WO2016169577A1 - Étalonnage de l'émetteur d'un dispositif de réseau - Google Patents

Étalonnage de l'émetteur d'un dispositif de réseau Download PDF

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Publication number
WO2016169577A1
WO2016169577A1 PCT/EP2015/058473 EP2015058473W WO2016169577A1 WO 2016169577 A1 WO2016169577 A1 WO 2016169577A1 EP 2015058473 W EP2015058473 W EP 2015058473W WO 2016169577 A1 WO2016169577 A1 WO 2016169577A1
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Prior art keywords
signal
transmitter
siso
radio frequency
network device
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PCT/EP2015/058473
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English (en)
Inventor
Shashi Kant
Bo Göransson
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Telefonaktiebolaget Lm Ericsson (Publ)
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Priority to PCT/EP2015/058473 priority Critical patent/WO2016169577A1/fr
Publication of WO2016169577A1 publication Critical patent/WO2016169577A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/10—Monitoring; Testing of transmitters
    • H04B17/11—Monitoring; Testing of transmitters for calibration
    • H04B17/12—Monitoring; Testing of transmitters for calibration of transmit antennas, e.g. of the amplitude or phase
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01Q—ANTENNAS, i.e. RADIO AERIALS
    • H01Q21/00—Antenna arrays or systems
    • H01Q21/28—Combinations of substantially independent non-interacting antenna units or systems
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01Q—ANTENNAS, i.e. RADIO AERIALS
    • H01Q3/00—Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an antenna or antenna system
    • H01Q3/26—Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an antenna or antenna system varying the relative phase or relative amplitude of energisation between two or more active radiating elements; varying the distribution of energy across a radiating aperture
    • H01Q3/267—Phased-array testing or checking devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H04B7/046—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting taking physical layer constraints into account
    • H04B7/0469—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting taking physical layer constraints into account taking special antenna structures, e.g. cross polarized antennas into account

Definitions

  • Embodiments presented herein relate to calibration, and particularly to a method, a network device, a computer program, and a computer program product for calibrating a transmitter of the network device.
  • communications networks there may be a challenge to obtain good performance and capacity for a given communications protocol, its parameters and the physical environment in which the communications network is deployed.
  • MIMO multiple input multiple output
  • UTRA UMTS Terrestrial Radio Access
  • E-UTRA evolved UTRA
  • LTE Release-10 supports up to rank-8 downlink transmission in order to achieve high data-rates.
  • Massive MIMO is one enabler for the beyond LTE wireless communication systems.
  • Massive MIMO is also known as very-large MIMO, full-dimension MIMO (FD- MIMO) or large-scale antenna array systems.
  • the base- station In massive MIMO, the base- station (BS) is equipped with a large number of physical transmit/receive antennas such that there are a high number of possible signal paths between the BS and a wireless device (WD) served by the BS. This may significantly improve the peak data rates and the link reliability.
  • Three-dimensional downlink beamforming (3D-DL-BF) is a technique in massive MIMO networks to improve downlink throughput performance by utilizing active antenna systems (AAS) permitting amplitude and phase tapering in both horizontal/azimuth and vertical/elevation dimensions. Due to the additional degrees of freedom in elevation domain, 3D BF (with AAS) can offer various applications, e.g., cell-splitting/sectorization in vertical domain, dedicated/user-specific elevation BF, etc.
  • AAS active antenna systems
  • Fig. 1 schematically illustrates, in terms of functional blocks, a generic MIMO-OFDM transmitter 10, where OFDM is short for orthogonal frequency-division multiplexing, the transmitter 10 being equipped with N transmit antennas.
  • signals are provided from transport blocks. Each signal is passed to an encoder for encoding and to an interleaver and modulator for interleaving and
  • the signals are then fed to a layer mapper for layer mapping and a pre-coder for pre-coding.
  • the thus pre-coded plurality of signals are then fed to and OFDM modulator for OFDM modulation and then to an antenna mapper for antenna mapping to the N antennas.
