Wireless communication method and related products

Abstract

The present disclosure provides a wireless communication method and related products. The wireless communication method comprises: a receiver acquires channel state information (CSI); the receiver determines a first estimated value of the CSI, wherein the first estimated value is used to represent canonical decomposition of the CSI and comprises a preset number of factor vectors, wherein at least one of the preset number of factor vectors is subjected to sub-dimension splitting; and the receiver generates a CSI report according to the first estimated value of the CSI. Through the above scheme, the parameters required for high-precision channel representation can be significantly reduced, thereby realizing CSI reporting with smaller feedback overhead.

Description

Technical Field

[0001] This disclosure relates to the field of data transmission, and more particularly to a wireless communication method and related products. Background Technology

[0002] The rapid development of modern telecommunications and the steady evolution of global networks according to market demands and local readiness necessitate significant improvements in the data transmission rates, coverage, and capacity of communication systems. To achieve these goals, one feasible technique is to increase the number of transmitting and receiving antennas.

[0003] One of the key challenges of Multiple Input Multiple Output (MIMO) systems lies in the ability of evolved NodeBs (eNodeBs or eNBs) to obtain high-precision channel state information (CSI). This necessitates fully leveraging the capabilities of this technology. For time division duplex (TDD) systems, this issue can be addressed through the reciprocity between downlink (DL) and uplink (UL) channels. For frequency division duplex (FDD) systems, for efficiency reasons, the eNodeB needs to obtain the DL channel conditions from the user equipment (UE). To this end, the eNodeB transmits reference symbols, which the UE can measure to obtain the DL channel state and report it to the eNodeB.

[0004] This background information is provided to disclose information that the applicant believes may be relevant to this disclosure. None of the foregoing information should be considered or construed as constituting prior art relative to this disclosure. Summary of the Invention

[0005] In a first aspect, this disclosure provides a wireless communication method, wherein the method includes:

[0006] Receiver acquires CSI;

[0007] The receiver determines a first estimate of the CSI, wherein the first estimate is used to represent the canonical decomposition of the CSI and includes a predetermined number of factor vectors, wherein at least one of the predetermined number of factor vectors is split by sub-dimensions;

[0008] The receiver generates a CSI report based on the first estimate of the CSI.

[0009] By using a first estimate to represent the canonical decomposition of CSI, and by splitting at least one factor vector of a predetermined number of factor vectors included in the first estimate through sub-dimension splitting, the parameters required for high-precision channel representation can be significantly reduced, thereby achieving CSI reporting with less feedback overhead.

[0010] In one possible implementation of the first aspect, the receiver determines the first estimate of the CSI by:

[0011] The receiver determines a sub-dimensional representation of each factor vector in at least one factor vector;

[0012] The receiver obtains a first estimate of the CSI based on the sub-dimension representation of each of the at least one factor vector.

[0013] To achieve sub-dimensional decomposition of at least one factor vector, a sub-dimensional representation of each factor vector in at least one factor vector is determined, thereby obtaining a first estimate of CSI based on this sub-dimensional representation. This makes it easier to obtain the first estimate of CSI, thereby simplifying the calculation, reducing the number of parameters used to represent CSI, and further improving the efficiency of channel estimation.

[0014] In one possible implementation of the first aspect, the receiver determines the sub-dimensional representation of each factor vector in at least one factor vector by:

[0015] The receiver determines the size of at least one sub-dimension parameter of each factor vector in at least one factor vector according to a first preset configuration corresponding to CSI;

[0016] The receiver represents each of the at least one factor vectors by the size of at least one sub-dimension parameter of each factor vector in the at least one factor vector.

[0017] In one possible implementation of the first aspect, for each factor vector in at least one factor vector: at least one sub-dimension parameter size includes a first sub-dimension parameter size and a second sub-dimension parameter size, wherein the first sub-dimension parameter size includes at least one factor indicating the size of the sub-dimension, and the second sub-dimension parameter size represents the number of sub-dimensions used to represent the factor vector; the sub-dimension representation of the factor vector is a Kronecker product of sub-split elements, wherein each sub-split element is represented exponentially according to the phase corresponding to the factor vector, the first sub-dimension parameter size of the factor vector, and the second sub-dimension parameter size of the factor vector.

[0018] In one possible implementation of the first aspect, the first preset configuration indicates the number of sub-dimensions into which each factor vector in at least one factor vector is split and the size of each of these sub-dimensions.

[0019] In one possible implementation of the first aspect, the receiver obtaining a first estimate of the CSI based on the sub-dimension representation of each of the at least one factor vector includes:

[0020] The receiver takes the sub-dimension representation of each factor vector in at least one factor vector as input to a first preset algorithm to obtain a first estimate of the CSI.

[0021] In one possible implementation of the first aspect, the receiver generating a CSI report based on a first estimate of the CSI includes:

[0022] The receiver generates a CSI report based on a second preset configuration and a first estimate of the CSI, wherein the second preset configuration indicates whether a preset structure is applied to the first factor vector among a preset number of factor vectors.

[0023] In one possible implementation of the first aspect, the second preset configuration indicates that a preset structure is applied to the first factor vector among a preset number of factor vectors;

[0024] The receiver generates the following CSI report:

[0025] The receiver performs factor approximation on the first estimate according to the second preset algorithm to derive the representation parameters of the first factor vector;

[0026] The receiver generates a CSI report based on the representation parameters of the first factor vector.

[0027] By assuming that one or more first factor vectors in a preset number of factor vectors adopt a preset structure, one or more first factor vectors can be factor approximated, thereby enabling the representation of one or more first factor vectors with fewer parameters and thus reducing feedback overhead.

[0028] In one possible implementation of the first aspect, the second preset configuration indicates that the preset structure is not applied to the first factor vector among a preset number of factor vectors; the receiver generates a CSI report including:

[0029] The receiver generates a CSI report based on the first estimate.

[0030] In one possible implementation of the first aspect, the preset structure is a parameter structure.

[0031] In one possible implementation of the first aspect, the parameter structure includes at least one of a guiding structure, a secondary structure, or a cubic structure.

[0032] Flexibility is achieved by introducing various parameter structures.

[0033] In one possible implementation of the first aspect, the method further includes:

[0034] The receiver receives first information from the wireless communication system indicating a second preset configuration.

[0035] In one possible implementation of the first aspect, the receiver generating the CSI report further includes:

[0036] The receiver performs an extrapolation operation on the first estimate to obtain the extrapolated first estimate for the next time unit.

[0037] The receiver generates a CSI report based on the first estimate of the extrapolated CSI value.

[0038] In one possible implementation of the first aspect, before the receiver takes a sub-dimension representation of each factor vector in at least one factor vector as input to a first preset algorithm to obtain a first estimate of the CSI, the method further includes:

[0039] The receiver obtains an initial sub-dimension representation of each factor vector in at least one factor vector based on the CSI;

[0040] The receiver takes a sub-dimension representation of each factor vector in at least one factor vector as input to a first preset algorithm to obtain a first estimate of the CSI, including:

[0041] The receiver takes the initial sub-dimension representation as input to the first preset algorithm to obtain the first estimate of CSI.

[0042] In one possible implementation of the first aspect, the receiver obtaining an initial sub-dimension representation of each factor vector in at least one factor vector according to the CSI includes:

[0043] The receiver obtains the initial sub-dimension representation of each factor vector in at least one factor vector and the rank of the canonical decomposition of CSI by performing the Discrete Fourier Transform (DFT) operation on CSI.

[0044] The receiver takes a sub-dimension representation of each factor vector in at least one factor vector as input to a first preset algorithm to obtain a first estimate of the CSI, including:

[0045] The receiver takes the initial sub-dimension representation and the rank of the canonical decomposition as input to the first preset algorithm to obtain the first estimate of CSI.

[0046] In one possible implementation of the first aspect, the receiver obtains the initial sub-dimensional representation of each factor vector in at least one factor vector and the rank of the canonical decomposition of the CSI based on the DFT operation of the CSI, including:

[0047] The receiver determines the expanded channel matrix based on the CSI and preset oversampling parameters;

[0048] The receiver performs a Discrete Fourier Transform (DFT) operation on the expanded channel matrix to obtain the power spectrum of the CSI.

[0049] The receiver determines the initial sub-dimension representation of each factor vector in at least one factor vector and the rank of the canonical decomposition of the CSI based on the power spectrum of the CSI.

[0050] In one possible implementation of the first aspect, the receiver obtaining an initial sub-dimension representation of each factor vector in at least one factor vector according to the CSI includes:

[0051] The receiver uses the extrapolated first estimate as the initial sub-dimension representation of each factor vector in at least one factor vector, wherein the extrapolated first estimate is obtained by extrapolating the first estimate determined in the previous time unit.

[0052] By introducing extrapolation into the channel estimation process, the extrapolated first estimate can be more robust to the "channel aging" problem.

[0053] In one possible implementation of the first aspect, the CSI includes first channel information in the time domain, second channel information in the frequency domain, and third channel information in the spatial domain; a predetermined number of factor vectors include a time factor vector for characterizing the first channel information, a subcarrier factor vector for characterizing the second channel information, and a transmit factor vector and a receive factor vector for characterizing the third channel information; wherein at least one of the time factor vector, subcarrier factor vector, transmit factor vector, and receive factor vector is represented by a regularly changing phase.

[0054] In one possible implementation of the first aspect, the transmission factor vector includes a first transmission factor vector and a second transmission factor vector, wherein the first transmission factor vector is used to characterize the channel information of at least one vertically polarized antenna port, and the second transmission factor vector is used to characterize the channel information of at least one horizontally polarized antenna port.

[0055] By further splitting the transmit factor vector into two sub-dimensions, the polarization of the transmit antenna is taken into account, thereby improving the applicability of the scheme.

[0056] In one possible implementation of the first aspect, the method further includes:

[0057] The receiver sends a CSI report to the wireless communication system.

[0058] In one possible implementation of the first aspect, the method further includes:

[0059] The receiver receives a second message from the wireless communication system indicating a preset number.

[0060] In a second aspect, this disclosure provides a wireless communication method, wherein the method includes:

[0061] The transmitter receives a CSI report from the receiver, wherein the CSI report is generated by the receiver based on a first estimate of the CSI, wherein the first estimate is used to represent the canonical decomposition of the CSI and includes a preset number of factor vectors, at least one of the preset number of factor vectors being split by sub-dimensions;

[0062] The transmitter determines the CSI based on the CSI report.

[0063] Since the CSI report is generated based on a first estimate of the CSI, which represents the canonical decomposition of the CSI and includes a preset number of factor vectors, where at least one of the preset number of factor vectors is split by sub-dimensions, the number of parameters required for high-precision channel representation can be significantly reduced, thereby enabling CSI reporting with less feedback overhead.

[0064] In one possible implementation of the second aspect, the method further includes:

[0065] The transmitter sends first information to the receiver indicating a second preset configuration, wherein the second preset configuration indicates whether a preset structure is applied to the first factor vector among a preset number of factor vectors.

[0066] In one possible implementation of the second aspect, the method further includes:

[0067] The transmitter sends a second message to the receiver indicating a preset number of items.

[0068] In a third aspect, this disclosure provides a wireless communication device, wherein the device includes: an acquisition module for acquiring a CSI; a determination module for determining a first estimate of the CSI, wherein the first estimate is used to represent a canonical decomposition of the CSI and includes a preset number of factor vectors, wherein at least one factor vector in the preset number of factor vectors is split by a sub-dimension; and a generation module for generating a CSI report based on the first estimate of the CSI.

[0069] In a fourth aspect, this disclosure provides a wireless communication apparatus, wherein the apparatus includes: a receiving module for receiving a CSI report from a receiver, wherein the CSI report is generated by the receiver based on a first estimate of the CSI, wherein the first estimate is used to represent a canonical decomposition of the CSI and includes a preset number of factor vectors, at least one of the preset number of factor vectors being subdivided by a sub-dimension; and a determining module for determining the CSI based on the CSI report.

[0070] In a fifth aspect, this disclosure provides a terminal device including processing circuitry for performing the wireless communication method according to the first aspect or any implementation thereof.

[0071] In a sixth aspect, this disclosure provides a network device including processing circuitry for performing the wireless communication method according to the second aspect or any implementation thereof.

[0072] In a seventh aspect, this disclosure provides a wireless communication system including a terminal device according to the fifth aspect and a network device according to the sixth aspect.

[0073] In an eighth aspect, this disclosure provides a chip including an input / output (I / O) interface and a processor, wherein the processor is configured to invoke and execute computer execution instructions stored in a memory to enable a device equipped with the chip to perform the wireless communication method according to the first aspect or any implementation thereof, or the second aspect or any implementation thereof.

[0074] In a ninth aspect, this disclosure provides a computer-readable storage medium that stores computer-executable instructions that, when executed by a processor, cause the processor to perform the wireless communication method according to the first aspect or any implementation thereof, or the second aspect or any implementation thereof.

[0075] In a tenth aspect, this disclosure provides a computer program product including computer-executable instructions that, when executed by a processor, cause the processor to perform the wireless communication method according to the first aspect or any implementation thereof, or the second aspect or any implementation thereof.

