Communication method and apparatus

Abstract

A communication method and apparatus. Taking the method applied to a terminal as an example, in the method, the terminal receives at least one downlink reference signal, and determines a first vector on the basis of a channel matrix respectively corresponding to the at least one downlink reference signal, the first vector being used by a second communication apparatus to determine a statistical covariance matrix, and the statistical covariance matrix being a covariance matrix obtained on the basis of the channel matrix respectively corresponding to the at least one downlink reference signal, wherein the dimension of the first vector is KN*1, the dimension of the statistical covariance matrix is N*N, K is an oversampling factor, N and K are positive integers, and K<N; and the terminal sends first information, the first information indicating the first vector. According to the method, the terminal feeds back the first vector to an access network device, thereby assisting the access network device in determining the statistical covariance matrix; and the signaling overhead for feeding back the first vector is less than the signaling overhead for directly feeding back the statistical covariance matrix.

Description

A communication method and apparatus

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese Patent Application No. 202411998870.2, filed with the State Intellectual Property Office of the People's Republic of China on December 31, 2024, entitled "A Communication Method and Apparatus", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of communications, and in particular to a communication method and apparatus. Background Technology

[0004] In time division duplex (TDD) systems, the uplink and downlink channels are reciprocal because they use the same frequency band. Base stations can utilize this reciprocity to perform channel estimation using uplink reference signals (e.g., sounding reference signals, SRS) to obtain channel estimation information.

[0005] Meanwhile, future communication systems are evolving towards higher frequency bands and larger bandwidths. However, at higher frequency bands, channel fading is severe, significantly reducing the signal-to-noise ratio (SNR) of the signal-to-noise ratio (SNR) of the signal-to-noise ratio (SNR) of the signal-to-noise ratio (SNR), thus affecting the SNR-based channel estimation performance. Consequently, SNR-based channel estimation suffers from severe performance degradation. Since the downlink SNR is higher than the uplink SNR, the terminal can measure the downlink reference signal and calculate the channel covariance matrix, feeding the covariance matrix back to the base station to assist in SNR channel estimation and reduce SNR interference noise. However, when there are many antenna ports and a large bandwidth, the covariance matrix has a large dimension, resulting in significant feedback overhead. Summary of the Invention

[0006] This application provides a communication method and apparatus to solve the problem of large feedback overhead of the covariance matrix.

[0007] In a first aspect, embodiments of this application provide a communication method that can be applied to a first communication device, such as a terminal or a communication module / processing module applicable to the terminal, or a circuit or chip responsible for communication functions (such as a modem chip, also known as a baseband chip, or a system-on-chip (SoC) chip containing a modem core or a system-in-package (SIP) chip), or a circuit or chip responsible for processing functions (such as a graphics processing unit (GPU)). Taking the application of this method to a terminal as an example, in this method, the terminal receives at least one downlink reference signal, determines a first vector based on the channel matrices corresponding to the at least one downlink reference signal, and the first vector is used by a second communication device to determine a statistical covariance matrix. The statistical covariance matrix is ​​a covariance matrix obtained based on the channel matrices corresponding to the at least one downlink reference signal, wherein the dimension of the first vector is KN*1, the dimension of the statistical covariance matrix is ​​N*N, K is the oversampling factor, N and K are positive integers, and K < N; the terminal sends first information, the first information indicating the first vector.

[0008] Using the above method, the terminal feeds back a first vector to the access network device, which assists the access network device in determining the statistical covariance matrix. Furthermore, the signaling overhead of feeding back the first vector is smaller than that of directly feeding back the statistical covariance matrix. In addition, because the uplink reference signal has a low signal-to-noise ratio (SNR) (e.g., low transmission power), the accuracy (or reliability) of the channel state information determined by the access network device based solely on the uplink reference signal is not high. However, the downlink reference signal has a higher SNR than the uplink reference signal (e.g., higher transmission power), and the statistical covariance matrix is ​​obtained based on the downlink reference signal. By combining the statistical covariance matrix with the uplink reference signal to jointly determine the channel state information, the access network device can improve the accuracy (or reliability) of the channel state information, thereby effectively improving communication quality.

[0009] In one possible design, the first vector is the result of the statistical covariance after undergoing a Discrete Fourier Transform (DFT).

