
  • Memory 64 may also store any configuration data 68 used by wireless device 50.
  • Processing circuitry 52 may he configured, e.g., through the use of appropriate program code stored in memory 64, to cany out all or parts of one or more of the methods detailed herein.
  • a wireless network such as the example wireless network illustrated in Figure 11.
  • the wireless network of Figure 11 only depicts network 1406, network nodes 1460 and 1460b, and WDs 1410, 1410b, and 1410c.
  • a wireless network can further include any additional elements suitable to support communication between wireless devices or between a wireless device and another communication device, such as a landline telephone, a service provider, or any other network node or end device.
  • network node 1460 and wireless device (WD) 1410 are depicted with additional detail.
  • the wireless network can provide communication and other types of services to one or more wireless devices to facilitate the wireless devices’ access to and/or use of the services provided by, or via, the wireless network.
  • the wireless network can comprise and/or interface with any type of communication, telecommunication, data, cellular, and/or radio network or other similar type of system.
  • the wireless network can be configured to operate according to specific standards or other types of predefined rules or procedures.
  • particular embodiments of the wireless network can implement communication standards, such as Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE), and/or other suitable 2G, 3G, 4G, or 5G standards; wireless local area network (WLAN) standards, such as the IEEE 802.11 standards; and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave and/or ZigBee standards.
  • GSM Global System for Mobile Communications
  • UMTS Universal Mobile Telecommunications System
  • LTE Long Term Evolution
  • WLAN wireless local area network
  • WiMax Worldwide Interoperability for Microwave Access
  • Bluetooth Z-Wave and/or ZigBee standards.
  • Network 1406 can comprise one or more backhaul networks, core networks, IP networks, public switched telephone networks (PSTNs), packet data networks, optical networks, wide-area networks (WANs), local area networks (LANs), wireless local area networks (WLANs), wired networks, wireless networks, metropolitan area networks, and other networks to enable communication between devices.
  • PSTNs public switched telephone networks
  • WANs wide-area networks
  • LANs local area networks
  • WLANs wireless local area networks
  • wired networks wireless networks, metropolitan area networks, and other networks to enable communication between devices.
  • Network node 1460 and WD 1410 comprise various components described in more detail below. These components work together in order to provide network node and/or wireless device functionality, such as providing wireless connections in a wireless network, in different embodiments, the wireless network can comprise any number of wired or wireless networks, network nodes, base stations, controllers, wireless devices, relay stations, and/or any other components or systems that can facilitate or participate in the communication of data and/or signals whether via wired or wireless connections.
  • network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)).
  • APs access points
  • BSs base stations
  • eNBs evolved Node Bs
  • gNBs NR NodeBs
  • Base stations can be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and can then also be referred to as femto base stations, pico base stations, micro base stations, or macro base stations.
  • a base station can be a relay node or a relay donor node controlling a relay.
  • a network node can also include one or more (or all) parts of a distributed radio base station such as centralized digital units and/or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Pails of a distributed radio base station can also be referred to as nodes in a distributed antenna system (DAS).
  • DAS distributed antenna system
  • network nodes include multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs), core network nodes (e.g., MSCs, MMEs), O&M nodes, OSS nodes, SON nodes, positioning nodes (e.g., E-SMLCs), and/or MD ' T ' s.
  • MSR multi-standard radio
  • RNCs radio network controllers
  • BSCs base station controllers
  • BTSs base transceiver stations
  • transmission points transmission nodes
  • MCEs multi-cell/multicast coordination entities
  • core network nodes e.g., MSCs, MMEs
  • O&M nodes e.g., OSS nodes, SON nodes, positioning nodes (e.g., E-SMLCs), and/or
  • network nodes can represent any suitable device (or group of devices) capable, configured, arranged, and/or operable to enable and/or provide a wireless device with access to tire wireless network or to provide some service to a wireless device that has accessed the wireless network.
  • network node 1460 includes processing circuitry 1470, device readable medium 1480, interface 1490, auxiliary equipment 1484, power source 1486, power circuitry 1487, and antenna 1462.
  • network node 1460 illustrated in the example wireless network of Figure 11 can represent a device that includes the illustrated combination of hard ware components, other embodiments can comprise network nodes with different combinations of components. It is to be understood that a network node comprises any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods and/or procedures disclosed herein.
  • network node 1460 fire depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, a network node can comprise multiple different physical components that make up a single illustrated component (e.g., device readable medium 1480 can comprise multiple separate hard drives as well as multiple RAM modules).
  • device readable medium 1480 can comprise multiple separate hard drives as well as multiple RAM modules.
  • network node 1460 can be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which can each have their own respective components.
  • network node 1460 comprises multiple separate components (e.g., BTS and BSC components)
  • one or more of the separate components can be shared among several network nodes.
  • a single RNC can control multiple NodeBs.
  • each unique NodeB and RNC pair can in some instances be considered a single separate network node.
  • network node 1460 can be configured to support multiple radio access technologies (RATs).
  • RATs radio access technologies
  • Network node 1460 can also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1460, such as, for example, GSM, WCDMA, LTE, NR, WiFi, or Bluetooth wireless technologies. These wireless technologies can be integrated into the same or different chip or set of chips and other components within network node 1460.
  • Processing circuitry 1470 can be configured to perform any determining, calculating, or similar operations (e.g., certain obtaining operations) described herein as being provided by a network node. These operations performed by processing circuitry 1470 can include processing information obtained by processing circuitry 1470 by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
  • processing information obtained by processing circuitry 1470 by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
  • Processing circuitry 1470 can comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application -specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node 1460 components, such as device readable medium 1480, network node 1460 functionality.
  • processing circuitry 1470 can execute instructions stored in device readable medium 1480 or in memory within processing circuitry 1470. Such functionality can include providing any of the various wireless features, functions, or benefits discussed herein.
  • processing circuitry 1470 can include a system on a chip (SOC).
  • SOC system on a chip
  • processing circuitry 1470 can include one or more of radio frequency (RF) transceiver circuitry 1472 and baseband processing circuitry 1474.
  • radio frequency (RF) transceiver circuitry 1472 and baseband processing circuitry 1474 can be on separate chips (or sets of chips), boards, or units, such as radio units and digital units.
  • part or ail of RF transceiver circuitry 1472 and baseband processing circuitry 1474 can be on the same chip or set of chips, boards, or units.
  • processing circuitry 1470 executing instructions stored on device readable medium 1480 or memory within processing circuitry 1470.
  • some or all of the functionality can be provided by processing circuitry 1470 without executing instructions stored on a separate or discrete device readable medium, such as in a hard-wired manner, in any of those embodiments, whether executing instructions stored on a device readable storage medium or not, processing circuitry 1470 can be configured to perform the described functionality.
  • the benefits provided by such functionality are not limited to processing circuitry 1470 alone or to other components of network node 1460, but are enjoyed by network node 1460 as a whole, and/or by end users and the wireless network generally.
  • Device readable medium 1480 can comprise any form of volatile or non-volatile computer readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device readable and/or computer-executable memory devices that store information, data, and/or instructions that can be used by processing circuitry 1470.
  • volatile or non-volatile computer readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or
  • Device readable medium 1480 can store any suitable instructions, data or information, including a computer program, software, an application including one or more of logic, rales, code, tables, etc. and/or other instructions capable of being executed by processing circuitry 1470 and, utilized by network node 1460.
  • Device readable medium 1480 can he used to store any calculations made by processing circuitry 1470 and/or any data received via interface 1490.
  • processing circuitry 1470 and device readable medium 1480 can be considered to be integrated.
  • Interface 1490 is used in the wired or wireless communication of signalling and/or data between network node 1460, network 1406, and/or WDs 1410. As illustrated, interface 1490 comprises port(s)/terminal(s) 1494 to send and receive data, for example to and from network 1406 over a wired connection. Interface 1490 also includes radio front end circuitry 1492 that can be coupled to, or in certain embodiments a part of, antenna 1462. Radio front end circuitry 1492 comprises filters 1498 and amplifiers 1496. Radio front end circuitry 1492 can be connected to antenna 1462 and processing circuitry 1470. Radio front end circuitry can be configured to condition signals communicated between antenna 1462 and processing circuitry 1470.
  • Radio front end circuitry 1492 can receive digital data that is to be sent out to other network nodes or WDs via a wireless connection. Radio front end circuitry 1492 can convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1498 and/or amplifiers 1496. The radio signal can then be transmitted via antenna 1462. Similarly, when receiving data, antenna 1462 can collect radio signals which are then converted into digital data by radio front end circuitry 1492. The digital data can be passed to processing circuitry 1470. In other embodiments, the interface can comprise different components and/or different combinations of components.
  • network node 1460 may not include separate radio front end circuitry 1492, instead, processing circuitry 1470 can comprise radio front end circuitry and can be connected to antenna 1462 without separate radio front end circuitry 1492.
  • processing circuitry 1470 can comprise radio front end circuitry and can be connected to antenna 1462 without separate radio front end circuitry 1492.
  • all or some of RF transceiver circuitry 1472 can be considered a part of interface 1490.
  • interface 1490 can include one or more ports or terminals 1494, radio front end circuitry 1492, and RF transceiver circuitry 1472, as part of a radio unit (not shown), and interface 1490 can communicate with baseband processing circuitry 1474, which is part of a digital unit (not shown).
  • Antenna 1462 can include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals.
  • Antenna 1462 can be coupled to radio front end circuitry 1490 and can be any type of antenna capable of transmitting and receiving data and/or signals wirelessly.
  • antenna 1462 can comprise one or more omni-directional, sector or panel antennas operable to transmit/receive radio signals between, for example, 2 GHz and 66 GHz.
  • An omni-directional antenna can be used to transmit/receive radio signals in any direction
  • a sector antenna can be used to transmit/receive radio signals from devices within a particular area
  • a panel antenna can be a line-of-sight antenna used to transmit/receive radio signals in a relatively straight line.
  • the use of more than one antenna can be referred to as MIMO.
  • antenna 1462 can be separate from network node 1460 and can be connectable to network node 1460 through an interface or port.
  • Antenna 1462, interface 1490, and/or processing circuitry 1470 can be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by a network node. Any information, data and/or signals can be received from a wireless device, another network node and/or any other network equipment. Similarly, antenna 1462, interface 1490, and/or processing circuitry 1470 can be configured to perform any transmitting operations described herein as being performed by a network node. Any information, data and/or signals can be transmitted to a wireless device, another network node and/or any other network equipment.
  • Power circuitry 1487 can comprise, or be coupled to, power management circuitry and can be configured to supply the components of network node 1460 with power for performing the functionality described herein. Power circuitry 1487 can receive power from power source 1486, Power source 1486 and/or power circuitry 1487 can be configured to provide power to the various components of network node 1460 in a form suitable for tire respective components (e.g., at a voltage and current level needed for each respective component). Power source 1486 can either be included in, or external to, power circuitry 1487 and/or network node 1460.
  • network node 1460 can he connectable to an external power source (e.g., an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry 1487.
  • an external power source e.g., an electricity outlet
  • power source 1486 can comprise a source of power in the form of a battery or batery pack which is connected to, or integrated in, power circuitry 1487.
  • the battery can provide backup power should the external power source fail.
  • Other types of power sources such as photovoltaic devices, can also be used.
  • network node 1460 can include additional components beyond those shown in Figure 11 that can be responsible for providing certain aspects of the network node’s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein.
  • network node 1460 can include user interface equipment to allow and/or facilitate input of information into network node 1460 and to allow and/or facilitate output of information from network node 1460. This can allow and/or facilitate a user to perform diagnostic, maintenance, repair, and other administrative functions for network node 1460.
  • a wireless device e.g. WD 1410
  • a wireless device can be configured to communicate wirelessly with network nodes (e.g., 1460) and/or other wireless devices (e.g., 1410b, c). Communicating wirelessly can involve transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information through air.
  • a WD can be configured to transmit and/or receive information without direct human interaction. For instance, a WD can be designed to transmit information to a network on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the network.
  • Examples of a WD include, but are not limited to, a smart phone, a mobile phone, a cell phone, a voice over IP (VoIP) phone, a wireless local loop phone, a desktop computer, a personal digital assistant (PDA), a wireless cameras, a gaming console or device, a music storage device, a playback appliance, a wearable terminal device, a wireless endpoint, a mobile station, a tablet, a laptop, a laptop-embedded equipment (LEE), a laptop-mounted equipment (LME), a smart device, a wireless customer-premise equipment (CPE), a vehicle-mounted wireless terminal device, etc.
  • VoIP voice over IP
  • PDA personal digital assistant
  • LOE laptop-embedded equipment
  • LME laptop-mounted equipment
  • CPE wireless customer-premise equipment
  • a WD can support device -to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, vehicle-to-vehicle (V2V), vehicle- to-infrastructure (V2I), vehicle-to-everything (V2X) and can in this case be referred to as a D2D communication device.
  • D2D device-to-device
  • V2V vehicle-to-vehicle
  • V2I vehicle- to-infrastructure
  • V2X vehicle-to-everything
  • a WD can represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another WD and/or a network node.
  • the WD can in this case be a machine-to-machine (M2M) device, which can in a 3GPP context he referred to as an MTC device.
  • M2M machine-to-machine
  • the WD can be a UE implementing the 3GPP narrow band internet of things (NB-IoT) standard.
  • NB-IoT narrow band internet of things
  • machines or devices are sensors, metering devices such as power meters, industrial machinery, or home or personal appliances (e.g., refrigerators, televisions, etc.) personal wearables (e.g., watches, fitness trackers, etc.), in other scenarios, a WD can represent a vehicle or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation.
  • a WD as described above can represent the endpoint of a wireless connection, in which case the device can be referred to as a vrireless terminal. Furthermore, a WD as described above can be mobile, in which case it can also be referred to as a mobile device or a mobile terminal.
  • wireless device 1410 includes antenna 1411, interface 1414, processing circuitry 1420, device readable medium 1430, user interface equipment 1432, auxiliary equipment 1434, power source 1436 and power circuitry 1437.
  • WD 1410 can include multiple sets of one or more of the illustrated components for different wireless technologies supported by WD 1410, such as, for example, GSM, WCDMA, LTE, NR, WiFi, WiMAX, or Bluetooth wireless technologies, just to mention a few. These wireless technologies can be integrated into the same or different chips or set of chips as other components within WD 1410.
  • Antenna 1411 can include one or more antennas or antenna arrays, configured to send and/or receive wireless signals, and is connected to interface 1414.
  • antenna 1411 can be separate from WD 1410 and be connectable to WD 1410 through an interface or port.
  • Antenna 1411, interface 1414, and/or processing circuitry 1420 can be configured to perform any receiving or transmitting operations described herein as being performed by a WD. Any information, data and/or signals can be received from a network node and/or another WD.
  • radio front end circuitry and/or antenna 1411 can be considered an interface.
  • interface 1414 comprises radio front end circuitry 1412 and antenna 1411.
  • Radio front end circuitry 1412 comprise one or more filters 1418 and amplifiers 1416.
  • Radio front end circuitry 1414 is connected to antenna 1411 and processing circuitry 1420 and can be configured to condition signals communicated between antenna 1411 and processing circuitry 1420, Radio front end circuitry 1412 can be coupled to or a part of antenna 1411.
  • WD 1410 may not include separate radio front end circuitry 1412; rather, processing circuitry 1420 can comprise radio front end circuitry and can be connected to antenna
  • Radio front end circuitry 1412 can receive digital data that is to be sent out to other network nodes or WDs via a wireless connection. Radio front end circuitry 1412 can convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1418 and/or ampli bombs 1416. The radio signal can then be transmitted via antenna 1411. Similarly, when receiving data, antenna 1411 can collect radio signals which are then converted into digital data by radio front end circuitry
  • the digital data can be passed to processing circuitry 1420.
  • the interface can comprise different components and/or different combinations of components.
  • Processing circuitry 1420 can comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software, and/or encoded logic operable to provide, either alone or in conjunction with other WD 1410 components, such as device readable medium 1430, WD 1410 functionality. Such functionality can include providing any of the various wireless features or benefits discussed herein.
  • processing circuitry 1420 can execute instructions stored in device readable medium 1430 or in memory within processing circuitry 1420 to provide the functionality disclosed herein.
  • processing circuitry 1420 includes one or more of RF transceiver circuitry 1422, baseband processing circuitry 1424, and application processing circuitry 1426.
  • the processing circuitry can comprise different components and/or different combinations of components.
  • processing circuitry 1420 of WD 1410 can comprise a SOC.
  • RF transceiver circuitry 1422, baseband processing circuitry 1424, and application processing circuitry 1426 can be on separate chips or sets of chips.
  • part or all of baseband processing circuitry 1424 and application processing circuitry 1426 can he combined into one chip or set of chips, and RF transceiver circuitry 1422 can be on a separate chip or set of chips.
  • part or ail of RF transceiver circuitry 1422 and baseband processing circuitry 1424 can be on the same chip or set of chips, and application processing circuitry 1426 can be on a separate chip or set of chips.
  • part or all of RF transceiver circuitry 1422, baseband processing circuitry 1424, and application processing circuitry 1426 can be combined in the same chip or set of chips.
  • RF transceiver circuitry 1422 can be a part of interface 1414.
  • RF transceiver circuitry 1422 can condition RF signals for processing circuitry 1420.
  • processing circuitry 1420 executing instructions stored on device readable medium 1430, which in certain embodiments can be a computer-readable storage medium.
  • some or all of the functionality can be provided by processing circuitry 1420 without executing instructions stored on a separate or discrete device readable storage medium, such as in a hard-wired manner.
  • processing circuitry 1420 can be configured to perform the described functionality. The benefits provided by such functionality are not limited to processing circuitry 1420 alone or to other components of WD 1410, but are enjoyed by WD 1410 as a whole, and/or by end users and the wireless network generally.
  • Processing circuitry 1420 can be configured to perform any determining, calculating, or similar operations (e.g., certain obtaining operations) described herein as being performed by a WD. These operations, as performed by processing circuitry 1420, can include processing information obtained by processing circuitry 1420 by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored by WD 1410, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
  • processing information obtained by processing circuitry 1420 by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored by WD 1410, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
  • Device readable medium 1430 can be operable to store a computer program, software, an application including one or more of logic, rules, code, tables, etc. and/or other instructions capable of being executed by processing circuitry 1420.
  • Device readable medium 1430 can include computer memory (e.g., Random Access Memory (RAM) or Read Only Memory (ROM)), mass storage media (e.g., a hard disk), removable storage media (e.g., a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device readable and/or computer executable memory devices that store information, data, and/or instructions that can be used by processing circuitry 1420.
  • processing circuitry 1420 and device readable medium 1430 can be considered to be integrated.