  • TBs transport blocks
  • OFDM modulator for OFDM modulation
  • antenna mapper for antenna mapping to the N antennas.
  • Cyclic redundancy check (CRC) bits are added to each TB and passed to the channel encoder (whereby each TB can be broken down into several smaller code-blocks).
  • the channel encoder adds parity bits to protect the data. Thereafter, the bit-stream is interleaved in order to randomize any block errors that may occur during transmission.
  • the interleaved data is mapped to an appropriate complex modulation-alphabet through a symbol-mapper (modulator).
  • the complex symbols are passed through a layer mapper and a precoder.
  • the resultant complex-symbol blocks are then passed through an inverse fast Fourier transform (IFFT) block followed by a cyclic prefix (CP) addition.
  • IFFT inverse fast Fourier transform
  • CP cyclic prefix
  • the IFFT block may be necessary for some communication systems which implements Orthogonal Frequency- Division Multiple Access (OFDMA) as the access technology (For example LTE/LTE- A, Wi-Fi, WiMax).
  • OFDMA Orthogonal Frequency- Division Multiple Access
  • CDMA code division multiple access
  • HSDPA High-Speed Downlink Packet Access
  • the baseband in-phase and quadrature (IQ) data is up-converted to a radio-frequency and thereby transmitted through the respective antennas.
  • the physical transmission (Tx) radio frequency (RF) branches not only have gain and phase imbalances among the branches but also some clipping noise (which can be seen as a part of the Tx error vector magnitude (EVM)).
  • EVM error vector magnitude
  • the gain and phase imbalances may generally have a more pronounced impact on the transmit beamforming since the beam-shape and transmit steering direction are in the unknown direction due to the (uncorrected) phase and gain imbalances among the Tx- RF branches.
  • antenna calibration may be employed in order to mitigate the gain and phase imbalances among the Tx branches.
  • mechanisms for the antenna calibration can generally be divided into two categories, namely offline mechanisms, and online mechanisms.
  • the calibration can be performed via special transmit signals which can sound all the available Tx branches (during start-up configuration).
  • the online mechanisms the calibration can be performed by utilizing the transmitted information data.
  • the gain and phase imbalances are time-varying and also frequency- dependent.
  • online mechanisms may be preferred in practice, but may also have higher complexity than the offline mechanisms since antenna calibration parameters estimation needs to be tailored based on the supported technologies. For example, in LTE-like systems so-called logical antenna ports need to be appropriately mapped to the physical transmit antennas.
  • the transmitted data or pilots can only be assigned to 1 logical antenna port while the number of physical transmit antennas can be higher; e.g., comprising 64, 128, 256, 512, 1024, etc., Tx branches.
  • the same information or pilots could be replicated to all the physical Tx branches, but this may be a problem for antenna calibration since the estimation of the gain/phase of individual Tx-branches is not feasible (due to the same signal over all the branches). Therefore, online calibration may need further mechanisms in order for the gain and phases of individual Tx-RF branches to be estimated in LTE-like systems.
  • An object of embodiments herein is to provide efficient calibration of a transmitter of a network device.
  • a method for calibrating a transmitter of a network device The method is performed by the network device.
  • the method comprises acquiring single-input single-output (SISO) signals, wherein each SISO signal is representative of a respective radio frequency transmission signal on a radio frequency path in the transmitter.
  • the method comprises acquiring, for each SISO signal, a noise covariance matrix of a noise vector of the radio frequency transmission signal.
  • the method comprises determining, for each SISO signal, a channel estimation of the radio frequency path using the acquired noise covariance matrix.
  • the method comprises adjusting amplitude and phase imbalances of the transmitter according to the channel estimation.
  • this provides efficient calibration of a transmitter of a network device.
  • this provides efficient online antenna calibration in a transmitter for massive MIMO.
  • this provides energy efficient, due to the low complexity in terms of calibration parameters estimation, calibration of a transmitter of a network device.
  • a network device for calibrating a transmitter of the network device.
  • the network device comprises a processing unit.