[0010] In one possible design, the first vector and the matrix of the first type are used by the second communication device to determine the statistical covariance matrix; wherein the matrix of the first type is determined based on K DFT matrices, the first DFT matrix and the second DFT matrix are any two DFT matrices among the K DFT matrices, wherein the product of the conjugate transpose of the first DFT matrix and the second DFT matrix is ​​not an identity matrix, the product of any one of the K DFT matrices and its own conjugate transpose is an identity matrix, the dimension of each DFT matrix is ​​N*N, and the dimension of the matrix of the first type is N*KN.

[0011] In one possible design, the statistical covariance matrix satisfies: Where s is the first vector, Diag(s) represents a diagonal matrix with vector s as its diagonal elements, D is a matrix of the first type, and R hh Let be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

[0012] In one possible design, the statistical covariance matrix satisfies: R hh =D*Diag(s)*D H Where s is the first vector, Diag(s) represents a diagonal matrix with vector s as its diagonal elements, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

[0013] In one possible design, the statistical covariance matrix satisfies: Among them, R hh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * It is the conjugate matrix of the first type of matrix. ⊙ represents the pseudo-inverse operation, and ⊙ represents the Khatri rao product operation.

[0014] In one possible design, the statistical covariance matrix satisfies: vec(R) hh )=(D * ⊙D)s; where R hh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension Rhh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

[0015] In one possible design, the first vector satisfies: s = diag(D H R hh D); where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

[0016] In one possible design, the first vector satisfies: Where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hh Let be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

[0017] With the above design, the first vector includes the diagonal elements of the first diagonal matrix. It can be seen that by feeding back the first vector (that is, the diagonal elements of the first diagonal matrix), the feedback overhead can be greatly reduced and the resource utilization efficiency can be improved.

[0018] In one possible design, the first vector satisfies: s = (D * ⊙D) H vec(R hh ); where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

[0019] In one possible design, the first vector satisfies: Where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. *Let be the conjugate matrix of the first type of matrix, and ⊙ denote the Khatri rao product operation. This indicates a pseudo-inverse operation.

[0020] By adopting the above design, since the terminal performs a pseudo-inverse operation during the process of determining the first vector, the number of zero elements in the first vector can be increased, thereby further reducing the feedback overhead.

[0021] In one possible design, before sending the first information, the terminal sends a second information, which instructs the second communication device to perform a pseudo-inverse operation when determining the statistical covariance matrix.

[0022] In one possible design, before sending the first information, the terminal sends a third information indicating that the first communication device supports a first capability, which includes performing pseudo-inverse operations on a matrix.

[0023] The above design can be used to notify access network devices whether they need to perform pseudo-inverse operations, or whether the terminal needs to perform pseudo-inverse operations.

[0024] In one possible design, the terminal sends an uplink reference signal.

[0025] Secondly, this application provides a communication method that can be applied to a second communication device, such as an access network device on the network side, or a module (e.g., circuit, chip, or chip system) applicable to the access network device, or a logic node, logic module, or software capable of implementing all or part of the functions of the access network device. Taking the application of this method to an access network device as an example, in this method, the access network device sends at least one downlink reference signal and receives first information. The first information is used to indicate a first vector. The first vector is used by the second communication device to determine a statistical covariance matrix. The statistical covariance matrix is ​​a covariance matrix obtained based on the channel matrices corresponding to the at least one downlink reference signal, wherein the dimension of the first vector is KN*1, the dimension of the statistical covariance matrix is ​​N*N, K is the oversampling factor, N and K are positive integers, and K < N. The access network device determines the statistical covariance matrix based on the first vector.

[0026] Using the above method, the terminal feeds back a first vector to the access network device, which assists the access network device in determining the statistical covariance matrix. Furthermore, the signaling overhead of feeding back the first vector is smaller than that of directly feeding back the statistical covariance matrix. In addition, because the uplink reference signal has a low signal-to-noise ratio (SNR) (e.g., low transmission power), the accuracy (or reliability) of the channel state information determined by the access network device based solely on the uplink reference signal is not high. However, the downlink reference signal has a higher SNR than the uplink reference signal (e.g., higher transmission power), and the statistical covariance matrix is ​​obtained based on the downlink reference signal. By combining the statistical covariance matrix with the uplink reference signal to jointly determine the channel state information, the access network device can improve the accuracy (or reliability) of the channel state information, thereby effectively improving communication quality.