  • User interface equipment 1432 can include components that allow and/or facilitate a human user to interact with WD 1410. Such interaction can be of many forms, such as visual, audial, tactile, etc. User interface equipment 1432 can be operable to produce output to the user and to allow and/or facilitate the user to provide input to WD 1410. The type of interaction can vary depending on the type of user interface equipment 1432 installed in WD 1410. For example, if WD 1410 is a smart phone, the interaction can be via a touch screen; if WD 1410 is a smart meter, the interaction can be through a screen that provides usage (e.g., the number of gallons used) or a speaker that provides an audible alert (e.g., if smoke is detected).
  • usage e.g., the number of gallons used
  • a speaker that provides an audible alert
  • User interface equipment 1432 can include input interfaces, devices and circuits, and output interfaces, devices and circuits. User interface equipment 1432 can be configured to allow and/or facilitate input of information into WD 1410, and is connected to processing circuitry 1420 to allow and/or facilitate processing circuitry 1420 to process the input information. User interface equipment 1432 can include, for example, a microphone, a proximity or other sensor, keys/buttons, a touch display, one or more cameras, a USB port, or other input circuitry. User interface equipment 1432 is also configured to allow and/or facilitate output of information from WD 1410, and to allow' and/or facilitate processing circuitry 1420 to output information from WD 1410.
  • User interface equipment 1432 can include, for example, a speaker, a display, vibrating circuitry, a USB port, a headphone interface, or other output circuitry. Using one or more input and output interfaces, devices, and circuits, of user interface equipment 1432, WD 1410 can communicate with end users and/or the wireless network, and allow and/or facilitate them to benefit from the functionality described herein.
  • Auxiliary equipment 1434 is operable to provide more specific functionality which may not be generally performed by WDs. This can comprise specialized sensors for doing measurements for various purposes, interfaces for additional types of communication such as wired communications etc. The inclusion and type of components of auxiliary equipment 1434 can vary depending on the embodiment and/or scenario.
  • Power source 1436 can, in some embodiments, be in the form of a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic devices or power ceils, can also be used.
  • WD 1410 can further comprise power circuitry 1437 for delivering power from power source 1436 to the various parts of WD 1410 which need power from power source 1436 to cany out any functionality described or indicated herein.
  • Power circuitry 1437 can in certain embodiments comprise power management circuitry.
  • Power circuitry 1437 can additionally or alternatively be operable to receive power from an external power source; in which case WD 1410 can be connectable to the external power source (such as an electricity outlet) via input circuitry or an interface such as an electrical power cable.
  • Power circuitry 1437 can also in certain embodiments be operable to deliver power from an external power source to power source 1436. This can be, for example, for the charging of power source 1436. Power circuitry 1437 can perform any converting or other modification to the power from power source 1436 to make it suitable for supply to the respective components of WD 1410.
  • Figure 12 illustrates one embodiment of a UE in accordance with various aspects described herein.
  • a user equipment or UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device.
  • a UE can represent a device that is intended for sale to, or operation by, a human user hut which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller).
  • a UE can represent a device that is not intended for sale to, or operation by, an end user but which can he associated with or operated for the benefit of a user (e.g., a smart power meter).
  • UE 1500 can be any UE identified by the 3rd Generation Partnership Project (3GPP), including a NB-IoT UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.
  • UE 1500 is one example of a WD configured for communication in accordance with one or more communication standards promulgated by the 3rd Generation Partnership Project (3GPP), such as 3GPP’s GSM, UMTS, LTE, and/or 5G standards.
  • 3GPP 3rd Generation Partnership Project
  • the term WD and UE can be used interchangeable. Accordingly, although Figure 12 is a UE, the components discussed herein are equally applicable to a WD, and vice-versa.
  • UE 1500 includes processing circuitry 1501 that is operatively coupled to input/output interface 1505, radio frequency (RF) interface 1509, network connection interface 1511, memory 1515 including random access memory (RAM) 917, read-only memory (ROM) 919, and storage medium 921 or the like, communication subsystem 931, power source 933, and/or any other component, or any combination thereof.
  • Storage medium 1521 includes operating system 1523, application program 1525, and data 1527. In other embodiments, storage medium 1521 can include other similar types of information.
  • Certain UEs can utilize ail of the components shown in Figure 12, or only a subset of the components. The level of integration between the components can vary from one UE to another UE. Further, certain UEs can contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.
  • processing circuitry 1501 can be configured to process computer instructions and data.
  • Processing circuitry 1501 can be configured to implement any sequential state machine operative to execute machine instructions stored as machine-readable computer programs in the memory, such as one or more hardware-implemented state machines (e.g., in discrete logic, FPGA, ASIC, etc.); programmable logic together with appropriate firmware; one or more stored program, general-purpose processors, such as a microprocessor or Digital Signal Processor (DSP), together with appropriate software; or any combination of the above.
  • the processing circuitry 1501 can include two central processing units (CPUs). Data can be information in a form suitable for use by a computer.
  • input/output interface 1505 can be configured to provide a communication interface to an input device, output device, or input and output device.
  • UE 1500 can be configured to use an output device via input/output interface 1505.
  • An output device can use the same type of interface port as an input device.
  • a USB port can be used to provide input to and output from UE 1500.
  • the output device can be a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof.
  • UE 1500 can be configured to use an input device via input/output interface 1505 to allow' and/or facilitate a user to capture information into UE 1500.
  • the input device can include a touch -sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like.
  • the presence- sensitive display can include a capacitive or resistive touch sensor to sense input from a user.
  • a sensor can be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, another like sensor, or any combination thereof.
  • the input device can be an accelerometer, a magnetometer, a digital camera, a microphone, and an optical sensor.
  • RF interface 1509 can be configured to provide a communication interface to RF components such as a transmitter, a receiver, and an antenna.
  • Network connection interface 1511 can be configured to provide a communication interface to network 1543a.
  • Network 1543a can encompass wared and/or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof.
  • network 1543a can comprise a Wi-Fi network.
  • Network connection interface 1511 can be configured to include a receiver and a transmitter interface used to communicate with one or more other devices over a communication network according to one or more communication protocols, such as Ethernet, TCP/IP, SONET, ATM, or the like.
  • Network connection interface 1511 can implement receiver and transmitter functionality appropriate to the communication network links (e.g., optical, electrical, and the like). The transmitter and receiver functions can share circuit components, software or firmware, or alternatively can be implemented separately.
  • R AM 1517 can be configured to interface via bus 1502 to processing circuitry 1501 to provide storage or caching of data or computer instructions during the execution of software programs such as the operating system, application programs, and device drivers.
  • ROM 1519 can be configured to provide computer instructions or data to processing circuitry 1501.
  • ROM 1519 can be configured to store invariant low-level system code or data for basic system functions such as basic input and output (I/O) , startup, or reception of keystrokes from a keyboard that are stored in a non-volatile memory.
  • Storage medium 1521 can be configured to include memory such as RAM, ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic di sks, optical disks, floppy disks, hard disks, removable cartridges, or flash drives.
  • storage medium 1521 can be configured to include operating system 1523, application program 1525 such as a web browser application, a widget or gadget engine or another application, and data file 1527.
  • Storage medium 1521 can store, for use by UE 1500, any of a variety of various operating systems or combinations of operating systems.
  • Storage medium 1521 can be configured to include a number of physical drive units, such as redundant array of independent disks (R AID), floppy disk drive, flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), externa! micro- DIMM SDRAM, smartcard memory such as a subscriber identity module or a removable user identity (SIM/RUIM) module, other memory, or any combination thereof.
  • R AID redundant array of independent disks
  • HD-DVD high-density digital versatile disc
  • HDDS holographic digital data storage
  • DIMM digital data storage
  • DIMM synchronous dynamic random access memory
  • smartcard memory such as a subscriber identity module or a removable user identity (SIM/RUIM) module, other memory, or
  • Storage medium 152,1 can allow and/or facilitate UE 1500 to access computer-executable instructions, application programs or the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data.
  • An article of manufacture, such as one utilizing a communication system can be tangibly embodied in storage medium 1521, which can comprise a device readable medium.
  • processing circuitry 1501 can be configured to communicate with network 1543b using communication subsystem 1531.
  • Network 1543a and network 1543b can be the same network or networks or different network or networks.
  • Communication subsystem 1531 can be configured to include one or more transceivers used to communicate with network 1543b.
  • communication subsystem 1531 can be configured to include one or more transceivers used to communicate with one or more remote transceivers of another device capable of wireless communication such as another WD, LIE, or base station of a radio access network (RAN) according to one or more communication protocols, such as IEEE 802.11, CDMA, WCDMA, GSM, LTE, UTRAN, WiMax, or the like.
  • RAN radio access network
  • Each transceiver can include transmitter 1533 and/or receiver 1535 to implement transmitter or receiver functionality, respectively, appropriate to the RAN links (e.g., frequency allocations and the like). Further, transmitter 1533 and receiver 1535 of each transceiver can share circuit components, software or firmware, or alternatively can be implemented separately.
  • the communication functions of communication subsystem 1531 can include data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof.
  • communication subsystem 1531 can include cellular communication, Wi-Fi communication, Bluetooth communication, and GPS communication.
  • Network 1543b can encompass wired and/or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof.
  • network 1543b can be a cellular network, a Wi-Fi network, and/or a nearfield network.
  • Power source 1513 can be configured to provide alternating current (AC) or direct current (DC) power to components of UE 1500.
  • communication subsystem 1531 can be configured to include any of the components described herein.
  • processing circuitry 1501 can be configured to communicate with any of such components over bus 1502.
  • any of such components can be represented by program instructions stored in memory that when executed by processing circuitry 1501 perform the corresponding functions described herein.
  • the functionality of any of such components can be partitioned between processing circuitry 1501 and communication subsystem 1531.
  • the non-cornputationally intensive functions of any of such components can be implemented in software or firmware and the computationally intensive functions can be implemented in hardware.
  • FIG. 13 is a schematic block diagram illustrating a virtualization environment 1600 in which functions implemented by some embodiments can be virtualized.
  • virtualizing means creating virtual versions of apparatuses or devices which can include virtualizing hardware platforms, storage devices and networking resources.
  • virtualization can be applied to a node (e.g., a virtualized base station, a virtualized radio access node, virtualized core network node) or to a device (e.g., a UE, a wireless device or any other type of communication device) or components thereof and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components (e.g., via one or more applications, components, functions, virtual machines or containers executing on one or more physical processing nodes in one or more networks).
  • a node e.g., a virtualized base station, a virtualized radio access node, virtualized core network node
  • a device e.g., a UE, a wireless device or any other type of communication device
  • some or all of the functions described herein can be implemented as virtual components executed by one or more virtual machines implemented in one or more virtual environments 1600 hosted by one or more of hardware nodes 1630. Further, in embodiments in which the virtual node is not a radio access node or does not require radio connectivity (e.g., a core network node), then the network node can be entirely virtualized.
  • the functions can be implemented by one or more applications 1620 (which can alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) operative to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.
  • Applications 1620 are run in virtualization environment 1600 which provides hardware 1630 comprising processing circuitry 1660 and memory 1690.
  • Memory 1690 contains instructions 1695 executable by processing circuitry 1660 whereby application 1620 is operative to provide one or more of the features, benefits, and/or functions disclosed herein.
  • Virtualization environment 1600 comprises general-purpose or special-purpose network hardware devices 1630 comprising a set of one or more processors or processing circuitry 1660, which can be commercial off-the-shelf (COTS) processors, dedicated Application Specific integrated Circuits (ASICs), or any other type of processing circuitry including digital or analog hardware components or special purpose processors.
  • processors or processing circuitry 1660 can be commercial off-the-shelf (COTS) processors, dedicated Application Specific integrated Circuits (ASICs), or any other type of processing circuitry including digital or analog hardware components or special purpose processors.
  • Each hardware device can comprise memory 1690-1 which can be non-persistent memory for temporarily storing instructions 1695 or software executed by processing circuitry 1660.
  • Each hardware device can comprise one or more network interface controllers (NICs) 1670, also known as network interface cards, which include physical network interface 1680.
  • NICs network interface controllers
  • Each hardware device can also include non-transitory, persistent, machine-readable storage media 1690-2 having stored therein software 1695 and/or instructions executable by processing circuitry 1660.
  • Software 1695 can include any type of software including software for instantiating one or more virtualization layers 1650 (also referred to as hypervisors), software to execute virtual machines 1640 as well as software allowing it to execute functions, features and/or benefits described in relation with some embodiments described herein.
  • Virtual machines 1640 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and can be run by a corresponding virtualization layer 1650 or hypervisor. Different embodiments of the instance of virtual appliance 1620 can he implemented on one or more of virtual machines 1640, and the implementations can be made in different ways.
  • processing circuitry 1660 executes software 1695 to instantiate the hypervisor or virtualization layer 1650, which can sometimes be referred to as a virtual machine monitor (VMM).
  • VMM virtual machine monitor
  • Virtualization layer 1650 can present a virtual operating platform that appears like networking hardware to virtual machine 1640.
  • hardware 1630 can be a standalone network node with generic or specific components.
  • Hardware 1630 can comprise antenna 16225 and can implement some functions via virtualization.
  • hardware 1630 can be part of a larger cluster of hardware (e.g., such as in a data center or customer premise equipment (CPE)) where many hardware nodes work together and are managed via management and orchestration (MANO) 1690, which, among others, oversees lifecycle management of applications 1620.
  • CPE customer premise equipment
  • Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV).
  • NFV network function virtualization
  • NFV can be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.
  • virtual machine 1640 can be a software implementation of a physical machine that runs programs as if they were executing on a physical, non- virtualized machine.
  • Each of virtual machines 1640, and that part of hardware 1630 that executes that virtual machine be it hardware dedicated to that virtual machine and/or hardware shared by that virtual machine with others of the virtual machines 1640, forms a separate virtual network elements (VNE).
  • VNE virtual network elements
  • VNF Virtual Network Function
  • one or more radio units 16200 that each include one or more transmitters 16220 and one or more receivers 16210 can be coupled to one or more antennas 16225.
  • Radio units 16200 can communicate directly with hardware nodes 1630 via one or more appropriate network interfaces and can be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station.
  • control system 16230 which can alternatively be used for communication between the hardware nodes 1630 and radio units 16200.
  • a communication system includes telecommunication network 1710, such as a 3GPP-type cellular network, which comprises access network 1711, such as a radio access network, and core network 1714.
  • Access network 1711 comprises a plurality ofbase stations 1712a, 1712b, 1712c, such as MBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 1713a, 1713b, 1713c.
  • Each base station 1712a, 1712b, 1712c is connectable to core network 1714 over a wired or wireless connection 1715.
  • a first UE 1791 located in coverage area 1713c can be configured to wirelessly connect to, or be paged by, the corresponding base station 1712c.
  • a second UE 1792 in coverage area 1713a is wirelessly connectable to the corresponding base station 1712a. While a plurality ofUEs 1791, 1792 are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole LIE is in the coverage area or where a sole UE is connecting to the
  • Telecommunication network 1710 is itself connected to host computer 1730, which can he embodied in the hardware and/or software of a standalone server, a cloud-implemented server, a distributed server or as processing resources in a server farm.
  • Host computer 1730 can be under the ownership or control of a service provider, or can be operated by the service provider or on behalf of the service provider.
  • Connections 1721 and 1722 between telecommunication network 1710 and host computer 1730 can extend directly from core network 1714 to host computer 1730 or can go via an optional intermediate network 1720.
  • Intermediate network 1720 can be one of, or a combination of more than one of, a public, private or hosted network; intermediate network 1720, if any, can be a backbone network or the Internet; in particular, intermediate network 1720 can comprise two or more sub-networks (not shown).
  • the communication system of Figure 14 as a whole enables connectivity between the connected UEs 1791. 1792 and host computer 1730.
  • the connectivity can be described as an over-the-top (OTT) connection 1750.
  • Host computer 1730 and the connected UEs 1791, 1792 are configured to communicate data and/or signaling via OTT connection 1750, using access network 1711, core network 1714, any intermediate network 1720 and possible further infrastructure (not shown) as intermediaries.
  • OTT connection 1750 can be transparent in the sense that the participating communication devices through which OTT connection 1750 passes are unaware of routing of uplink and downlink communications.
  • base station 1712 may not or need not be informed about the past routing of an incoming downlink communication with data originating from host computer 1730 to be forwarded (e.g., handed over) to a connected UE 1791. Similarly, base station 1712 need not be aware of the future routing of an outgoing uplink communication originating from the LIE 1791 towards the host computer 1730.
  • host computer 1810 comprises hardware 1815 including communication interface 1816 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of communication system 1800.
  • Host computer 1810 further comprises processing circuitry 1818, which can have storage and/or processing capabilities.
  • processing circuitry 1818 can comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions.
  • Host computer 1810 further comprises software 1811, which is stored in or accessible by host computer 1810 and executable by processing circuitry 1818.
  • Software 1811 includes host application 1812.
  • Host application 1812 can be operable to provide a service to a remote user, such as UE 1830 connecting via OTT connection 1850 terminating at UE 1830 and host computer 1810. In providing the service to the remote user, host application 1812 can provide user data which is transmitted using OTT connection 1850.
  • Communication system 1800 can also include base station 1820 provided in a telecommunication system and comprising hardware 1825 enabling it to communicate with host computer 1810 and with UE 1830.
  • Hardware 1825 can include communication interface 1826 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of communication system 1800, as well as radio interface 1827 for setting up and maintaining at least wireless connection 1870 with UE 1830 located in a coverage area (not shown in Figure 15) served by base station 1820.
  • Communication interface 1826 can he configured to facilitate connection 1860 to host computer 1810. Connection 1860 can be direct or it can pass through a core network (not shown in Figure 15) of the telecommunication system and/or through one or more intermediate networks outside the telecommunication system.
  • hardware 1825 of base station 1820 can also include processing circuitry 1828, which can comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions.
  • Base station 1820 further has software 1821 stored internally or accessible via an external connection.
  • Communication system 1800 can also include UE 1830 already referred to. Its hardware 1835 can include radio interface 1837 configured to set up and maintain wireless connection 1870 with a base station serving a coverage area in which UE 1830 is currently located.
  • Hardware 1835 of UE 1830 can also include processing circuitry 1838, which can comprise one or more programmable processors, application -specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions, UE 1830 further comprises software 1831, which is stored in or accessible by UE 1830 and executable by processing circuitry 1838.
  • Software 1831 includes client application 1832.
  • Client application 1832 can be operable to provide a service to a human or non-human user via UE 1830, with the support of host computer 1810.
  • an executing host application 1812 can communicate with the executing client application 1832 via OTT connection 1850 terminating at UE 1830 and host computer 1810.