  • the processing unit is configured to cause the network device to acquire single-input single-output (SISO) signals, wherein each SISO signal is representative of a respective radio frequency
  • the processing unit is configured to cause the network device to acquire, for each SISO signal, a noise covariance matrix of a noise vector of radio frequency transmission signal.
  • the processing unit is configured to cause the network device to determine, for each SISO signal, a channel estimation of the radio frequency path using the acquired noise covariance matrix.
  • the processing unit is configured to cause the network device to adjust amplitude and phase imbalances of the transmitter according to the channel estimation.
  • a computer program for calibrating a transmitter of the network device comprising computer program code which, when run on a processing unit of the network device, causes the network device to perform a method according to the first aspect.
  • a computer program product comprising a computer program according to the third aspect and a computer readable means on which the computer program is stored. It is to be noted that any feature of the first, second, third and fourth aspects may be applied to any other aspect, wherever appropriate. Likewise, any advantage of the first aspect may equally apply to the second, third, and/or fourth aspect, respectively, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following detailed disclosure, from the attached dependent claims as well as from the drawings.
  • Fig. l is a schematic diagram illustrating a known transmitter
  • FIGS. 2 and 3 are schematic diagrams illustrating a transmitter according to embodiments
  • Fig. 4 is a schematic diagram illustrating part of a transmitter according to an embodiment
  • Fig. 5 is a schematic diagram illustrating a set of instructions according to an embodiment
  • Fig. 6a is a schematic diagram showing functional units of a network device according to an embodiment
  • Fig. 6b is a schematic diagram showing functional modules of a network device according to an embodiment
  • Fig. 7 shows one example of a computer program product comprising computer readable means according to an embodiment
  • FIGs. 8 and 9 are flowcharts of methods according to embodiments.
  • radio network node or simply network node is used and it refers to any type of network node serving a user equipment (UE) and/or being operatively connected to at least one other network node or network element or any radio node from where a UE receives a signal.
  • radio network nodes are Node B, base station (BS), multi-standard radio (MSR) radio node such as MSR BS, eNode B, network controller, radio network controller (RNC), base station controller (BSC), relay, donor node controlling relay, base transceiver station (BTS), access point (AP), transmission point, transmission node, remote radio unit (RRU), remote radio head (RRH), nodes in distributed antenna system (DAS), etc.
  • UE user equipment
  • D2D device-to-device
  • M2M machine-to-machine
  • PDA personal digital assistants
  • tablet computers mobile terminals
  • smart phones laptop embedded equipment
  • LME laptop mounted equipment
  • USB universal serial bus
  • At least some embodiments are described in particular for massive downlink MIMO operation in LTE-like systems supporting either single-user or multi- user BF. However, the embodiments are applicable to any (massive) MIMO systems, HSPA, Wi-Fi/WLAN, WiMax, etc.
  • At least some embodiments relate to efficient estimation of transmit antenna calibration parameters suitable for online for Massive MIMO based on space- alternating generalized expectation maximization (SAGE) principles combined with maximum a posteriori (MAP) estimation.
  • SAGE space- alternating generalized expectation maximization
  • MAP maximum a posteriori
  • the embodiments disclosed herein relate to calibration of a transmitter of a network device.
  • a network device a method performed by the network device, a computer program comprising code, for example in the form of a computer program product, that when run on a processing unit of the network device, causes the network device to perform the method.
  • Fig. 6a schematically illustrates, in terms of a number of functional units, the components of a network device 60 according to an embodiment.
  • a processing unit 61 is provided using any combination of one or more of a suitable central processing unit (CPU), multiprocessor, microcontroller, digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate arrays (FPGA) etc., capable of executing software instructions stored in a computer program product 71 (as in Fig. 7), e.g. in the form of a storage medium 63.
  • a computer program product 71 as in Fig. 7
  • the processing unit 61 is thereby arranged to execute methods as herein disclosed.
  • the storage medium 63 may also comprise persistent storage, which, for example, can be any single one or combination of magnetic memory, optical memory, solid state memory or even remotely mounted memory. As such, the storage medium 63 may store a set of instructions 50.