[0027] In one possible design, the first vector is the result of the statistical covariance after DFT.

[0028] In one possible design, the first vector and the matrix of the first type are used by the second communication device to determine the statistical covariance matrix; wherein the matrix of the first type is determined based on K DFT matrices, the first DFT matrix and the second DFT matrix are any two DFT matrices among the K DFT matrices, wherein the product of the conjugate transpose of the first DFT matrix and the second DFT matrix is ​​not an identity matrix, the product of any one of the K DFT matrices and its own conjugate transpose is an identity matrix, the dimension of each DFT matrix is ​​N*N, and the dimension of the matrix of the first type is N*KN.

[0029] In one possible design, the statistical covariance matrix satisfies: Where s is the first vector, Diag(s) represents a diagonal matrix with vector s as its diagonal elements, D is a matrix of the first type, and R hh Let be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

[0030] In one possible design, the statistical covariance matrix satisfies: R hh =D*Diag(s)*D H Where s is the first vector, Diag(s) represents a diagonal matrix with vector s as its diagonal elements, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

[0031] In one possible design, the statistical covariance matrix satisfies: Among them, R hh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * It is the conjugate matrix of the first type of matrix. ⊙ represents the pseudo-inverse operation, and ⊙ represents the Khatri rao product operation.

[0032] In one possible design, the statistical covariance matrix satisfies: vec(R) hh )=(D *⊙ D)s; where R hh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

[0033] In one possible design, the first vector satisfies: s = diag(D H R hh D); where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

[0034] In one possible design, the first vector satisfies: Where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hh Let be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

[0035] In one possible design, the first vector satisfies: s = (D * ⊙D) H vec(R hh ); where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

[0036] In one possible design, the first vector satisfies: Where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denote the Khatri rao product operation. This indicates a pseudo-inverse operation.

[0037] In one possible design, before receiving the first information, the access network device receives second information, which instructs the second communication device to perform a pseudo-inverse operation when determining the statistical covariance matrix.

[0038] In one possible design, before receiving the first information, the access network device receives third information, which indicates that the first communication device supports a first capability, the first capability including performing pseudo-inverse operations on a matrix.

[0039] In one possible design, the access network device receives an uplink reference signal and determines channel state information based on the uplink reference signal and the statistical covariance matrix.

[0040] Using the above method, access network devices can determine channel state information based on a defined statistical covariance matrix and the received uplink reference signal.

[0041] Thirdly, this application provides a communication device, which includes a transceiver unit and a processing unit; the transceiver unit is configured to receive at least one downlink reference signal; the processing unit is configured to determine a first vector based on the channel matrices corresponding to the at least one downlink reference signal, wherein the first vector is used by a second communication device to determine a statistical covariance matrix, the statistical covariance matrix being a covariance matrix obtained based on the channel matrices corresponding to the at least one downlink reference signal, wherein the dimension of the first vector is KN*1, the dimension of the statistical covariance matrix is ​​N*N, K is an oversampling factor, N and K are positive integers, and K < N; the transceiver unit is configured to transmit first information, the first information indicating the first vector.

[0042] In one possible design, the first vector is the result of the statistical covariance after undergoing a Discrete Fourier Transform (DFT).

[0043] In one possible design, the first vector and the matrix of the first type are used by the second communication device to determine the statistical covariance matrix; wherein the matrix of the first type is determined based on K DFT matrices, the first DFT matrix and the second DFT matrix are any two DFT matrices among the K DFT matrices, wherein the product of the conjugate transpose of the first DFT matrix and the second DFT matrix is ​​not an identity matrix, the product of any one of the K DFT matrices and its own conjugate transpose is an identity matrix, the dimension of each DFT matrix is ​​N*N, and the dimension of the matrix of the first type is N*KN.

[0044] In one possible design, the statistical covariance matrix satisfies: Where s is the first vector, Diag(s) represents a diagonal matrix with vector s as its diagonal elements, D is a matrix of the first type, and R hh Let be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

[0045] In one possible design, the statistical covariance matrix satisfies: R hh =D*Diag(s)*D H Where s is the first vector, Diag(s) represents a diagonal matrix with vector s as its diagonal elements, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

[0046] In one possible design, the statistical covariance matrix satisfies: Among them, R hh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * It is the conjugate matrix of the first type of matrix. ⊙ represents the pseudo-inverse operation, and ⊙ represents the Khatri rao product operation.

