WO2022214935A1 - Approximation de matrice de covariance de canal de liaison descendante dans des systèmes duplex par répartition en fréquence - Google Patents

Approximation de matrice de covariance de canal de liaison descendante dans des systèmes duplex par répartition en fréquence Download PDF

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Publication number
WO2022214935A1
WO2022214935A1 PCT/IB2022/053108 IB2022053108W WO2022214935A1 WO 2022214935 A1 WO2022214935 A1 WO 2022214935A1 IB 2022053108 W IB2022053108 W IB 2022053108W WO 2022214935 A1 WO2022214935 A1 WO 2022214935A1
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WIPO (PCT)
Prior art keywords
covariance
downlink
uplink
matrix
network node
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PCT/IB2022/053108
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English (en)
Inventor
Salime BAMERI
Khalid ALMAHROG
Amr El-Keyi
Ramy H. GOHARY
Ioannis LAMBADARIS
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Telefonaktiebolaget Lm Ericsson (Publ)
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Publication of WO2022214935A1 publication Critical patent/WO2022214935A1/fr

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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
    • H04L25/021—Estimation of channel covariance
    • 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
    • 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
    • H04L25/0228—Channel estimation using sounding signals with direct estimation from sounding signals

Definitions

  • the present disclosure relates to wireless communications, and in particular, to estimating a downlink covariance matrix using samples or a portion of the uplink covariance matrix.
  • the Third Generation Partnership Project (3 GPP) has developed and is developing standards for Fourth Generation (4G) (also referred to as Long Term Evolution (LTE)) and Fifth Generation (5G) (also referred to as New Radio (NR)) wireless communication systems.
  • 4G Fourth Generation
  • 5G Fifth Generation
  • NR New Radio
  • Such systems provide, among other features, broadband communication between network nodes, such as base stations, and mobile wireless devices (WD), as well as communication between network nodes and between WDs.
  • Sixth Generation (6G) wireless communication systems are also under development.
  • Wireless communications systems employing relatively large number of antennas at the network node (e.g., base station (BS)) to serve multiple wireless devices, are known as massive Multiple-Input Multiple-Output (MIMO) systems.
  • MIMO massive Multiple-Input Multiple-Output
  • CSI channel state information
  • DL downlink
  • UL uplink
  • TDD Time Division Duplex
  • the channel spatial covariance matrix plays a role in CSI acquisition.
  • the coherence time of the channel spatial covariance matrix is longer than the channel coherence time; therefore, estimating this matrix can be performed less frequently and this reduces the training overhead.
  • the propagation directions are frequency invariant for the UL and DL bands
  • the angular power spectrum (APS) i.e., the signal power distribution in the angular domain
  • the frequency invariance of the APS is exploited in several existing methods to estimate the DL spatial covariance matrix from an observation of the UL spatial covariance matrix.
  • an idea of these existing methods is to estimate the UL covariance matrix, then using this matrix: either to explicitly estimate the APS and use it to compute an estimate of the DL covariance matrix, or to apply some transformation to the UL covariance matrix estimate to get the DL covariance matrix.
  • the APS frequency invariance assumption is implicitly preserved in the applied transformation.
  • legacy solutions for FDD systems rely on using the covariance matrix estimated from uplink measurements without any frequency correction.
  • This can lead to significant performance degradation when the duplex gap between uplink and downlink transmission bands is large, e.g., for LTE band 4, the uplink band is 1710-1755 MHz while the downlink band is 2110-2155 MHz, i.e., the duplex gap is 400 MHz.
  • Existing frequency correction methods for covariance matrices in some existing methods have high complexity. For example, the algorithm in one existing method explicitly estimates the APS samples by solving a complex optimization problem and then estimates the downlink covariance from the estimated APS.
  • the total complexity of this algorithm can be shown to be 0(gM 3 ) where M is the number of network node antennas and g » M is the number of samples in the APS estimate.
  • the scheme in another existing method uses a truncated Fourier series expansion to represent the APS and obtains the downlink covariance matrix using a matrix multiplication operation that multiples the vectorized uplink covariance matrix by a transformation matrix. The complexity of this scheme is dominated by this matrix multiplication and is of 0(M 4 ).
  • Some embodiments advantageously provide methods, systems, and apparatuses for estimating a downlink covariance matrix using samples or a portion of the uplink covariance matrix.
  • massive MIMO systems with wireless devices communicating with a network node operating in FDD mode is considered.
  • FDD systems unlike TDD systems, frequency separation between uplink and downlink results in a lack of channel reciprocity and consequently, different uplink/downlink channel covariance matrices.
  • a scheme described herein provides an approximate downlink covariance matrix using only uplink covariance samples or a specific portion of the uplink covariance matrix.
  • One or more embodiments described herein provide a method that can be implemented as a low complexity matrix multiplication using a fixed matrix that depends only on the network node array manifold and uplink/downlink frequency separation. The accuracy of some embodiments described herein advantageously increases with an increasing number of antennas at the network node. In one or more embodiments, the theoretical results were confirmed by numerical simulations.
  • a network node is configured to communicate with a wireless device, WD, using frequency division duplex, FDD, communications.
  • the network node includes processing circuitry configured to: determine an uplink covariance Toeplitz matrix based at least in part on measurements of signals received from the WD in an uplink frequency band on a number of antenna elements in an array of antenna elements of an antenna of the network node; determine a downlink covariance Toeplitz matrix based at least in part on a single row and column of the uplink covariance Toeplitz matrix; and apply the downlink covariance Toeplitz matrix for downlink transmissions to the WD in a downlink frequency band.