  • client application 1832 can receive request data from host application 1812 and provide user data in response to the request data, OTT connection 1850 can transfer both the request data and the user data. Client application 1832 can interact with the user to generate the user data that it provides.
  • host computer 1810, base station 1820 and UE 1830 illustrated in Figure 15 can be similar or identical to host computer 1730, one of base stations 1712a, 1712b, 1712c and one of UEs 1791, 1792 of Figure 14, respectively.
  • the inner workings of these entities can be as shown in Figure 15 and independently, the surrounding network topology can he that of Figure 14.
  • OTT connection 1850 has been drawn abstractly to illustrate the communication between host computer 1810 and UE 1830 via base station 1820, without explicit reference to any intermediary devices and the precise routing of messages via these devices.
  • Network infrastructure can determine the routing, which it can be configured to hide from UE 1830 or from the service provider operating host computer 1810, or both. While OTT connection 1850 is active, the network infrastructure can further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).
  • Wireless connection 1870 between UE 1830 and base station 1820 is in accordance with the teachings of the embodiments described throughout this disclosure.
  • One or more of the various embodiments improve the performance of OTT services provided to UE 1830 using OTT connection 1850, in which wireless connection 1870 forms the last segment.
  • the exemplary embodiments disclosed herein can improve flexibility for the network to monitor end- to-end quality-of-service (QoS) of data flows, including their corresponding radio bearers, associated with data sessions between a user equipment (UE) and another entity, such as an OTT data application or service external to the 5G network.
  • QoS quality-of-service
  • a measurement procedure can be provided for the purpose of monitoring data rate, latency and other network operational aspects on which the one or more embodiments improve.
  • the measurement procedure and/or the network functionality for reconfiguring OTT connection 1850 can be implemented in software 1811 and hardware 1815 of host computer 1810 or in software 1831 and hardware 1835 of UE 1830, or both.
  • sensors can be deployed in or in association with communication devices through which OTT connection 1850 passes; the sensors can participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 1811, 1831 can compute or estimate the monitored quantities.
  • the reconfiguring of OTT connection 1850 can include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect base station 1820, and it can be unknown or imperceptible to base station 1820. Such procedures and functionalities can be known and practiced in the art.
  • measurements can involve proprietary HE signaling facilitating host computer 1810’s measurements of throughput, propagation times, latency and the like.
  • the measurements can be implemented in that software 1811, 1831 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using OTT connection 1850 while it monitors propagation times, errors, etc.
  • FIG 16 is a flowchart illustrating an exemplary method and/or procedure implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station and a UE which, in some exemplary embodiments, can be those described with reference to Figures 9 and 10.
  • the host computer provides user data.
  • substep 1911 (which can be optional) of step 1910, the host computer provides the user data by executing a host application, in step 1920, the host computer initiates a transmission carrying the user data to the UE.
  • step 1930 the base station transmits to the UE the user data which was carried in the transmission that the host computer initiated, in accordance with the teachings of the embodiments described throughout this disclosure.
  • step 1940 the UE executes a client application associated with the host application executed by the host computer.
  • FIG 17 is a flowchart illustrating an exemplary method and/or procedure implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station and a UE which can be those described with reference to Figures 9 and 10. For simplicity of the present disclosure, only drawing references to Figure 17 will be included in this section.
  • the host computer provides user data.
  • the host computer provides the user data by executing a host application.
  • the host computer initiates a transmission carrying the user data to the UE.
  • the transmission can pass via the base station, in accordance with the teachings of the embodiments described throughout this disclosure.
  • step 2030 (which can be optional), the UE receives the user data carried in the transmission.
  • FIG 18 is a flowchart illustrating an exemplary method and/or procedure implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station and a UE which can be those described with reference to Figures 9 and 10. For simplicity of the present disclosure, only drawing references to Figure 18 will be included in this section.
  • step 2110 the UE receives input data provided by the host computer. Additionally or alternatively, in step 2120, the UE provides user data.
  • substep 2121 (which can be optional) of step 2120, the UE provides tire user data by executing a client application.
  • substep 2111 (which can be optional) of step 2110, the UE executes a client application which provides the user data in reaction to the received input data provided by the host computer.
  • the executed client application can further consider user input received from the user.
  • the UE initiates, in suhstep 2130 (which can he optional), transmission of the user data to the host computer.
  • the host computer receives the user data transmitted from the UE, in accordance with the teachings of the embodiments described throughout this disclosure.
  • FIG 19 is a flowchart illustrating an exemplary method and/or procedure implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station and a UE which can be those described with reference to Figures 9 and 10. For simplicity of the present disclosure, only drawing references to Figure 19 will be included in this section.
  • the base station receives user data from the UE.
  • the base station initiates transmission of the received user data to the host computer.
  • the host computer receives the user data carried in the transmission initiated by the base station.
  • the exemplary embodiments described herein provide techniques for pre-configuring a UE for operation in a 3GPP non-terrestrial network (NTN). Such embodiments reduce the time needed for initial acquisition of an NTN (e.g., PLMN) and a cell within the NTN. This can provide various benefits and/or advantages, including reducing UE energy consumption (or, equivalently, increasing UE operational time on one battery charge) and improving user access to services provided by an NTN.
  • NTN non-terrestrial network
  • exemplary embodiments described herein can enable UEs to access network resources and OTT services more consistently and without interruption.
  • the term unit can have conventional meaning in the field of electronics, electrical devices and/or electronic devices and can include, for example, electrical and/or electronic circuitry, devices, modules, processors, memories, logic solid state and/or discrete devices, computer programs or instructions for carrying out respective tasks, procedures, computations, outputs, find/or displaying functions, and so on, as such as those that are described herein.
  • any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses.
  • Each virtual apparatus may comprise a number of these functional units.
  • These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include Digital Signal Processor (DSPs), special-purpose digital logic, and the like.
  • the processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM), Random Access Memory (RAM), cache memory, flash memory devices, optica! storage devices, etc.
  • Program code stored in memory includes program instructions for executing one or more telecommunications and/or data communications protocols as well as instructions for carrying out one or more of the techniques described herein, in some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according one or more embodiments of the present disclosure.
  • device and/or apparatus can be represented by a semiconductor chip, a chipset, or a (hardware) module comprising such chip or chipset; this, however, does not exclude the possibility that a functionality of a device or apparatus, instead of being hardware implemented, be implemented as a software module such as a computer program or a computer program product comprising executable software code portions for execution or being run on a processor.
  • functionality of a device or apparatus can he implemented by any combination of hardware and software.
  • a device or apparatus can also be regarded as an assembly of multiple devices and/or apparatuses, whether functionally in cooperation with or independently of each other.
  • devices and apparatuses can be implemented in a distributed fashion throughout a system, so long as the functionality of the device or apparatus is preserved. Such and similar principles are considered as known to a skilled person.
  • Embodiments of the presently disclosed techniques and apparatuses include, but are not limited to, the following enumerated examples:
  • a method for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element comprising: estimating a first set of one or more parameters for the channel, based on training symbols transmitted from the second device to the first device; estimating a second set of one or more parameters for the channel, based on training symbols or reference signals transmitted from die first device to the second device and based on the first set of one or more parameters.
  • the first set of one or more parameters includes: a path gain magnitude corresponding to each radio channel path between the second device and the first device; a delay corresponding to each radio channel path between the second device and the first device; and an angle of arrival for each radio channel path between the second device and the first device.
  • estimating the first set of one or more parameters for the channel comprises estimating a distribution function for the first set of one or more parameters using a deep generative model and observations of the training symbols transmitted from the second device to the first device, as received at the first device, and using a least-squares estimation approach to jointly estimate, based on the estimated distribution function and the observations of the training symbols, the first set of one or more parameters and a phase change parameter for each radio channel path between the second device and the first device, in the direction toward the first device from the estimated distribution.
  • estimating the second set of one or more parameters comprises using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on observations of training symbols transmitted from the first device to the second device, as received at the first device.
  • estimating the first set of one or more parameters and estimating the second set of one or more parameters are carried out by the first device, and wherein estimating the second set of one or more parameters comprises using a least- squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on quantized channel-state information feedback received from the second device, the quantized channel-state information feedback being based on the reference signals.
  • An apparatus for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element comprising a processing circuit configured to: estimate a first set of one or more parameters for the channel, based on training symbols transmited from the second device to the first device; estimate a second set of one or more parameters for the channel, based on training symbols or reference signals transmited from the first device to the second device and based on the first set of one or more parameters.
  • the first set of one or more parameters includes: a path gain magnitude corresponding to each radio channel path between the second device and the first device; a delay corresponding to each radio channel path between the second device and the first device; and an angle of arrival for each radio channel path between the second device and the first device.
  • the second set of one or more parameters includes a phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device.
  • the processing circuit is configured to estimate the first set of one or more parameters for the channel by estimating a distribution function for the first set of one or more parameters using a deep generative model and observations of the training symbols transmited from the second device to the first device, as received at the first device, and using a least-squares estimation approach to jointly estimate, based on the estimated distribution function and the observations of the training symbols, the first set of one or more parameters and a phase change parameter for each radio channel path between the second device and the first device, in the direction toward the first device from the estimated distribution.
  • processing circuit is configured to estimate the second set of one or more parameters by using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on observations of training symbols transmitted from the first device to the second device, as received at the first device.
  • the processing circuit is configured to estimate the first set of one or more parameters find estimate the second set of one or more parameters, and to estimate the second set of one or more parameters by using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on quantized channel-state information feedback received from the second device, the quantized channel -state information feedback being based on the reference signals.
  • processing circuit is further configured to determine an estimated channel response for the channel in the direction from the first device to the second device, based on the estimated first set of one or more parameters, the estimated second set of one or more parameters, and an antenna array response that depends on tire physical arrangement of the antenna elements of the first device.
  • the processing circuit is further configured to use the transmitter circuit to transmit a signal to the second device from the first device, using antenna weights determined from the estimated channel response for the channel in the direction from the first device to the second device.
  • AoA Angle of arrival AoD Angle of departure BS
  • Base station Compressed sensing CSI Channel state information
  • DFT Discrete Fourier transform
  • DGM Deep generative models
  • FDD Frequency division duplex
  • GAN Generative adversarial network LS Least squares

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Abstract

Techniques for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element. An example method includes the steps of estimating (810) a first set of one or more parameters for the channel, based on training symbols transmitted from the second device to the first device, and estimating (820) a second set of one or more parameters for the channel, based on training symbols or reference signals transmitted from the first device to the second device and based on the first set of one or more parameters.

Description

DEEP GENERATIVE MODELS FOR DOWNLINK CHANNEL ESTIMATION IN
FDD MASSIVE MIMO SYSTEMS
TECHNICAL FIELD
The present disclosure is related to channel estimation in wireless communication systems and is more particularly related to techniques for estimating channel conditions for the downlink in a frequency-division duplexing (FDD) wireless communications utilizing many downlink antenna elements.
BACKGROUND
Wireless communication systems in 3GPP
Figure 1 illustrates a simplified wireless communication system, with a user equipment (UE) 102 that communicates with one or multiple access nodes 103, 104, which in turn are connected to a network node 106. The access nodes 103, 104 tire part of the radio access network (RAN) 100. The network node 106 may be, for example, part of a core network.
For wireless communication systems confirming to the 3rd Generation Partnership Project (3GPP) specifications for the Evolved Packet System (EPS), also referred to as Long Term Evolution (LTE) or 4G, as specified in 3GPP TS 36.300 and related specifications, the access nodes 103, 104 correspond typically to base stations referred to in 3GPP specifications as Evolved NodeBs (eNBs), while the network node 106 corresponds typically to either a Mobility Management Entity (MME) and/or a Serving Gateway (SGW). The eNB is part of the RAN 100, which in this case is the E-UTRAN (Evolved Universal Terrestrial Radio Access Network), while the MME and SGW are both part of the EPC (Evolved Packet Core network). The eNBs are inter-connected via the X2 interface, and connected to EPC via the SI interface, more specifically via SI -C to the MME and Sl-U to the SGW.
On the other hand, for wireless communication systems pursuant to 3GPP specifications for the 3GPP 5G System, 5GS (also referred to as New Radio, NR, or 5G), as specified in 3GPP TS 38.300 and related specifications, the access nodes 103, 104 correspond typically to base stations referred to as 5G NodeBs, or gNBs, while the network node 106 corresponds typically to either an Access and Mobility Management Function (AMF) and/or a User Plane Function (UPF). In this example, the gNB is part of the RAN 100, which in this case is the NG-RAN (Next Generation Radio Access Network), while the AMF and UPF are both part of the 5G Core Network (5GC). The gNBs are inter-connected via the Xu interface, and connected to 5GC via the NG interface, more specifically via NG-C to the AMF and NG-U to the UPF.
Massive multiple-input multiple-output (MIMO) is a key technology in helping to meet the demands of next generation wireless technologies. This technology can be deployed in time- division duplex (TDD) mode, where the uplink and downlink occur in the same frequency band but at different time slots, as well as in frequency-division duplex (FDD) mode, where the uplink and downlink operate simultaneously on different frequency bands. To unlock the full potential of massive MIMO in both FDD and TDD, accurate channel state information (CSI) between the base station (BS) and the user equipment (UE) is required, so that the scheduling BS can take the fullest advantage of opportunities for spatial multiplexing.
In TDD massive-MIMO systems, CSI acquisition can rely on the assumption of channel reciprocity between the uplink and downlink. Even in such systems, however, due to calibration errors between the uplink and downlink radio frequency (RF) chains, such channel reciprocity may not hold. In FDD, due to different band of frequencies in the uplink and downlink, the downlink channel is not the same as the uplink channel, nor can it be inferred from the uplink channel without any downlink training.
In FDD systems, the UE traditionally estimates its own downlink channel from received pilot symbols, or reference symbols, transmitted from the BS. For the BS to use this information in scheduling and beamforming downlink transmissions to the UE, the UE must signal this estimated CSI to the BS. Transmitting the downlink CSI estimated by the UE from the UE to the base station is feasible when only a few antennas are used at the BS. In this case, orthogonal pilots may be provided for each of the relatively small number of antennas, and the transmitting of the estimated CSI to the BS incurs only a small feedback overhead. However, in massive- MIMO systems, where there may be many antenna elements at the BS, it may become prohibitively difficult to provide completely orthogonal pilot patterns for the large number of antennas, and the large feedback overhead required to transmit the estimated CSI to the BS means that this approach may not be feasible.
Alternatively, in practical 4G and 5G systems, the UE may feed a quantized, and possibly wideband, version of the estimated downlink channel information back to the BS to be used for subsequent signal transmission and resource allocation. This wideband quantized CSI information may be utilized by the BS in downlink transmission precoding and link adaptation. However, because this wideband quantized CSI information necessarily lacks frequency- dependent detail, the resulting downlink throughput is inferior to that achieved in TDD systems, where detailed downlink CSI information can be acquired at the BS through channel reciprocity.
Accordingly, improved channel estimation techniques that are better suited for massive- MIMO deployments utilizing FDD are needed. SUMMARY
Various embodiments of the techniques described herein utilize the fact that the channel matrix over each subcarrier is a function of a smaller set of parameters, namely, tire number of propagation paths, the path gains, phases, and delays, as well as the angie-of-arrival (AoA) and angle-of-departure (AoD) for the signals transmitted between the base station and a user equipment (UE). According to these techniques, these parameters are estimated, instead of a channel matrix. Unlike conventional techniques, where a long training sequence is transmitted over all antennas and over all subcarriers, in the techniques described herein these underlying channel parameters may be estimated using a short training signal, over a much smaller set of antennas and subcarriers.
Motivated by the partial reciprocity of uplink and downlink channels, various embodiments of the techniques described in detail below utilize the following steps to estimate lire downlink channel: 1) estimating the frequency-independent underlying channel parameters, namely, the magnitudes of path gains, delays, AoAs and AoDs during the uplink training; and 2) estimating the frequency-specific underlying channel parameters, i.e., the phase of each propagation path, via downlink training. Using this strategy, the burden in FDD downlink channel estimation is shifted to the BS, which is already responsible for uplink channel estimation.
In the first step, the least squares (LS) estimation approach may be used to estimate those parameters that are frequency- independent, or at least very nearly so. The optimization problem in this step is difficult to solve analytically, mainly due to non-linear and non-convex structure of its objective function. To address this problem, deep generative models (DGMs) may be used to capture the distribution of the underlying parameters, with this distribution then being used as a prior, to simplify the optimization problem. In the second step, the parameters estimated in the first step may then be used to estimate the remaining frequency-specific parameters, via an LS technique.
An example method for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element comprises the steps of estimating a first set of one or more parameters for the channel, based on training symbols transmitted from tire second device to tire first device, and estimating a second set of one or more parameters for tire channel, based on training symbols or reference signals transmitted from the first device to the second device and based on the first set of one or more parameters.
The first set of one or more parameters may include (but are not limited to) a path gain magnitude corresponding to each radio channel path between the second device and the first device, a delay corresponding to each radio channel path between the second device and the first device, and an angle of arrival for each radio channel path between the second device and the first device, estimating the first set of one or more parameters for the channel comprises estimating a distribution function for the first set of one or more parameters using a deep generative model and observations of the training symbols transmitted from the second device to the first device, as received at the first device, and using a least-squares estimation approach to jointly estimate, based on the estimated distribution function and the observations of the training symbols, the first set of one or more parameters and a phase change parameter for each radio channel path between the second device find the first device, in the direction toward the first device from the estimated distribution.
The second set of one or more parameters may include a phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device. Estimating the second set of one or more parameters may comprise using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on observations of training symbols transmitted from the first device to the second device, as received at the first device.
Also described in detail herein are several apparatuses configured to carry out all or parts of one or more of the techniques described herein. An example apparatus for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element comprises a processing circuit configured to estimate a first set of one or more parameters for the channel, based on training symbols transmited from the second device to the first device, and estimate a second set of one or more parameters for the channel, based on training symbols or reference signals transmited from the first device to the second device and based on the first set of one or more parameters. Various embodiments of these apparatuses may be configured to carry out any of the several variations described below.
The solutions described herein rely on the fact that the channel matrix over each subcarrier is a function of a smaller set of parameters, namely, the number of propagation paths, the path gains, phases, delays, as well as AoA and AoD. These are the parameters that are estimated, instead of the channel matrix directly. These parameters depend on the physical properties of the propagation environment and on the operating frequencies and, importantly, they are independent of the number of antennas at the BS as well as the number of subcarriers. This allows the channel parameters to be estimated with a much lower pilot overhead. The described solutions exploit the partial reciprocity between uplink and downlink channels, meaning that the frequency-independent downlink channel parameters are first estimated in the uplink. Then, using these estimated parameters, the frequency-specific underlying channel parameters, i.e., the phases of the individual propagation paths, are estimated via downlink training. This strategy ensures that the burden in FDD downlink channel estimation is shifted from the UE to the BS, which is, in any case, responsible for uplink channel estimation.