  • the network device 60 further comprise a
  • communications interface 62 for communications with at least one other network device 60, at least one user equipment, etc.
  • the communications interface 62 may comprise one or more transmitters 20, 30, 40 and receivers, comprising analogue and digital components and a suitable number of antennas for wireless communications and ports for wireline communications.
  • the processing unit 61 controls the general operation of the network device 60 e.g. by sending data and control signals to the
  • the network device 60 may be provided as a network node or a user equipment.
  • Fig. 6b schematically illustrates, in terms of a number of functional modules, the components of a network device 60 according to an embodiment.
  • 6b comprises a number of functional modules; an acquire module 61a configured to perform below steps S106, S108, and a determine module 61b configured to perform below steps Sio6b, Sio6c, Sio8a, S110, Snoa, Snob, Siioc.
  • each functional module 6ia-j may be implemented in hardware or in software.
  • one or more or all functional modules 6ia-j may be implemented by the processing unit 61, possibly in cooperation with functional units 62 and/or 63.
  • the processing unit 61 may thus be arranged to from the storage medium 63 fetch
  • FIG. 7 shows one example of a computer program product 71 comprising computer readable means 73.
  • a computer program 72 can be stored, which computer program 72 can cause the processing unit 61 and thereto operatively coupled entities and devices, such as the communications interface 62 and the storage medium 63, to execute methods according to embodiments described herein.
  • the computer program 72 and/or computer program product 71 may thus provide means for performing any steps as herein disclosed.
  • the computer program product 71 is illustrated as an optical disc, such as a CD (compact disc) or a DVD (digital versatile disc) or a Blu-Ray disc.
  • the computer program product 71 could also be embodied as a memory, such as a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), or an electrically erasable programmable read-only memory (EEPROM) and more particularly as a non-volatile storage medium of a device in an external memory such as a USB (Universal Serial Bus) memory or a Flash memory, such as a compact Flash memory.
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • FIGs. 8 and 9 are flow chart illustrating embodiments of methods for calibrating a transmitter 20, 30, 40 of a network device 60. The methods are performed by the network device 60. The methods are advantageously provided as computer programs 32.
  • a transmitter 20, 30 comprising a single-path feedback receiver as illustrated in Fig. 2 and Fig. 3.
  • the transmitter 20 is configured for antenna calibration in the frequency domain.
  • the transmitter 20 comprises all features of the transmitter 10.
  • the transmitter 20 additionally comprises a frequency domain antenna calibrator, a feedback receiver, and an antenna calibration parameters estimator.
  • the transmitter 30 is configured for antenna calibration in the time domain.
  • the transmitter 30 comprises all features of the transmitter 10.
  • the transmitter 30 additionally comprises a time domain antenna calibrator, a feedback receiver, and an antenna calibration parameters estimator.
  • the feedback receiver down-converts the received analog signal from the passband to the digital/discrete-time baseband (can be converted in one or multiple steps to digital domain).
  • Fig. 2 and Fig. 3 thus schematically illustrate transmitters 20, 30 suitable for implementing the proposed mechanisms for calibrating a transmitter of a network device 60 that can be employed in frequency-domain and in time- domain, respectively, as part of an online/real-time antenna calibration mechanism, respectively.
  • frequency-domain antenna calibration mechanism is described and then time-domain calibration is described.
  • the time-domain antenna calibration mechanism is mainly suitable for multi- standard radios.
  • the herein disclosed mechanisms for online/real -time antenna calibration of a transmitter 20, 30, 40 of a network device 60 can utilize either whole transmitted data as the known reference symbols to estimate the parameters.
  • the received frequency-domain signal at the [k ] -th resource element (RE)/sub-carrier in the feedback receiver is denoted: [k ]x j [k i £] +n[k ] t (i) where 7[£,/]eC lx l is a post-FFT received symbol complex scalar at the [k ] - th RE, where JC .