  • determining the downlink covariance Toeplitz matrix includes determining a frequency invariant angular power spectrum, APS, the APS being determined as a Fourier series, coefficients of the Fourier series being based at least in part on the single row and column of the uplink covariance Toeplitz matrix.
  • a number of coefficients in the Fourier series is based at least in part on the number of antenna elements in the array of antenna elements.
  • determining the downlink covariance Toeplitz matrix includes determining a product of a matrix of Sine functions and a vector containing elements of the single row and column of the uplink covariance Toeplitz matrix.
  • the processing circuitry is further configured to store the matrix of Sine functions, the matrix of Sine functions being independent of the signal measurements and being preoperationally computed.
  • determining the uplink covariance Toeplitz matrix includes finding a sample Toeplitz matrix that minimizes a Frobenius norm of a difference between the sample Toeplitz matrix and an uplink covariance matrix determined from the signal measurements.
  • determining the downlink covariance Toeplitz matrix includes determining a first downlink covariance Toeplitz matrix corresponding to a first dimension of a two-dimensional array of the antenna elements and determining a second downlink covariance Toeplitz matrix corresponding to the second dimension of the two-dimensional array.
  • determining the downlink covariance Toeplitz matrix includes determining a Kronecker product of the first and second downlink covariance Toeplitz matrices corresponding to the first and second dimensions of the two-dimensional array. In some embodiments, determining the downlink covariance Toeplitz matrix includes determining a polarization-independent downlink covariance Toeplitz matrix that is applied for each polarization of an array of dual polarized antenna elements of the antenna. In some embodiments, the single row and column are a first row and column, respectively, of the uplink covariance Toeplitz matrix.
  • a method in a network node configured to communicate with a wireless device, WD, using frequency division duplex, FDD, communications includes: determining an uplink covariance Toeplitz matrix based at least in part on measurements of signals received from the WD in an uplink frequency band on a number of antenna elements in an array of antenna elements of an antenna of the network node; determining a downlink covariance Toeplitz matrix based at least in part on a single row and column of the uplink covariance Toeplitz matrix; and applying the downlink covariance Toeplitz matrix for downlink transmissions to the WD in a downlink frequency band.
  • determining the downlink covariance Toeplitz matrix includes determining a frequency invariant angular power spectrum, APS, the APS being determined as a Fourier series, coefficients of the Fourier series being based at least in part on the single row and column of the uplink covariance Toeplitz matrix.
  • a number of coefficients in the Fourier series is based at least in part on the number of antenna elements in the array of antenna elements.
  • determining the downlink covariance Toeplitz matrix includes determining a product of a matrix of Sine functions and a vector containing elements of the single row and column of the uplink covariance Toeplitz matrix.
  • the method also includes storing the matrix of Sine functions, the matrix of Sine functions being independent of the signal measurements and being preoperationally computed.
  • determining the uplink covariance Toeplitz matrix includes finding a sample Toeplitz matrix that minimizes a Frobenius norm of a difference between the sample Toeplitz matrix and an uplink covariance matrix determined from the signal measurements.
  • determining the downlink covariance Toeplitz matrix includes determining a first downlink covariance Toeplitz matrix corresponding to a first dimension of a two-dimensional array of the antenna elements and determining a second downlink covariance Toeplitz matrix corresponding to the second dimension of the two-dimensional array.
  • determining the downlink covariance Toeplitz matrix includes determining a Kronecker product of the first and second downlink covariance Toeplitz matrices corresponding to the first and second dimensions of the two-dimensional array. In some embodiments, determining the downlink covariance Toeplitz matrix includes determining a polarization-independent downlink covariance Toeplitz matrix that is applied for each polarization of an array of dual polarized antenna elements of the antenna. In some embodiments, the single row and column are a first row and column, respectively, of the uplink covariance Toeplitz matrix.
  • FIG. 1 is a schematic diagram of an example network architecture illustrating a communication system connected via an intermediate network to a host computer according to the principles in the present disclosure
  • FIG. 2 is a block diagram of a host computer communicating via a network node with a wireless device over an at least partially wireless connection according to some embodiments of the present disclosure
  • FIG. 3 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for executing a client application at a wireless device according to some embodiments of the present disclosure
  • FIG. 4 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a wireless device according to some embodiments of the present disclosure
  • FIG. 5 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data from the wireless device at a host computer according to some embodiments of the present disclosure
  • FIG. 6 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a host computer according to some embodiments of the present disclosure
  • FIG. 7 is a flowchart of an example process in a network node according to some embodiments of the present disclosure.
  • FIG. 8 is a flowchart of an example process in a wireless device according to some embodiments of the present disclosure
  • FIG. 9 is a flowchart of another example process in a network node according to principles disclosed herein;
  • FIG. 10 is a diagram of the scheme/method according to some embodiments of the present disclosure
  • FIG. 11 is a diagram of a uniformly space dual polarized array
  • Some existing methods for estimating DL spatial covariance matrix from the observed UL covariance matrix for FDD systems suffer from high complexity that disadvantageously uses limited processing resources.
  • One or more embodiments described herein advantageously help solve some of the problems with existing systems by, for example, using only the first row and column of the uplink covariance matrix to compute the downlink covariance matrix for FDD systems.
  • the Fourier series expansion of the APS may be used where only a specific portion of the uplink covariance matrix is used for computation.
  • only the first row and column of the uplink covariance matrix is used to compute the downlink covariance matrix.
  • the complexity of the algorithm described herein is of 0
  • the embodiments reside primarily in combinations of apparatus components and processing steps related to estimating a downlink covariance matrix using samples or a portion of the uplink covariance matrix.