Unlike existing solutions, the techniques described herein need not assume any sparsity in the underlying channel parameters, instead, this constraint is relaxed, so that it can be considered that the channel parameters have a particular structure that is not necessarily sparse. This structure depends on the physical properties of the environment that the signal is propagating in to. A DGM is used to capture this structure and then incorporate it as a prior into the channel estimation process to improve the accuracy and reduce the pilot overhead. The immediate benefit of the proposed DGM-based channel estimation is that the channel estimation becomes much simpler, where even the least squares technique can provide a significant performance, compared to conventional benchmarks. Furthermore, incorporating the underlying distribution of channel parameters provides resilience to the noise level, i.e., a significant performance improvement is achieved even at very low SNR.
Of course, the present invention is not limited to the above features and advantages. Indeed, those skilled in the art will recognize additional features and advantages upon reading the following detailed description, and upon viewing the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 is a simplified illustration of a wireless communication system. Figure 2 is a block diagram illustrating an example downlink channel estimation algorithm.
Figure 3 illustrates a generative adversarial network (GAN) structure.
Figure 4 illustrates the Reg-GAN training procedure.
Figure 5, Figure 6, and Figure 7 illustrate performance results for a simulation of an implementation of the techniques described herein.
Figure 8 is a flow diagram illustrating an exemplary method according to the techniques described herein.
Figure 9 is a block diagram illustrating an example network node.
Figure 10 is a block diagram illustrating an example UE. Figure 11 is a block diagram of an exemplary wireless network configurable according to various exemplary embodiments of the present disclosure.
Figure 12 is a block diagram of an exemplary user equipment (UE) configurable according to various exemplary embodiments of the present disclosure.
Figure 13 is a block diagram of illustrating a virtualization environment that can facilitate virtualization of various functions implemented according to various exemplary embodiments of die present disclosure.
Figures 14-15 are block diagrams of exemplary communication systems configurable according to various exemplary embodiments of the present disclosure.
Figures 16, 17, 18, and 19 are flow diagrams illustrating various exemplary methods and/or procedures implemented in a communication system, according to various exemplary embodiments of the present disclosure.
DETAILED DESCRIPTION
In the discussion that follows, techniques for channel estimation will be described in the context of downlink channel estimation, in a wireless system employing FDD, i.e., where the base stations transmit to user equipment (UEs) in one frequency band while receiving from die UEs in another. Thus, references to the “downlink” refer to transmissions from the base station to die UE, while references to the “uplink” refer to transmissions from the UE to the base station. The description below also describes these techniques in the context of massive-MIMO, where the base station employs a relatively large number (perhaps tens or even hundreds) of antenna elements for transmitting to and receiving from the UEs it serves. In such systems, the UEs may have only one or a relatively small number of antennas. However, the present techniques are not limited to estimating downlink channels in FDD systems, nor are they limited to use in massive MIMO systems. Accordingly, these techniques should be understood as more generally applicable to estimating a channel between first and second devices, where at least one of the devices (and possibly both) uses multiple (and perhaps many ) antenna elements for transmitting to and receiving from the other.
Studies on FDD channel estimation have aimed at reducing feedback overhead by exploiting die sparsity in the underlying channel parameters, e.g., path gain, direction- of- arrival (DoA), and direction-of-departure (DoD). Leveraging this sparsity, the channel can be reformulated using the channel gain, DoA, and DoD. One way to recover these underlying channel parameters is to use compressive sensing (CS) techniques, e.g., as described in C. R. Berger, Z. Wang, J. Huang and S. Zhou, "Application of compressive sensing to sparse channel estimation," IEEE Commun. Mag., vol. 48, pp. 164-174, 2010. However, CS-based techniques require strong channel sparsity in a Discrete Fourier Transform (DFT)-basis, which is not strictly held in some cases. In addition, CS-based techniques also require a large number of pilots, and these techniques tire often iterative and computationally intensive during decoding, which may lead to long delays.
As an alternative to CS-based techniques, some proposed techniques for downlink CS1 estimation rely on spatial reciprocity between uplink and downlink channels operating on close- by carrier frequencies. Given that the uplink and downlink communication occur in the same propagation environment, the uplink channel estimates can be used in the estimation of downlink channel.
Uplink/downlink reciprocity can be considered in two ways. In one way, one can assume full reciprocity, where the multi-path components of the channel (including phase, amplitude, delay, angle of arrival, departure, etc.) are the same for both uplink and downlink. Based on this assumption, it has been proposed to eliminate downlink training and feedback in LTE systems, e.g., in D. Vasisht, S. Kumar, H. Rahul and D. Katabi, "Eliminating Channel Feedback in Next- Generation Cellular Networks," in Proc. ACM SIGCOMM Conf., 2016.
However, there is not enough evidence to confirm such full-reciprocity assumptions, especially when the duplex gap between uplink and downlink transmission bands is large. For example, for LTE band 4, the uplink band is 1710-1755 MHz while the downlink band is 21 J O- 2155 MHz, which means that the duplex gap is 400 MHz. This is much too large to assume full reciprocity in the channel.
In addition, it has been shown via measurements and theoretical investigations that not ail channel parameters are reciprocal in the uplink and downlink. In particular, there is no reciprocity between the phases of different multi-path components for FDD uplink and downlink channels, as shown in Z. Zhong, L. Fan and S. Ge, "FDD Massive MIMG Uplink and Downlink Channel Reciprocity Properties: Full or Partial Reciprocity?", CoRR, vol. ahs/1912.11221v2, 2019. Intuitively, this is not surprising, given the sensitivity of the phase to operating frequency. Therefore, uplink and downlink channels Eire only partially reciprocal.
The techniques described herein utilize the fact that the channel matrix at a given frequency, e.g., at a given subcarrier of a signal transmitted using multicarrier modulation techniques like Orthogonal Frequency Division Multiplexing (OFDM) or Discrete Fourier Transform-spread OFDM (DFT-OFDM), is a function of a smaller set of parameters, namely, the number of propagation paths by which the radio signal travels from one device to the other, the gains, phases, and delays for these paths, as well as the AoA and AoD for each path. According to the presently disclosed techniques, these parameters are estimated, rather than directly estimating the channel matrix. Unlike conventional techniques, where a long training sequence is transmitted over all antennas and over all subcarriers, the techniques described here estimate these underlying channel parameters using a short training signal, over a much smaller set of antennas and subcarriers.
Motivated by the partial reciprocity of uplink and downlink channels, various embodiments of the techniques described in detail herein utilize the following steps to estimate the downlink channel: 1) estimating the frequency-independent underlying channel parameters, namely, the magnitudes of path gains, delays, AoAs and AoDs during the uplink training; and 2) estimating the frequency -specific underlying channel parameters, i.e., the phase of each propagation path, via downlink training. Using this strategy, the burden in FDD downlink channel estimation is shifted to the BS, which is already responsible for uplink channel estimation.
In the first step, the least squares (LS) estimation approach may be used to estimate the frequency-independent parameters. The optimization problem in this step is difficult to solve analytically, mainly due to non-linear and non-convex structure of its objective function. To address this problem, deep generative models (DGMs) may be used to capture the distribution of the underlying parameters, with this distribution then being used as a prior to simplify the optimization problem. In the second step, the frequency-independent parameters estimated in the first step may then be used to estimate the frequency-specific parameters, via an LS technique. In both steps, the optimization problem may be carried out numerically using the gradient descent algorithm.
The techniques described herein exploit the fact that the channel matrix over each subcarrier is a function of a smaller set of parameters, namely, the number of propagation paths, the path gains, phases, and delays, as well as the AoA and AoD, find estimate these parameters instead of estimating the channel matrix directly. These parameters depend on the physical properties of the propagation environment and on the operating frequencies, and, importantly, they are independent of the number of antennas at the BS as well as the number of subcarriers. Unlike conventional techniques, where a long training sequence is transmitted over all antennas and over all subcarriers, these underlying channel parameters may be estimated using a short training signal over a much smaller set of antennas and subcarriers.
Various embodiments of the techniques described herein use a DGM framework that can learn the underlying distribution of the channel parameters as a function of the propagation environment, which is complex and difficult to obtain analytically. In particular, the generative adversarial network (GAN) structure may be utilized to find a deterministic mapping function (i.e., a generator) that is capable of drawing samples from tire underlying distribution of channel parameters, by feeding it with samples from a low- dimensional standard Gaussian distribution. By exploiting the uplink/downlink partial reciprocity, the techniques described herein reduce the pilot and feedback overhead for downlink channel estimation. In addition, the sparsity assumption in channel parameters is relaxed. Instead, using a generator, obtained from GAN, the learned structure of channel parameters is incorporated as a prior into the channel estimation procedure. Consequently, the optimization problem operates in a low-dimensional subspace, with a dimensionality defined by the generator and, importantly, is independent of the number of received pilots. Therefore, a significant reduction in computational complexity as well as CSI feedback overhead may be achieved.
Simulation results indicate that the DGM-based channel estimation techniques described here outperform the conventional channel estimation technique in practical ranges of signal-to- noise ratio (SNR). This is mainly due to the capabilities of the generator in representing the underlying distribution of the channel parameters. Incorporating this prior knowledge into the channel estimation significantly improves the performance, even at low SNR. This indicates how the proposed technique is resilient to the noise level. Additionally, for fixed SNR, it can be shown that the proposed techniques may yield near-optimal performance using only few pilot measurements. This can significantly reduce the pilot overhead in FDD massive MIMO systems.
System Model
For purposes of explaining the techniques, a single-cell single-user communication system is considered. The base station (BS) in this model system is equipped with a uniform linear array (ULA) with M » 1 antenna elements. For the sake of simplicity of presentation, it is assumed that the UE is single antenna. Nevertheless, the proposed algorithm can be directly extended to the case where the UE is equipped with multiple antennas, either by assuming a common set of propagation parameters, i.e., path gains, delays, and AoA and AoD, for all the channels to different UE antennas, or by assuming a distinct set of propagation parameters for each UE antenna, or a combination thereof.
The communication between the BS and UE is performed in FDD mode. In the uplink, the UE communicates with the BS at frequency ƒup, while in the downlink the BS communicates with the UE at frequency ƒdl. Both uplink and downlink frequency bands are of bandwidth B.
Received Signal Model
It is assumed that orthogonal frequency division duplex (OFDM) technology is used in both uplink and downlink commutation with K subcarriers. Let 7iu denote the set of subcarrier indices used for uplink training. During the uplink training at the kth subcarrier, the UE transmit training symbol sk, k ∈ KU, where |sfej2 = PT, and PT is the transmit power. The received signal at BS over the k-th subcarrier is given by:
Figure imgf000011_0001
where tif is an M X 1 uplink channel vector between the BS and the UE and is an
Figure imgf000012_0011
M X 1 noise vector at the k-tb subcarrier that is drawn independently and identically from a complex Gaussian distribution with zero mean and variance ,
Figure imgf000012_0010
In the downlink, let 3 ' Cd and >fd denote, respectively, the set of subcarrier indices and the set of antenna elements dedicated for training. It is assumed that p training symbols are transmitted over the m-th antenna (m ∈ Md) over the k-th subcarrier (k ∈ Kd).
The received signal at the UE over the k-th subcarrier at the f-th time slot is given by and the
Figure imgf000012_0003
symbols transmitted at the i-th time slot over the k-th subcarrier across all antenna elements in the set Md. It is assumed that denotes the noise term at the i-th
Figure imgf000012_0005
time slot over the k-th subcarrier, drawn independently and identically from a complex Gaussian distribution with zero mean and variance s2. Note that, in general,
Figure imgf000012_0004
£ M. For the case when !.Mdj < M, it is assumed that the antenna elements in the set Md do not transmit during the downlink training, where M is the compliment of the set Md.
Figure imgf000012_0006
Collecting the received signal across all p training time slots over the k-th subcarrier, the received signal can be written as: where Sf is a p x \Md j matrix of downlink training symbols with on its i-th row,
Figure imgf000012_0007
Channel Model
To characterize the wireless channel between the BS antenna array and the UE, the following geometric channel model is considered. It is assumed that the propagation channel between the BS and the UE in the uplink consists of paths. Through the 1-th path, the signal
Figure imgf000012_0009
travels the distance dɭ between the UE and the BS. Also, let
Figure imgf000012_0008
[0,2π ], and denote the random path gain, the random phase
Figure imgf000012_0002
change, the random azimuth angle of the signal received, and the random delay corresponding to the i-th path in the uplink, respectively. Using this notation, the channel response between the UE and the BS at the k-th subcarrier can be given by:
Figure imgf000012_0001
where is the wavelength of the K-th subcarrier in the uplink, and c is the speed of light
Figure imgf000013_0007
. Since kB/K is very small compared to /fp, the subcarrier index in the array response a(θɭ, l) can he ignored.
Denoting d as the antenna spacing in the ULA, the array response is given by:
Figure imgf000013_0001
Similarly, the downlink communication channel at the fcth subcarrier is given by: where Ldl is number of path in the downlink, is the wavelength of the downlink carrier frequency,
Figure imgf000013_0006
respectively denote the random path gain, the random phase change, the random azimuth angle of the received signal and the random delay corresponding to the ith path in the downlink. b (0j, λ) is a subvector of a(θɭ, λ) where its ith entry with Md being the ith
Figure imgf000013_0002
smallest member of set Md.
FDD Partial Reciprocity in FDD communication, since uplink and downlink communication between the BS and UE occurs over different frequency bands, reciprocity between does not hold in
Figure imgf000013_0005
general. However, it has been shown that since uplink and downlink channels share the same propagation environment, partial reciprocity exists between uplink and downlink channels.
It is observed via measurements and verified using theoretical analysis that a portion of uplink and downlink channel parameters are frequency independent or at least largely so. Specifically, since the signal of each propagation path travels the same distance at the same speed in both uplink and downlink communication link, the delay of each propagation path is the same in both uplink and downlink, i.e., Furthermore, it is shown via both
Figure imgf000013_0004
measurement and ray tracing simulations, that the directional and the power gain of each communication path are the same in both uplink and downlink, i.e.,
Figure imgf000013_0003
θ; and On the other hand, the existing measurements show that This
Figure imgf000013_0009
Figure imgf000013_0008
implies that the channel translation requires downlink training.
Example Al gorithm
Leveraging the partial reciprocity of channel parameters in FDD, a strategy for downlink channel estimation as explained in the sequel may be developed. This strategy aims to estimate the frequency-independent parameters during the uplink training, while the frequency-specific channel parameters are being estimated via downlink training. To better characterize the channel estimation process, the following variables may be defined:
Figure imgf000014_0004
Figure imgf000014_0007
using uplink training, as these parameters are common to both the uplink and downlink channels, while ld is estimated during downlink training, with a much lower training overhead. Figure 2 shows a block diagram of an example downlink channel estimation algorithm using the techniques described herein. The algorithm utilizes received uplink pilots to perform uplink training to estimate the common uplink/downlink parameters q as well as the uplink-only parameters lu. As used herein, the terms “pilots,” “training symbols,” “training signals,” “reference signals,” and “reference symbols” are generally used interchangeably, to refer to symbols of a transmitted signal having values that are known to the receiver. The terms “training symbols” or “training signals” may suggest a series of such symbols transmitted expressly for the purpose of training an algorithm used for channel estimation purposes, but reference symbols transmitted for other purposes may be repurposed as training symbols/signals. Next, the common parameters from the estimated uplink parameters are chosen and utilized in estimating the downlink parameters Φd, along with information obtained from downlink pilots. Finally, the downlink channel is e
Figure imgf000014_0005
mated, or reconstructed, by plugging the estimated common and downlink parameters in the above expression for . Details of this process are presented in the next two subsections.
Figure imgf000014_0008
Uplink Training Here, the aim is to estimate qup using the uplink training. To do so, observations of the uplink pilots across all subcarriers are stacked well as nup are defined. The co
Figure imgf000014_0006
llected received signal in the uplink can then be expresse
Figure imgf000014_0003
d as: where A(xup) is a non-lin
Figure imgf000014_0001
ear function of xup. Using the LS estimation approach, the estimate of tup is obtained by solving the following minimization problem:
Figure imgf000014_0002
Figure imgf000015_0001
The above optimization problem is difficult to solve analytically, due to the non-linear structure of the objective function. Even solving numerically (using, for example, coordinate descent or gradient projection) is challenging, mainly because of the high dimensionality of the search space, as well as because the objective function is not convex. Therefore, any numerical approach will suffer from the convergence issues. To alleviate such difficulties, instead of solving the above problem directly, an approach based on DGMs is used. Variational auto- encoders (VAEs) and GANs are well-known examples of DGMs. In this technique, the tuple (a, r, Q) is replaced by G (z) , which is a mapping from the z domain to the domain of (a, Ʈ, θ) . Doing so implicitly assumes that that the tuple (a, Ʈ, θ) is described in terms of a low dimension z through vector function (?(·). How to find G(z) using a GAN architecture is elaborated later, below.
Given G(z), the LS problem may be rewritten as:
Figure imgf000015_0002
Note that the above two optimization problems are not equivalent. However, given the above assumption, there is no optimality loss, as the optimal (a, Ʈ, θ) in the first problem is on the manifold that G (z) generates samples on. Therefore, the optimal solution to the first problem is in tire range of G(z), Furthermore, it can be shown that the constraint in the above optimization problem can be ignored, such that the following unconstrained optimization problem can be solved:
Figure imgf000015_0003
To efficiently solve this problem, z and f" are jointly and iteratively updated, using the gradient descent algorithm. To do this, the mapping G(- ) is needed. How this mapping can be determined using the GAN architecture is described later, below.
Downlink Training
Relying on the partial reciprocity of channel parameters in FDD, else frequency- independent channel parameters (i.e., (a, r, 8)) as estimated during the uplink training are used to estimate Φd , based on downlink training but using fewer training symbols in downlink. To estimate Φd:. the LS technique is used, similar to what was described above for the uplink training. Given is defined, as are
Figure imgf000015_0004
Figure imgf000015_0005
Figure imgf000015_0006
Now, stacking the observations across all subcarriers in the set , and defining in the
Figure imgf000016_0001
ownlink can be given by:
Figure imgf000016_0002
Defining the following LS problem may be solved: e above optimization problem, the constraint
Figure imgf000016_0003
is ignored, because the object periodic function of for integer ^. The above optimization problem is an unconstrained least-squares roblem and can be solved using the gradient descent algorithm.