[0076] This disclosure provides a wireless communication method and related products. The wireless communication method includes: a receiver acquiring a Channel Identity Filter (CSI); the receiver determining a first estimate of the CSI, wherein the first estimate represents a canonical decomposition of the CSI and includes a preset number of factor vectors, wherein at least one factor vector in the preset number of factor vectors is subdivided; and the receiver generating a CSI report based on the first estimate of the CSI. By using the first estimate to represent the canonical decomposition of the CSI and subdividing at least one factor vector in the preset number of factor vectors included in the first estimate, the number of parameters required for high-precision channel representation can be significantly reduced, thereby achieving CSI reporting with less feedback overhead. Attached Figure Description

[0077] The following figures illustrate exemplary embodiments of this disclosure by way of example.

[0078] Figure 1 This is a schematic diagram of a communication system provided by one or more exemplary embodiments of this disclosure.

[0079] Figure 2 This is another schematic diagram of a communication system provided by one or more exemplary embodiments of this disclosure.

[0080] Figure 3 This is a schematic diagram of the basic component structure of a communication system provided by one or more exemplary embodiments of this disclosure.

[0081] Figure 4 A block diagram of a device in a communication system provided by one or more exemplary embodiments of the present disclosure is shown.

[0082] Figure 5 This is a schematic flowchart of a wireless communication method provided by one or more exemplary embodiments of the present disclosure.

[0083] Figure 6 This is an example of a tensor provided by one or more exemplary embodiments of this disclosure.

[0084] Figure 7A and Figure 7B The dimensional splitting of the channel tensor provided by one or more exemplary embodiments of this disclosure is illustrated.

[0085] Figure 8 This is a diagram illustrating the peak selection when R=6, provided by one or more exemplary embodiments of this disclosure.

[0086] Figure 9 This is a diagram illustrating the approximation of the TX1 factor when rank R=8 provided by one or more exemplary embodiments of this disclosure.

[0087] Figure 10The ALS output (i.e., coarsening factor) of UE1 is shown, which involves the TTI dimension, 16 time / TTI samples, and rank 8.

[0088] Figure 11 The ALS output (i.e., coarsening factor) of UE2 is shown, which involves the TTI dimension, 16 time / TTI samples, and rank 8.

[0089] Figure 12 This is a flowchart of factor extrapolation provided by one or more exemplary embodiments of this disclosure.

[0090] Figure 13 This is an example of a device with 32 transmitting antennas provided by one or more exemplary embodiments of this disclosure.

[0091] Figure 14 These are simulation results of single-layer transmission provided by one or more exemplary embodiments of this disclosure.

[0092] Figure 15 This is another simulation result of single-layer transmission provided by one or more exemplary embodiments of this disclosure.

[0093] Figure 16 These are simulation results of two-layer transmission provided by one or more exemplary embodiments of this disclosure.

[0094] Figure 17 These are simulation results of three-layer transport provided by one or more exemplary embodiments of this disclosure.

[0095] Figure 18 This is a simulation result of four-layer transport provided by one or more exemplary embodiments of this disclosure.

[0096] Figure 19 This is another illustrative flowchart of a wireless communication method provided by one or more exemplary embodiments of this disclosure.

[0097] Figure 20 This is a schematic diagram of the structure of a wireless communication device provided by one or more exemplary embodiments of this disclosure.

[0098] Figure 21 This is a schematic diagram of the structure of a wireless communication device provided by one or more exemplary embodiments of this disclosure. Detailed Implementation

[0099] In the following description, reference is made to the accompanying drawings, which form a part of this disclosure, which illustrate by way of description specific aspects of embodiments of the disclosure or aspects that may be used with embodiments of the disclosure. It should be understood that embodiments of the disclosure can be used in other ways and include structural or logical variations not depicted in the drawings. Therefore, the following detailed description should not be construed in a limiting sense, and the scope of this disclosure is defined by the appended claims.

[0100] To aid in understanding this disclosure, examples of wireless communication systems and devices are described below.

[0101] Exemplary communication systems and devices

[0102] refer to Figure 1 The following simplified schematic diagram of a communication system is provided as an illustrative example and is not limiting. Communication system 100 includes a radio access network 120. Radio access network 120 may be a next-generation (e.g., sixth-generation, 6G, or later) radio access network or a traditional (e.g., 5G, 4G, 3G, or 2G) radio access network. In radio access network 120, one or more electronic devices (EDs) 110a to 120j (generally referred to as 110) may be interconnected with each other or connected to one or more network nodes (170a and 170b, generally referred to as 170). Core network 130 may be part of the communication system and may depend on or be independent of the radio access technology used in communication system 100. Furthermore, communication system 100 includes a public switched telephone network (PSTN) 140, the Internet 150, and other networks 160.

[0103] Figure 2An exemplary communication system 100 is illustrated. Generally, the communication system 100 enables multiple wireless or wired units to transmit data and other content. The purpose of the communication system 100 may be to provide content such as voice, data, video, and / or text via broadcast, multicast, and unicast. The communication system 100 can operate by sharing resources such as carrier spectrum bandwidth among its constituent units. The communication system 100 may include terrestrial communication systems and / or non-terrestrial communication systems. The communication system 100 can provide a wide range of communication services and applications (e.g., earth monitoring, remote sensing, passive sensing and positioning, navigation and tracking, autonomous delivery, and mobility). The communication system 100 can provide high availability and robustness through the joint operation of terrestrial and non-terrestrial communication systems. For example, integrating a non-terrestrial communication system (or components thereof) into a terrestrial communication system can create a multi-layered heterogeneous network. Compared to traditional communication networks, heterogeneous networks can achieve better overall performance through efficient multi-link joint operation between terrestrial and non-terrestrial networks, more flexible functional sharing, and faster physical layer link switching.

[0104] Terrestrial and non-terrestrial communication systems can be considered as subsystems of a communication system. In the example shown, communication system 100 includes electronic devices (EDs) 110a to 110d (generally referred to as ED 110), radio access networks (RANs) 120a and 120b, a non-terrestrial communication network 120c, a core network 130, a public switched telephone network (PSTN) 140, the Internet 150, and other networks 160. RANs 120a and RAN 120b include corresponding base stations (BSs) 170a and 170b, which can generally be referred to as terrestrial transmit and receive points (T-TRPs) 170a and 170b. The non-terrestrial communication network 120c includes access nodes 120c, which can generally be referred to as non-terrestrial transmit and receive points (NT-TRPs) 172.

[0105] Alternatively or additionally, any ED 110 can be used to connect to, access, or communicate with any other T-TRP 170a, T-TRP 170b, and NT-TRP 172, the Internet 150, the core network 130, the PSTN 140, other networks 160, or any combination thereof. In some examples, ED 110a can communicate uplink and / or downlink with T-TRP 170a via interface 190a. In some examples, ED 110a, 110b, and 110d can also communicate directly with each other via one or more sidelink air interfaces 190b. In some examples, ED 110d can communicate uplink and / or downlink with NT-TRP 172 via interface 190c.

[0106] Air interfaces 190a and 190b can use similar communication technologies, such as any suitable wireless access technology. For example, communication system 100 can implement one or more channel access methods in air interfaces 190a and 190b, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or single-carrier FDMA (SC-FDMA). Air interfaces 190a and 190b can utilize other higher-dimensional signal spaces, which may involve combinations of orthogonal and / or non-orthogonal dimensions.

[0107] The 190c air interface enables communication between the ED 110d and one or more NT-TRP172s via a wireless link or simply through a link. In some examples, the link is a dedicated connection for unicast transmission, a connection for broadcast transmission, or a connection for multicast transmission between a group of EDs and one or more NT-TRPs.

[0108] RAN 120a and RAN 120b communicate with core network 130 to provide various services, such as voice, data, and other services, to ED 110a, ED 110b, and ED 110c. RAN 120a and RAN 120b and / or core network 130 may communicate directly or indirectly with one or more other RANs (not shown). These other RANs may or may not be directly served by core network 130, and may or may not use the same radio access technology as RAN 120a and / or RAN 120b. Core network 130 may also serve as a gateway access between (i) RAN 120a and RAN 120b and / or ED 110a, ED 110b, and ED 110c and (ii) other networks (e.g., PSTN 140, Internet 150, and other networks 160). Additionally, some or all of ED110a, ED110b, and ED110c may include the ability to communicate with different wireless networks via different wireless links using different wireless technologies and / or protocols. ED110a, ED110b, and ED110c may communicate with a service provider or exchange (not shown) via a wired communication channel and with the Internet 150, rather than wirelessly (or also wirelessly). PSTN 140 may include a circuit-switched telephone network for providing plain old telephone service (POTS). The Internet 150 may include a network of computers and / or subnets (intranets) and incorporate protocols such as Internet Protocol (IP), Transmission Control Protocol (TCP), and User Datagram Protocol (UDP). ED110a, ED110b, and ED110c may be multimode devices capable of operating according to multiple wireless access technologies and include multiple transceivers required to support these technologies.

[0109] Basic component structure

[0110] Figure 3Another example of an ED 110 and base stations 170a, 170b, and / or 170c is shown. The ED 110 is used to connect people, objects, machines, etc. The ED 110 can be widely used in various scenarios, such as cellular communication, device-to-device (D2D), vehicle-to-everything (V2X), peer-to-peer (P2P), machine-to-machine (M2M), machine-type communication (MTC), Internet of Things (IoT), virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery, and mobility.

[0111] Each ED 110 represents any suitable end-user equipment for wireless operation and may include (or be referred to as): user equipment / device (UE), wireless transmit / receive unit (WTRU), mobile station, fixed or mobile subscriber unit, cellular phone, station (STA), machine type communication (MTC) device, personal digital assistant (PDA), smartphone, laptop, computer, tablet, wireless sensor, consumer electronics device, smart book, vehicle, automobile, truck, bus, train, or IoT device, industrial equipment, or devices within the aforementioned equipment (e.g., communication module, modem, or chip). Next-generation ED 110 may be referred to using other terms. Base stations 170a and 170b are T-TRPs, hereinafter referred to as T-TRP 170. Also in Figure 3 As shown, NT-TRP is referred to below as NT-TRP 172. Each ED 110 connected to T-TRP 170 and / or NT-TRP 172 can be dynamically or semi-statically turned on (i.e., established, activated, or enabled), turned off (i.e., released, deactivated, or disabled), and / or used in response to one or more of the following: connectivity availability and connectivity necessity.

[0112] ED 110 includes a transmitter 201 and a receiver 203 coupled to one or more antennas 204. Only one antenna 204 is shown in the figure. Alternatively, one, some, or all of the antennas may be panels. The transmitter 201 and receiver 203 may, for example, be integrated as a transceiver. The transceiver is used to modulate data or other content for transmission by at least one antenna 204 or via a network interface controller (NIC). The transceiver is also used to demodulate data or other content received by at least one antenna 204. Each transceiver includes any suitable structure for generating signals for wireless or wired transmission and / or for processing signals received wirelessly or wiredly. Each antenna 204 includes any suitable structure for transmitting and / or receiving wireless or wired signals.

[0113] ED 110 includes at least one memory 208. Memory 208 stores instructions and data used, generated, or collected by ED 110. For example, memory 208 may store software instructions or modules for implementing some or all of the functions and / or embodiments described herein and executed by one or more processing units 210. Each memory 208 includes any suitable one or more volatile and / or non-volatile storage and retrieval devices. Any suitable type of memory can be used, such as random access memory (RAM), read-only memory (ROM), hard disk, optical disk, subscriber identity module (SIM) card, memory stick, secure digital (SD) memory card, or on-processor cache.

[0114] ED 110 may also include one or more input / output devices (not shown) or interfaces (e.g., Figure 1 (A wired interface connecting to the Internet 150). Input / output devices support interaction with users or other devices on the network. Each input / output device includes any suitable structure for providing or receiving information from the user, such as a speaker, microphone, keypad, keyboard, display, or touchscreen, including network interface communication.

[0115] ED 110 also includes a processor 210 for performing various operations, including operations related to preparing for uplink transmissions to NT-TRP 172 and / or T-TRP 170, operations related to processing downlink transmissions received from NT-TRP 172 and / or T-TRP 170, and operations related to processing sidelink transmissions to and from another ED 110. Processing operations related to preparing for uplink transmissions may include operations such as encoding, modulation, transmit beamforming, and generating symbols for transmission. Processing operations related to processing downlink transmissions may include operations such as receive beamforming, demodulation, and decoding of received symbols. According to an embodiment, the downlink transmission may be received by receiver 203, possibly using receive beamforming, and processor 210 may extract signaling from the downlink transmission (e.g., by detecting and / or decoding signaling). Examples of signaling may be reference signals transmitted by NT-TRP 172 and / or T-TRP 170. In some embodiments, processor 276 performs transmit beamforming and / or receive beamforming based on beam direction indications received from T-TRP 170, such as beam angle information (BAI). In some embodiments, processor 210 may perform operations related to network access (e.g., initial access) and / or downlink synchronization, such as operations related to detecting synchronization sequences, decoding, and acquiring system information. In some embodiments, processor 210 may perform channel estimation, for example, using reference signals received from NT-TRP 172 and / or from T-TRP 170.

[0116] Processor 210 may be part of transmitter 201 and / or receiver 203, but is not shown in the figure. Memory 208 may be part of processor 210, but is not shown in the figure.

[0117] The processing components of processor 210, transmitter 201, and receiver 203 can each be implemented by the same or different processors, which execute instructions stored in memory (e.g., memory 208). Alternatively, some or all of the processing components of processor 210, transmitter 201, and receiver 203 can be implemented using special-purpose circuits such as field-programmable gate arrays (FPGAs), graphics processing units (GPUs), or application-specific integrated circuits (ASICs).