  • the joining term, “in communication with” and the like may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example.
  • electrical or data communication may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example.
  • Coupled may be used herein to indicate a connection, although not necessarily directly, and may include wired and/or wireless connections.
  • network node can be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multi- standard radio (MSR) radio node such as MSR BS, multi-cell/multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (
  • BS base station
  • wireless device or a user equipment (UE) are used interchangeably.
  • the WD herein can be any type of wireless device capable of communicating with a network node or another WD over radio signals, such as wireless device (WD).
  • the WD may also be a radio communication device, target device, device to device (D2D) WD, machine type WD or WD capable of machine to machine communication (M2M), low-cost and/or low-complexity WD, a sensor equipped with WD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (IoT) device, or a Narrowband IoT (NB-IOT) device, etc.
  • D2D device to device
  • M2M machine to machine communication
  • M2M machine to machine communication
  • Tablet mobile terminals
  • smart phone laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles
  • CPE Customer Premises Equipment
  • IoT Internet of Things
  • NB-IOT Narrowband IoT
  • radio network node can be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi-cell/multicast Coordination Entity (MCE), IAB node, relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH).
  • RNC evolved Node B
  • MCE Multi-cell/multicast Coordination Entity
  • IAB node IAB node
  • relay node access point
  • radio access point radio access point
  • RRU Remote Radio Unit
  • RRH Remote Radio Head
  • Transmitting in downlink may pertain to transmission from the network or network node to the wireless device.
  • Transmitting in uplink may pertain to transmission from the wireless device to the network or network node.
  • Transmitting in sidelink may pertain to (direct) transmission from one wireless device to another.
  • Uplink, downlink and sidelink (e.g., sidelink transmission and reception) may be considered communication directions.
  • uplink and downlink may also be used to described wireless communication between network nodes, e.g. for wireless backhaul and/or relay communication and/or (wireless) network communication for example between base stations or similar network nodes, in particular communication terminating at such. It may be considered that backhaul and/or relay communication and/or network communication is implemented as a form of sidelink or uplink communication or similar thereto.
  • WCDMA Wide Band Code Division Multiple Access
  • WiMax Worldwide Interoperability for Microwave Access
  • UMB Ultra Mobile Broadband
  • GSM Global System for Mobile Communications
  • functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes.
  • the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.
  • Some embodiments provide estimating a downlink covariance matrix using samples or a portion of the uplink covariance matrix.
  • FIG. 1 a schematic diagram of a communication system 10, according to an embodiment, such as a 3GPP-type cellular network that may support standards such as LTE and/or NR (5G), which comprises an access network 12, such as a radio access network, and a core network 14.
  • the access network 12 comprises a plurality of network nodes 16a, 16b, 16c (referred to collectively as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (referred to collectively as coverage areas 18).
  • Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20.
  • a first wireless device (WD) 22a located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a.
  • a second WD 22b in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of WDs 22a, 22b (collectively referred to as wireless devices 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole WD is in the coverage area or where a sole WD is connecting to the corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include many more WDs 22 and network nodes 16.
  • a WD 22 can be in simultaneous communication and/or configured to separately communicate with more than one network node 16 and more than one type of network node 16.
  • a WD 22 can have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR.
  • WD 22 can be in communication with an eNB for LTE/E-UTRAN and a gNB for NR/NG-RAN.
  • the communication system 10 may itself be connected to a host computer 24, which may be 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.
  • the host computer 24 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider.
  • the connections 26, 28 between the communication system 10 and the host computer 24 may extend directly from the core network 14 to the host computer 24 or may extend via an optional intermediate network 30.
  • the intermediate network 30 may be one of, or a combination of more than one of, a public, private or hosted network.
  • the intermediate network 30, if any, may be a backbone network or the Internet.
  • the intermediate network 30 may comprise two or more sub-networks (not shown).
  • the communication system of FIG. 1 as a whole enables connectivity between one of the connected WDs 22a, 22b and the host computer 24.
  • the connectivity may be described as an over-the-top (OTT) connection.
  • the host computer 24 and the connected WDs 22a, 22b are configured to communicate data and/or signaling via the OTT connection, using the access network 12, the core network 14, any intermediate network 30 and possible further infrastructure (not shown) as intermediaries.
  • the OTT connection may be transparent in the sense that at least some of the participating communication devices through which the OTT connection passes are unaware of routing of uplink and downlink communications.
  • a network node 16 may not or need not be informed about the past routing of an incoming downlink communication with data originating from a host computer 24 to be forwarded (e.g., handed over) to a connected WD 22a. Similarly, the network node 16 need not be aware of the future routing of an outgoing uplink communication originating from the WD 22a towards the host computer 24.
  • a network node 16 is configured to include an estimation unit 32 which is configured to perform one or more network node 16 functions as described herein such as with respect to estimating a downlink covariance matrix using samples or a portion of the uplink covariance matrix. More particularly, the estimation unit 32 may be configured to determine a downlink covariance Toeplitz matrix based at least in part on a single row and column of the uplink covariance Toeplitz matrix.
  • a wireless device 22 is configured to include a signaling unit 34 which is configured to perform one or more wireless device 22 function as described herein such as with respect to a downlink covariance matrix that is estimated using samples or a portion of the uplink covariance matrix.
  • a host computer 24 comprises hardware (HW) 38 including a communication interface 40 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 10.
  • the host computer 24 further comprises processing circuitry 42, which may have storage and/or processing capabilities.
  • the processing circuitry 42 may include a processor 44 and memory 46.