Figure imgf000016_0004
In a variation of the techniques described above, quantized downlink CSI feedback from he UE can be used to estimate Φd. In 4G and 5G systems, for example, the UE can periodically ransmit CSI to the BS, with that CSI including a channel quality indicator (CQI), rank indicator RI), and precoding matrix indicator (PMI). This information can be used to construct an stimate of the downlink channel Given a set of virtual downlink training symbols irtual received signal vector can be constructed from this estimated downlink hannel, and this set used for by solving:
Figure imgf000016_0005
whe containing the downlink subcarrier indices for which the C
Figure imgf000016_0006
SI available. Deep Generative Model The previous section described an LS-based procedure to obtain the parameters that efine the downlink channel. A crucial step in this procedure is determining the mapping hat leads to qup. This section explains how to find ^"^^ using DGMs. First, DGMs are briefly ntroduced. The core idea of DGMs is to represent a high-dimensional and complex distribution of ata q (in this case (a, Ʈ, θ) using a deterministic mapping over a low-dimensional random ector ^ which has a well-behaved probability density function (e.g., uniform or Gaussian). pecifically, a DGM is a function ^"^^ that maps a low-dimensional random vector z ∈ Rd ypically drawn independently from a Gaussian or uniform distribution, to a high-dimensional vector where n ≥ 8 (in practice, due to the structure of q, % can be much greater than d). The mapping G(·) is determined such that the distribution of xg, generated by G(·), matches the distribution of the real-world data vector x. in other words, using the transformation G(-) from a simple and low-dimensional distribution, samples that belong to the same manifold as x does are generated. This implies that any generated sample from G(-) already satisfies the constraints in the original downlink training optimization problem. The function G (·) is parameterized by a deep neural network, which is trained in an unsupervised way as explained below. One well-known example of DGM is the family of GANs. The GAN architecture may he used to find G(·), as described below.
GANs
GANs are among the most powerful DGMs that are used to capture the distribution of data. The basic structure of a GAN is illustrated in Figure 3. As shown in Figure 3, a GAN consists of two fully-connected feed-forward neural networks, namely a generator network a discriminator network DWd(x): Rn → [0,1] where Wg and Wd
Figure imgf000017_0004
represent, respecti vely, the sets of weights of the generator and discriminator networks. The generator network Gw (z) maps the input random vector z ~ Pz(z), into the data space xg ~
Pg(x). Here, Pz(z) represents the pdf of the input random vector z and is usually chosen to be Pz(z) = N(0,Ia), and Pa(x) represents the pdf of the generated samples. The discriminator network, Dw (·), receives the two sets of inputs: one set consists of the samples xg generated by Gwa iz) and the other set consists of the true samples x. The discriminator network Dwd (·) is meant to correctly distinguish between the fake samples xq and the true samples x. Effectively, the goal of the generator network Gwg (·) is to generate fake samples, such that the discriminator network DWd(x) cannot distinguish them from the true samples. Meanwhile, the goal of the discriminator network ϋm> (·) is to correctly distinguish between xg and x. To do so, Dwd (x) provides the probability that x is a real sample. If Pr(x) represents the distribution of true samples, the goal is to have Pg(x) = Pr(x), by optimally adjusting Wg and Wd. How to adjust the weights is described below.
Training of GANs are trained simultaneously and iteratively via the following two-
Figure imgf000017_0003
player min-max game:
Figure imgf000017_0001
where is the loss function and is defined as:
Figure imgf000017_0002
Figure imgf000018_0005
Note that the objective of training Gwg (·) is to fool DWd, (·) by generating the realistic samples xg such that ( .)signs a high probability to xg being true samples. This can be done by maximizing
Figure imgf000018_0001
(or equivalently, minimizing over Wg,
Figure imgf000018_0010
as given in the second term in the above expression for L In the meantime,
Figure imgf000018_0011
Divrf(·) aims to correctly distinguish xg, generated by Gwg(·)» from the hue samples x. In other words, Dw
Figure imgf000018_0002
is meant to be close to zero while Dw (x) has to be close to one. Therefore,
Wd is chosen such that log DW j (x) + log 1 - , is maximized. Such a
Figure imgf000018_0003
competitive interplay between Gw (z) and DWi (x)converges to an equilibrium where Pg(x) = Pr(x). At this point, the generator produces realistic xq such that the discriminator is unable to differentiate between x and xg, Le.,
Figure imgf000018_0004
= \-
Mode collapse issue
Despite the success ofGANs in learning the underlying distribution of data, training of GANs is challenging, due to the instability in the training and sensitivity to the hyperparameters. Mode collapse, an issue that can hinder the training of GANs, refers to a collapsing of large volumes of probability mass into a few modes. This means that although the generator produces meaningful samples, these samples belong to only few modes of the data distribution; therefore, the samples produced by the generator do not fully represent the underlying distribution of real data.
Different solutions have been proposed to address the mode collapse issue. In several embodiments of the techniques described herein, a regularized GAN (Reg-GAN) may be used. Compared to the original form of G ANs, a Reg-GAN uses a regularization term that is meant to penalize the missing modes. To do so, together with the generator and discriminator, an encoder network Ew (x) : x → z is trained to help the generator avoid the missing modes, where We is the set of weights of the encoder network.
To perform this training, two regularizing terms are considered in training of the
Figure imgf000018_0009
and Gw^(·) networks. One regularize: is based on the fact that if is a good auto-
Figure imgf000018_0008
encoder, then, for any x0 E M0, where M0 is the set of missing modes,
Figure imgf000018_0007
obtained. Therefore is the loss function considered in the training
Figure imgf000018_0006
of EWe(·) and Gw (·) networks, as a regularizer to penalize the generator for any missing samples including the samples of minor modes. A second regularizer,
Ex [iogDwd( Gwg, (EWe(x)) )j is used to encourage GWr ^ EWe( .) ) to generate realistic samples such that Dw (·) assigns, to these samples, a high probability of being a true sample. Therefore, a fair probability distribution is achieved across different modes.
The regularized loss functions for the generator, the encoder and discriminator are respectively given by:
Figure imgf000019_0003
where A* and l2 are the regularizer's coefficients.
During the training, tire aim is to find Wd, W^ and We. This can be done by jointly and iteratively maximizing TD, and minimizing TG and TE. Before starting the training, Wd, W^ and We are randomly initialized. In each of the subsequent iterations, a mini-batch of size m is sampled from each of the training set and noise samples j. For fixed W^
Figure imgf000019_0001
Figure imgf000019_0002
and Wg, Wd is updated by ascending in the direction of the gradient of TD. Then, while fixing W4 and We, Wg is updated by descending in the opposite direction of the gradient of TG. Similarly, We is updated by descending in the opposite direction of the gradient of TE for fixed l¾ andl¾.
The Reg-GAN training procedure is summarized in Figure 4. Note that the gradient- based update can be implemented using any standard gradient-based update. In the numerical simulations, to speed up the convergence, a momentum-based gradient update may be used.
Simulation Results
Throughout the simulations, an indoor massive MIMO scenario is considered. An example of such scenario is the "11 " scenario provided by DeepMIMO dataset. (A. Alkhateeb, "DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications," CoRR, vol. abs/1902.06435, 2019.) The "11" scenario comprises a 10 X 10 X 2.5 meters room with 2 tables inside the room. There are M = 64 antennas mounted on the ceiling. The users are spread inside the room across the x-y plane with each of them being 1 meter above the floor. The communication between the base station antennas and each user is in FDD mode and uses X OFDM subcarriers. The uplink and downlink operating frequencies are respectively 2.4 GHz and 2.5 GHz. The following simulation scenarios are considered: • UP-MMSE: In this scenario, MMSE-based channel estimation is used for uplink training only. It is assumed that all 64 antennas as well as all K=16 subcarriers are used for training. • UP-GAN: This is a DGM-based channel estimation technique in the uplink. Similar to the MMSE-based technique, all 64 antennas as well as all 16 subcarriers are used in the uplink. In this scenario, the unconstrained LS optimization problem is solved to find ­ (or ":^ A^ @^) and =u followed by channel reconstruction using the uplink channel model. • DL-MMSE: In this scenario, the MMSE-based channel estimation is used for downlink training without using any information from the uplink channel training. Here all ¹º antennas as well as all ^¹ subcarriers are used for downlink training. • DL-GAN: In this scenario, the aim is to obtain » using the estimates of obtained using UP-GAN, and Φd. Φd is found using ": obtained via the uplink training. • DL-Full-Reciprocity: In this scenario, as in the DL-GAN scenario, the aim is to obtain » . Here, there is no downlink training, and it is assumed that full reciprocity exists ween the uplink and downlink. Specifically, it is assumed and then »
Figure imgf000020_0002
is reconstructed using P:
Figure imgf000020_0003
• DL-Modified-R2F2: In this scenario, the mapping function ^^^"^^ is ignored, and the constrained LS optimization problem is directly solved for the uplink analytically, using an approach based on the R2F2 algorithm proposed in D. Vasisht, S. Kumar, H. Rahul and D. Katabi, "Eliminating Channel Feedback in Next-Generation Cellular Networks," in Proc. ACM SIGCOMM Conf., 2016. Note that the R2F2 algorithm, in its original form, is based on the assumption that the downlink channel parameters are the same as its uplink counterparts. As explained, this assumption is found to be invalid due to the frequency dependency of . To account for the disparity in phases, the constrained LS optimizat
Figure imgf000020_0001
ion problem for the uplink is first solved using a similar technique provided in D. Vasisht, S. Kumar, H. Rahul and D. Katabi, "Eliminating Channel Feedback in Next-Generation Cellular Networks," in Proc. ACM SIGCOMM Conf., 2016. Then, using the so obtained ":^ A^ @^, the downlink LS optimization problem is solved to find Throughout the simulations, this technique is referred to as DL- Modified-R2F2. In this scenario, it is assumed that . To implement DL-Modified-R2F2, the uplink LS optimization problem is solved using coordinate descent. This approach involves the division of the parameters of the optimization problem into smaller sets for which the constraints are separable. The optimization is then carried out over each of these sets iteratively while treating the variables in the other sets to be constants, thereby reducing the computation complexity. Conceptually, in the feasible set, the algorithm iterati vely converges to a minimum by taking strides along directions parallel to the parameter-set axes. In this case, the separability of constraints is obtained by taking a,t, Q, and Φd as four parameter sets all of which have box constraints. Since the objective function is non- convex, the global optimality of this technique is not guaranteed. To avoid local minima, the optimization is initiated from 10 randomly chosen initial points and choose the solution with the least value of the objective function.
Note that the above R2F2-based technique is highly sensitive to the gradient descent step that is chosen for each parameter. This technique is also very slow since it requires multiple random initializations as well as a large number of iterations to converge.
The impact of the proposed technique on achievable rate has been explored. Figure 5 plots the rate per subcarrier versus SNR, for |kdj = |kdj = K = 16, and p = |kdj. In the uplink, the UP-GAN outperforms the UP-MMSE technique in practical range of SNR. This is due to a better estimate of channel matrix in this range of SNR. This, by itself, is attributed to the rich prior stored in tire weights of network. For the same reason, the DL-GAN yields a
Figure imgf000021_0001
much better rate performance compared to what DL-Full-Reciprocity and DL-Modified-R2F2 do. Note that the saturation in rate at high SNR is related to the error floor in channel estimation, which comes from the limits in representation capability of GWq (z) .
Figure 6 shows the rate performance of the DGM-based technique versus p , with jXd| = p, |kuj = K = 16, and SNR= 20 (dB). Figure 7 shows the rate performance of the DGM-based technique versus and SNR- 20 (dB). (p is
Figure imgf000021_0002
the number of training symbols used; |Mdj is the number of antenna elements.) Here, it is assumed that the frequency-independent features (a, t, Q) are estimated in the uplink as in the UP-GAN scenario. The goal here is to show how the number of downlink pilot observations affect the rate performance of the proposed technique. As shown in these figures, the rate quickly saturates by increasing p or |Mdj . In other words, adding more training observations does not always improve the rate performance. This can be explained as follows. Due to partial reciprocity in downlink, the proposed DGM-based technique will use the estimate of (a, r, Q) obtained via the uplink training. Since these features are not perfectly estimated, mainly because of the representation capability of Gwg (·). they will affect the estimation of Φd in downlink in a way that adding more training observation does not improve the estimation of Φd, and therefore, the estimate of channel. The same argument holds true when the training power is increased, as shown in Figure 6.
As should be apparent from the detailed description and explanation provided above, improved channel estimation can be obtained by learning the distribution of channel parameters. The unknown underlying distribution of the channel parameters is a function of the propagation environment and is complex and difficult to obtain analytically. As detailed above, DGMs may he used to learn this function. In particular, a GAN structure can be used to find a deterministic mapping function (i.e., a generator) that is capable of drawing samples from the underlying distribution of channel parameters, by feeding it with samples from a low-dimensional standard Gaussian distribution.
These techniques are particularly useful for solving problems arising from attempting to perform channel estimation in an FDD scenario. As was shown above, partial reciprocity between the uplink and downlink channels in an FDD scenario can be exploited to reduce the pilot and feedback overhead in downlink.
Using the disclosed techniques, the sparsity assumption in channel parameters is relaxed. Instead, by using a generator obtained from GAN, a learned structure of channel parameters may he incorporated into the channel estimation procedure as a prior. The result of this is that the optimization problem operates in a low-dimensional subspace, with a dimensionality defined by the generator. Importantly, the optimization problem is independent of number of received pilots. Therefore, a significant reduction in computational complexity as well as CSI feedback is achieved, while maintaining a simple channel estimation.
In view of the detailed examples and explanation provide above, it will he appreciated that the process flow diagram shown in Figure 8 illustrates an example method for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element. The first device may be a base station, while the second device may be a UE, for example. Note that the term “antenna element” may refer to a single discrete antenna, or to a combination of antenna structures that are operated as a unit. Note also that while the second device need only have one antenna element, it may have several (or many), in some embodiments of the illustrated technique.
As shown at block 810, the method includes a step of estimating a first set of one or more parameters for the channel, based on training symbols transmitted from the second device to the first device, i.e., training symbols transmitted in a first direction, such as in the uplink direction . As shown at block 820, the method further comprises estimating a second set of one or more parameters for the channel, based on training symbols or reference signals transmitted from the first device to the second device, i.e., in a second direction, such as the downlink direction, and based on the already estimated first set of one or more parameters.
The first set of one or more parameters may include (but are not limited to) a path gain magnitude corresponding to each radio channel path between the second device and the first device, a delay corresponding to each radio channel path between the second device and the first device, and an angle of arrival for each radio channel path between the second device and the first device. These parameters are generally (although not necessarily strictly) frequency- independent, and thus can be considered to be applicable to both directions. The estimation shown at block 810 may also produce phase change parameters for each radio channel path between the second device and the first device (the first direction), hut these phase change parameters are frequency -dependent, and are not subsequently used to estimate the second set of parameters, for the second direction.
The second set of one or more parameters estimated in the step shown at block 820 may include a phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device. This is a frequency- dependent parameter, and such a parameter may be obtained for each of several frequencies, e.g., for each of multiple subcarrier frequencies. As such, these parameters are used for constructing the channel estimate for the second direction, i.e., from the first device towards the first device.
Referring to the step shown in block 810, in various embodiments of the techniques described herein, estimating the first set of one or more parameters for the channel comprises estimating a distribution function for the first set of one or more parameters using a deep generative model and observations of the training symbols transmitted from the second device to the first device, as received at the first device. This is shown at block 812 in Figure 8; details of this process were provided above. In these embodiments, estimating the first set of one or more parameters for the channel further comprises using a least-squares estimation approach to jointly estimate, based on the estimated distribution function and the observations of the training symbols, the first set of one or more parameters and a phase change parameter for each radio channel path between the second device and the first device, in the direction toward the first device from the estimated distribution. This is shown at block 814 in Figure 8; again, details of this process were provided above. The deep generative model may be based on a generative adversarial network (GAN), for example.
In some of these and in other embodiments, estimating the second set of one or more parameters, as shown at block 820 of Figure 8, may comprise using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on observations of training symbols transmitted from the first device to the second device, as received at the first device.
This is shown at block 822 in Figure 8.
In a variation of this approach, estimating the first set of one or more parameters and estimating the second set of one or more parameters are both carried out by the first device, and estimating the second set of one or more parameters comprises using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on quantized channel-state information feedback received from the second device, where die quantized channel-state information feedback being based on the reference signals. As was described above, the quantized channel-state information feedback may be used to estimate a downlink channel model, which in turn may be used to generate “virtual” training signal vectors, for use in training the deep generative model algorithm.
As shown at block 830, the method may comprise determining an estimated channel response for the channel in the direction from the first device to the second device, based on the estimated first set of one or more parameters, the estimated second set of one or more parameters, and an antenna array response that depends on the physical arrangement of the antenna elements of the first device. As shown at block 840, the method may further comprise transmitting a signal to the second device from the first device, using antenna weights determined from the estimated channel response for the channel in the direction from the first device to the second device.
Figure 9 shows an example network node 30 that may correspond to any of the devices described herein. In particular, network node 30 may be a base station configured to carry out all or parts of the techniques described herein. Network node 30 may be an evolved Node B (eNodeB), Node B or gNB, for example. Network node may represent a radio network node such as base station, radio base station, base transceiver station, base station controller, network controller, NR BS, Multi-cell/multicast Coordination Entity (MCE), relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH), or a multi-standard BS (MSR BS).
In the discussion herein, network node 30 is described as being configured to operate as a cellular network access node in an LTE network or NR network, but network node 30 may also correspond to similar access nodes in other types of network. Those skilled in the art will readily appreciate how each type of node may be adapted to carry out one or more of the methods described herein, e.g,, through the modification of and/or addition of appropriate program instructions for execution by processing circuits 32.
Network node 30 facilitates communication between wireless terminals (e.g., UEs), other network access nodes and/or the core network. Network node 30 may include communication interface circuitry 38 that includes circuitry for communicating with other nodes in the core network, radio nodes, and/or other types of nodes in the network for the purposes of providing data and/or cellular communication services. Network node 30 communicates with wireless devices using antennas 34 and transceiver circuitry 36. Transceiver circuitry 36 may include transmitter circuits, receiver circuits, and associated control circuits that are collectively configured to transmit and receive signals according to a radio access technology, for the purposes of providing cellular communication services.
Network node 30 also includes one or more processing circuits 32 that are operatively associated with the transceiver circuitry 36 and, in some cases, the communication interface circuitry 38. Processing circuitry 32 comprises one or more digital processors 42, e.g., one or more microprocessors, microcontrollers, Digital Signal Processors (DSPs), Field Programmable Gate Arrays (FPGAs), Complex Programmable Logic Devices (CPLDs), Application Specific Integrated Circuits (ASICs), or any mix thereof. More generally, processing circuitry 32 may comprise fixed circuitry, or programmable circuitry that is specially configured via the execution of program instructions implementing the functionality taught herein, or some mix of fixed and programmed circuitry. Processor 42 may be multi-core, i.e., having two or more processor cores utilized for enhanced performance, reduced power consumption, and more efficient simultaneous processing of multiple tasks.