[0118] In some implementations, ED 110 may be a device (also referred to as a component) such as a communication module, modem, chip, or chipset, including at least one processor 210 and an interface or at least one pin. In this scenario, the transmitter 201 and receiver 203 may be replaced by an interface or at least one pin, wherein the interface or at least one pin is used to connect the device (e.g., a chip) and other devices (e.g., a chip, memory, or bus). Therefore, sending information to NT-TRP 172 and / or T-TRP 170 and / or another ED 110 can be referred to as sending information to an interface or at least one pin, or as sending information to NT-TRP 172 and / or T-TRP 170 and / or another ED 110 via an interface or at least one pin, while receiving information from NT-TRP 172 and / or T-TRP 170 and / or another ED 110 can be referred to as receiving information from an interface or at least one pin, or as receiving information from NT-TRP 172 and / or T-TRP 170 and / or another ED 110 via an interface or at least one pin. This information may include control signaling and / or data.

[0119] In some implementations, the T-TRP 170 may be referred to by other names, such as base station, base transceiver station (BTS), wireless base station, network node, network device, network-side device, transmit / receive node, Node B, eNB, home base station, next generation NodeB (gNB), transmission point (TP), site controller, access point (AP), or wireless router, relay station, remote radio head, ground node, ground network device, or ground base station, base band unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distribution unit (DU), positioning node, etc. The T-TRP 170 can be a macro BS, pico BS, relay node, host node, or a combination thereof. T-TRP170 may refer to the aforementioned device or a component within the aforementioned device (e.g., a communication module, modem, or chip).

[0120] In some embodiments, the various parts of T-TRP 170 can be distributed. For example, some modules of T-TRP 170 may be located remotely from the device housing the T-TRP 170 antenna and may be coupled to the device housing the antenna via a communication link (not shown), sometimes referred to as a fronthaul, such as a Common Public Radio Interface (CPRI). Therefore, in some embodiments, the term T-TRP 170 may also refer to modules on the network side that perform processing operations such as ED 110 location determination, resource allocation (scheduling), message generation, and encoding / decoding; these modules are not necessarily part of the device housing the T-TRP 170 antenna. These modules may also be coupled to other T-TRPs. In some embodiments, T-TRP 170 may actually be multiple T-TRPs operating together to serve ED 110 through cooperative multicast or similar methods.

[0121] T-TRP 170 includes at least one transmitter 252 and at least one receiver 254 coupled to one or more antennas 256. Only one antenna 256 is shown in the figure. Alternatively, one, some, or all of the antennas may be panels. The transmitter 252 and receiver 254 may be integrated as a transceiver. T-TRP 170 also includes a processor 260 for performing various operations, including operations related to: preparing a transmission for downlink transmission to ED 110, processing uplink transmissions received from ED 110, preparing a transmission for backhaul transmission to NT-TRP 172, and processing a transmission received from NT-TRP 172 via backhaul. Processing operations related to preparing a transmission for downlink or backhaul transmission may include operations such as encoding, modulation, precoding (e.g., MIMO precoding), transmit beamforming, and generating symbols for transmission. Processing operations related to processing received uplink transmissions or transmissions received via backhaul may include operations such as receive beamforming, demodulation, and decoding of received symbols. Processor 260 may also perform operations related to network access (e.g., initial access) and / or downlink synchronization, such as generating the contents of a synchronization signal block (SSB) and generating system information. In some embodiments, processor 260 also generates beam direction indications, such as BAI, which can be scheduled for transmission by scheduler 253. Processor 260 performs other network-side processing operations described herein, such as determining the location of ED 110, determining the location for deploying NT-TRP 172, etc. In some embodiments, processor 260 may generate signaling, such as for configuring one or more parameters of ED 110 and / or one or more parameters of NT-TRP 172. Any signaling generated by processor 260 is transmitted by transmitter 252. It should be noted that, alternatively, the term "signaling" as used herein may be referred to as control signaling. Dynamic signaling can be transmitted in control channels such as the physical downlink control channel (PDCCH), while static or semi-static higher-layer signaling can be included in messages transmitted in data channels such as the physical downlink shared channel (PDSCH).

[0122] Scheduler 253 may be coupled to processor 260. Scheduler 253 may be included in or operate separately from T-TRP 170. Scheduler 253 may schedule uplink, downlink, and / or backhaul transmissions, including issuing scheduling grants and / or configuring unscheduled (“configuration grants”) resources. T-TRP 170 also includes memory 258 for storing information and data. Memory 258 stores instructions and data used, generated, or collected by T-TRP 170. For example, memory 258 may store software instructions or modules for implementing some or all of the functions and / or embodiments described herein and executed by processor 260.

[0123] Processor 260 may be part of transmitter 252 and / or receiver 254, but is not shown in the figure. Similarly, processor 260 may implement scheduler 253, but is not shown in the figure. Memory 258 may be part of processor 260, but is not shown in the figure.

[0124] The processing components of processor 260, scheduler 253, transmitter 252, and receiver 254 can each be implemented by the same or different one or more processors, which execute instructions stored in memory (e.g., memory 258). Alternatively, some or all of the processing components of processor 260, scheduler 253, transmitter 252, and receiver 254 can be implemented using dedicated circuitry such as FPGA, GPU, or ASIC.

[0125] When T-TRP 170 is a device (also referred to as a component) such as a communication module, modem, chip, or chipset in a device, it includes at least one processor and an interface or at least one pin. In this scenario, transmitter 252 and receiver 254 can be replaced by an interface or at least one pin, wherein the interface or at least one pin is used to connect the device (e.g., a chip) and other devices (e.g., a chip, memory, or bus). Therefore, sending information to NT-TRP 172 and / or T-TRP 170 and / or ED110 can be referred to as sending information to an interface or at least one pin, while receiving information from NT-TRP 172 and / or T-TRP 170 and / or ED 110 can be referred to as receiving information from an interface or at least one pin. This information may include control signaling and / or data.

[0126] Although the NT-TRP 172 is shown as an example of a drone only, the NT-TRP 172 can be implemented in any suitable non-terrestrial form. Furthermore, the NT-TRP 172 may be referred to by other names in some implementations, such as a non-terrestrial node, a non-terrestrial network device, or a non-terrestrial base station. The NT-TRP 172 includes a transmitter 272 and a receiver 274 coupled to one or more antennas 280. Only one antenna 280 is shown in the figure. Alternatively, one, some, or all of the antennas may be panels. The transmitter 272 and receiver 274 may be integrated as a transceiver. The NT-TRP 172 also includes a processor 276 for performing various operations, including operations related to: preparing transmissions for downlink transmission to ED 110, processing uplink transmissions received from ED 110, preparing transmissions for backhaul transmission to T-TRP 170, and processing transmissions received from T-TRP 170 via backhaul. Processing operations related to preparing a transmission for downlink or backhaul transmission may include operations such as encoding, modulation, precoding (e.g., MIMO precoding), transmit beamforming, and generating symbols for transmission. Processing operations related to processing received uplink transmissions or transmissions received via backhaul may include operations such as receive beamforming, demodulation, and decoding of received symbols. In some embodiments, processor 276 performs transmit beamforming and / or receive beamforming based on beam direction information (e.g., BAI) received from T-TRP 170. In some embodiments, processor 276 may generate signaling, such as for configuring one or more parameters of ED 110. In some embodiments, NT-TRP 172 implements physical layer processing but does not implement higher-level functions such as medium access control (MAC) or radiolink control (RLC) layer functions. Since this is only an example, in general, NT-TRP 172 may implement higher-level functions in addition to physical layer processing.

[0127] The NT-TRP 172 also includes a memory 278 for storing information and data. A processor 276 may be part of the transmitter 272 and / or receiver 274, but is not shown in the figure. The memory 278 may be part of the processor 276, but is not shown in the figure.

[0128] The processing components of processor 276, transmitter 272, and receiver 274 can each be implemented by the same or different processors, which execute instructions stored in memory (e.g., memory 278). Alternatively, some or all of the processing components of processor 276, transmitter 272, and receiver 274 can be implemented using programmable special-purpose circuitry such as FPGAs, GPUs, or ASICs. In some embodiments, NT-TRP 172 can actually be multiple NT-TRPs operating together to serve ED 110 via cooperative multicast or similar methods.

[0129] When NT-TRP 172 is a device within a machine (e.g., a communication module, modem, chip, or chipset), it includes at least one processor and an interface or at least one pin. In this scenario, transmitter 272 and receiver 257 can be replaced by an interface or at least one pin, wherein the interface or at least one pin is used to connect the device (e.g., a chip) and other devices (e.g., a chip, memory, or bus). Therefore, sending information to T-TRP 170 and / or another NT-TRP 172 and / or ED 110 can be referred to as sending information to an interface or at least one pin, while receiving information from T-TRP 170 and / or another NT-TRP 172 and / or ED 110 can be referred to as receiving information from an interface or at least one pin. This information may include control signaling and / or data.

[0130] It should be noted that the term "TRP" used in this article can refer to either T-TRP or NT-TRP. T-TRP can be alternatively referred to as terrestrial network TRP (TN TRP), and NT-TRP can be alternatively referred to as non-terrestrial network TRP (NTN TRP).

[0131] T-TRP 170, NT-TRP 172 and / or ED 110 may include other components, but these components have been omitted for clarity.

[0132] Basic module structure

[0133] One or more steps of the methods in the embodiments provided herein can be derived from... Figure 4 The corresponding unit or module provided will be executed. Figure 4Units or modules in devices such as ED 110, T-TRP 170, or NT-TRP 172 are illustrated. For example, signals may be transmitted by a transmitting unit or transmitting module. Signals may be received by a receiving unit or receiving module. Signals may be processed by a processing unit or processing module. Other steps may be performed by artificial intelligence (AI) or machine learning (ML) modules. The corresponding units or modules may be implemented using hardware, one or more components or devices executing software, or a combination thereof. For example, one or more of these units or modules may be integrated circuits, such as a programmable FPGA, GPU, or ASIC. It should be understood that if these modules are implemented by a processor using software, these modules may be retrieved by the processor, wholly or partially, individually or collectively, in single or multiple instances, as needed, and these modules themselves may include instructions for further deployment and instantiation.

[0134] Further details regarding ED 110, T-TRP 170, and NT-TRP 172 are known to those skilled in the art. Therefore, these details are omitted herein.

[0135] Abbreviations and vocabulary definitions

[0136] 3GPP – 3rd Generation Partnership Project

[0137] NR – New Radio

[0138] BS – Base Station

[0139] CSI – Channel State Information

[0140] DFT - Discrete Fourier Transform

[0141] DL - Downlink

[0142] eNodeB – Base Station System in 3GPP Terminology

[0143] TDD – Time Division Duplexing

[0144] FDD – Frequency Division Duplexing

[0145] FFT - Fast Fourier Transform

[0146] SU – Single-User

[0147] MU – Multi-User

[0148] MIMO—Multiple Input Multiple Output

[0149] UE – User Equipment

[0150] UL - Uplink

[0151] Uma – Urban Macro

[0152] As an application in the field of massive MIMO, canonical decomposition can be employed. However, some existing methods may use a predefined number of orthogonal components in the spatial, frequency, and Doppler domains (these orthogonal components are determined by the terminal and reported to the base station), which can lead to performance loss compared to a joint nonorthogonal basis across the spatial-frequency-Doppler domains.

[0153] The CSI reporting volume strikes a balance between the accuracy and overhead (system resources and air signaling for CSI reporting in UL) of the DL channel representation. Several CSI compression methods exist in the NR standard, such as Type I and Type II codebooks. Type I codebooks are used for SU mode transmission, and Type II codebooks are used for MU mode. Type II codebooks offer higher accuracy than Type I codebooks, but have some drawbacks, such as lower accuracy for higher channel ranks. Furthermore, these reports are aperiodic, and there is a delay between DL and UL messages (round-trip delay), resulting in a certain usage delay in the acquired CSI, leading to more significant performance degradation over time. To address these shortcomings of existing standard codebooks, a more efficient channel compression algorithm needs to be designed that can achieve ideal performance with a maximum rank of 4, while maintaining overhead comparable to the existing standard Type II codebook.

[0154] When faced with increased channel rank (up to 4), the accuracy of channel representation / compression may become relatively low. Currently, many terminals have at least four receive antennas. Based on system-level simulations and actual field tests, the rank reported by the UE to the eNodeB is at most 2 to 4. Existing solutions exhibit poor accuracy in high-rank representation / compression.

[0155] The present disclosure presents a solution to this problem. The purpose of this disclosure is to provide channel state information compression to reduce feedback overhead in various scenarios with similar problems. This disclosure is based on: increasing the dimensionality of the original channel tensor (using virtual extra dimensions), performing tensor decomposition, and approximating specific factors (factor vectors) through steering vectors and extrapolated time factors. This approach is applicable to any scenario that may require the transmission of channel state information under constraints of limited feedback. Some examples that can utilize this disclosure include: FDD MIMO feedback mechanisms, backhaul-constrained cloud RANs, channel feedback in millimeter-wave scenarios, and cooperative multipoint MIMO feedback mechanisms.

[0156] The idea behind this disclosure is to use the concept of canonical decomposition to reduce the amount of channel space-frequency-time information to be reported. Since it can achieve a sufficiently high-quality MIMO channel representation with lower complexity and requires fewer parameters to represent CSI, canonical decomposition is used instead of other types of decomposition (Tucker decomposition, tensor chain, etc.).