  • the processing circuitry 42 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • processors and/or processor cores and/or FPGAs Field Programmable Gate Array
  • ASICs Application Specific Integrated Circuitry
  • the processor 44 may be configured to access (e.g., write to and/or read from) memory 46, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read- Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • memory 46 may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read- Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • Processing circuitry 42 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by host computer 24.
  • Processor 44 corresponds to one or more processors 44 for performing host computer 24 functions described herein.
  • the host computer 24 includes memory 46 that is configured to store data, programmatic software code and/or other information described herein.
  • the software 48 and/or the host application 50 may include instructions that, when executed by the processor 44 and/or processing circuitry 42, causes the processor 44 and/or processing circuitry 42 to perform the processes described herein with respect to host computer 24.
  • the instructions may be software associated with the host computer 24.
  • the software 48 may be executable by the processing circuitry 42.
  • the software 48 includes a host application 50.
  • the host application 50 may be operable to provide a service to a remote user, such as a WD 22 connecting via an OTT connection 52 terminating at the WD 22 and the host computer 24.
  • the host application 50 may provide user data which is transmitted using the OTT connection 52.
  • the “user data” may be data and information described herein as implementing the described functionality.
  • the host computer 24 may be configured for providing control and functionality to a service provider and may be operated by the service provider or on behalf of the service provider.
  • the processing circuitry 42 of the host computer 24 may enable the host computer 24 to observe, monitor, control, transmit to and/or receive from the network node 16 and or the wireless device 22.
  • the processing circuitry 42 of the host computer 24 may include an information unit 54 configured to enable the service provider to provide, process, estimate, store, transmit, receive, analyze, forward, relay, etc., information related to estimating a downlink covariance matrix using samples or a portion of the uplink covariance matrix.
  • the communication system 10 further includes a network node 16 provided in a communication system 10 and including hardware 58 enabling it to communicate with the host computer 24 and with the WD 22.
  • the hardware 58 may include a communication interface 60 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 10, as well as a radio interface 62 for setting up and maintaining at least a wireless connection 64 with a WD 22 located in a coverage area 18 served by the network node 16.
  • the radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.
  • the communication interface 60 may be configured to facilitate a connection 66 to the host computer 24.
  • the connection 66 may be direct or it may pass through a core network 14 of the communication system 10 and/or through one or more intermediate networks 30 outside the communication system 10.
  • the hardware 58 of the network node 16 further includes processing circuitry 68.
  • the processing circuitry 68 may include a processor 70 and a memory 72.
  • the processing circuitry 68 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • FPGAs Field Programmable Gate Array
  • ASICs Application Specific Integrated Circuitry
  • the processor 70 may be configured to access (e.g., write to and/or read from) the memory 72, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or
  • ROM Read-Only Memory
  • EPROM Erasable Programmable Read-Only Memory
  • the network node 16 further has software 74 stored internally in, for example, memory 72, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection.
  • the software 74 may be executable by the processing circuitry 68.
  • the processing circuitry 68 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by network node 16.
  • Processor 70 corresponds to one or more processors 70 for performing network node 16 functions described herein.
  • the memory 72 is configured to store data, programmatic software code and/or other information described herein.
  • the software 74 may include instructions that, when executed by the processor 70 and/or processing circuitry 68, causes the processor 70 and/or processing circuitry 68 to perform the processes described herein with respect to network node 16.
  • processing circuitry 68 of the network node 16 may include estimation unit 32 configured to perform one or more network node 16 functions as described herein such as with respect to estimating a downlink covariance matrix using samples or a portion of the uplink covariance matrix.
  • the communication system 10 further includes the WD 22 already referred to.
  • the WD 22 may have hardware 80 that may include a radio interface 82 configured to set up and maintain a wireless connection 64 with a network node 16 serving a coverage area 18 in which the WD 22 is currently located.
  • the radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.
  • the hardware 80 of the WD 22 further includes processing circuitry 84.
  • the processing circuitry 84 may include a processor 86 and memory 88.
  • the processing circuitry 84 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • the processor 86 may be configured to access (e.g., write to and/or read from) memory 88, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • memory 88 may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • the WD 22 may further comprise software 90, which is stored in, for example, memory 88 at the WD 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the WD 22.
  • the software 90 may be executable by the processing circuitry 84.
  • the software 90 may include a client application 92.
  • the client application 92 may be operable to provide a service to a human or non-human user via the WD 22, with the support of the host computer 24.
  • an executing host application 50 may communicate with the executing client application 92 via the OTT connection 52 terminating at the WD 22 and the host computer 24.
  • the client application 92 may receive request data from the host application 50 and provide user data in response to the request data.
  • the OTT connection 52 may transfer both the request data and the user data.
  • the client application 92 may interact with the user to generate the user data that it provides.
  • the processing circuitry 84 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by WD 22.
  • the processor 86 corresponds to one or more processors 86 for performing WD 22 functions described herein.
  • the WD 22 includes memory 88 that is configured to store data, programmatic software code and/or other information described herein.
  • the software 90 and/or the client application 92 may include instructions that, when executed by the processor 86 and/or processing circuitry 84, causes the processor 86 and/or processing circuitry 84 to perform the processes described herein with respect to WD 22.
  • the processing circuitry 84 of the wireless device 22 may include a signaling unit 34 configured to perform one or more wireless device 22 function as described herein such as with respect to estimating a downlink covariance matrix using samples or a portion of the uplink covariance matrix.
  • the inner workings of the network node 16, WD 22, and host computer 24 may be as shown in FIG. 2 and independently, the surrounding network topology may be that of FIG. 1.
  • the OTT connection 52 has been drawn abstractly to illustrate the communication between the host computer 24 and the wireless device 22 via the network node 16, without explicit reference to any intermediary devices and the precise routing of messages via these devices.