Processing circuitry 32 also includes a memory 44. Memory 44, in some embodiments, stores one or more computer programs 46 and, optionally, configuration data 48. Memory 44 provides non -transitory storage for the computer program 46 and it may comprise one or more types of computer-readable media, such as disk storage, solid-state memory storage, or any mix thereof. Here, “non-transitory” means permanent, semi-permanent, or at least temporarily persistent storage and encompasses both long-term storage in non-volatile memory and storage in working memory, e.g., for program execution. By way of non-limiting example, memory 44 comprises any one or more of SRAM, DRAM, EEPROM, and FLASH memory, which may be in processing circuitry 32 and/or separate from processing circuitry 32. Memory 44 may also store any configuration data 48 used by the network access node 30. Processing circuitry 32 may be configured, e.g., through the use of appropriate program code stored in memory 44, to carry out all or parts of one or more of the methods detailed hereinafter. Processing circuitry 32 of the network node 30 is thus configured, according to some embodiments, to perform all or parts of the techniques described herein as carried out by a base station or a “first device,” for example.
Figure 10 illustrates a diagram of a user equipment 50 configured to carry out the techniques described above, according to some embodiments. User equipment 50 may be considered to represent any wireless devices or terminals that may operate in a network, such as a UE in a cellular network. Other examples may include a communication device, target device, MTC device, loT device, device to device (D2D) UE, machine type UE or UE capable of machine·· to- machine communication (M2M), a sensor equipped with UE, PDA (personal digital assistant), tablet, IPAD tablet, mobile terminal, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), LTSB dongles, Customer Premises Equipment (CPE), etc.
User equipment 50 is configured to communicate with a network node or base station in a wide-area cellular network via antennas 54 and transceiver circuitry 56. Transceiver circuitry 56 may include transmitter circuits, receiver circuits, and associated control circuits that are collectively configured to transmit and receive signals according to a radio access technology, for the purposes of using cellular communication services. The radio access technology can be NR or LTE, for the purposes of this discussion.
User equipment 50 also includes one or more processing circuits 52 that are operatively associated with the radio transceiver circuitry 56. Processing circuitry 52 comprises one or more digital processing circuits, e.g., one or more microprocessors, microcontrollers, DSPs, FPGAs, CPLDs, ASICs, or any mix thereof. More generally, processing circuitry 52 may comprise fixed circuitry, or programmable circuitry that is specially adapted via tire execution of program instructions implementing the functionality taught herein, or may comprise some mix of fixed and programmed circuitry. Processing circuitry 52 may be multi-core.
Processing circuitry 52 also includes a memory 64. Memory 64, in some embodiments, stores one or more computer programs 66 and, optionally, configuration data 68. Memory 64 provides non -transitory storage for computer program 66 and it may comprise one or more types of computer-readable media, such as disk storage, solid-state memory storage, or any mix thereof. By way of non-limiting example, memory 64 comprises any one or more of SRAM, DRAM, EEPROM, and FLASH memory, which may be in processing circuitry 52 and/or separate from processing circuitry 52. Memory 64 may also store any configuration data 68 used by wireless device 50. Processing circuitry 52 may he configured, e.g., through the use of appropriate program code stored in memory 64, to cany out all or parts of one or more of the methods detailed herein.
Although the subject matter described herein can be implemented in any appropriate type of system using any suitable components, the embodiments disclosed herein are described in relation to a wireless network, such as the example wireless network illustrated in Figure 11. For simplicity, the wireless network of Figure 11 only depicts network 1406, network nodes 1460 and 1460b, and WDs 1410, 1410b, and 1410c. In practice, a wireless network can further include any additional elements suitable to support communication between wireless devices or between a wireless device and another communication device, such as a landline telephone, a service provider, or any other network node or end device. Of the illustrated components, network node 1460 and wireless device (WD) 1410 are depicted with additional detail. The wireless network can provide communication and other types of services to one or more wireless devices to facilitate the wireless devices’ access to and/or use of the services provided by, or via, the wireless network.
The wireless network can comprise and/or interface with any type of communication, telecommunication, data, cellular, and/or radio network or other similar type of system. In some embodiments, the wireless network can be configured to operate according to specific standards or other types of predefined rules or procedures. Thus, particular embodiments of the wireless network can implement communication standards, such as Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE), and/or other suitable 2G, 3G, 4G, or 5G standards; wireless local area network (WLAN) standards, such as the IEEE 802.11 standards; and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave and/or ZigBee standards.
Network 1406 can comprise one or more backhaul networks, core networks, IP networks, public switched telephone networks (PSTNs), packet data networks, optical networks, wide-area networks (WANs), local area networks (LANs), wireless local area networks (WLANs), wired networks, wireless networks, metropolitan area networks, and other networks to enable communication between devices.
Network node 1460 and WD 1410 comprise various components described in more detail below. These components work together in order to provide network node and/or wireless device functionality, such as providing wireless connections in a wireless network, in different embodiments, the wireless network can comprise any number of wired or wireless networks, network nodes, base stations, controllers, wireless devices, relay stations, and/or any other components or systems that can facilitate or participate in the communication of data and/or signals whether via wired or wireless connections.
Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)). Base stations can be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and can then also be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station can be a relay node or a relay donor node controlling a relay. A network node can also include one or more (or all) parts of a distributed radio base station such as centralized digital units and/or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Pails of a distributed radio base station can also be referred to as nodes in a distributed antenna system (DAS).
Further examples of network nodes include multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs), core network nodes (e.g., MSCs, MMEs), O&M nodes, OSS nodes, SON nodes, positioning nodes (e.g., E-SMLCs), and/or MD'T's. As another example, a network node can be a virtual network node as described in more detail below. More generally, however, network nodes can represent any suitable device (or group of devices) capable, configured, arranged, and/or operable to enable and/or provide a wireless device with access to tire wireless network or to provide some service to a wireless device that has accessed the wireless network.
In Figure 11, network node 1460 includes processing circuitry 1470, device readable medium 1480, interface 1490, auxiliary equipment 1484, power source 1486, power circuitry 1487, and antenna 1462. Although network node 1460 illustrated in the example wireless network of Figure 11 can represent a device that includes the illustrated combination of hard ware components, other embodiments can comprise network nodes with different combinations of components. It is to be understood that a network node comprises any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods and/or procedures disclosed herein. Moreover, while the components of network node 1460 fire depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, a network node can comprise multiple different physical components that make up a single illustrated component (e.g., device readable medium 1480 can comprise multiple separate hard drives as well as multiple RAM modules).
Similarly, network node 1460 can be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which can each have their own respective components. In certain scenarios in which network node 1460 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components can be shared among several network nodes. For example, a single RNC can control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, can in some instances be considered a single separate network node. In some embodiments, network node 1460 can be configured to support multiple radio access technologies (RATs). In such embodiments, some components can be duplicated (e.g., separate device readable medium 1480 for the different RATs) and some components can be reused (e.g., the same antenna 1462 can be shared by the RATs). Network node 1460 can also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1460, such as, for example, GSM, WCDMA, LTE, NR, WiFi, or Bluetooth wireless technologies. These wireless technologies can be integrated into the same or different chip or set of chips and other components within network node 1460.
Processing circuitry 1470 can be configured to perform any determining, calculating, or similar operations (e.g., certain obtaining operations) described herein as being provided by a network node. These operations performed by processing circuitry 1470 can include processing information obtained by processing circuitry 1470 by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
Processing circuitry 1470 can comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application -specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node 1460 components, such as device readable medium 1480, network node 1460 functionality. For example, processing circuitry 1470 can execute instructions stored in device readable medium 1480 or in memory within processing circuitry 1470. Such functionality can include providing any of the various wireless features, functions, or benefits discussed herein. In some embodiments, processing circuitry 1470 can include a system on a chip (SOC).
In some embodiments, processing circuitry 1470 can include one or more of radio frequency (RF) transceiver circuitry 1472 and baseband processing circuitry 1474. In some embodiments, radio frequency (RF) transceiver circuitry 1472 and baseband processing circuitry 1474 can be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or ail of RF transceiver circuitry 1472 and baseband processing circuitry 1474 can be on the same chip or set of chips, boards, or units. In certain embodiments, some or all of the functionality described herein as being provided by a network node, base station, eNB or other such network device can be performed by processing circuitry 1470 executing instructions stored on device readable medium 1480 or memory within processing circuitry 1470. In alternative embodiments, some or all of the functionality can be provided by processing circuitry 1470 without executing instructions stored on a separate or discrete device readable medium, such as in a hard-wired manner, in any of those embodiments, whether executing instructions stored on a device readable storage medium or not, processing circuitry 1470 can be configured to perform the described functionality. The benefits provided by such functionality are not limited to processing circuitry 1470 alone or to other components of network node 1460, but are enjoyed by network node 1460 as a whole, and/or by end users and the wireless network generally.
Device readable medium 1480 can comprise any form of volatile or non-volatile computer readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device readable and/or computer-executable memory devices that store information, data, and/or instructions that can be used by processing circuitry 1470. Device readable medium 1480 can store any suitable instructions, data or information, including a computer program, software, an application including one or more of logic, rales, code, tables, etc. and/or other instructions capable of being executed by processing circuitry 1470 and, utilized by network node 1460. Device readable medium 1480 can he used to store any calculations made by processing circuitry 1470 and/or any data received via interface 1490. In some embodiments, processing circuitry 1470 and device readable medium 1480 can be considered to be integrated.
Interface 1490 is used in the wired or wireless communication of signalling and/or data between network node 1460, network 1406, and/or WDs 1410. As illustrated, interface 1490 comprises port(s)/terminal(s) 1494 to send and receive data, for example to and from network 1406 over a wired connection. Interface 1490 also includes radio front end circuitry 1492 that can be coupled to, or in certain embodiments a part of, antenna 1462. Radio front end circuitry 1492 comprises filters 1498 and amplifiers 1496. Radio front end circuitry 1492 can be connected to antenna 1462 and processing circuitry 1470. Radio front end circuitry can be configured to condition signals communicated between antenna 1462 and processing circuitry 1470. Radio front end circuitry 1492 can receive digital data that is to be sent out to other network nodes or WDs via a wireless connection. Radio front end circuitry 1492 can convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1498 and/or amplifiers 1496. The radio signal can then be transmitted via antenna 1462. Similarly, when receiving data, antenna 1462 can collect radio signals which are then converted into digital data by radio front end circuitry 1492. The digital data can be passed to processing circuitry 1470. In other embodiments, the interface can comprise different components and/or different combinations of components.
In certain alternative embodiments, network node 1460 may not include separate radio front end circuitry 1492, instead, processing circuitry 1470 can comprise radio front end circuitry and can be connected to antenna 1462 without separate radio front end circuitry 1492. Similarly, in some embodiments, all or some of RF transceiver circuitry 1472 can be considered a part of interface 1490. In still other embodiments, interface 1490 can include one or more ports or terminals 1494, radio front end circuitry 1492, and RF transceiver circuitry 1472, as part of a radio unit (not shown), and interface 1490 can communicate with baseband processing circuitry 1474, which is part of a digital unit (not shown).
Antenna 1462 can include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. Antenna 1462 can be coupled to radio front end circuitry 1490 and can be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In some embodiments, antenna 1462 can comprise one or more omni-directional, sector or panel antennas operable to transmit/receive radio signals between, for example, 2 GHz and 66 GHz. An omni-directional antenna can be used to transmit/receive radio signals in any direction, a sector antenna can be used to transmit/receive radio signals from devices within a particular area, and a panel antenna can be a line-of-sight antenna used to transmit/receive radio signals in a relatively straight line. In some instances, the use of more than one antenna can be referred to as MIMO. In certain embodiments, antenna 1462 can be separate from network node 1460 and can be connectable to network node 1460 through an interface or port.
Antenna 1462, interface 1490, and/or processing circuitry 1470 can be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by a network node. Any information, data and/or signals can be received from a wireless device, another network node and/or any other network equipment. Similarly, antenna 1462, interface 1490, and/or processing circuitry 1470 can be configured to perform any transmitting operations described herein as being performed by a network node. Any information, data and/or signals can be transmitted to a wireless device, another network node and/or any other network equipment.
Power circuitry 1487 can comprise, or be coupled to, power management circuitry and can be configured to supply the components of network node 1460 with power for performing the functionality described herein. Power circuitry 1487 can receive power from power source 1486, Power source 1486 and/or power circuitry 1487 can be configured to provide power to the various components of network node 1460 in a form suitable for tire respective components (e.g., at a voltage and current level needed for each respective component). Power source 1486 can either be included in, or external to, power circuitry 1487 and/or network node 1460. For example, network node 1460 can he connectable to an external power source (e.g., an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry 1487. As a further example, power source 1486 can comprise a source of power in the form of a battery or batery pack which is connected to, or integrated in, power circuitry 1487. The battery can provide backup power should the external power source fail. Other types of power sources, such as photovoltaic devices, can also be used.
Alternative embodiments of network node 1460 can include additional components beyond those shown in Figure 11 that can be responsible for providing certain aspects of the network node’s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein. For example, network node 1460 can include user interface equipment to allow and/or facilitate input of information into network node 1460 and to allow and/or facilitate output of information from network node 1460. This can allow and/or facilitate a user to perform diagnostic, maintenance, repair, and other administrative functions for network node 1460.
In some embodiments, a wireless device (WD, e.g. WD 1410) can be configured to communicate wirelessly with network nodes (e.g., 1460) and/or other wireless devices (e.g., 1410b, c). Communicating wirelessly can involve transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information through air. In some embodiments, a WD can be configured to transmit and/or receive information without direct human interaction. For instance, a WD can be designed to transmit information to a network on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the network.
Examples of a WD include, but are not limited to, a smart phone, a mobile phone, a cell phone, a voice over IP (VoIP) phone, a wireless local loop phone, a desktop computer, a personal digital assistant (PDA), a wireless cameras, a gaming console or device, a music storage device, a playback appliance, a wearable terminal device, a wireless endpoint, a mobile station, a tablet, a laptop, a laptop-embedded equipment (LEE), a laptop-mounted equipment (LME), a smart device, a wireless customer-premise equipment (CPE), a vehicle-mounted wireless terminal device, etc. A WD can support device -to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, vehicle-to-vehicle (V2V), vehicle- to-infrastructure (V2I), vehicle-to-everything (V2X) and can in this case be referred to as a D2D communication device. As yet another specific example, in an Internet of Things (IoT) scenario, a WD can represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another WD and/or a network node. The WD can in this case be a machine-to-machine (M2M) device, which can in a 3GPP context he referred to as an MTC device. As one particular example, the WD can be a UE implementing the 3GPP narrow band internet of things (NB-IoT) standard. Particular examples of such machines or devices are sensors, metering devices such as power meters, industrial machinery, or home or personal appliances (e.g., refrigerators, televisions, etc.) personal wearables (e.g., watches, fitness trackers, etc.), in other scenarios, a WD can represent a vehicle or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation. A WD as described above can represent the endpoint of a wireless connection, in which case the device can be referred to as a vrireless terminal. Furthermore, a WD as described above can be mobile, in which case it can also be referred to as a mobile device or a mobile terminal.
As illustrated, wireless device 1410 includes antenna 1411, interface 1414, processing circuitry 1420, device readable medium 1430, user interface equipment 1432, auxiliary equipment 1434, power source 1436 and power circuitry 1437. WD 1410 can include multiple sets of one or more of the illustrated components for different wireless technologies supported by WD 1410, such as, for example, GSM, WCDMA, LTE, NR, WiFi, WiMAX, or Bluetooth wireless technologies, just to mention a few. These wireless technologies can be integrated into the same or different chips or set of chips as other components within WD 1410.
Antenna 1411 can include one or more antennas or antenna arrays, configured to send and/or receive wireless signals, and is connected to interface 1414. In certain alternati ve embodiments, antenna 1411 can be separate from WD 1410 and be connectable to WD 1410 through an interface or port. Antenna 1411, interface 1414, and/or processing circuitry 1420 can be configured to perform any receiving or transmitting operations described herein as being performed by a WD. Any information, data and/or signals can be received from a network node and/or another WD. In some embodiments, radio front end circuitry and/or antenna 1411 can be considered an interface.
As illustrated, interface 1414 comprises radio front end circuitry 1412 and antenna 1411. Radio front end circuitry 1412 comprise one or more filters 1418 and amplifiers 1416. Radio front end circuitry 1414 is connected to antenna 1411 and processing circuitry 1420 and can be configured to condition signals communicated between antenna 1411 and processing circuitry 1420, Radio front end circuitry 1412 can be coupled to or a part of antenna 1411. in some embodiments, WD 1410 may not include separate radio front end circuitry 1412; rather, processing circuitry 1420 can comprise radio front end circuitry and can be connected to antenna
1411. Similarly, in some embodiments, some or all of RF transceiver circuitry 1422 can be considered a part of interface 1414. Radio front end circuitry 1412 can receive digital data that is to be sent out to other network nodes or WDs via a wireless connection. Radio front end circuitry 1412 can convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1418 and/or ampli fiers 1416. The radio signal can then be transmitted via antenna 1411. Similarly, when receiving data, antenna 1411 can collect radio signals which are then converted into digital data by radio front end circuitry
1412. The digital data can be passed to processing circuitry 1420. In other embodiments, the interface can comprise different components and/or different combinations of components.
Processing circuitry 1420 can comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software, and/or encoded logic operable to provide, either alone or in conjunction with other WD 1410 components, such as device readable medium 1430, WD 1410 functionality. Such functionality can include providing any of the various wireless features or benefits discussed herein. For example, processing circuitry 1420 can execute instructions stored in device readable medium 1430 or in memory within processing circuitry 1420 to provide the functionality disclosed herein.
As illustrated, processing circuitry 1420 includes one or more of RF transceiver circuitry 1422, baseband processing circuitry 1424, and application processing circuitry 1426. In other embodiments, the processing circuitry can comprise different components and/or different combinations of components. In certain embodiments processing circuitry 1420 of WD 1410 can comprise a SOC. In some embodiments, RF transceiver circuitry 1422, baseband processing circuitry 1424, and application processing circuitry 1426 can be on separate chips or sets of chips. In alternative embodiments, part or all of baseband processing circuitry 1424 and application processing circuitry 1426 can he combined into one chip or set of chips, and RF transceiver circuitry 1422 can be on a separate chip or set of chips. In still alternative embodiments, part or ail of RF transceiver circuitry 1422 and baseband processing circuitry 1424 can be on the same chip or set of chips, and application processing circuitry 1426 can be on a separate chip or set of chips. In yet other alternative embodiments, part or all of RF transceiver circuitry 1422, baseband processing circuitry 1424, and application processing circuitry 1426 can be combined in the same chip or set of chips. In some embodiments, RF transceiver circuitry 1422 can be a part of interface 1414. RF transceiver circuitry 1422 can condition RF signals for processing circuitry 1420.