[0157] Furthermore, based on canonical decomposition, this disclosure proposes the use of virtual dimensions (or sub-dimensions), which provides the opportunity to represent MIMO channels with high expected accuracy and a minimum number of parameters. Each such virtual dimension (or factor vector) of the tensor can contain the smallest possible size (e.g., 2 elements in each dimension). Channel approximation is performed on each dimension using vectors with a “guided” structure. The decomposition of the channel virtual dimensions / factors can be computed using an alternating least squares (ALS) algorithm, which yields vectors with non-orthogonal properties. To provide robustness against time-varying CSI degradation (in order to resist channel aging), factors with a parametric structure (e.g., a “guided” structure, described in detail later) are extrapolated over time to obtain future channel predictions.

[0158] Furthermore, the provided propagation channel compression scheme exhibits good performance under different propagation channel scenarios (e.g., urban, rural, and suburban channels), signal-to-interference-plus-noise ratio (SINR), and different UE speeds (e.g., 3 km / h to 10 km / h).

[0159] This disclosure also provides an embodiment in which channel approximation is performed in each dimension using a vector with a parametric structure. This parametric structure is not limited to a "guided" structure; it can also be a quadratic structure, a cubic structure, or other structures. Dimensions or portions of the channel representation can also be enabled or disabled, and the algorithm's parameters can be adjusted to correspond to the current propagation channel conditions.

[0160] A Discrete Fourier Transform (DFT) can be performed as an initial approximation of the channel in the first ALS iteration to reduce the algorithm's complexity and the number of ALS iterations required. Besides ALS, other approximation methods can also be used, such as the Levenberg-Marquardt algorithm.

[0161] This disclosure proposes a wireless communication method and / or device for representing (approximating) MIMO channels using piecewise linear nonorthogonal vectors. These nonorthogonal vectors can span the full dimensions of the spatial, frequency, and time domains, or sub-dimensions of the spatial, frequency, and time domains. The proposed method can significantly reduce the overhead of transmitting CSI from the UE to the eNodeB and achieve good-quality predicted channels under various propagation channel conditions, SNR, the number and configuration of various transmit and receive antennas, and signal bandwidth.

[0162] The proposed scheme enables rank adaptation in MU mode for eNodeB. The considered public content compresses the channel itself (a channel-based approach), thus enabling the extraction of eigenvalues ​​and their application to rank selection adaptation in eNodeB.

[0163] The proposed solution is robust to the aperiodicity of UE mobility or high-precision CSI reports. High-precision CSI reports are typically aperiodic due to UL resource constraints. Maintaining stable performance over time intervals is a desirable property of CSI. The disclosed information considered can provide robust performance against the aperiodicity of CSI or UE mobility.

[0164] The principles of this disclosure include representing MIMO channels in the spatial-frequency-time domain using canonical decomposition. To implement a two-dimensional antenna array, the TX antenna undergoes an additional two-dimensional split.

[0165] The details of this disclosure will be described in detail in the following description.

[0166] Figure 5This is a schematic flowchart illustrating a wireless communication method provided by one or more exemplary embodiments of this disclosure. The method can be implemented by a receiver. Optionally, the receiver can be a terminal device or other device with similar functionality (e.g., the receiver can be a chip), which is not limited herein. Figure 5 As shown, the method may include the following steps.

[0167] S510: Receiver acquires CSI.

[0168] Here, CSI refers to the actual channel state information between the transmitter and receiver. CSI can also be referred to as, for example, the precise CSI measured by the receiver. This measurement is based on the signal transmitted from the transmitter to the receiver. Existing techniques can be used to obtain CSI, which will not be elaborated upon here for the sake of brevity.

[0169] S520: The receiver determines a first estimate of the CSI, wherein the first estimate is used to represent the canonical decomposition of the CSI and includes a preset number of factor vectors, wherein at least one of the preset number of factor vectors is split by sub-dimensions.

[0170] After obtaining the CSI as described above, due to limited feedback resources, some processing may be necessary to obtain a suitable CSI estimate for generating a CSI report. Therefore, a canonical decomposition is proposed to be introduced when estimating the CSI. Furthermore, based on the canonical decomposition of the CSI, a sub-dimension splitting of the tensors used in the canonical decomposition is also proposed.

[0171] The sub-dimension split here can also be called virtual dimension split. The principle is to divide the factor vector into the smallest possible size so that the factor vector can be represented by sub-split elements in the sub-dimension (virtual dimension). The size of the factor vector before the sub-dimension split can be larger than the size of each sub-dimension of the factor vector after the sub-dimension split.

[0172] By splitting the factor vector by its subdimensions, we can introduce the smallest possible size to represent it. This splitting can help reduce the number of parameters used to represent the factor vector. Typically, the size of the factor vector can be split in such a way that the size of the subdimension is the smallest possible integer.

[0173] It should be noted that at least one factor vector here can be one of a preset number of factor vectors, or all of the factor vectors in the preset number of factor vectors, or only a portion thereof; this disclosure does not limit this. When multiple factor vectors in the preset number of factor vectors are split by sub-dimensions, the sub-dimensions involved in such splitting of each factor vector can be the same or different; this disclosure does not limit this.

[0174] Furthermore, the preset number here refers to the number of factor vectors used in the canonical decomposition, which can be a predefined value before estimating the CSI. This value can be a fixed value known to both the receiver and the transmitter, or a value notified by the transmitter. This predefined value can depend on the amount of information in the CSI to be estimated. For example, if the CSI includes information in the time domain, frequency domain, and spatial domain, then as an implementation, the preset number is set to 4, meaning that four factor vectors can be used for the canonical decomposition of the CSI: one factor vector to represent the information in the time domain, one factor vector to represent the information in the frequency domain, and two factor vectors to represent the information in the spatial domain. In the following text, a preset number of 5 can be used as an example (one factor vector to represent the time domain channel state, one factor vector to represent the frequency domain channel state, and three factor vectors to represent the spatial domain channel state—two for the transmit antenna and one for the receive antenna). However, it should be noted that more or fewer factor vectors can be used, depending on the amount of information contained in the CSI or other factors (e.g., the specific algorithm used to implement the canonical decomposition).

[0175] As described above, at least one factor vector can be subdivided, or in other words, subdivided using virtual dimensions. In one implementation, this subdivision can be achieved by the receiver determining a subdimension representation for each factor vector in the at least one factor vector, and then obtaining a first estimate of the CSI based on this subdimension representation. To achieve subdimension subdivision of the at least one factor vector, determining a subdimension representation for each factor vector in the at least one factor vector, and then obtaining a first estimate of the CSI based on this subdimension representation, makes it easier to obtain the first estimate of the CSI, thereby simplifying the computation, reducing the number of parameters used to represent the CSI, and further improving the efficiency of channel estimation.

[0176] Each factor vector can have a corresponding sub-dimension representation. If all factor vectors in a preset number of factor vectors have been split by sub-dimensions, the first estimate of CSI can be obtained based on the sub-dimension representations of all factor vectors in the preset number of factor vectors. If some factor vectors in a preset number of factor vectors have been split by sub-dimensions, while one or more other factor vectors have not been split by sub-dimensions, the first estimate of CSI can be obtained based on the sub-dimension representations of some factor vectors in the preset number of factor vectors.

[0177] To determine the sub-dimension representation of each factor vector in at least one factor vector, in one possible implementation, the receiver can determine the size of at least one sub-dimension parameter for each factor vector in at least one factor vector according to a first preset configuration corresponding to the CSI, and represent each factor vector in at least one factor vector with the size of the at least one sub-dimension parameter. Here, the first preset configuration may indicate the number of sub-dimensions into which the sub-dimension of each factor vector in at least one factor vector is split, and the size of each of these sub-dimensions. The receiver can then use the sub-dimension representation of each factor vector in at least one factor vector as input to a first preset algorithm to obtain a first estimate of the CSI. For example, the first preset configuration may be the number of transmit antennas, based on which the number of sub-dimensions into which the factor vector (used to represent channel information characterizing the transmit antennas) is split can be determined.

[0178] For each of the at least one factor vector, the at least one sub-dimension parameter size includes a first sub-dimension parameter size and a second sub-dimension parameter size, wherein the first sub-dimension parameter size includes at least one factor indicating the size of the sub-dimension, and the second sub-dimension parameter size represents the number of sub-dimensions used to represent the factor vector. The sub-dimension representation of the factor vector is a Kronecker product of the sub-split elements, wherein each sub-split element is represented exponentially based on the phase corresponding to the factor vector, the first sub-dimension parameter size of the factor vector, and the second sub-dimension parameter size of the factor vector.

[0179] After determining the sub-dimensional representation of each factor vector in at least one factor vector, the receiver can obtain a first estimate of the CSI based on the sub-dimensional representation of each factor vector in at least one factor vector. In one possible implementation, the receiver can use the sub-dimensional representation of each factor vector in at least one factor vector as input to a first preset algorithm to obtain the first estimate of the CSI. That is, the first preset algorithm can be used to determine a preset number of factor vectors included in the first estimate, at least one of which is represented in the form of a sub-dimensional representation. Here, the first preset algorithm can be any algorithm that can be used to compute the canonical decomposition of the CSI factor vectors, for example, the ALS algorithm. Other algorithms can also be used, such as the Levenberg-Marquardt algorithm, etc. It should be noted that if not all factor vectors in the preset number of factor vectors have been subdivided, the input to the first preset algorithm can be the sub-dimensional representation of each factor vector that has been subdivided.

[0180] To facilitate understanding, a concrete example is introduced to more clearly explain the relevant terminology. It should be noted that the number of factor vectors before subdivision (the preset number), the number of factor vectors after subdivision (referred to as "at least one factor vector" in the above description), and the number and size of subdivisions for each factor vector after subdivision in this example are for illustrative purposes only and are not limitations.

[0181] Assume CSI can contain a number of elements of size 1. tensor The time-domain channel information is represented by a value of . tensor The frequency domain channel information is represented by a tensor. (including sizes respectively) and of and The channel information associated with the transmitting antenna is represented by ), and the data consists of a size of . tensor This represents channel information related to the receiving antenna. The terms channel tensor, tensor, and factor vector are used interchangeably throughout the text.

[0182] The propagation channel (the actual CSI mentioned above) can be represented by canonical decomposition in the following form:

[0183]

[0184] in, , , , , , — Rank (rank refers to the rank of canonical decomposition).

[0185] Here we use and The channel information is used to characterize the two polarizations (vertical polarization and horizontal polarization) of the transmitting antenna. It should be noted that a single tensor can also be used to represent the channel information related to the transmitting antenna, and this disclosure does not limit this.

[0186] like Figure 6 As shown, Figure 6 This is an example of a tensor provided by one or more exemplary embodiments of this disclosure. Only three tensors are shown here. , and For each receiving antenna, such a decomposition can be performed.

[0187] In this example, the channel is approximated in each dimension by a vector with a “guided” structure, where:

[0188]

[0189]

[0190]

[0191]

[0192]

[0193] Each tensor is subdivided (partitioned) into its smallest possible size to improve the performance of the overall channel representation (dimension splitting). If all five tensors are subdivided, the subdimension representation of each of the five tensors is:

[0194]

[0195]

[0196] (2)

[0197]

[0198]

[0199] in, It represents the Kronecker product.

[0200] The stability of a tensor to noise largely depends on the tensor dimension. .

[0201] Feasibility of Dimensional Splitting of Channel Tensor

[0202] consider All tensors have a size equal to a certain size Dimension equals scalar noise tensor Each element follows a Gaussian distribution with a mean of 0 and a standard deviation of 1.

[0203] If tensor Total number of elements If it is fixed, then the theoretical limit of the Frobenius norm of the error does not exceed...

[0204] , (3)

[0205] For a fixed large quantity The minimum value of the above expression is .

[0206] It is a decomposition method, (3) for the decomposition method The dependence is relatively weak.

[0207] Sub-dimension partitioning of the channel tensor (dimension partitioning)

[0208] For any r, each The size can be expressed as Then for all dimensions:

[0209]

[0210] For example, if Then it can be represented as a 2×2×2 Kronecker product. and ,in, .

[0211] for Then it can be expressed as the Kronecker product of 2×2×2×2×3, where, , hour , hour .

[0212] In each The middle represents the index. In each The middle represents the index. .

[0213] In this case, the index i of each dimension can be represented as:

[0214]

[0215] Finally, (2) can be expressed in the following form:

[0216] (4)

[0217] in,

[0218] Finally, quantized canonical decomposition, or ordinary canonical decomposition of tensors with virtual dimensions of prime size, is considered the most promising tensor tool for channel denoising and subsequent extrapolation, which will be described later. In one possible implementation, the ALS algorithm (alternating least squares) can be used to obtain the values. and More details will follow later.

[0219] Figure 7A and Figure 7BThe dimensional splitting of the channel tensor provided by one or more exemplary embodiments of this disclosure is illustrated.

[0220] like Figure 7A As shown, the original channel is If the channel is split into two parts (vertical polarization and horizontal polarization), then we get:

[0221]

[0222]

[0223] After splitting each dimension into sub-dimensions, we can obtain:

[0224]

[0225]

[0226]

[0227]

[0228] Therefore, each tensor is broken down into its smallest possible size. For example, tensor The size changes from 1 to 4 sub-dimensions, tensor The size changes from 1 to 5 sub-dimensions, tensor The size is changed from 1 to 4 sub-dimensions. The size reduction achieved by splitting the sub-dimensions can help save on feedback overhead.