  • Network infrastructure may determine the routing, which it may be configured to hide from the WD 22 or from the service provider operating the host computer 24, or both. While the OTT connection 52 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).
  • the wireless connection 64 between the WD 22 and the network node 16 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 the WD 22 using the OTT connection 52, in which the wireless connection 64 may form the last segment. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and/or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc.
  • a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve.
  • the measurement procedure and/or the network functionality for reconfiguring the OTT connection 52 may be implemented in the software 48 of the host computer 24 or in the software 90 of the WD 22, or both.
  • sensors (not shown) may be deployed in or in association with communication devices through which the OTT connection 52 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 48, 90 may compute or estimate the monitored quantities.
  • the reconfiguring of the OTT connection 52 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect the network node 16, and it may be unknown or imperceptible to the network node 16. Some such procedures and functionalities may be known and practiced in the art.
  • measurements may involve proprietary WD signaling facilitating the host computer’s 24 measurements of throughput, propagation times, latency and the like.
  • the measurements may be implemented in that the software 48, 90 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 52 while it monitors propagation times, errors, etc.
  • the host computer 24 includes processing circuitry 42 configured to provide user data and a communication interface 40 that is configured to forward the user data to a cellular network for transmission to the WD 22.
  • the cellular network also includes the network node 16 with a radio interface 62.
  • the network node 16 is configured to, and/or the network node’s 16 processing circuitry 68 is configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/supporting/ending a transmission to the WD 22, and/or preparing/terminating/maintaining/supporting/ending in receipt of a transmission from the WD 22.
  • the host computer 24 includes processing circuitry 42 and a communication interface 40 that is configured to a communication interface 40 configured to receive user data originating from a transmission from a WD 22 to a network node 16.
  • the WD 22 is configured to, and/or comprises a radio interface 82 and/or processing circuitry 84 configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/supporting/ending a transmission to the network node 16, and/or preparing/terminating/maintaining/supporting/ending in receipt of a transmission from the network node 16.
  • FIGS. 1 and 2 show various “units” such as estimation unit 32, and signaling unit 34 as being within a respective processor, it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry. In other words, the units may be implemented in hardware or in a combination of hardware and software within the processing circuitry.
  • FIG. 3 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIGS. 1 and 2, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIG. 2.
  • the host computer 24 provides user data (Block S100).
  • the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50 (Block S102).
  • the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block S104).
  • the network node 16 transmits to the WD 22 the user data which was carried in the transmission that the host computer 24 initiated, in accordance with the teachings of the embodiments described throughout this disclosure (Block S106).
  • the WD 22 executes a client application, such as, for example, the client application 92, associated with the host application 50 executed by the host computer 24 (Block s 108).
  • FIG. 4 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2.
  • the host computer 24 provides user data (Block SI 10).
  • the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50.
  • the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 12).
  • the transmission may pass via the network node 16, in accordance with the teachings of the embodiments described throughout this disclosure.
  • the WD 22 receives the user data carried in the transmission (Block S 114).
  • FIG. 5 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2.
  • the WD 22 receives input data provided by the host computer 24 (Block S 116).
  • the WD 22 executes the client application 92, which provides the user data in reaction to the received input data provided by the host computer 24 (Block SI 18).
  • the WD 22 provides user data (Block S120).
  • the WD provides the user data by executing a client application, such as, for example, client application 92 (Block S122).
  • client application 92 may further consider user input received from the user.
  • the WD 22 may initiate, in an optional third substep, transmission of the user data to the host computer 24 (Block S124).
  • the host computer 24 receives the user data transmitted from the WD 22, in accordance with the teachings of the embodiments described throughout this disclosure (Block S126).
  • FIG. 6 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2.
  • the network node 16 receives user data from the WD 22 (Block S128).
  • the network node 16 initiates transmission of the received user data to the host computer 24 (Block S130).
  • the host computer 24 receives the user data carried in the transmission initiated by the network node 16 (Block S132).
  • FIG. 7 is a flowchart of an example process in a network node 16 according to some embodiments of the present disclosure.
  • One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the estimation unit 32), processor 70, radio interface 62 and/or communication interface 60.
  • Network node 16 is configured to estimate (Block S134) a downlink covariance matrix associated with a downlink channel based at least on only a portion of an uplink covariance matrix associated with an uplink channel where the downlink channel lacks channel reciprocity with the uplink channel, as described herein.
  • Network node 16 is further configured to perform (Block S136) at least one action based at least on the downlink covariance matrix, as described herein.
  • the portion of the uplink covariance matrix corresponds to a first row and first column of the uplink covariance matrix.
  • the estimating of the downlink covariance matrix includes computing a downlink covariance vector based at least on the first row and first column of the uplink covariance matrix, and constructing a Toeplitz downlink covariance matrix based at least on the computing downlink covariance vector.
  • the processing circuitry is configured to perform uplink measurements based on at least one uplink signal from the wireless device, and determine the uplink covariance matrix based at least on the uplink measurements.
  • the at least one action includes performing downlink transmission based at least on the estimated downlink covariance matrix.
  • FIG. 8 is a flowchart of an example process in a wireless device 22 according to some embodiments of the present disclosure. One or more blocks described herein may be performed by one or more elements of wireless device 22 such as by one or more of processing circuitry 84 (including the signaling unit 34), processor 86, radio interface 82 and/or communication interface 60.
  • Wireless device 22 is configured to receive (Block S 138) a downlink transmission that is based on an estimate downlink covariance matrix associated with a downlink channel where the estimated downlink covariance matrix is based at least on only a portion of an uplink covariance matrix associated with an uplink channel, and the downlink channel lacks channel reciprocity with the uplink channel, as described herein.