[0047] In one possible design, the statistical covariance matrix satisfies: vec(R) hh )=(D * ⊙D)s; where Rhh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

[0048] In one possible design, the first vector satisfies: s = diag(D H R hh D); where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

[0049] In one possible design, the first vector satisfies: Where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hh Let be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

[0050] In one possible design, the first vector satisfies: s = (D * ⊙D) H vec(R hh ); where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

[0051] In one possible design, the first vector satisfies: Where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denote the Khatri rao product operation. This indicates a pseudo-inverse operation.

[0052] In one possible design, the transceiver unit is also configured to send a second message before sending the first message, the second message instructing the second communication device to perform a pseudo-inverse operation when determining the statistical covariance matrix.

[0053] In one possible design, the transceiver unit is further configured to send a third message before sending the first message, the third message indicating that the first communication device supports a first capability, the first capability including performing a pseudo-inverse operation on a matrix.

[0054] In one possible design, the transceiver unit is also used to transmit an uplink reference signal.

[0055] Fourthly, this application provides a communication device, which includes a transceiver unit and a processing unit; the transceiver unit is configured to transmit at least one downlink reference signal; receive first information, the first information being used to indicate a first vector, the first vector being used by a second communication device to determine a statistical covariance matrix, the statistical covariance matrix being a covariance matrix obtained based on the channel matrices corresponding to the at least one downlink reference signal respectively, wherein the dimension of the first vector is KN*1, the dimension of the statistical covariance matrix is ​​N*N, K is an oversampling factor, N and K are positive integers, and K < N; the processing unit is configured to determine the statistical covariance matrix based on the first vector.

[0056] In one possible design, the first vector is the result of the statistical covariance after DFT.

[0057] In one possible design, the first vector and the matrix of the first type are used by the second communication device to determine the statistical covariance matrix; wherein the matrix of the first type is determined based on K DFT matrices, the first DFT matrix and the second DFT matrix are any two DFT matrices among the K DFT matrices, wherein the product of the conjugate transpose of the first DFT matrix and the second DFT matrix is ​​not an identity matrix, the product of any one of the K DFT matrices and its own conjugate transpose is an identity matrix, the dimension of each DFT matrix is ​​N*N, and the dimension of the matrix of the first type is N*KN.

[0058] In one possible design, the statistical covariance matrix satisfies: Where s is the first vector, Diag(s) represents a diagonal matrix with vector s as its diagonal elements, D is a matrix of the first type, and R hh Let be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

[0059] In one possible design, the statistical covariance matrix satisfies: R hh =D*Diag(s)*D H Where s is the first vector, Diag(s) represents a diagonal matrix with vector s as its diagonal elements, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

[0060] In one possible design, the statistical covariance matrix satisfies: Among them, R hh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * It is the conjugate matrix of the first type of matrix. ⊙ represents the pseudo-inverse operation, and ⊙ represents the Khatri rao product operation.

[0061] In one possible design, the statistical covariance matrix satisfies: vec(R) hh )=(D * ⊙D)s; where R hh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

[0062] In one possible design, the first vector satisfies: s = diag(D H R hh D); where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

[0063] In one possible design, the first vector satisfies: Where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hhLet be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

[0064] In one possible design, the first vector satisfies: s = (D * ⊙D) H vec(R hh ); where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

[0065] In one possible design, the first vector satisfies: Where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denote the Khatri rao product operation. This indicates a pseudo-inverse operation.

[0066] In one possible design, the transceiver unit is also configured to receive second information before receiving the first information, the second information instructing the second communication device to perform a pseudo-inverse operation when determining the statistical covariance matrix.

[0067] In one possible design, the transceiver unit is further configured to receive third information before receiving the first information, the third information indicating that the first communication device supports a first capability, the first capability including performing pseudo-inverse operations on a matrix.

[0068] In one possible design, the transceiver unit is further configured to receive an uplink reference signal; the processing unit is further configured to determine channel state information based on the uplink reference signal and the statistical covariance matrix.

[0069] For the technical effects that can be achieved by the third and fourth aspects mentioned above, please refer to the description of the technical effects that can be achieved by the corresponding design schemes in the first or second aspects mentioned above. This application will not repeat them here.