  • [t,/]eC lxl is a known transmitted complex symbol (e.g., M- ary quadrature amplitude modulation (M-QAM)) corresponding to the j-th Tx-RF branch at the [k,i] -th RE, and where the complex channel
  • M-QAM M- ary quadrature amplitude modulation
  • H j [k,l] ⁇ C N - , xl describes the unknown (correlated) channel, i.e., the unknown gain and phase of j-t Tx-RF branch at the [k ] -th RE.
  • the transmitted symbols will pass through the transmitter paths of the transmitter 20, 30, 40, which transmitter paths will distort the symbols. Since this channel is highly correlated in time and frequency direction, a two- dimensional space-alternating generalized expectation maximization (SAGE) maximum a posteriori based estimation may be used for estimating the channel defined by the transmitter paths.
  • SAGE space-alternating generalized expectation maximization
  • a posteriori based estimation may be used for estimating the channel defined by the transmitter paths.
  • the received symbols described in Equation (1) are vectorized by being stacked in a column vector within a considered 2D window, which encompasses several REs; M f REs in time-direction and M k
  • Y e c MkM ' x l is a column vector of received complex-symbols, where X j e Q M t M t * M k M t - g a (jj a g 0na i matrix comprising the known transmitted complex symbols corresponding to the j-t Tx-RF branch, where
  • H e C M " M ' X 1 is a column vector of j-th Tx-RF channel which is the
  • H X is column vector of channel comprising
  • Table 1 Summary ofjoint-LS channel estimation andjoint-LMMSE channel estimation based calibration parameters estimation.
  • the effective noise co-variance RN needs to be estimated. So, for the estimation of the effective noise co-variance, a first joint- LS based channel estimation can first be determined and then an estimate R N can be determined as follows:
  • R N can be determined as follows:
  • a channel matrix of size M k M f N t ⁇ M k M f N t needs to be inverted.
  • a byproduct can be exploited to estimate the distortion noise variance.
  • At least some embodiments of the proposed mechanisms for calibrating a transmitter 20, 30, 40 of a network device 60 involve to decomposes the MISO system into a single-input single-output (SISO) system such that for every SISO system a low-complexity MAP -based (equivalently to LMMSE) channel estimation can be employed. Online matrix inversion is avoided since the autocorrelation matrix is known a-priori (based on the delay spread per branch and the time selectivity). Thus, the filters can be stored and looked up based on the computed signal to noise ratio (SNR) as given by .
  • SNR signal to noise ratio
  • the network device 60 is configured to, in a step S106, acquire N T single- input single-output (SISO) signals, denoted z ; , for all j e ⁇ l, ... , N T ⁇ .
  • SISO single- input single-output
  • SISO signal is representative of a respective radio frequency transmission signal j on a radio frequency path in the transmitter 20, 30, 40.
  • the calibration is based on a noise covariance matrix.
  • the network device 60 is configured to, in a step S108, acquire, for each SISO signal z ; , a noise covariance matrix, denoted , of a noise vector of radio frequency transmission signal j .
  • a channel estimate is then determined.
  • the network device 60 is configured to, in a step S110, determine, for each SISO signal i j , a channel estimation of the considered radio frequency path.
  • the network device 60 may then perform the calibration. Particularly, the network device 60 is configured to, in a step S112, adjust amplitude and phase imbalances of the transmitter 20, 30, 40 according to the channel estimation.
  • the transmitter 20, 30, 40 may have N T antenna elements, and/or the radio frequency transmission signal may be signal component j of a MIMO transmission signal.
  • the herein disclosed method for calibration is suitable for an antenna with a large number of antenna elements and/or for a network device 60 configured for MIMO transmission.
  • Fig. 9 illustrating methods for calibrating a transmitter of a network device 60 as performed by the network device 60 according to further embodiments. Parallel reference is continued to Fig. 5.
  • the herein disclosed mechanisms for calibration may comprise an
  • the network device is configured to, in a step S102, initialize a set of channel estimates j ⁇ ] for all j e ⁇ l, ... , N T ⁇ .
  • the initial noise vector may be estimated by subtracting the received signal vector with the re-created noise-free signal as given in Equation (4) in Fig. 5.
  • the network device is configured to, in a step Si04a, estimate an initial noise vector by subtracting a re-created noise-free signal from the received signal vector .