  • Wireless device 22 is further configured to process (Block S 140) the downlink transmission.
  • the portion of the uplink covariance matrix corresponds to a first row and first column of the uplink covariance matrix.
  • the processing circuitry is further configured to cause transmission of at least one uplink signal to the network node for performing uplink measurements where the uplink covariance matrix is based at least on the uplink measurements.
  • FIG. 9 is a flowchart of another example process in a network node 16 according to some embodiments of the present disclosure.
  • One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the estimation unit 32), processor 70, radio interface 62 and/or communication interface 60.
  • Network node 16 is configured to determine an uplink covariance Toeplitz matrix based at least in part on measurements of signals received from the WD in an uplink frequency band on a number of antenna elements in an array of antenna elements of an antenna of the network node (Block S142).
  • the process also includes determining a downlink covariance Toeplitz matrix based at least in part on a single row and column of the uplink covariance Toeplitz matrix (Block S 144). The process further includes applying the downlink covariance Toeplitz matrix for downlink transmissions to the WD in a downlink frequency band (Block S146).
  • determining the downlink covariance Toeplitz matrix includes determining a frequency invariant angular power spectrum, APS, the APS being determined as a Fourier series, coefficients of the Fourier series being based at least in part on the single row and column of the uplink covariance Toeplitz matrix. In some embodiments, a number of coefficients in the Fourier series is based at least in part on the number of antenna elements in the array of antenna elements. In some embodiments, determining the downlink covariance Toeplitz matrix includes determining a product of a matrix of Sine functions and a vector containing elements of the single row and column of the uplink covariance Toeplitz matrix.
  • the method also includes storing the matrix of Sine functions, the matrix of Sine functions being independent of the signal measurements and being preoperationally computed.
  • determining the uplink covariance Toeplitz matrix includes finding a sample Toeplitz matrix that minimizes a Frobenius norm of a difference between the sample Toeplitz matrix and an uplink covariance matrix determined from the signal measurements.
  • determining the downlink covariance Toeplitz matrix includes determining a first downlink covariance Toeplitz matrix corresponding to a first dimension of a two-dimensional array of the antenna elements and determining a second downlink covariance Toeplitz matrix corresponding to the second dimension of the two-dimensional array.
  • determining the downlink covariance Toeplitz matrix includes determining a Kronecker product of the first and second downlink covariance Toeplitz matrices corresponding to the first and second dimensions of the two- dimensional array. In some embodiments, determining the downlink covariance
  • Toeplitz matrix includes determining a polarization-independent downlink covariance Toeplitz matrix that is applied for each polarization of an array of dual polarized antenna elements of the antenna.
  • the single row and column are a first row and column, respectively, of the uplink covariance Toeplitz matrix.
  • Some embodiments provide estimating a downlink covariance matrix using samples or a portion of the uplink covariance matrix.
  • Some functionality described below with respect to network node 16 may be performed by network node 16 such as via processing circuitry 68, processor 70, estimation unit 32, radio interface 62, etc.
  • Some functionality described below with respect to wireless device 22 may be performed by wireless device 22 such as via processing circuitry 84, processor 86, signaling unit 34, radio interface 82, etc.
  • a MIMO channel between a network node 16 and a single antenna wireless device 22 is considered. It is assumed that the network node 16 is equipped with an M-antenna uniform linear array (ULA) with M » 1.
  • the transmission relies on a FDD system in which the uplink transmission, from wireless device 22 to network node 16, occurs over the frequency interval while the downlink transmission occurs on the frequency interval • A narrow-band transmission is considered, i.e., . Therefore, the entire uplink and downlink frequency intervals can be each seen as a single frequency, f u and f d , respectively.
  • a wide sense stationary (WSS) uncorrelated scattering channel model is considered in which the channel vectors evolve in time according to a WSS process.
  • T c channel coherence time
  • the spatial uplink and downlink covariance matrices are respectively given by: wherein q is the angle of arrival (AoA) and r(q) is the APS and () ⁇ denotes the Hermitian transpose.
  • a M (0) and CL d (&) denote the uplink and downlink steering vectors, respectively, and for a ULA are given by: where d is the antenna spacing in the ULA, A u and l 1 are uplink and downlink wavelengths, respectively, given by A where c 0 denotes the speed of light.
  • channel covariance matrices are Hermitian, semi-definite and Toeplitz. Therefore, each covariance matrix can be constructed from its first column. This property will be used in the covariance matrix approximation method described herein.
  • FIG. 10 is a diagram of a downlink covariance estimation algorithm that may be implemented by network node 16.
  • the network node 16 may compute (Block S148) a covariance matrix based at least on channel estimates and a noise variance estimate.
  • Network node 16 gets/determines (Block S150) a Toeplitz covariance (e.g., uplink Toeplitz covariance matrix) based at least on the computed covariance matrix referred to in Block S148.
  • Network node 16 extracts (Block S152) a first row and first column of the Toeplitz covariance and constructs * » .
  • Network node 16 computes (Block S154) downlink covariance vectors, as described herein.
  • Network node 16 constructs (Block S156) a Toeplitz downlink covariance matrix which may correspond to the downlink covariance matrix.
  • an approximate APS is implicitly calculated by obtaining a different form of the integral that computes the uplink covariance matrix. This can be achieved by using the expression for the steering vectors of a uniform linear array
  • a Fourier series of a periodic function /(x) with period T is given by: where and ⁇ a fc ⁇ are the Fourier series coefficients.
  • each covariance matrix can be constructed by network node 16 using only the first column.