In certain embodiments, some or all of the functionality described herein as being performed by a WD can be provided by processing circuitry 1420 executing instructions stored on device readable medium 1430, which in certain embodiments can be a computer-readable storage medium. In alternative embodiments, some or all of the functionality can be provided by processing circuitry 1420 without executing instructions stored on a separate or discrete device readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a device readable storage medium or not, processing circuitry 1420 can be configured to perform the described functionality. The benefits provided by such functionality are not limited to processing circuitry 1420 alone or to other components of WD 1410, but are enjoyed by WD 1410 as a whole, and/or by end users and the wireless network generally.
Processing circuitry 1420 can be configured to perform any determining, calculating, or similar operations (e.g., certain obtaining operations) described herein as being performed by a WD. These operations, as performed by processing circuitry 1420, can include processing information obtained by processing circuitry 1420 by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored by WD 1410, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
Device readable medium 1430 can be operable to store a computer program, software, an application including one or more of logic, rules, code, tables, etc. and/or other instructions capable of being executed by processing circuitry 1420. Device readable medium 1430 can include computer memory (e.g., Random Access Memory (RAM) or Read Only Memory (ROM)), mass storage media (e.g., a hard disk), removable storage media (e.g., a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device readable and/or computer executable memory devices that store information, data, and/or instructions that can be used by processing circuitry 1420. In some embodiments, processing circuitry 1420 and device readable medium 1430 can be considered to be integrated.
User interface equipment 1432 can include components that allow and/or facilitate a human user to interact with WD 1410. Such interaction can be of many forms, such as visual, audial, tactile, etc. User interface equipment 1432 can be operable to produce output to the user and to allow and/or facilitate the user to provide input to WD 1410. The type of interaction can vary depending on the type of user interface equipment 1432 installed in WD 1410. For example, if WD 1410 is a smart phone, the interaction can be via a touch screen; if WD 1410 is a smart meter, the interaction can be through a screen that provides usage (e.g., the number of gallons used) or a speaker that provides an audible alert (e.g., if smoke is detected). User interface equipment 1432 can include input interfaces, devices and circuits, and output interfaces, devices and circuits. User interface equipment 1432 can be configured to allow and/or facilitate input of information into WD 1410, and is connected to processing circuitry 1420 to allow and/or facilitate processing circuitry 1420 to process the input information. User interface equipment 1432 can include, for example, a microphone, a proximity or other sensor, keys/buttons, a touch display, one or more cameras, a USB port, or other input circuitry. User interface equipment 1432 is also configured to allow and/or facilitate output of information from WD 1410, and to allow' and/or facilitate processing circuitry 1420 to output information from WD 1410. User interface equipment 1432 can include, for example, a speaker, a display, vibrating circuitry, a USB port, a headphone interface, or other output circuitry. Using one or more input and output interfaces, devices, and circuits, of user interface equipment 1432, WD 1410 can communicate with end users and/or the wireless network, and allow and/or facilitate them to benefit from the functionality described herein.
Auxiliary equipment 1434 is operable to provide more specific functionality which may not be generally performed by WDs. This can comprise specialized sensors for doing measurements for various purposes, interfaces for additional types of communication such as wired communications etc. The inclusion and type of components of auxiliary equipment 1434 can vary depending on the embodiment and/or scenario.
Power source 1436 can, in some embodiments, be in the form of a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic devices or power ceils, can also be used. WD 1410 can further comprise power circuitry 1437 for delivering power from power source 1436 to the various parts of WD 1410 which need power from power source 1436 to cany out any functionality described or indicated herein. Power circuitry 1437 can in certain embodiments comprise power management circuitry. Power circuitry 1437 can additionally or alternatively be operable to receive power from an external power source; in which case WD 1410 can be connectable to the external power source (such as an electricity outlet) via input circuitry or an interface such as an electrical power cable. Power circuitry 1437 can also in certain embodiments be operable to deliver power from an external power source to power source 1436. This can be, for example, for the charging of power source 1436. Power circuitry 1437 can perform any converting or other modification to the power from power source 1436 to make it suitable for supply to the respective components of WD 1410.
Figure 12 illustrates one embodiment of a UE in accordance with various aspects described herein. As used herein, a user equipment or UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device. Instead, a UE can represent a device that is intended for sale to, or operation by, a human user hut which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE can represent a device that is not intended for sale to, or operation by, an end user but which can he associated with or operated for the benefit of a user (e.g., a smart power meter). UE 1500 can be any UE identified by the 3rd Generation Partnership Project (3GPP), including a NB-IoT UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE. UE 1500, as illustrated in Figure 12, is one example of a WD configured for communication in accordance with one or more communication standards promulgated by the 3rd Generation Partnership Project (3GPP), such as 3GPP’s GSM, UMTS, LTE, and/or 5G standards. As mentioned previously, the term WD and UE can be used interchangeable. Accordingly, although Figure 12 is a UE, the components discussed herein are equally applicable to a WD, and vice-versa.
In Figure 12, UE 1500 includes processing circuitry 1501 that is operatively coupled to input/output interface 1505, radio frequency (RF) interface 1509, network connection interface 1511, memory 1515 including random access memory (RAM) 917, read-only memory (ROM) 919, and storage medium 921 or the like, communication subsystem 931, power source 933, and/or any other component, or any combination thereof. Storage medium 1521 includes operating system 1523, application program 1525, and data 1527. In other embodiments, storage medium 1521 can include other similar types of information. Certain UEs can utilize ail of the components shown in Figure 12, or only a subset of the components. The level of integration between the components can vary from one UE to another UE. Further, certain UEs can contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.
In Figure 12, processing circuitry 1501 can be configured to process computer instructions and data. Processing circuitry 1501 can be configured to implement any sequential state machine operative to execute machine instructions stored as machine-readable computer programs in the memory, such as one or more hardware-implemented state machines (e.g., in discrete logic, FPGA, ASIC, etc.); programmable logic together with appropriate firmware; one or more stored program, general-purpose processors, such as a microprocessor or Digital Signal Processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 1501 can include two central processing units (CPUs). Data can be information in a form suitable for use by a computer.
In the depicted embodiment, input/output interface 1505 can be configured to provide a communication interface to an input device, output device, or input and output device. UE 1500 can be configured to use an output device via input/output interface 1505. An output device can use the same type of interface port as an input device. For example, a USB port can be used to provide input to and output from UE 1500. The output device can be a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. UE 1500 can be configured to use an input device via input/output interface 1505 to allow' and/or facilitate a user to capture information into UE 1500. The input device can include a touch -sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence- sensitive display can include a capacitive or resistive touch sensor to sense input from a user. A sensor can be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, another like sensor, or any combination thereof. For example, the input device can be an accelerometer, a magnetometer, a digital camera, a microphone, and an optical sensor.
In Figure 12, RF interface 1509 can be configured to provide a communication interface to RF components such as a transmitter, a receiver, and an antenna. Network connection interface 1511 can be configured to provide a communication interface to network 1543a. Network 1543a can encompass wared and/or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof. For example, network 1543a can comprise a Wi-Fi network. Network connection interface 1511 can be configured to include a receiver and a transmitter interface used to communicate with one or more other devices over a communication network according to one or more communication protocols, such as Ethernet, TCP/IP, SONET, ATM, or the like. Network connection interface 1511 can implement receiver and transmitter functionality appropriate to the communication network links (e.g., optical, electrical, and the like). The transmitter and receiver functions can share circuit components, software or firmware, or alternatively can be implemented separately.
R AM 1517 can be configured to interface via bus 1502 to processing circuitry 1501 to provide storage or caching of data or computer instructions during the execution of software programs such as the operating system, application programs, and device drivers. ROM 1519 can be configured to provide computer instructions or data to processing circuitry 1501. For example, ROM 1519 can be configured to store invariant low-level system code or data for basic system functions such as basic input and output (I/O) , startup, or reception of keystrokes from a keyboard that are stored in a non-volatile memory. Storage medium 1521 can be configured to include memory such as RAM, ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic di sks, optical disks, floppy disks, hard disks, removable cartridges, or flash drives. In one example, storage medium 1521 can be configured to include operating system 1523, application program 1525 such as a web browser application, a widget or gadget engine or another application, and data file 1527. Storage medium 1521 can store, for use by UE 1500, any of a variety of various operating systems or combinations of operating systems.
Storage medium 1521 can be configured to include a number of physical drive units, such as redundant array of independent disks (R AID), floppy disk drive, flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), externa! micro- DIMM SDRAM, smartcard memory such as a subscriber identity module or a removable user identity (SIM/RUIM) module, other memory, or any combination thereof. Storage medium 152,1 can allow and/or facilitate UE 1500 to access computer-executable instructions, application programs or the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system can be tangibly embodied in storage medium 1521, which can comprise a device readable medium.
In Figure 12, processing circuitry 1501 can be configured to communicate with network 1543b using communication subsystem 1531. Network 1543a and network 1543b can be the same network or networks or different network or networks. Communication subsystem 1531 can be configured to include one or more transceivers used to communicate with network 1543b. For example, communication subsystem 1531 can be configured to include one or more transceivers used to communicate with one or more remote transceivers of another device capable of wireless communication such as another WD, LIE, or base station of a radio access network (RAN) according to one or more communication protocols, such as IEEE 802.11, CDMA, WCDMA, GSM, LTE, UTRAN, WiMax, or the like. Each transceiver can include transmitter 1533 and/or receiver 1535 to implement transmitter or receiver functionality, respectively, appropriate to the RAN links (e.g., frequency allocations and the like). Further, transmitter 1533 and receiver 1535 of each transceiver can share circuit components, software or firmware, or alternatively can be implemented separately. In the illustrated embodiment, the communication functions of communication subsystem 1531 can include data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. For example, communication subsystem 1531 can include cellular communication, Wi-Fi communication, Bluetooth communication, and GPS communication. Network 1543b can encompass wired and/or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof. For example, network 1543b can be a cellular network, a Wi-Fi network, and/or a nearfield network. Power source 1513 can be configured to provide alternating current (AC) or direct current (DC) power to components of UE 1500.
The features, benefits and/or functions described herein can he implemented in one of the components of LIE 1500 or partitioned across multiple components of UE 1500. Further, the features, benefits, and/or functions described herein can be implemented in any combination of hardware, software or firmware, in one example, communication subsystem 1531 can be configured to include any of the components described herein. Further, processing circuitry 1501 can be configured to communicate with any of such components over bus 1502. In another example, any of such components can be represented by program instructions stored in memory that when executed by processing circuitry 1501 perform the corresponding functions described herein. In another example, the functionality of any of such components can be partitioned between processing circuitry 1501 and communication subsystem 1531. In another example, the non-cornputationally intensive functions of any of such components can be implemented in software or firmware and the computationally intensive functions can be implemented in hardware.
Figure 13 is a schematic block diagram illustrating a virtualization environment 1600 in which functions implemented by some embodiments can be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which can include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to a node (e.g., a virtualized base station, a virtualized radio access node, virtualized core network node) or to a device (e.g., a UE, a wireless device or any other type of communication device) or components thereof and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components (e.g., via one or more applications, components, functions, virtual machines or containers executing on one or more physical processing nodes in one or more networks). In some embodiments, some or all of the functions described herein can be implemented as virtual components executed by one or more virtual machines implemented in one or more virtual environments 1600 hosted by one or more of hardware nodes 1630. Further, in embodiments in which the virtual node is not a radio access node or does not require radio connectivity (e.g., a core network node), then the network node can be entirely virtualized.
The functions can be implemented by one or more applications 1620 (which can alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) operative to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein. Applications 1620 are run in virtualization environment 1600 which provides hardware 1630 comprising processing circuitry 1660 and memory 1690. Memory 1690 contains instructions 1695 executable by processing circuitry 1660 whereby application 1620 is operative to provide one or more of the features, benefits, and/or functions disclosed herein.
Virtualization environment 1600, comprises general-purpose or special-purpose network hardware devices 1630 comprising a set of one or more processors or processing circuitry 1660, which can be commercial off-the-shelf (COTS) processors, dedicated Application Specific integrated Circuits (ASICs), or any other type of processing circuitry including digital or analog hardware components or special purpose processors. Each hardware device can comprise memory 1690-1 which can be non-persistent memory for temporarily storing instructions 1695 or software executed by processing circuitry 1660. Each hardware device can comprise one or more network interface controllers (NICs) 1670, also known as network interface cards, which include physical network interface 1680. Each hardware device can also include non-transitory, persistent, machine-readable storage media 1690-2 having stored therein software 1695 and/or instructions executable by processing circuitry 1660. Software 1695 can include any type of software including software for instantiating one or more virtualization layers 1650 (also referred to as hypervisors), software to execute virtual machines 1640 as well as software allowing it to execute functions, features and/or benefits described in relation with some embodiments described herein.
Virtual machines 1640, comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and can be run by a corresponding virtualization layer 1650 or hypervisor. Different embodiments of the instance of virtual appliance 1620 can he implemented on one or more of virtual machines 1640, and the implementations can be made in different ways.
During operation, processing circuitry 1660 executes software 1695 to instantiate the hypervisor or virtualization layer 1650, which can sometimes be referred to as a virtual machine monitor (VMM). Virtualization layer 1650 can present a virtual operating platform that appears like networking hardware to virtual machine 1640.
As shown in Figure 13, hardware 1630 can be a standalone network node with generic or specific components. Hardware 1630 can comprise antenna 16225 and can implement some functions via virtualization. Alternatively, hardware 1630 can be part of a larger cluster of hardware (e.g., such as in a data center or customer premise equipment (CPE)) where many hardware nodes work together and are managed via management and orchestration (MANO) 1690, which, among others, oversees lifecycle management of applications 1620.
Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV can be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment. in the context of NFV, virtual machine 1640 can be a software implementation of a physical machine that runs programs as if they were executing on a physical, non- virtualized machine. Each of virtual machines 1640, and that part of hardware 1630 that executes that virtual machine, be it hardware dedicated to that virtual machine and/or hardware shared by that virtual machine with others of the virtual machines 1640, forms a separate virtual network elements (VNE).
In the context of NFV, Virtual Network Function (VNF) is responsible for handling specific network functions that run in one or more virtual machines 1640 on top of hardware networking infrastructure 1630, and can correspond to application 1620 in Figure 13. in some embodiments, one or more radio units 16200 that each include one or more transmitters 16220 and one or more receivers 16210 can be coupled to one or more antennas 16225. Radio units 16200 can communicate directly with hardware nodes 1630 via one or more appropriate network interfaces and can be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station.
In some embodiments, some signaling can be affected with die use of control system 16230 which can alternatively be used for communication between the hardware nodes 1630 and radio units 16200.
With reference to Figure 14, in accordance with an embodiment, a communication system includes telecommunication network 1710, such as a 3GPP-type cellular network, which comprises access network 1711, such as a radio access network, and core network 1714. Access network 1711 comprises a plurality ofbase stations 1712a, 1712b, 1712c, such as MBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 1713a, 1713b, 1713c. Each base station 1712a, 1712b, 1712c is connectable to core network 1714 over a wired or wireless connection 1715. A first UE 1791 located in coverage area 1713c can be configured to wirelessly connect to, or be paged by, the corresponding base station 1712c. A second UE 1792 in coverage area 1713a is wirelessly connectable to the corresponding base station 1712a. While a plurality ofUEs 1791, 1792 are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole LIE is in the coverage area or where a sole UE is connecting to the
Telecommunication network 1710 is itself connected to host computer 1730, which can he embodied in the hardware and/or software of a standalone server, a cloud-implemented server, a distributed server or as processing resources in a server farm. Host computer 1730 can be under the ownership or control of a service provider, or can be operated by the service provider or on behalf of the service provider. Connections 1721 and 1722 between telecommunication network 1710 and host computer 1730 can extend directly from core network 1714 to host computer 1730 or can go via an optional intermediate network 1720. Intermediate network 1720 can be one of, or a combination of more than one of, a public, private or hosted network; intermediate network 1720, if any, can be a backbone network or the Internet; in particular, intermediate network 1720 can comprise two or more sub-networks (not shown).
The communication system of Figure 14 as a whole enables connectivity between the connected UEs 1791. 1792 and host computer 1730. The connectivity can be described as an over-the-top (OTT) connection 1750. Host computer 1730 and the connected UEs 1791, 1792 are configured to communicate data and/or signaling via OTT connection 1750, using access network 1711, core network 1714, any intermediate network 1720 and possible further infrastructure (not shown) as intermediaries. OTT connection 1750 can be transparent in the sense that the participating communication devices through which OTT connection 1750 passes are unaware of routing of uplink and downlink communications. For example, base station 1712 may not or need not be informed about the past routing of an incoming downlink communication with data originating from host computer 1730 to be forwarded (e.g., handed over) to a connected UE 1791. Similarly, base station 1712 need not be aware of the future routing of an outgoing uplink communication originating from the LIE 1791 towards the host computer 1730.
Example implementations, in accordance with an embodiment, of the UE, base station and host computer discussed in the preceding paragraphs will now be described with reference to Figure 15. In communication system 1800, host computer 1810 comprises hardware 1815 including communication interface 1816 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of communication system 1800. Host computer 1810 further comprises processing circuitry 1818, which can have storage and/or processing capabilities. In particular, processing circuitry 1818 can comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions. Host computer 1810 further comprises software 1811, which is stored in or accessible by host computer 1810 and executable by processing circuitry 1818. Software 1811 includes host application 1812.
Host application 1812 can be operable to provide a service to a remote user, such as UE 1830 connecting via OTT connection 1850 terminating at UE 1830 and host computer 1810. In providing the service to the remote user, host application 1812 can provide user data which is transmitted using OTT connection 1850.
Communication system 1800 can also include base station 1820 provided in a telecommunication system and comprising hardware 1825 enabling it to communicate with host computer 1810 and with UE 1830. Hardware 1825 can include communication interface 1826 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of communication system 1800, as well as radio interface 1827 for setting up and maintaining at least wireless connection 1870 with UE 1830 located in a coverage area (not shown in Figure 15) served by base station 1820. Communication interface 1826 can he configured to facilitate connection 1860 to host computer 1810. Connection 1860 can be direct or it can pass through a core network (not shown in Figure 15) of the telecommunication system and/or through one or more intermediate networks outside the telecommunication system. In the embodiment shown, hardware 1825 of base station 1820 can also include processing circuitry 1828, which can comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions. Base station 1820 further has software 1821 stored internally or accessible via an external connection.