  • the herein disclosed mechanisms for calibration may comprise a
  • the MISO signal Y may be iteratively decomposed into respective SISO signals z y for a considered j-t Tx antenna.
  • the network device is configured to, in a step Sio6a, acquire theN r SISO signals by decomposing a multiple-input single- output, MISO, signal Y into the N T SISO signals i j , wherein the MISO signal is representative of all the transmission signals.
  • a SISO signal z y is obtained (or rather re-created) in Equation (5) in Fig. 5 by utilizing the known data X, , the current estimated channel at o-th iteration and the estimated noise vector . That is, the MISO signal Y may be determined as
  • acquiring the N T SISO signals comprises, in a step Sio6b, determine SISO signal i j from a diagonal matrix X ; of baseband transmission symbols of radio frequency transmission signal j , channel estimation at iteration g , and the noise vector .
  • the herein disclosed mechanisms for calibration may comprise an estimation phase where channel estimates corresponding to each decomposed SISO signals and noise-variance estimates are obtained. Details thereof will now be disclosed. l8
  • the channel estimates corresponding to the j-th Tx antenna may estimated at
  • the channel estimation// ⁇ at iteration is the channel estimation// ⁇ at iteration
  • Equation (8) is further based on an autocorrelation matrix R HH of the channel, and a diagonal matrix X ; comprising baseband transmission symbols of radio frequency transmission signal j .
  • Equation (8) is given in Equation (8) in Fig. 5, i.e.,
  • the network device 60 is configured to, in a step Snoa, determine the channel estimation by determining the channel estimates / (g+1) corresponding to
  • H ⁇ +l R HH Xf (R ⁇ + X j R HH Xf Y Zj , where parameter R HH is a autocorrelation matrix of the channel, and where X ; is a diagonal matrix of baseband transmission symbols of radio frequency transmission signal j .
  • Equation (8) in Fig. 5 which are still exact, namely,
  • the channel estimation J at iteration (g + 1) may further based on a channel autocorrelation matrix R HH of the channel, a diagonal matrix X ; of baseband transmission symbols of radio frequency transmission signal j , and a noise covariance matrix of the noise vector .
  • the network device 60 is configured to, in a step
  • Snob determine the channel estimates /y( g+1 ' corresponding to antenna
  • element j of the transmitter 20, 30, 40 at iteration (g + 1) R HH (R HH + X "1 R A ⁇ - ⁇ Y X "1 Zj , where parameter R HH is a autocorrelation matrix of the channel, where X ; is a diagonal matrix of baseband transmission symbols of radio frequency transmission signal j , and where parameter is a noise covariance matrix of the noise vector .
  • the network device 60 is configured to, in a step Sio8a, acquire the noise covariance matrix by determining a noise variance estimate ⁇ ? from the noise covariance matrix.
  • the noise variance estimate cr? may then be determined as ⁇ ?
  • parameter R HH is a autocorrelation matrix of the channel, and where X ; is a diagonal matrix of baseband transmission symbols of radio frequency transmission signal j .
  • the parameter ⁇ is dependent on a modulation alphabet for modulating the MISO signal Y . That is, the parameter ⁇ in Equation (9) is dependent on the considered modulation alphabet such that ⁇ is 1, 1.8889, and 2.6854 for 4- QAM, 16-QAM, and 64-QAM, respectively, where QAM denotes quadrature amplitude modulation.
  • the herein disclosed mechanisms for calibration may comprise a
  • Fig. 4 schematically illustrates part of a transmitter 40 suitable for implementing the herein disclosed calibration.
  • the part of the transmitter 40 comprises the antenna calibration parameters estimator and the (frequency domain) antenna calibrator.
  • the antenna calibration parameters estimator is configured to perform herein disclosed steps S102 to S110, and the antenna calibrator is configured to perform steps S112, Sii2a, Sii2b.