  • the transfer matrix ⁇ given depends on the number of antennas in the ULA and downlink-uplink frequency ratio Therefore, this matrix can be computed offline, in advance of operation. Note that the dimension of the matrix F is given by M X 2 M — 1, and hence, the complexity of computing
  • the full covariance matrix R_d can be acquired by exploiting the Hermitian symmetry and Toeplitz properties of the covariance matrix.
  • the downlink single layer precoder can be computed as the principal eigenvector of the estimated downlink covariance matrix.
  • y 1 h1 + n I
  • h ⁇ is the channel vector
  • n 1 Gaussian noise vector with zero mean and covariance matrix
  • Lde the number of available samples of y ⁇ .
  • the covariance matrix sample can be computed by: given It is noted that this is Hermitian but not necessarily Toeplitz which is a factor in the scheme described herein.
  • the computed uplink covariance matrix sample may be projected to the cone of Toeplitz positive semi-definite matrices by using the following convex optimization problem: wherein T + denotes the set of positive semi-definite Toeplitz M X M matrices and
  • This projected uplink covariance R u ' can be used in the scheme described herein instead of actual R u .
  • a network node 16 employing a 2-dimensional polarized array as shown in FIG. 11 is considered.
  • M v and M H denote the number of rows and columns of the 2-dimensional antenna array, respectively, i.e., the total number of antenna elements is given by 2 M V M H .
  • the downlink covariance approximation algorithm described herein can be applied separately in both directions and the per-polarization downlink covariance matrix R(p) can be estimated from the Kronecker product of the two matrices.
  • the input measurement set for the vertical direction uses the columns of the matrix H(t ) as its input measurement set where 2 M H samples are available from each full channel vector measurement.
  • the horizontal covariance estimation algorithm uses the rows of H(t ) as its input measurements and 2 M v samples are available from each full channel vector measurement.
  • the single user (SU)-MIMO precoders for each wireless device 22 can be constructed using the dominant eigen vectors of its tracked covariance matrix.
  • the rank r SU-MIMO precoder can be obtained by co-phasing the eigen vectors of the covariance matrix per polarization.
  • the rank 2 SU-MIMO precoder is given by: where f 0 is the co-phasing factor and v 0 is the dominant eigen vector of the estimated per-polarization downlink covariance matrix R(p) Note that a fixed or random co-phasing factor can be used by the network node 16.
  • the dominant eigen vector of the covariance matrix v 0 can be directly obtained from the estimated horizontal and vertical eigen vectors as: are the dominant eigen vectors of the estimated horizontal and vertical covariance matrices, respectively.
  • the performance of the scheme described herein is compared with an existing scheme.
  • a baseline is considered where there is no downlink covariance matrix estimation and the noisy uplink covariance is used directly as the estimated downlink covariance matrix.
  • the performance metric used in the simulations is the relative loss in the downlink received signal-to-interference-plus- noise-ratio (SINR) ratio which can be measured by where v is the principal eigenvector of the downlink covariance of the tested method, R d is the actual downlink covariance matrix (not the estimated one) and is the largest eigenvalue of the actual downlink covariance matrix.
  • SINR downlink received signal-to-interference-plus- noise-ratio
  • the metric shows the relative loss in the received SINR at the wireless device 22 using a precoder composed of the principal eigenvector of the estimated downlink covariance matrix and the received SINR using the principal eigenvector of the exact downlink covariance matrix.
  • r(q ) is simulated as a summation of some Gaussian distributions given by: wherein, P is drawn uniformly random from ⁇ 1,2, ... ,5 ⁇ ,/ p (0) denotes a Gaussian distribution with mean q r uniformly drawn from and standard deviation ⁇ uniformly drawn from [3°, 5°].
  • ⁇ w p ⁇ are drawn uniformly random from [0,1] and normalized such that It is noted that, in this model, P denotes the number of clusters of scatterers, are the cluster powers, are clusters mean AoAs and denote angular spreads.
  • a noisy version of the uplink channel covariance matrix is available at network node 16.
  • network node 16 has access to uplink covariance samples through 1000 noisy channel estimates.
  • the APS is used to generate the actual uplink covariance matrix R u and then the actual channel vector is computed as
  • ⁇ n1 is a Gaussian random vector with zero mean and identity covariance matrix.
  • h ⁇ is assumed to be available at network node 16 and used for computing the estimated uplink covariance matrix.
  • the scheme described herein and the existing scheme have very close performance.
  • one or more embodiments described herein provide for estimating the downlink covariance matrix from uplink channel estimates in FDD systems with low computational complexity. Further, significant performance improvement in received downlink SINR is shown through simulations compared to a baseline legacy scheme where the uplink covariance matrix is used for downlink precoding.
  • one or more embodiments described herein provide an algorithm that may be used for FDD systems where the network node 16 employs a uniform linear array to approximate the downlink covariance matrix using only uplink covariance samples.
  • the method can be implemented as a low complexity matrix multiplication using a fixed matrix that depends only on the array manifold and uplink/downlink frequency separation.
  • the downlink covariance estimation algorithm may be extended to the case of uniform two-dimensional polarized arrays by estimating the downlink covariance matrix in the vertical and horizontal directions separately and merging the covariance matrices and/or the eigen vectors of the two directions to estimate the full-dimension covariance matrix and/or the downlink precoding vectors.
  • Embodiment A1 A network node configured communicate with a wireless device (WD), the network node configured to, and/or comprising a radio interface and/or comprising processing circuitry configured to: estimate a downlink covariance matrix associated with a downlink channel based at least on only a portion of an uplink covariance matrix associated with an uplink channel, the downlink channel lacking channel reciprocity with the uplink channel; and perform at least one action based at least on the downlink covariance matrix.