[0070] Fifthly, this application provides a communication device that has the functions of implementing the first or second aspect described above. For example, the communication device includes modules, units, or means corresponding to the operations involved in the first or second aspect described above. These modules, units, or means can be implemented by software, hardware, or a combination of software and hardware.

[0071] Sixthly, this application provides a communication device including an interface circuit and one or more processors. The one or more processors are coupled to a memory. The memory stores part or all of the necessary computer program or instructions for implementing the functions described in the first or second aspect. The one or more processors are executable to the computer program or instructions, which, when executed, cause the communication device to implement the methods in any possible design or implementation of the first or second aspect. The interface circuit is used to implement the communication functions within the communication device and / or the communication functions between the communication device and other devices or components.

[0072] In one possible design, the processor is used to communicate with other devices or components through the interface circuit.

[0073] In one possible design, the communication device may also include the memory.

[0074] In a seventh aspect, this application provides a communication system comprising an access network device and a terminal, wherein the terminal is configured to perform the method in any possible design of the first aspect described above, and the access network device is configured to perform the method in any possible design of the second aspect described above.

[0075] Eighthly, this application provides a computer-readable storage medium storing computer-readable instructions that, when read and executed by a computer, cause the computer to perform any of the possible designs in the first to second aspects described above.

[0076] Ninthly, this application provides a computer program product that, when read and executed by a computer, causes the computer to perform any of the possible designs in the first to second aspects described above. Attached Figure Description

[0077] Figure 1 shows a possible, non-limiting system schematic diagram;

[0078] Figure 2 shows a schematic diagram of the structure of the various modules included inside the access network equipment and terminal;

[0079] Figure 3 shows a schematic diagram of the baseband hardware implementation in the access network equipment;

[0080] Figure 4 shows a flowchart of how access network devices obtain channel state information based on SRS;

[0081] Figure 5 shows an overview flowchart of a communication method;

[0082] Figure 6 shows a schematic diagram of the structure of a communication device;

[0083] Figure 7 shows a schematic diagram of another communication device. Detailed Implementation

[0084] The embodiments of this application can be applied to various communication systems, such as: General Packet Radio Service (GPRS), Long Term Evolution (LTE) systems, LTE Frequency Division Duplex (FDD) systems, LTE TDD, Worldwide Interoperability for Microwave Access (WIMAX) communication systems, 5th Generation Mobile Communication Technology (5G) systems, or New Radio (NR), or applied to future communication systems or other similar communication systems, or Ultra Wide Band (UWB) systems, or Wireless Fidelity (WiFi) systems.

[0085] Figure 1 illustrates a possible, non-limiting system diagram. As shown in Figure 1, the communication system 1000 includes a wireless access network 100 and a core network 200. Optionally, the communication system 1000 may also include an Internet 300. The wireless access network 100 may include at least one wireless access network device (110a and 110b in Figure 1) and at least one terminal (120a-120j in Figure 1). The terminal connects wirelessly to the wireless access network device, and the wireless access network device connects wirelessly or via a wired connection to the core network. The core network device and the wireless access network device can be independent physical devices, or the functions of the core network device and the logical functions of the wireless access network device can be integrated into the same physical device, or a single physical device can integrate some of the functions of the core network device and some of the functions of the wireless access network device. Terminals can be interconnected with each other, and wireless access network devices can be interconnected via wired or wireless connections. Figure 1 is only a schematic diagram; the communication system may also include other network devices, such as wireless relay devices and wireless backhaul devices, which are not shown in Figure 1.

[0086] Radio access network equipment can be a base station, an evolved NodeB (eNodeB), a transmission reception point (TRP), a next-generation NodeB (gNB) in a 5G mobile communication system, a next-generation base station in a future mobile communication system, a base station in a future mobile communication system, or an access node in a WiFi system. Radio access network equipment can also be an open RAN (O-RAN or ORAN) or a cloud radio access network (CRAN). Radio access network equipment can also be a communication system integrating two or more of the above systems. Radio access network equipment can be a macro base station (as shown in Figure 1, 110a), a micro base station or an indoor station (as shown in Figure 1, 110b), a relay node, or a donor node, etc.

[0087] Furthermore, the wireless access network equipment can also be a module or unit that performs some of the functions of a base station. For example, it can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU), etc. In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules.