  • the network device 60 is configured to adjust the amplitude and phase imbalances of the transmitter 20, 30, 40 by, in a step Sii2a, select one antenna element k of the transmitter 20, 30, 40 as reference; and, in a step Sii2b, adjust amplitude gain « 7 and phase correction A ⁇ f>j for antenna element j of the transmitter 20, 30, 40 based on the channel estimation and the channel estimation .
  • the gain a and
  • phase correction ⁇ . for the j-th Tx antenna can be expressed as a j ⁇ ).
  • the network device 60 is configured to, in a step S116, transmit calibrated data x' on antenna element j of the transmitter 20, 30, 40.
  • the received system model in discrete time-domain can be expressed as follows, considering linear convolution):
  • S j [n] is a known discrete time-domain data sample corresponding to j- th Tx antenna
  • h ⁇ [n] describes the channel impulse response sample having length Lj
  • * denotes the linear convolution.
  • the discrete time-domain complex Gaussian noise realization having zero mean and unknown noise variance ⁇ , .
  • Vectorizing the received time-domain samples within the desired window length AT for the channel estimation yields:
  • y e C ⁇ x 1 is a column vector of received AT complex-samples in discrete time-domain
  • S 7 E C"" L ' is a matrix comprising the known transmitted complex data samples corresponding toj-th Tx-RF branch, h
  • e C z x 1 is a column vector of j-t Tx-RF channel impulse responses which is the calibration parameter of interest to be estimated
  • w e C ⁇ x 1 is a zero- mean complex Gaussian unknown noise vector having covariance Rw
  • the above disclosed mechanisms for calibrating a transmitter 20, 30, 40 of a network device can be utilized for time-domain channel estimation.
  • the channel frequency response is estimated by taking the FFT.
  • the estimated channel frequency responses i.e., the calibration parameters, are utilized to obtain the calibration parameters in the frequency domain as given above for calibration parameters estimation directly in the frequency-domain.
  • the calibration parameter estimates are re-transformed into the time-domain via an IFFT. Thereby the appropriate filtering of discrete data samples is performed by utilizing the estimated impulse response corresponding to calibration.
  • steps S106 to S112 may be iteratively performed.
  • the network device 60 may be configured to, in a step S114, iteratively perform steps S106 to S112, wherein SISO signal z ; at iteration stage (g + 1) is based on the channel estimation from iteration stage g j
  • steps S106-S112 may be iteratively performed until convergence. Hence, the steps S106-S112 may be iteratively performed until a difference between parameters obtained at one stage g and parameters obtained at a next stage (g + 1) is relatively small, for example smaller than a threshold.
  • the steps S106-S112 may be iteratively performed for a selected number of iterations. Hence, the steps S106-S112 may be iteratively performed for a fixed number G of iterations.

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Abstract

L'invention concerne des mécanismes pour étalonner un émetteur d'un dispositif de réseau. Le procédé est réalisé par le dispositif de réseau. Le procédé consiste à acquérir des signaux à entrée unique, sortie unique (SISO), chaque signal SISO étant représentatif d'un signal d'émission radiofréquence respectif sur un chemin radiofréquence dans l'émetteur. Le procédé consiste à acquérir, pour chaque signal SISO, une matrice de covariance de bruit d'un vecteur de bruit du signal d'émission radiofréquence. Le procédé consiste à déterminer, pour chaque signal SISO, une estimation de canal du chemin radiofréquence à l'aide de la matrice de covariance de bruit acquise. Le procédé consiste à régler les déséquilibres d'amplitude et de phase de l'émetteur selon l'estimation de canal.
PCT/EP2015/058473 2015-04-20 2015-04-20 Étalonnage de l'émetteur d'un dispositif de réseau WO2016169577A1 (fr)

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CN110178315A (zh) * 2017-01-24 2019-08-27 华为技术有限公司 一种天线校正方法及装置
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WO2022010389A1 (fr) * 2020-07-07 2022-01-13 Telefonaktiebolaget Lm Ericsson (Publ) Procédé et dispositif(s) pour prendre en charge l'étalonnage d'un réseau multi-antennes compris dans un dispositif d'antenne fonctionnant avec un réseau de communication sans fil

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