  • WD wireless device
  • processing circuitry configured to: estimate a downlink covariance matrix associated with a downlink channel based at least on only a portion of an uplink covariance matrix associated with an uplink channel, the downlink channel lacking channel reciprocity with the uplink channel; and perform at least one action based at least on the downlink covariance matrix.
  • Embodiment A2 The network node of Embodiment Al, wherein the portion of the uplink covariance matrix corresponds to a first row and first column of the uplink covariance matrix.
  • Embodiment A3 The network node of any one of Embodiments A1-A2, wherein the estimating of the downlink covariance matrix includes: computing a downlink covariance vector based at least on the first row and first column of the uplink covariance matrix; and constructing a Toeplitz downlink covariance matrix based at least on the computing downlink covariance vector.
  • Embodiment A4. The network node of any one of Embodiments A1-A3, wherein the processing circuitry is configured to: perform uplink measurements based on at least one uplink signal from the wireless device; and determine the uplink covariance matrix based at least on the uplink measurements.
  • Embodiment A5 The network node of any one of Embodiments A1-A4, wherein the at least one action includes performing downlink transmission based at least on the estimated downlink covariance matrix.
  • Embodiment Bl A method implemented in a network node that is configured to communicate with a wireless device, the method comprising: estimating a downlink covariance matrix associated with a downlink channel based at least on only a portion of an uplink covariance matrix associated with an uplink channel, the downlink channel lacking channel reciprocity with the uplink channel; and performing at least one action based at least on the downlink covariance matrix.
  • Embodiment B2 The method of Embodiment B 1, wherein the portion of the uplink covariance matrix corresponds to a first row and first column of the uplink covariance matrix.
  • Embodiment B3 The method of any one of Embodiments B 1-B2, wherein the estimating of the downlink covariance matrix includes: computing a downlink covariance vector based at least on the first row and first column of the uplink covariance matrix; and constructing a Toeplitz downlink covariance matrix based at least on the computing downlink covariance vector.
  • Embodiment B4 The network node of any one of Embodiments B 1-B3, wherein the processing circuitry is configured to: perform uplink measurements based on at least one uplink signal from the wireless device; and determine the uplink covariance matrix based at least on the uplink measurements.
  • Embodiment B5. The network node of any one of Embodiments B 1-B4, wherein the at least one action includes performing downlink transmission based at least on the estimated downlink covariance matrix.
  • a wireless device configured to communicate with a network node, the WD configured to, and/or comprising a radio interface and/or processing circuitry configured to: receive a downlink transmission that is based on an estimate downlink covariance matrix associated with a downlink channel, the estimated downlink covariance matrix being based at least on only a portion of an uplink covariance matrix associated with an uplink channel, the downlink channel lacking channel reciprocity with the uplink channel; and process the downlink transmission.
  • Embodiment C2 The WD of Embodiment Cl, wherein the portion of the uplink covariance matrix corresponds to a first row and first column of the uplink covariance matrix.
  • Embodiment C3 The WD of any one of Embodiments C1-C2, wherein the processing circuitry is further configured to cause transmission of at least one uplink signal to the network node for performing uplink measurements, the uplink covariance matrix being based at least on the uplink measurements.
  • Embodiment Dl A method implemented in a wireless device that is configured to communicate with a network node, the method comprising: receiving a downlink transmission that is based on an estimate downlink covariance matrix associated with a downlink channel, the estimated downlink covariance matrix being based at least on only a portion of an uplink covariance matrix associated with an uplink channel, the downlink channel lacking channel reciprocity with the uplink channel; and processing the downlink transmission.
  • Embodiment D2 The method of Embodiment Dl, wherein the portion of the uplink covariance matrix corresponds to a first row and first column of the uplink covariance matrix.
  • Embodiment D3 The method of any one of Embodiments D1-D2, further comprising causing transmission of at least one uplink signal to the network node for performing uplink measurements, the uplink covariance matrix being based at least on the uplink measurements.
  • the concepts described herein may be embodied as a method, data processing system, computer program product and/or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and/or functionality described herein may be performed by, and/or associated to, a corresponding module, which may be implemented in software and/or firmware and/or hardware. Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that can be executed by a computer. Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.
  • These computer program instructions may also be stored in a computer readable memory or storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
  • the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Java® or C++.
  • the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the "C" programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer.
  • the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • LAN local area network
  • WAN wide area network
  • Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.

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Abstract

Un procédé, un système et un appareil sont divulgués. Selon un ou plusieurs modes de réalisation, un nœud de réseau configuré pour communiquer avec un dispositif sans fil utilisant des communications en duplex par répartition en fréquence (FDD) est divulgué. Une matrice de Toeplitz de covariance de liaison montante est déterminée sur la base, au moins en partie, de mesures de signaux reçus du WD dans une bande de fréquence de liaison montante sur un certain nombre d'éléments d'antenne dans un réseau d'éléments d'antenne d'une antenne du nœud de réseau. Une matrice de Toeplitz de covariance de liaison descendante est déterminée sur la base, au moins en partie, d'une seule rangée et d'une seule colonne de la matrice de Toeplitz de covariance de liaison montante. La matrice de Toeplitz de covariance de liaison descendante est appliquée pour des transmissions de liaison descendante au WD dans une bande de fréquence de liaison descendante.
PCT/IB2022/053108 2021-04-07 2022-04-04 Approximation de matrice de covariance de canal de liaison descendante dans des systèmes duplex par répartition en fréquence WO2022214935A1 (fr)

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