[0229] For each tensor, the sub-dimension representation of the tensor can be represented as a Kronecker product of the sub-split elements, and the tensor can be represented by at least one sub-dimension parameter size, which can include a first sub-dimension parameter size and a second sub-dimension parameter size, wherein the first sub-dimension parameter size can include at least one factor indicating the size of the sub-dimension, and the second sub-dimension parameter size represents the number of sub-dimensions used to represent the factor vector.

[0230] For example, for tensors Its sub-dimension representation is Its child splitting element is , , ... The size of the first sub-dimension parameter of this tensor includes (The size of the first sub-dimension parameter mentioned above) and (The size of the second sub-dimension parameter mentioned above). In this example, and This means the tensor has a sub-dimension size of 2 and a total of 3 sub-dimensions. It should be noted that the first sub-dimension parameter can have multiple sizes, because for each sub-dimension, their sizes can be different, or some sub-dimensions can differ from others.

[0231] For example, regarding tensors Its sub-dimension representation is Its child splitting element is , ... The size of the first sub-dimension parameter of this tensor includes (The size of the first sub-dimension parameter mentioned above) and (The size of the second sub-dimension parameter mentioned above). In this example, This means that the sizes of these sub-dimensions of the tensor are different, so there are two first sub-dimension parameter sizes of 2 and 3, and the number of sub-dimensions is 5.

[0232] In this example, the first preset configuration corresponding to the CSI mentioned above may include the number of transmit antennas, the number of receive antennas, the number of subcarriers, the number of transmission time intervals (TTIs), etc.

[0233] For easier understanding, please refer to Figure 7B The tensor (factor vector) TX1 is further represented by sub-splitting elements, thus reducing the number of different phases between parts from 7 to 3.

[0234] Therefore, by performing sub-dimension splitting, the number of parameters used to represent the factor vector can be reduced, thereby reducing feedback overhead.

[0235] S530: The receiver generates a CSI report based on the first estimate of the CSI.

[0236] In one possible implementation, the receiver can generate a CSI report based on a second preset configuration and a first estimate of the CSI, wherein the second preset configuration indicates whether a preset structure is applied to the first factor vector among a preset number of factor vectors. Here, if there are multiple first factor vectors for which one or more preset structures are applied, each first factor vector may adopt the same preset structure from different preset structures, and this disclosure does not limit this.

[0237] As described in the example above, the factor vector can assume a certain preset structure. In one possible implementation, the preset structure can be a parameter structure, which can include at least one of a guided structure, a quadratic structure, or a cubic structure, thereby achieving flexibility by introducing various parameter structures.

[0238] For example, using the above example as an illustration, as shown in equation (1), it is assumed that the five tensors adopt a guided structure, which means that the phase of each element of the tensor follows a certain rule.

[0239] To inform the receiver of the above assumptions, a second preset configuration can be indicated by a wireless communication system. In one implementation, the receiver can receive first information indicating the second preset configuration and second information indicating a preset quantity from the wireless communication system. The first information may be the second preset configuration or some information including the second preset configuration; this disclosure does not limit this. Similarly, the second information may be a preset quantity or some information including a preset quantity; this disclosure does not limit this.

[0240] It is possible to enable / disable one or more dimensions in the channel representation, and adjust the algorithm parameters to correspond to the current propagation channel conditions. Enabling here means applying a preset structure to the factor vectors, while disabling means not applying the preset structure to the factor vectors. As mentioned above, this enabling / disabling can be achieved through a second preset configuration.

[0241] In one specific implementation, the second preset configuration instructs the application of a preset structure to the first factor vector. The receiver can perform factor approximation on the first estimate according to the second preset algorithm to derive the representation parameters of the first factor vector, and generate a CSI report based on the representation parameters of the first factor vector. Here, when the preset structure is applied, since the preset structure is a parametric structure, factor approximation can be performed to estimate the parameters that can be used to represent the factor vector. Taking the above example as an illustration, as shown in equation (1), assuming that the five tensors adopt a guided structure, the receiver can obtain the tensors by performing factor approximation. , , and The phase, and obtain the tensor The phase and amplitude of these tensors are the aforementioned first factor vectors. Since it is assumed that they all employ a steering structure, these phase and amplitude parameters, as representation parameters of these first factor vectors, can be used by the receiver to generate a CIS report. For example, the receiver can directly report these representation parameters so that the eNodeB can reconstruct the channel based on them. In this scheme, by assuming that one or more of the first factor vectors in a predetermined number of factor vectors employ a predetermined structure, one or more first factor vectors can be factored, thereby enabling the representation of one or more first factor vectors with fewer parameters and reducing feedback overhead.

[0242] In another specific implementation, the second preset configuration indicates that a preset structure is not applied to the first factor vector. The receiver can generate a CSI report based on the first estimate. For example, if the first estimate also includes five factor vectors, but none of them are indicated to use the preset structure, the receiver can directly report these five factor vectors to the wireless communication system.

[0243] It should be noted that the factor vectors split by sub-dimensions and the factor vectors indicated to use a preset structure may not be the same factor vectors. Furthermore, if all factor vectors included in the first estimate are indicated to use one or more preset structures, the receiver can perform factor approximation to obtain the representation parameters of all these factor vectors and report the representation parameters to the wireless communication system. If some factor vectors included in the first estimate are indicated to use one or more preset structures, while others are not indicated to use a preset structure, the receiver can report the representation parameters of the factor vectors using the preset structure and directly report the other factor vectors.

[0244] In one implementation, the receiver can also extrapolate the first estimate to obtain the extrapolated first estimate for the next time unit, and generate a CSI report based on the extrapolated first estimate of CSI. Since channel aging may exist, the estimated CSI measured at this time may differ from the future channel (e.g., the future channel may be used by the wireless communication system for subsequent downlink scheduling). The receiver can choose to generate a CSI report based on the first estimate, or it can choose to extrapolate the future first estimate and generate a CSI report based on the extrapolated first estimate. By introducing extrapolation in the channel estimation process, the extrapolated first estimate can have better robustness to the "channel aging" problem. Using the example above, as shown in equation (1), if a dimension, i.e., the time dimension, is directly selected for extrapolation, the representation parameters obtained through multiple factor approximations can be accumulated to derive the tensor. The variation patterns of the amplitude and phase of the elements are determined, and then the tensor is extrapolated based on these variation patterns. The future amplitude and future phase of the element are then used as a tensor. The extrapolated value is obtained and reported to the wireless communication system.

[0245] As described above, the first preset algorithm can be used to determine a first estimate by taking the sub-dimension representation of each factor vector in at least one factor vector as input. In one possible implementation, random values ​​can be used as initial values ​​for the factor vectors; in another possible implementation, before obtaining the first estimate of the CSI by taking the sub-dimension representation of each factor vector in at least one factor vector as input to the first preset algorithm, the receiver can obtain the initial sub-dimension representation of each factor vector in at least one factor vector based on the CSI. Then, the receiver can take the initial sub-dimension representation as input to the first preset algorithm to obtain the first estimate of the CSI.

[0246] In one example, the receiver can obtain the initial sub-dimension representation of each factor vector in at least one factor vector and the rank of the canonical decomposition of the CSI based on the DFT operation of the CSI, and then obtain a first estimate of the CSI by using the initial sub-dimension representation and the rank of the canonical decomposition as input to a first preset algorithm.

[0247] Specifically, the receiver can determine the expanded channel matrix based on the CSI and preset oversampling parameters, perform a Discrete Fourier Transform (DFT) operation on the expanded channel matrix to obtain the power spectrum of the CSI, determine the initial sub-dimension representation of each factor vector in at least one factor vector and the rank of the canonical decomposition of the CSI based on the power spectrum of the CSI, and use the initial sub-dimension representation and the rank of the canonical decomposition as input to a first preset algorithm to obtain a first estimate of the CSI.

[0248] In another example, the receiver may use the extrapolated first estimate as an initial sub-dimension representation of each factor vector in at least one factor vector, wherein the extrapolated first estimate is obtained by extrapolating a first estimate determined in the previous time unit. The receiver then uses the initial sub-dimension representation as input to a first preset algorithm to obtain a first estimate of the CSI.

[0249] In one implementation, CSI includes first channel information in the time domain, second channel information in the frequency domain, and third channel information in the spatial domain; a predetermined number of factor vectors include a time factor vector representing the first channel information, a subcarrier factor vector representing the second channel information, and a transmit factor vector and a receive factor vector representing the third channel information; at least one of the time factor vector, subcarrier factor vector, transmit factor vector, and receive factor vector is represented by a regularly changing phase. As shown in equation (1) above, the time factor vector can be... The subcarrier factor vector can be The receiver factor vector can be .

[0250] The transmission factor vector can include a first transmission factor vector and a second transmission factor vector. The first transmission factor vector is used to characterize the channel information of at least one vertically polarized antenna port, and the second transmission factor vector is used to characterize the channel information of at least one horizontally polarized antenna port. By further splitting the transmission factor vector into two sub-dimensions, the polarization of the transmitting antenna is considered, thereby improving the applicability of the scheme. As shown in equation (1) above, the first transmission factor vector and the second transmission factor vector can be respectively and To achieve a two-dimensional antenna array, the TX antenna is further split into two dimensions.

[0251] After generating a CSI report based on the first estimate of the CSI, the receiver can also send the CSI report to the wireless communication system.

[0252] It should be noted that the receiver can send a CSI report to the wireless communication system either to the aforementioned transmitter or to any network element in the wireless communication system; this disclosure does not limit this. Similarly, the receiver can receive the signal used to measure CSI from the transmitter or from other network elements in the wireless communication system; this is also not limited herein.

[0253] The following section will describe a more specific implementation using the above example, where the channel state information is represented by five tensors.

[0254] DFT as an initial approximation

[0255] One possible approach to significantly reduce the number of iterations in the ALS algorithm while maintaining acceptable accuracy is to construct an initial approximation using the Discrete Fourier Transform (DFT). Considering the aforementioned assumptions—that the factor vectors associated with the subcarrier frequency and the transmit antenna closely follow the guiding structure—the following procedure is proposed for constructing the initial approximation.

[0256] A DFT can be performed as an initial approximation of the channel in the first ALS iteration to reduce the complexity of the algorithm and the number of ALS iterations required.

[0257] 1) Assuming that for and All possible values ​​and some initial values ​​for the time-domain index Select size as The corresponding two-dimensional channel matrix is ​​represented as follows:

[0258] 2) Assuming oversampling parameters are selected (corresponding to the above preset oversampling parameters) (for example, ), introducing a size of matrix (Corresponding to the extended channel matrix mentioned above). The top left submatrix is ​​equal to All other values ​​are equal to zero.

[0259] 3) Assume that a 2D-FFT is performed on each extended matrix, and the calculation is... ,in, It is a Fourier matrix of size s (this step corresponds to the DFT operation mentioned above).

[0260] 4) Assume that the results are averaged by taking the absolute value of each sample element: Let... (The power spectrum corresponding to the CSI above) is of size [value missing].

[0261]

[0262] The initial approximation is constructed by selecting the canonical rank. As the number of peaks, the corresponding peak index Convert to Fourier phase and And select the paradigm factor and As a guide vector with a selected phase, where the magnitude of each element is equal to 1. Figure 8 This is provided by one or more exemplary embodiments of the present disclosure. A diagram illustrating the peak selection at that time.

[0263] A Discrete Fourier Transform (DFT) can be performed as an initial approximation of the channel in the first ALS iteration to reduce the complexity of the algorithm and the number of ALS iterations required.

[0264] The remaining factors of the 5D tensor are determined as follows: Factors are sought in quantized form.

[0265]

[0266] Therefore, the remaining factors are calculated. , and The initialization is then performed. It's easy to see that the guiding vector can also be easily represented as a quantized tensor. Thus, an initial approximation of the quantization of the tensor is obtained. For each dimension, the obtained factor refers to the aforementioned initial sub-dimension representation of each factor vector in at least one factor vector that has been split into sub-dimensions.

[0267] It is worth noting that the described process can quickly achieve acceptable quality. However, a more robust optimization process can further improve the initialization quality. Therefore, the ALS process is directly applied to the original tensor with the following dimensions:

[0268]

[0269]

[0270] Then, for example, using Quantify each factor.

[0271] Then, an additional optimization process is performed on the tensors with split dimensions to obtain better initialization. For comparison, Table 1 below shows the Frobenius norm error of the initial initialization, as well as the final quality of extrapolation and approximation of the simulation results. It can be seen that ALS initialization can improve the quality to some extent, but in practice, the time consumed is too long and unacceptable.

[0272] Table 1

[0273]

[0274] Alternating Least Squares Algorithm for Canonical Tensor Decomposition

[0275] To construct the canonical decomposition, each tensor dimension and its corresponding factor are considered separately. Besides... Apart from one of them, all other factors are fixed.

[0276] The multidimensional ALS algorithm is shown below.

[0277]

[0278]

[0279]

[0280] repeat

[0281] Use factors Solving linear square problems using QR decomposition

[0282]

[0283] Consider one factor as a variable and the others as variables.

[0284] Recalculate

[0285]

[0286]

[0287] In this formula and further in the text, It is the symbol for the Kronecker product.

[0288] Thresholds can be set ,For example, .