Communication system 1800 can also include UE 1830 already referred to. Its hardware 1835 can include radio interface 1837 configured to set up and maintain wireless connection 1870 with a base station serving a coverage area in which UE 1830 is currently located.
Hardware 1835 of UE 1830 can also include processing circuitry 1838, which can comprise one or more programmable processors, application -specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions, UE 1830 further comprises software 1831, which is stored in or accessible by UE 1830 and executable by processing circuitry 1838. Software 1831 includes client application 1832. Client application 1832 can be operable to provide a service to a human or non-human user via UE 1830, with the support of host computer 1810. In host computer 1810, an executing host application 1812 can communicate with the executing client application 1832 via OTT connection 1850 terminating at UE 1830 and host computer 1810. In providing the service to the user, client application 1832 can receive request data from host application 1812 and provide user data in response to the request data, OTT connection 1850 can transfer both the request data and the user data. Client application 1832 can interact with the user to generate the user data that it provides.
It is noted that host computer 1810, base station 1820 and UE 1830 illustrated in Figure 15 can be similar or identical to host computer 1730, one of base stations 1712a, 1712b, 1712c and one of UEs 1791, 1792 of Figure 14, respectively. This is to say, the inner workings of these entities can be as shown in Figure 15 and independently, the surrounding network topology can he that of Figure 14.
In Figure 15, OTT connection 1850 has been drawn abstractly to illustrate the communication between host computer 1810 and UE 1830 via base station 1820, without explicit reference to any intermediary devices and the precise routing of messages via these devices. Network infrastructure can determine the routing, which it can be configured to hide from UE 1830 or from the service provider operating host computer 1810, or both. While OTT connection 1850 is active, the network infrastructure can further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).
Wireless connection 1870 between UE 1830 and base station 1820 is in accordance with the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments improve the performance of OTT services provided to UE 1830 using OTT connection 1850, in which wireless connection 1870 forms the last segment. More precisely, the exemplary embodiments disclosed herein can improve flexibility for the network to monitor end- to-end quality-of-service (QoS) of data flows, including their corresponding radio bearers, associated with data sessions between a user equipment (UE) and another entity, such as an OTT data application or service external to the 5G network. These and other advantages can facilitate more timely design, implementation, and deployment of 5G/NR solutions. Furthermore, such embodiments can facilitate flexible and timely control of data session QoS, which can lead to improvements in capacity, throughput, latency, etc. that are envisioned by 5G/NR and important for the growth of OTT services.
A measurement procedure can be provided for the purpose of monitoring data rate, latency and other network operational aspects on which the one or more embodiments improve. There can further be an optional network functionality for reconfiguring OTT connection 1850 between host computer 1810 and UE 1830, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring OTT connection 1850 can be implemented in software 1811 and hardware 1815 of host computer 1810 or in software 1831 and hardware 1835 of UE 1830, or both. In embodiments, sensors (not shown) can be deployed in or in association with communication devices through which OTT connection 1850 passes; the sensors can participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 1811, 1831 can compute or estimate the monitored quantities. The reconfiguring of OTT connection 1850 can include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect base station 1820, and it can be unknown or imperceptible to base station 1820. Such procedures and functionalities can be known and practiced in the art. In certain embodiments, measurements can involve proprietary HE signaling facilitating host computer 1810’s measurements of throughput, propagation times, latency and the like. The measurements can be implemented in that software 1811, 1831 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using OTT connection 1850 while it monitors propagation times, errors, etc.
Figure 16 is a flowchart illustrating an exemplary method and/or procedure implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which, in some exemplary embodiments, can be those described with reference to Figures 9 and 10. For simplicity of the present disclosure, only drawing references to Figure 16 will be included in this section, in step 1910, the host computer provides user data. In substep 1911 (which can be optional) of step 1910, the host computer provides the user data by executing a host application, in step 1920, the host computer initiates a transmission carrying the user data to the UE. In step 1930 (which can be optional), the base station transmits to the UE the user data which was carried in the transmission that the host computer initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1940 (which can also be optional), the UE executes a client application associated with the host application executed by the host computer.
Figure 17 is a flowchart illustrating an exemplary method and/or procedure implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which can be those described with reference to Figures 9 and 10. For simplicity of the present disclosure, only drawing references to Figure 17 will be included in this section. In step 2010 of the method, the host computer provides user data. In an optional substep (not shown) the host computer provides the user data by executing a host application. In step 2020, the host computer initiates a transmission carrying the user data to the UE. The transmission can pass via the base station, in accordance with the teachings of the embodiments described throughout this disclosure. In step 2030 (which can be optional), the UE receives the user data carried in the transmission. Figure 18 is a flowchart illustrating an exemplary method and/or procedure implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which can be those described with reference to Figures 9 and 10. For simplicity of the present disclosure, only drawing references to Figure 18 will be included in this section. In step 2110 (which can be optional), the UE receives input data provided by the host computer. Additionally or alternatively, in step 2120, the UE provides user data. In substep 2121 (which can be optional) of step 2120, the UE provides tire user data by executing a client application. In substep 2111 (which can be optional) of step 2110, the UE executes a client application which provides the user data in reaction to the received input data provided by the host computer. In providing the user data, the executed client application can further consider user input received from the user. Regardless of the specific manner in which the user data was provided, the UE initiates, in suhstep 2130 (which can he optional), transmission of the user data to the host computer. In step 2140 of the method, the host computer receives the user data transmitted from the UE, in accordance with the teachings of the embodiments described throughout this disclosure.
Figure 19 is a flowchart illustrating an exemplary method and/or procedure implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which can be those described with reference to Figures 9 and 10. For simplicity of the present disclosure, only drawing references to Figure 19 will be included in this section. In step 2210 (which can be optional), in accordance with the teachings of the embodiments described throughout this disclosure, the base station receives user data from the UE. In step 2220 (which can be optional), the base station initiates transmission of the received user data to the host computer. In step 2230 (which can he optional), the host computer receives the user data carried in the transmission initiated by the base station.
The exemplary embodiments described herein provide techniques for pre-configuring a UE for operation in a 3GPP non-terrestrial network (NTN). Such embodiments reduce the time needed for initial acquisition of an NTN (e.g., PLMN) and a cell within the NTN. This can provide various benefits and/or advantages, including reducing UE energy consumption (or, equivalently, increasing UE operational time on one battery charge) and improving user access to services provided by an NTN. When used in UEs and/or network nodes, exemplary embodiments described herein can enable UEs to access network resources and OTT services more consistently and without interruption. This improves the availability and/or performance of these services as experienced by OTT service providers and end-users, including more consistent data throughout and fewer delays without excessive UE power consumption or other reductions in user experience. The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures that, although not explicitly shown or described herein, embody the principles of the disclosure and ean be thus within the spirit and scope of the disclosure. Various exemplary embodiments can he used together with one another, as well as interchangeably therewith, as should be understood by those having ordinary skill in the ait.
The term unit, as used herein, can have conventional meaning in the field of electronics, electrical devices and/or electronic devices and can include, for example, electrical and/or electronic circuitry, devices, modules, processors, memories, logic solid state and/or discrete devices, computer programs or instructions for carrying out respective tasks, procedures, computations, outputs, find/or displaying functions, and so on, as such as those that are described herein.
Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include Digital Signal Processor (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM), Random Access Memory (RAM), cache memory, flash memory devices, optica! storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and/or data communications protocols as well as instructions for carrying out one or more of the techniques described herein, in some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according one or more embodiments of the present disclosure.
As described herein, device and/or apparatus can be represented by a semiconductor chip, a chipset, or a (hardware) module comprising such chip or chipset; this, however, does not exclude the possibility that a functionality of a device or apparatus, instead of being hardware implemented, be implemented as a software module such as a computer program or a computer program product comprising executable software code portions for execution or being run on a processor. Furthermore, functionality of a device or apparatus can he implemented by any combination of hardware and software. A device or apparatus can also be regarded as an assembly of multiple devices and/or apparatuses, whether functionally in cooperation with or independently of each other. Moreover, devices and apparatuses can be implemented in a distributed fashion throughout a system, so long as the functionality of the device or apparatus is preserved. Such and similar principles are considered as known to a skilled person.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In addition, certain terms used in the present disclosure, including the specification, drawings and exemplary embodiments thereof, can be used synonymously in certain instances, including, but not limited to, e.g., data and information. It should be understood that while these words and/or other words that can be synonymous to one another can be used synonymously herein, that there can be instances when such words can be intended to not be used synonymously. Further, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it is explicitly incorporated herein in its entirety. All publications referenced are incorporated herein by reference in their entireties.
EXAMPLE EMBODIMENTS
Embodiments of the presently disclosed techniques and apparatuses include, but are not limited to, the following enumerated examples:
1. A method for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element, the method comprising: estimating a first set of one or more parameters for the channel, based on training symbols transmitted from the second device to the first device; estimating a second set of one or more parameters for the channel, based on training symbols or reference signals transmitted from die first device to the second device and based on the first set of one or more parameters.
2. The method of example embodiment 1 , wherein the first set of one or more parameters includes: a path gain magnitude corresponding to each radio channel path between the second device and the first device; a delay corresponding to each radio channel path between the second device and the first device; and an angle of arrival for each radio channel path between the second device and the first device.
3. The method of example embodiment 2 , wherein the second set of one or more parameters includes a phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device.
4. The method of any one of example embodiments 1-3, wherein estimating the first set of one or more parameters for the channel comprises estimating a distribution function for the first set of one or more parameters using a deep generative model and observations of the training symbols transmitted from the second device to the first device, as received at the first device, and using a least-squares estimation approach to jointly estimate, based on the estimated distribution function and the observations of the training symbols, the first set of one or more parameters and a phase change parameter for each radio channel path between the second device and the first device, in the direction toward the first device from the estimated distribution.
5. The method of example embodiment 4, wherein the deep generative model is based on a generative adversarial network (GAN).
6. The method of example embodiment 4 or 5, wherein estimating the second set of one or more parameters comprises using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on observations of training symbols transmitted from the first device to the second device, as received at the first device.
7. The method of example embodiment 4 or 5, wherein estimating the first set of one or more parameters and estimating the second set of one or more parameters are carried out by the first device, and wherein estimating the second set of one or more parameters comprises using a least- squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on quantized channel-state information feedback received from the second device, the quantized channel-state information feedback being based on the reference signals.
8. The method of any one of example embodiments 1-7, further comprising determining an estimated channel response for the channel in the direction from the first device to the second device, based on the estimated first set of one or more parameters, the estimated second set of one or more parameters, and an antenna array response that depends on the physical arrangement of the antenna elements of the first device.
9. The method of example embodiment 8, further comprising transmitting a signal to the second device from the first device, using antenna weights determined from the estimated channel response for the channel in the direction from the first device to the second device.
10. An apparatus for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element, the apparatus comprising a processing circuit configured to: estimate a first set of one or more parameters for the channel, based on training symbols transmited from the second device to the first device; estimate a second set of one or more parameters for the channel, based on training symbols or reference signals transmited from the first device to the second device and based on the first set of one or more parameters.
11. The apparatus of example embodiment 10, wherein the first set of one or more parameters includes: a path gain magnitude corresponding to each radio channel path between the second device and the first device; a delay corresponding to each radio channel path between the second device and the first device; and an angle of arrival for each radio channel path between the second device and the first device.
12. The apparatus of example embodiment 11, wherein the second set of one or more parameters includes a phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device. 13. The apparatus of any one of example embodiments 10-12, wherein the processing circuit is configured to estimate the first set of one or more parameters for the channel by estimating a distribution function for the first set of one or more parameters using a deep generative model and observations of the training symbols transmited from the second device to the first device, as received at the first device, and using a least-squares estimation approach to jointly estimate, based on the estimated distribution function and the observations of the training symbols, the first set of one or more parameters and a phase change parameter for each radio channel path between the second device and the first device, in the direction toward the first device from the estimated distribution.
14. The apparatus of example embodiment 13, wherein the deep generative model is based on a generative adversarial network (GAN).
15. The apparatus of example embodiment 13 or 14, wherein the processing circuit is configured to estimate the second set of one or more parameters by using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on observations of training symbols transmitted from the first device to the second device, as received at the first device.
16. The apparatus of example embodiment 13 or 14, wherein the processing circuit is configured to estimate the first set of one or more parameters find estimate the second set of one or more parameters, and to estimate the second set of one or more parameters by using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on quantized channel-state information feedback received from the second device, the quantized channel -state information feedback being based on the reference signals.
17. The apparatus of any one of example embodiments 10-16, wherein the processing circuit is further configured to determine an estimated channel response for the channel in the direction from the first device to the second device, based on the estimated first set of one or more parameters, the estimated second set of one or more parameters, and an antenna array response that depends on tire physical arrangement of the antenna elements of the first device. 18. The apparatus of example embodiment 17, further comprising a transmitter circuit, and wherein the processing circuit is further configured to use the transmitter circuit to transmit a signal to the second device from the first device, using antenna weights determined from the estimated channel response for the channel in the direction from the first device to the second device.
ABBREVIATIONS
AoA Angle of arrival AoD Angle of departure BS Base station CS Compressed sensing CSI Channel state information DFT Discrete Fourier transform DGM Deep generative models FDD Frequency division duplex GAN Generative adversarial network LS Least squares
MIMO Multiple-input multiple-output RF Radio chains SNR Signal to noise ratio TDD Time division duplex UE User Equipment

Claims

CLAIMS What is claimed is:
1. A method for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element, the method comprising: estimating (810) a first set of one or more parameters for the channel, based on training symbols transmitted from the second device to the first device; estimating (820) a second set of one or more parameters for the channel, based on training symbols or reference signals transmitted from the first device to tire second device and based on the first set of one or more parameters.
2. The method of claim 1, wherein the first set of one or more parameters includes: a path gain magnitude corresponding to each radio channel path between the second device and the first device; a delay corresponding to each radio channel path between the second device and the first device; and an angle of arrival for each radio channel path between the second device and the first device.
3. The method of claim 2, wherein the second set of one or more parameters includes a phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device.
4. The method of any one of claims 1-3, wherein estimating (810) the first set of one or more parameters for the channel comprises estimating (812) a distribution function for the first set of one or more parameters using a deep generative model and observations of the training symbols transmitted from the second device to the first device, as received at the first device, and using (814) a least-squares estimation approach to jointly estimate, based on the estimated distribution function and the observations of the training symbols, the first set of one or more parameters and a phase change parameter for each radio channel path between the second device and the first device, in the direction toward the first device from the estimated distribution.
5. The method of claim 4, wherein the deep generative model is based on a generative adversarial network (GAN).
6. The method of claim 4 or 5, wherein estimating (820) the second set of one or more parameters comprises using (822) a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on observations of training symbols transmitted from the first device to the second device, as received at tire first device.
7. The method of claim 4 or 5, wherein estimating (810) the first set of one or more parameters and estimating (820) the second set of one or more parameters are carried out by the first device, and wherein estimating the second set of one or more parameters comprises using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on quantized channel-state information feedback received from the second device, the quantized channel-state information feedback being based on the reference signals.
8. The method of any one of claims 1-7, further comprising determining (830) an estimated channel response for the channel in the direction from the first device to the second device, based on the estimated first set of one or more parameters, the estimated second set of one or more parameters, and an antenna array response that depends on the physical arrangement of the antenna elements of the first device.
9. The method of claim 8, further comprising transmitting (840) a signal to the second device from the first device, using antenna weights determined from the estimated channel response for the channel in the direction from the first device to the second device.
10. An apparatus (30, 50) for estimating channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element, the apparatus (30, SO) comprising a processing circuit (32, 52) configured to: estimate a first set of one or more parameters for the channel, based on training symbols transmitted from the second device to the first device; estimate a second set of one or more parameters for the channel, based on training symbols or reference signals transmitted from the first device to the second device and based on the first set of one or more parameters.
11. The apparatus (30, 50) of claim 10, wherein the first set of one or more parameters includes: a path gain magnitude corresponding to each radio channel path between the second device and the first device; a delay corresponding to each radio channel path between the second device and the first device; and an angle of arrival for each radio channel path between the second device and the first device.
12. The apparatus (30, 50) of claim 11, wherein the second set of one or more parameters includes a phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device.
13. The apparatus (30, 50) of any one of claims 10-12, wherein the processing circuit (32, 52) is configured to estimate the first set of one or more parameters for the channel by estimating a distribution function for the first set of one or more parameters using a deep generative model and observations of the training symbols transmitted from the second device to the first device, as received at the first device, and using a least-squares estimation approach to jointly estimate, based on the estimated distribution function and the observations of the training symbols, the first set of one or more parameters and a phase change parameter for each radio channel path between the second device and the first device, in the direction toward the first device from the estimated distribution.
14. The apparatus (30, 50) of claim 13, wherein the deep generative model is based on a generative adversarial network (GAN).
15. The apparatus (30, 50) of claim 13 or 14, wherein the processing circuit (32, 52) is configured to estimate the second set of one or more parameters by using a least-squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on observations of training symbols transmitted from the first device to the second device, as received at the first device.
16. The apparatus of (30, 50) claim 13 or 14, wherein the processing circuit (32, 52) is configured to estimate the first set of one or more parameters and estimate the second set of one or more parameters, and to estimate the second set of one or more parameters by using a least- squares estimation approach to obtain an estimated phase change parameter corresponding to each radio channel path between the first device and the second device, in the direction toward the second device, based on the estimated first set of one or more parameters and based on quantized channel-state information feedback received from the second device, the quantized channel-state information feedback being based on the reference signals.
17. The apparatus (30, 50) of any one of claims 10-16, wherein the processing circuit (32, 52) is further configured to determine an estimated channel response for the channel in the direction from the first device to the second device, based on the estimated first set of one or more parameters, the estimated second set of one or more parameters, and an antenna array response that depends on the physical arrangement of the antenna elements of the first device.
18. The apparatus (30, 50) of claim 17, further comprising a transceiver circuit (36, 56), and wherein the processing circuit is further configured to use the transceiver circuit (32, 52) to transmit a signal to the second device from the first device, using antenna weights determined from the estimated channel response for the channel in the direction from the first device to the second device.
19. A computer program product (46, 66) comprise program instructions for execution by processing circuitry (32, 52), the program instructions being configured to cause the processing circuitry (32, 52) to estimate channel characteristics for a channel between a first device having a plurality of antenna elements and a second device having at least one antenna element by: estimating a first set of one or more parameters for the channel, based on training symbols transmitted from the second device to the first device; estimating a second set of one or more parameters for the channel, based on training symbols or reference signals transmitted from the first device to the second device and based on the first set of one or more parameters.
20. A computer-readable medium comprising, stored thereupon, the computer program product of claim 19.
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