[0289] The rough factor obtained by applying the ALS algorithm can be considered as the first estimate of the CSI mentioned above. Factor approximation can then be performed, since all five tensors in this example employ guided structures.

[0290] Factor approximation

[0291] In calculating the roughness factor After being output by ALS, through and Smooth the factors. Least squares can be used for this purpose.

[0292] Figure 9 The rank provided by one or more exemplary embodiments of this disclosure A diagram approximating the TX1 factor at time. Figure 9 In the diagram, the green curve represents the ALS output, while the red curve is filled by a linear approximation (interpolation).

[0293] Figure 10 The ALS output (i.e., coarsening factor) of UE1 is shown, involving the TTI dimension, 16 time / TTI samples, and rank 8. Figure 10 The image shows the real-valued factors obtained from simulations using the Quasi Deterministic RadioChannel Generator (Quadriga, User Manual and Documentation, Documentation Version: v2.6.1, July 12, 2021). The following simulations use a 5 ms time interval for each time sample. It is 16.

[0294] Figure 11 The ALS output (i.e., coarsening factor) of UE2 is shown, involving the TTI dimension, 16 time / TTI samples, and rank 8. Figure 11The image shows the real-valued factors obtained from simulations using the Quasi Deterministic RadioChannel Generator (Quadriga, User Manual and Documentation, Documentation Version: v2.6.1, July 12, 2021). The following simulations use a 5 ms time interval for each time sample. It is 16.

[0295] Figure 12 This is a flowchart illustrating factor extrapolation provided by one or more exemplary embodiments of this disclosure. For example... Figure 12 As shown, channel extrapolation is performed to obtain future channel prediction samples.

[0296] Typically, each element of a factor can be represented as the product of its absolute value and its complex part: Here, 'i' is the factor index for each dimension.

[0297] Assumption And taking into account the assumption that the phase changes almost linearly, the dependence of the phase on the index can be approximated by the following straight line: .

[0298] However, other functions can be used in the approximation (extrapolation) process.

[0299] It is also assumed that the dependency between the magnitude logarithm and the index can be approximated by the following linear function: .

[0300] However, other functions can also be used in the approximation (extrapolation) process.

[0301] The coefficients p and g are determined as least-squares solutions, which minimizes the differences in the interpolation stages. For example, the amplitude and phase are accumulated multiple times, and the above coefficients are derived from the least-squares solutions.

[0302] In addition, by adding indexes It is easy to obtain the extrapolation of the corresponding factors of the tensor in subsequent time intervals.

[0303] The best results were obtained when the amplitude was extrapolated twice during the simulation.

[0304] .

[0305] Other functions can be used for approximation purposes.

[0306] For each rank r, the values ​​of p0, p1, g0, g1, g2, and g3 are estimated using the following formula:

[0307]

[0308] This disclosure includes a channel compression mechanism used in channel state information feedback. The channel tensor can be viewed as a source of random numbers in different dimensions (i.e., space (eNodeB and UE), time, and frequency). To improve the accuracy of channel approximation and achieve low-parameter representation, a joint basis across the entire channel domain needs to be designed. Such a basis is constructed using the canonical decomposition method. The channel tensor contains some properties required for MIMO channel representation, as follows: (1) The sum of 6 to 8 rank tensors contains almost all the energy of the MIMO channel. There are 6 to 8 dominant clusters in the channel, represented by such a sum; (2) The core size depends on the dimension d, denoted as Tucker (the core size depends on the dimension d, denoted as Tucker). , where d is the number of dimensions of the channel tensor and R is the rank of the tensor. (The core size increases exponentially with the number of virtual dimensions) or other decomposition methods such as tensor chains require fewer representation parameters; (3) the approximate bound of the canonical decomposition is smaller than that of other decompositions and decays linearly with R; (4) the canonical decomposition provides a non-orthogonal representation of the MIMO channel. As is well known, MIMO sparse channels have a compact representation in a non-orthogonal basis.

[0309] Introducing virtual dimensions, or splitting the dimensions of the original channel tensor (the factor vector before sub-dimension splitting) into sub-dimensions, reduces the number of parameters required to represent a MIMO channel. The proposed method for reducing this number of parameters is to obtain virtual dimensions containing the smallest possible number of elements (each virtual dimension has 2 elements). This significantly reduces the number of parameters required for high-precision channel representation. Mathematically, using virtual dimensions provides a constraint on the type of non-orthogonal basis. This means that the MIMO channel is represented by a piecewise linear non-orthogonal basis.

[0310] Channel approximation is performed on each virtual dimension using vectors with a "guided" structure. This allows the UE to reduce overhead and decrease the size of the tensor without sacrificing performance.

[0311] To avoid the "channel aging" problem caused by round-trip delays or aperiodicity in CSI reports, a predictive approach is employed. Therefore, factors with a "guided" structure are extrapolated over time to obtain future channel predictions.

[0312] In addition to the main idea of ​​this disclosure, this disclosure can be extended by using vectors with not only "guided" structures to perform channel approximations in various dimensions (e.g., quadratic approximations, cubic approximations, etc.).

[0313] Different variations of channel approximation can improve decomposition characteristics, especially in fast propagation channels, such as complex multipath distributions and high user speeds.

[0314] In addition to ALS, other methods (such as the Levenberg-Marquardt algorithm) can be used for approximation, which can reduce complexity or obtain the regularity of factors over time.

[0315] Use a common magnitude coefficient associated with only one dimension (e.g., time) across all dimensions to reduce the overall size of the representation and reduce overhead.

[0316] This scheme can be implemented in any large-scale MIMO FDD communication system by adding corresponding equipment to the user station, which compresses the channel according to the algorithm described in this disclosure and sends the compression coefficients of the channel to the base station through a dedicated service channel.

[0317] Figure 13 This is an example of a device with 32 transmitting antennas provided in one or more exemplary embodiments of this disclosure. Figure 13 As shown, the antenna array has a structure of 8 columns, 2 rows, and ±45° cross-polarization pairs of vibrators. The center frequency is set to 2.14 GHz, with a horizontal spacing of 0.5λ and a vertical spacing of 0.78λ between the vibrators. In the experiment, a 10 MHz FDD system was considered, with 7×3 eNodeBs (21 cells) used for network deployment. Other relevant parameters are shown in Table 2.

[0318] Table 2 Main Simulation Parameters

[0319]

[0320] Figures 14 to 18 The results of implementing the proposed algorithm in a telecommunications system are shown, wherein, Figure 14 These are simulation results of single-layer transmission provided by one or more exemplary embodiments of this disclosure; Figure 15 This is another simulation result of single-layer transmission provided by one or more exemplary embodiments of this disclosure; Figure 16 These are simulation results of two-layer transmission provided by one or more exemplary embodiments of this disclosure; Figure 17 These are simulation results of three-layer transport provided by one or more exemplary embodiments of this disclosure; Figure 18 This is a simulation result of four-layer transport provided by one or more exemplary embodiments of this disclosure.

[0321] Figure 14 Simulation results of single-layer transmission in the Quadriga 3GPP Uma NLOS channel are shown, with a frequency of 2.14 GHz, a user speed of 3 km / h, and extrapolation over 20 ms. Figure 15Simulation results of single-layer transmission of the Quadriga Berlin Uma NLOS channel are shown, with a frequency of 2.14 GHz, a user speed of 10 km / h, and extrapolation over 40 ms. Figure 16 Simulation results of two-layer transmission in the Quadriga Berlin Uma NLOS channel are shown, with a frequency of 2.14 GHz, a user speed of 10 km / h, and extrapolation over 20 ms. Figure 17 Simulation results of three-layer transmission of the Quadriga Berlin Uma NLOS channel are shown, with a frequency of 2.14 GHz, a user speed of 10 km / h, and extrapolation over 20 ms. Figure 18 Simulation results of four-layer transmission of the Quadriga Berlin Uma NLOS channel are shown, with a frequency of 2.14 GHz, a user speed of 10 km / h, and extrapolation over 20 ms.

[0322] To estimate the approximate channel efficiency, a metric called "spectral efficiency" is used. For each subcarrier domain index and each time domain index value, a metric of size is considered independently. The corresponding channel matrix.

[0323] Assuming for each The calculated size is The truth matrix H and approximate matrix of the propagation channel The row orthogonal basis is represented as follows: and .

[0324] Then, for ,calculate and The singular values ​​of .

[0325] Then, the spectral efficiency is defined as .

[0326] Ideal spectral efficiency is defined as ,in, Indicates noise power.

[0327] Figure 14 and Figure 15 Simulation results for single-layer transmission are shown. Figure 14 In the middle, extrapolation was performed based on a value of 20 ms. Figure 15 In the simulation, an extrapolation was performed using a value of 40 ms. The signal-to-noise ratio was chosen to be 30 dB. Simulations were conducted for different users at random locations and varying distances from the BS. Figure 14 In the settings, all users selected a speed of 3 km / h. Figure 15Select 10 km / h. Here, in Figure 14 and Figure 15 In this context, "extrapolator" and "ALS output"—the raw data output by the ALS algorithm that is not extrapolated and is not filled with the guide vector—are considered as a variant of the potential achievable features.

[0328] Figure 16 , Figure 17 , Figure 18 The simulation results for two-layer, three-layer, and four-layer transmission are shown accordingly.

[0329] The overall characteristics are shown in Tables 3 and 4 below. The ratio of the algorithm's spectral efficiency to the ideal spectral efficiency is shown here, in percentages (%).

[0330] Table 3 Overall Characteristics

[0331]

[0332] Table 4 Overall Characteristics

[0333]

[0334] The wireless communication method provided in this disclosure generates a CSI report based on a first estimate of the CSI, wherein the first estimate represents the canonical decomposition of the CSI and includes a predetermined number of factor vectors, wherein at least one of the predetermined number of factor vectors is subdivided. By using the first estimate to represent the canonical decomposition of the CSI and subdividing at least one of the predetermined number of factor vectors included in the first estimate, the number of parameters required for high-precision channel representation can be significantly reduced, thereby achieving CSI reporting with less feedback overhead.

[0335] The above text combined Figures 5 to 18 The wireless communication method of this disclosure is described from the perspective of the receiver (e.g., a terminal device). The following will combine... Figure 19 The wireless communication method disclosed herein is described from the perspective of the transmitter (e.g., a network device).

[0336] Figure 19 This is another schematic flowchart illustrating a wireless communication method provided by one or more exemplary embodiments of this disclosure. The method can be implemented by a transmitter. Optionally, the transmitter can be a network device or other device with similar functionality (e.g., the transmitter can be a chip), which is not limited herein. Figure 19 As shown, the method may include the following steps.

[0337] S1910: The transmitter receives a CSI report from the receiver, wherein the CSI report is generated by the receiver based on a first estimate of the CSI, wherein the first estimate is used to represent a canonical decomposition of the CSI and includes a preset number of factor vectors, at least one of the preset number of factor vectors being split by sub-dimensions.

[0338] Here, CSI refers to the actual channel state information between the transmitter and receiver. CSI can also be referred to as, for example, the precise CSI measured by the receiver. This measurement is based on the signal transmitted from the transmitter to the receiver. Existing techniques can be used to obtain CSI, which will not be elaborated upon here for the sake of brevity.

[0339] The sub-dimension split here can also be called virtual dimension split. The principle is to divide the factor vector into the smallest possible size so that the factor vector can be represented by sub-split elements in the sub-dimension (virtual dimension). The size of the factor vector before the sub-dimension split can be larger than the size of each sub-dimension of the factor vector after the sub-dimension split.

[0340] By splitting the factor vector by its subdimensions, we can introduce the smallest possible size to represent it. This splitting can help reduce the number of parameters used to represent the factor vector. Typically, the size of the factor vector can be split in such a way that the size of the subdimension is the smallest possible integer.

[0341] It should be noted that at least one factor vector here can be one of a preset number of factor vectors, or all of the factor vectors in the preset number of factor vectors, or only a portion thereof; this disclosure does not limit this. When multiple factor vectors in the preset number of factor vectors are split by sub-dimensions, the sub-dimensions involved in such splitting of each factor vector can be the same or different; this disclosure does not limit this.

[0342] It should be noted that the number of factor vectors before subdivision (preset number), the number of factor vectors after subdivision (referred to as "at least one factor vector" in the above description), and other terms in this exemplary embodiment are for illustrative purposes only and are not limiting.

[0343] S1920: The transmitter determines the CSI based on the CSI report.

[0344] In one implementation, the transmitter may send to the receiver first information indicating a second preset configuration and second information indicating a preset quantity, wherein the second preset configuration indicates whether a preset structure is applied to the first factor vector in the preset quantity of factor vectors. The first information may be the second preset configuration or some information including the second preset configuration; this embodiment of the present disclosure does not limit this. Similarly, the second information may be a preset quantity or some information including the preset quantity; this embodiment of the present disclosure does not limit this.

[0345] In one specific implementation, the second preset configuration indicates the application of a preset structure to the first factor vector. As described on the receiving side, the receiver can perform factor approximation on the first estimate according to the second preset algorithm to derive the representation parameters of the first factor vector, and generate a CSI report based on the representation parameters of the first factor vector. Here, when the preset structure is applied, since the preset structure is a parametric structure, factor approximation can be performed to estimate the parameters that can be used to represent the factor vector. Taking the above example as an illustration, as shown in equation (1), assuming that the five tensors adopt a guided structure, the receiver can obtain the tensors by performing factor approximation. , , and The phase, and obtain the tensor The phase and amplitude. All these tensors are the aforementioned first factor vectors, because it is assumed that they are all in the steering structure. Therefore, these phase and amplitude, as the aforementioned characterization parameters of these first factor vectors, can be used by the receiver to generate a CIS report. For example, the receiver can directly report these characterization parameters so that the eNodeB can reconstruct the channel based on these characterization parameters.

[0346] In another specific implementation, the second preset configuration indicates that a preset structure is not applied to the first factor vector. As described on the receiving side, the receiver can generate a CSI report based on the first estimate. For example, if the first estimate also includes five factor vectors, but none of the factor vectors are indicated to use the preset structure, the receiver can directly report these five factor vectors to the wireless communication system.

[0347] For channel reconstruction on the transmitting side (e.g., eNodeB), canonical decomposition can be used in the same way as on the receiving side. For details, please refer to the description on the receiving side. The technical effect is similar and will not be repeated here.

[0348] The specific implementation of the wireless communication method on the transmitting side can be understood by referring to the exemplary embodiment of the wireless communication on the receiving side. The technical effects achieved are similar, and will not be repeated here.

[0349] The following section will describe product examples related to wireless communication methods.

[0350] Figure 20 This is a schematic diagram of the structure of a wireless communication device 2000 provided in one or more exemplary embodiments of this disclosure.

[0351] like Figure 20 As shown, the wireless communication device 2000 may include:

[0352] Module 2010 is used to acquire CSI.

[0353] The determination module 2020 is used to determine a first estimate of CSI, wherein the first estimate is used to represent the canonical decomposition of CSI and includes a preset number of factor vectors, wherein at least one factor vector in the preset number of factor vectors is split by sub-dimensions;

[0354] The generation module 2030 is used to generate a CSI report based on the first estimate of the CSI.

[0355] In one possible implementation, the determining module 2020 includes:

[0356] A determining unit is used to determine the sub-dimensional representation of each factor vector in at least one factor vector;

[0357] The first acquisition unit is used to acquire a first estimate of the CSI based on the sub-dimension representation of each factor vector in at least one factor vector.

[0358] In one possible implementation, the determining unit is used for:

[0359] Based on a first preset configuration corresponding to CSI, determine the size of at least one sub-dimension parameter of each factor vector in at least one factor vector;

[0360] Each factor vector in at least one factor vector is represented by the size of at least one sub-dimension parameter of each factor vector in at least one factor vector.

[0361] In one possible implementation, for each factor vector in at least one factor vector: at least one sub-dimension parameter size includes a first sub-dimension parameter size and a second sub-dimension parameter size, wherein the first sub-dimension parameter size includes at least one factor indicating the size of the sub-dimension, and the second sub-dimension parameter size represents the number of sub-dimensions used to represent the factor vector; the sub-dimension representation of the factor vector is a Kronecker product of sub-split elements, wherein each sub-split element is represented exponentially according to the phase corresponding to the factor vector, the first sub-dimension parameter size of the factor vector, and the second sub-dimension parameter size of the factor vector.

[0362] In one possible implementation, the first preset configuration indicates the number of sub-dimensions into which each factor vector in at least one factor vector is split, and the size of each of these sub-dimensions.

[0363] In one possible implementation, the first acquisition unit is used to: take the sub-dimension representation of each factor vector in at least one factor vector as input to a first preset algorithm to obtain a first estimate of CSI.

[0364] In one possible implementation, the generation module 2030 is configured to: generate a CSI report based on a second preset configuration and a first estimate of the CSI, wherein the second preset configuration indicates whether a preset structure is applied to a first factor vector among a preset number of factor vectors.

[0365] In one possible implementation, the second preset configuration indicates that a preset structure is applied to the first factor vector among a preset number of factor vectors; wherein, the generation module 2030 is used for:

[0366] The first estimated value is approximated by factors according to the second preset algorithm to derive the representation parameters of the first factor vector;

[0367] A CSI report is generated based on the representation parameters of the first factor vector.

[0368] In one possible implementation, the second preset configuration indicates that the preset structure is not applied to the first factor vector among the preset number of factor vectors; wherein the generation module 2030 is used to: generate a CSI report based on the first estimate.

[0369] In one possible implementation, the preset structure is a parameterized structure.

[0370] In one possible implementation, the parameter structure includes at least one of a guiding structure, a secondary structure, or a tertiary structure.

[0371] In one possible implementation, the device 2000 further includes: a first receiving module for receiving first information indicating a second preset configuration from a wireless communication system.

[0372] In one possible implementation, the generation module 2030 is also used for:

[0373] Extrapolate the first estimate to obtain the extrapolated first estimate for the next time unit;

[0374] A CSI report is generated based on the first estimate extrapolated from the CSI.

[0375] In one possible implementation, the determining module 2020 further includes a second acquisition unit, which is configured to: acquire an initial sub-dimension representation of each factor vector in at least one factor vector based on the CSI before the first acquisition unit takes the sub-dimension representation of each factor vector in at least one factor vector as input to the first preset algorithm to acquire a first estimate of the CSI; the first acquisition unit is configured to: take the initial sub-dimension representation as input to the first preset algorithm to acquire a first estimate of the CSI.

[0376] In one possible implementation, the second acquisition unit is used to acquire the initial sub-dimension representation of each factor vector in at least one factor vector and the rank of the canonical decomposition of CSI based on the DFT operation of CSI; the first acquisition unit is used to take the initial sub-dimension representation and the rank of the canonical decomposition as input to a first preset algorithm to acquire a first estimate of CSI.

[0377] In one possible implementation, the first acquisition unit is used for:

[0378] The expanded channel matrix is ​​determined based on the CSI and preset oversampling parameters;

[0379] The power spectrum of CSI is obtained by performing a Discrete Fourier Transform (DFT) operation on the expanded channel matrix.

[0380] Based on the power spectrum of CSI, determine the initial sub-dimension representation of each factor vector in at least one factor vector and the rank of the canonical decomposition of CSI.

[0381] In one possible implementation, the second acquisition unit is used to represent the extrapolated first estimate as an initial sub-dimension representation of each factor vector in at least one factor vector, wherein the extrapolated first estimate is obtained by extrapolating the first estimate determined in the previous time unit.

[0382] In one possible implementation, CSI includes first channel information in the time domain, second channel information in the frequency domain, and third channel information in the spatial domain; a predetermined number of factor vectors include a time factor vector for characterizing the first channel information, a subcarrier factor vector for characterizing the second channel information, and a transmit factor vector and a receive factor vector for characterizing the third channel information; wherein at least one of the time factor vector, subcarrier factor vector, transmit factor vector, and receive factor vector is represented by a regularly changing phase.

[0383] In one possible implementation, the apparatus 2000 further includes a transmitting module for transmitting a CSI report to a wireless communication system.

[0384] In one possible implementation, the device 2000 further includes a second receiving module for receiving second information indicating a preset quantity from a wireless communication system.

[0385] The wireless communication device 2000 can be applied to the receiver described in the above-described possible method implementations. Those skilled in the art should understand that, in conjunction with the descriptions of the wireless communication methods in these possible implementations of this disclosure, the descriptions of the modules described above in these possible implementations can be understood. The technical effects achieved by the wireless communication device 2000 are similar to those achieved by the above-described possible method implementations, and will not be repeated here.

[0386] Figure 21 This is a schematic diagram of the structure of a wireless communication device 2100 provided in one or more exemplary embodiments of the present disclosure.

[0387] like Figure 21 As shown, the wireless communication device 2100 may include:

[0388] The receiving module 2110 is configured to receive a CSI report from the receiver, wherein the CSI report is generated by the receiver based on a first estimate of the CSI, wherein the first estimate is used to represent the canonical decomposition of the CSI and includes a preset number of factor vectors, at least one of the preset number of factor vectors being split by sub-dimensions.

[0389] Module 2120 is used to determine CSI based on the CSI report.

[0390] In one possible implementation, the wireless communication device 2100 further includes:

[0391] The first transmitting module is used to transmit first information indicating a second preset configuration to the receiver, wherein the second preset configuration indicates whether a preset structure is applied to the first factor vector among a preset number of factor vectors.

[0392] In one possible implementation, the wireless communication device 2100 further includes:

[0393] The second transmitting module is used to send a second message indicating a preset quantity to the receiver.

[0394] The wireless communication device 2100 can be applied to the transmitter described in the above-described possible method implementations. Those skilled in the art should understand that, in conjunction with the descriptions of the communication methods in these possible implementations of this disclosure, the descriptions of the modules described above in these possible implementations can be understood. The technical effects achieved by the wireless communication device 2100 are similar to those achieved by the above-described possible method implementations, and will not be repeated here.

[0395] One possible implementation of this disclosure provides a terminal device including processing circuitry for performing any of the aforementioned wireless communication methods on the receiving side, which will not be described in detail here.

[0396] One possible implementation of this disclosure provides a network device including processing circuitry for performing any wireless communication method on the transmitting side, which will not be described in detail here.

[0397] One possible implementation of this disclosure provides a wireless communication system, comprising: a terminal device for performing any of the aforementioned wireless communication methods on the receiving side, or a wireless communication apparatus for performing any of the aforementioned wireless communication methods on the receiving side; a network device for performing any of the aforementioned wireless communication methods on the transmitting side, or a wireless communication apparatus for performing any of the aforementioned wireless communication methods on the transmitting side.

[0398] One possible implementation of this disclosure provides a chip including an input / output (I / O) interface and a processor, wherein the processor is configured to invoke and execute computer execution instructions stored in memory to enable a device equipped with the chip to perform any wireless communication method on the receiving side or any wireless communication method on the transmitting side.

[0399] One possible implementation of this disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, cause the processor to perform any wireless communication method on the receiving side or any wireless communication method on the transmitting side.

[0400] One possible implementation of this disclosure provides a computer program product including computer-executable instructions that, when executed by a processor, cause the processor to perform any wireless communication method on the receiving side or any wireless communication method on the transmitting side.

[0401] Although this disclosure describes methods and processes by way of steps performed in a certain order, one or more steps in the methods and processes may be omitted or modified as appropriate. Where appropriate, one or more steps may be performed in an order other than that described.

[0402] Please note that the expression "at least one of A or B" used in this article is interchangeable with the expression "A and / or B". This expression refers to a list in which you can choose either A or B, or A and B. Similarly, the expression "at least one of A, B, or C" used in this article is interchangeable with "A and / or B and / or C" or "A, B, and / or C". This refers to a list in which you can choose: A or B or C, or A and B, or A and C, or B and C, or all of A, B, and C. The same principle applies to longer lists with the same format.

[0403] Although this disclosure describes at least part of the methodological aspects, those skilled in the art will understand that this disclosure also relates to various components for performing at least some aspects and features of the method, whether by hardware components, software, or any combination thereof. Accordingly, the technical solutions of this disclosure can be embodied in the form of a software product. Suitable software products can be stored in pre-recorded storage devices or other similar non-volatile or non-transitory computer-readable media, including DVDs, CD-ROMs, USB flash drives, removable hard drives, or other storage media. The software product includes instructions tangibly stored thereon that cause a processing device (e.g., a personal computer, server, or network device) to perform examples of the methods disclosed herein. Machine-executable instructions can be in the form of sequences of code, configuration information, or other data that, when executed, cause a machine (e.g., a processor or other processing device) to perform the steps in the methods according to the examples of this disclosure.

[0404] This disclosure may be implemented in other specific forms without departing from the subject matter of the claims. The exemplary embodiments described are merely illustrative in all respects and not restrictive. Features selected from one or more of the foregoing embodiments may be combined to create alternative embodiments not explicitly described, and features suitable for such combinations will be understood within the scope of this disclosure.

[0405] All values ​​and sub-ranges within the scope of the disclosure are also disclosed. Furthermore, although the systems, devices, and processes disclosed and illustrated herein may include a specific number of elements / components, modifications may be made to include more or fewer of these elements / components. For example, while any element / component disclosed may be referenced as a single quantity, embodiments disclosed herein may be modified to include multiple such elements / components. The subject matter described herein is intended to cover and encompass all appropriate technical changes.

[0406] Although embodiments have been described above with reference to the accompanying drawings, those skilled in the art will understand that variations and modifications can be made without departing from the scope defined by the appended claims.

Claims

1. A wireless communication method, characterized in that, include: The receiver acquires Channel State Information (CSI). The receiver determines a first estimate of the CSI, wherein the first estimate represents a canonical decomposition of the CSI and includes a predetermined number of factor vectors, wherein at least one of the predetermined number of factor vectors is split into sub-dimensions; and The receiver generates a CSI report based on the first estimate of the CSI.

2. The method according to claim 1, characterized in that, The receiver determines the first estimate of the CSI by: The receiver determines a sub-dimension representation of each factor vector in the at least one factor vector; and The receiver obtains the first estimate of the CSI based on the sub-dimension representation of each of the at least one factor vector.

3. The method according to claim 2, characterized in that, The receiver determines the sub-dimension representation of each factor vector in the at least one factor vector by including: The receiver determines the size of at least one sub-dimension parameter of each factor vector in the at least one factor vector according to a first preset configuration corresponding to the CSI; and The receiver represents each of the at least one factor vectors by the size of the at least one sub-dimension parameter of each factor vector.

4. The method according to claim 3, characterized in that, For each of the at least one factor vector: The at least one sub-dimension parameter size includes a first sub-dimension parameter size and a second sub-dimension parameter size, wherein the first sub-dimension parameter size includes at least one factor indicating the size of the sub-dimension, and the second sub-dimension parameter size represents the number of sub-dimensions used to represent the factor vector; The sub-dimension representation of the factor vector is a Kronecker product of the sub-split elements, wherein each sub-split element is represented exponentially based on the phase corresponding to the factor vector, the size of the first sub-dimension parameter of the factor vector, and the size of the second sub-dimension parameter of the factor vector.

5. The method according to claim 3 or 4, characterized in that, The first preset configuration indicates the number of sub-dimensions into which each factor vector in the at least one factor vector is split and the size of each sub-dimension in the sub-dimension.

6. The method according to any one of claims 2 to 5, characterized in that, The receiver obtains the first estimate of the CSI based on the sub-dimension representation of each of the at least one factor vector, including: The receiver takes the sub-dimension representation of each factor vector in the at least one factor vector as input to a first preset algorithm to obtain the first estimated value of the CSI.

7. The method according to claim 6, characterized in that, The receiver generates a CSI report based on the first estimate of the CSI, including: The receiver generates the CSI report based on a second preset configuration and the first estimate of the CSI, wherein the second preset configuration indicates whether a preset structure is applied to the first factor vector among the preset number of factor vectors.

8. The method according to claim 7, characterized in that, The second preset configuration indicates that the preset structure is applied to the first factor vector among the preset number of factor vectors; The receiver generates the CSI report by including: The receiver performs factor approximation on the first estimated value according to the second preset algorithm to derive the representation parameters of the first factor vector; as well as The receiver generates the CSI report based on the characterization parameters of the first factor vector.

9. The method according to claim 7, characterized in that, The second preset configuration indicates that the preset structure is not applied to the first factor vector among the preset number of factor vectors; The receiver generates the CSI report by including: The receiver generates the CSI report based on the first estimate.

10. The method according to any one of claims 7 to 9, characterized in that, The preset structure is a parameter structure.

11. The method according to claim 10, characterized in that, The parameter structure includes at least one of a guiding structure, a secondary structure, or a tertiary structure.

12. The method according to any one of claims 7 to 11, characterized in that, Also includes: The receiver receives first information from the wireless communication system that indicates the second preset configuration.

13. The method according to any one of claims 7 to 12, characterized in that, The receiver also includes the following in generating the CSI report: The receiver performs an extrapolation operation on the first estimate to obtain the extrapolated first estimate for the next time unit; and The receiver generates the CSI report based on the first estimate extrapolated from the CSI.

14. The method according to any one of claims 6 to 13, characterized in that, Before the receiver takes the sub-dimension representation of each factor vector in the at least one factor vector as input to a first preset algorithm to obtain the first estimate of the CSI, the method further includes: The receiver obtains the initial sub-dimension representation of each factor vector in the at least one factor vector according to the CSI; The receiver takes the sub-dimension representation of each factor vector in the at least one factor vector as input to a first preset algorithm to obtain the first estimate of the CSI, including: The receiver takes the initial sub-dimension representation as the input to the first preset algorithm to obtain the first estimated value of the CSI.

15. The method according to claim 14, characterized in that, The receiver obtains the initial sub-dimension representation of each factor vector in the at least one factor vector according to the CSI, including: The receiver obtains the initial sub-dimension representation of each factor vector in the at least one factor vector and the rank of the canonical decomposition of the CSI based on the discrete Fourier transform (DFT) operation of the CSI. The receiver takes the sub-dimension representation of each factor vector in the at least one factor vector as input to a first preset algorithm to obtain the first estimate of the CSI, including: The receiver takes the initial sub-dimension representation and the rank of the canonical decomposition as inputs to the first preset algorithm to obtain the first estimated value of the CSI.

16. The method according to claim 15, characterized in that, The receiver obtains the initial sub-dimension representation of each factor vector in the at least one factor vector and the rank of the canonical decomposition of the CSI based on the DFT operation of the CSI, including: The receiver determines the extended channel matrix based on the CSI and preset oversampling parameters; The receiver performs a Discrete Fourier Transform (DFT) operation on the expanded channel matrix to obtain the power spectrum of the CSI; and The receiver determines the initial sub-dimension representation of each factor vector in the at least one factor vector and the rank of the canonical decomposition of the CSI based on the power spectrum of the CSI.

17. The method according to claim 14, characterized in that, The receiver obtains the initial sub-dimension representation of each factor vector in the at least one factor vector according to the CSI, including: The receiver uses the extrapolated first estimate as the initial sub-dimension representation of each of the at least one factor vector, wherein the extrapolated first estimate is obtained by extrapolating the first estimate determined in the previous time unit.

18. The method according to any one of claims 1 to 17, characterized in that, The CSI includes first channel information in the time domain, second channel information in the frequency domain, and third channel information in the spatial domain; The preset number of factor vectors includes a time factor vector for characterizing the first channel information, a subcarrier factor vector for characterizing the second channel information, and a transmit factor vector and a receive factor vector for characterizing the third channel information. Wherein, at least one of the time factor vector, the subcarrier factor vector, the transmit factor vector, and the receive factor vector is represented by a regularly changing phase.

19. The method according to any one of claims 1 to 18, characterized in that, Also includes: The receiver sends the CSI report to the wireless communication system.

20. The method according to any one of claims 1 to 19, characterized in that, Also includes: The receiver receives second information from the wireless communication system indicating the preset quantity.

21. A wireless communication method, characterized in that, include: The transmitter receives a Channel State Information (CSI) report from the receiver, wherein the CSI report is generated by the receiver based on a first estimate of the CSI, wherein the first estimate represents a canonical decomposition of the CSI and includes a predetermined number of factor vectors, at least one of the predetermined number of factor vectors being subdivided into sub-dimensions; and The transmitter determines the CSI based on the CSI report.

22. The method according to claim 21, characterized in that, Also includes: The transmitter sends first information to the receiver indicating a second preset configuration, wherein the second preset configuration indicates whether a preset structure is applied to the first factor vector among the preset number of factor vectors.

23. The method according to claim 21 or 22, characterized in that, Also includes: The transmitter sends a second message to the receiver indicating the preset quantity.

24. A wireless communication device, characterized in that, include: The acquisition module is used to acquire Channel State Information (CSI). A determining module is configured to determine a first estimate of the CSI, wherein the first estimate represents a canonical decomposition of the CSI and includes a preset number of factor vectors, wherein at least one factor vector in the preset number of factor vectors is split into sub-dimensions; and A generation module is used to generate a CSI report based on the first estimate of the CSI.

25. The apparatus according to claim 24, characterized in that, The determining module includes: A determining unit is configured to determine the sub-dimension representation of each factor vector in the at least one factor vector; and The first acquisition unit is configured to acquire the first estimate of the CSI based on the sub-dimension representation of each of the at least one factor vector.

26. The apparatus according to claim 25, characterized in that, The determining unit is used for: Based on a first preset configuration corresponding to the CSI, determine the size of at least one sub-dimension parameter of each factor vector in the at least one factor vector; and Each factor vector in the at least one factor vector is represented by the size of the at least one sub-dimension parameter of each factor vector.

27. The apparatus according to claim 26, characterized in that, For each of the at least one factor vector: The at least one sub-dimension parameter size includes a first sub-dimension parameter size and a second sub-dimension parameter size, wherein the first sub-dimension parameter size includes at least one factor indicating the size of the sub-dimension, and the second sub-dimension parameter size represents the number of sub-dimensions used to represent the factor vector; The sub-dimension representation of the factor vector is a Kronecker product of the sub-split elements, wherein each sub-split element is represented exponentially based on the phase corresponding to the factor vector, the size of the first sub-dimension parameter of the factor vector, and the size of the second sub-dimension parameter of the factor vector.

28. The apparatus according to claim 26 or 27, characterized in that, The first preset configuration indicates the number of sub-dimensions into which each factor vector in the at least one factor vector is split and the size of each sub-dimension in the sub-dimension.

29. The apparatus according to any one of claims 25 to 28, characterized in that, The first acquisition unit is used for: The sub-dimension representation of each factor vector in the at least one factor vector is used as input to a first preset algorithm to obtain the first estimated value of the CSI.

30. The apparatus according to claim 29, characterized in that, The generation module is used for: The CSI report is generated based on a second preset configuration and the first estimate of the CSI, wherein the second preset configuration indicates whether a preset structure is applied to the first factor vector among the preset number of factor vectors.

31. The apparatus according to claim 30, characterized in that, The second preset configuration indicates that the preset structure is applied to the first factor vector among the preset number of factor vectors; The generation module is used for: The first estimated value is approximated by factors according to a second preset algorithm to derive the representation parameters of the first factor vector; and The CSI report is generated based on the characterization parameters of the first factor vector.

32. The apparatus according to claim 30, characterized in that, The second preset configuration indicates that the preset structure is not applied to the first factor vector among the preset number of factor vectors; The generation module is used to generate the CSI report based on the first estimate.

33. The apparatus according to any one of claims 30 to 32, characterized in that, The preset structure is a parameter structure.

34. The apparatus according to claim 10, characterized in that, The parameter structure includes at least one of a guiding structure, a secondary structure, or a tertiary structure.

35. The apparatus according to any one of claims 30 to 34, characterized in that, Also includes: The first receiving module is used to receive first information indicating the second preset configuration from the wireless communication system.

36. The apparatus according to any one of claims 30 to 35, characterized in that, The generation module is also used for: Extrapolate the first estimate to obtain the extrapolated first estimate for the next time unit; and The CSI report is generated based on the first estimate extrapolated from the CSI.

37. The apparatus according to any one of claims 29 to 36, characterized in that, The determining module further includes a second acquisition unit; The second acquisition unit is configured to: acquire an initial sub-dimension representation of each factor vector in the at least one factor vector according to the CSI before the first acquisition unit acquires the first estimated value of the CSI by using the sub-dimension representation of each factor vector in the at least one factor vector as input to a first preset algorithm; and The first acquisition unit is used to: take the initial sub-dimension representation as the input of the first preset algorithm to obtain the first estimated value of the CSI.

38. The apparatus according to claim 37, characterized in that, The second acquisition unit is used to acquire the initial sub-dimension representation of each factor vector in the at least one factor vector and the rank of the canonical decomposition of the CSI according to the discrete Fourier transform (DFT) operation of the CSI; as well as The first acquisition unit is used to take the initial sub-dimension representation and the rank of the canonical decomposition as the input of the first preset algorithm to obtain the first estimated value of the CSI.

39. The apparatus according to claim 38, characterized in that, The first acquisition unit is used for: The expanded channel matrix is ​​determined based on the CSI and preset oversampling parameters; Perform a Discrete Fourier Transform (DFT) operation on the expanded channel matrix to obtain the power spectrum of the CSI; as well as Based on the power spectrum of the CSI, determine the initial sub-dimension representation of each factor vector in the at least one factor vector and the rank of the canonical decomposition of the CSI.

40. The apparatus according to claim 37, characterized in that, The second acquisition unit is used to use the extrapolated first estimate as the initial sub-dimension representation of each factor vector in the at least one factor vector, wherein the extrapolated first estimate is obtained by extrapolating the first estimate determined in the previous time unit.

41. The apparatus according to any one of claims 24 to 40, characterized in that, The CSI includes first channel information in the time domain, second channel information in the frequency domain, and third channel information in the spatial domain; The preset number of factor vectors includes a time factor vector for characterizing the first channel information, a subcarrier factor vector for characterizing the second channel information, and a transmit factor vector and a receive factor vector for characterizing the third channel information. Wherein, at least one of the time factor vector, the subcarrier factor vector, the transmit factor vector, and the receive factor vector is represented by a regularly changing phase.

42. The apparatus according to any one of claims 24 to 41, characterized in that, Also includes: The transmitting module is used to send the CSI report to the wireless communication system.

43. The apparatus according to any one of claims 24 to 42, characterized in that, Also includes: The second receiving module is used to receive second information indicating the preset quantity from the wireless communication system.

44. A wireless communication device, characterized in that, include: A receiving module is configured to receive a Channel State Information (CSI) report from a receiver, wherein the CSI report is generated by the receiver based on a first estimate of the CSI, wherein the first estimate represents a canonical decomposition of the CSI and includes a preset number of factor vectors, at least one of the preset number of factor vectors being subdivided by a sub-dimension; and A determination module is used to determine the CSI based on the CSI report.

45. The apparatus according to claim 44, characterized in that, Also includes: A first transmitting module is configured to transmit first information indicating a second preset configuration to the receiver, wherein the second preset configuration indicates whether a preset structure is applied to the first factor vector among the preset number of factor vectors.

46. ​​The apparatus according to claim 44 or 45, characterized in that, Also includes: The second transmitting module is used to send second information indicating the preset quantity to the receiver.

47. A terminal device, characterized in that, Includes processing circuitry for performing the method according to any one of claims 1 to 20.

48. A network device, characterized in that, Includes processing circuitry for performing the method according to any one of claims 21 to 23.

49. A communication system, characterized in that, This includes the terminal device according to claim 47 and the network device according to claim 48.

50. A chip, characterized in that, It includes an input / output I / O interface and a processor, wherein the processor is configured to invoke and execute computer execution instructions stored in memory to enable a device equipped with the chip to perform the method according to any one of claims 1 to 20 or the method according to any one of claims 21 to 23.

51. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, cause the processor to perform any one of claims 1 to 20 or to perform any one of claims 21 to 23.

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