[0088] The embodiments of this application do not limit the specific technology or device form used in the wireless access network equipment. For ease of description, the wireless access network equipment will be referred to as access network equipment below. It is understood that access network equipment can be called a communication device. For example, access network equipment can be understood as a device with access network equipment functions. For example, a device with access network equipment functions can be an access network equipment; or some components in the access network equipment, such as CU, DU, etc. It can also be a device that can support the access network equipment to realize this function, such as a chip system, hardware circuit, software module, or hardware circuit plus software module. This device can be installed in the access network equipment or can be used in conjunction with the access network equipment. In the embodiments of this application, the chip system can be composed of chips or can include chips and other discrete devices.

[0089] A terminal can also be called a terminal device, user equipment (UE), mobile station, mobile terminal, etc. Terminals can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, etc. Terminals can be mobile phones, tablets, computers with wireless transceiver capabilities, wearable devices, vehicles, drones, helicopters, airplanes, ships, robots, robotic arms, smart home devices, etc.

[0090] The embodiments of this application do not limit the specific technology or device form used in the terminal. It is understood that a terminal can be referred to as a communication device. For example, a terminal can be understood as a device with terminal functions. For example, a device with terminal functions can be a terminal itself; it can also be a device capable of supporting the terminal in implementing that function, such as a chip system, hardware circuit, software module, or hardware circuit plus software module. This device can be installed in a terminal or can be used in conjunction with a terminal.

[0091] Access network devices and terminals can be fixed in location or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can be deployed on aircraft, balloons, and satellites. The embodiments of this application do not limit the application scenarios of the access network devices and terminals.

[0092] The roles of access network devices and terminals can be relative. For example, the helicopter or drone 120i in Figure 1 can be configured as a mobile access network device. For terminals 120j that access the wireless access network 100 via 120i, drone 120i is an access network device; however, for access network device 110a, 120i is a terminal, meaning that 110a and 120i communicate via a wireless air interface protocol. Alternatively, 110a and 120i can also communicate via an interface protocol between access network devices. In this case, 120i is also an access network device relative to 110a. 110a and 110b in Figure 1 can be referred to as communication devices with access network device functions, and 120a-120j in Figure 1 can be referred to as communication devices with terminal functions.

[0093] Communication between access network devices and terminals, between access network devices, and between terminals can be conducted using licensed spectrum, unlicensed spectrum, or both simultaneously. Communication can be conducted using spectrum below 6 GHz, spectrum above 6 GHz, or both simultaneously. The embodiments of this application do not limit the spectrum resources used for wireless communication.

[0094] Figure 2 shows a schematic diagram of the internal structure of the various modules included in the access network equipment and terminal involved in this application. Among them, the radio resource control (RRC) signaling interaction module is used by the access network equipment and terminal to send and receive RRC signaling. The medium access control (MAC) signaling interaction module is used by the access network equipment and terminal to send and receive MAC control element (MAC-CE) signaling. The physical layer (PHY) signaling and data interaction module is used by the access network equipment and terminal to send and receive uplink / downlink control signaling and uplink / downlink data.

[0095] Figure 3 illustrates the hardware implementation of the baseband in an access network device. The baseband can be implemented using a processing system that includes one or more processors. Processors include microprocessors (e.g., x86, ARM), microcontrollers, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), GPUs, programmable logic devices (PLDs), state machines, gating logic, discrete hardware circuits, and other suitable hardware configured for various functions. In other words, the processor used in the baseband can be used to implement the processes described below and any one or more steps within those processes.

[0096] Processing systems can be implemented using a bus architecture, typically represented by a bus. A bus can include any number of interconnect buses and bridges, depending on the specific application and overall design constraints of the processing system. A bus can couple various circuits together, including one or more processors (typically represented by a processor), memory, and computer-readable medium. A bus can also link various other circuits, such as timing sources, peripherals, voltage regulators, and power management circuits, and therefore will not be described further. A bus interface provides the interface between the bus and transceivers, as well as between the bus and the interface.

[0097] A transceiver provides a communication interface or means for communicating with various other devices via a wireless transmission medium. The transceiver may be coupled to an antenna array, and the transceiver and antenna array may be used together for communication with a corresponding network type. At least one interface (e.g., a network interface and / or a user interface) provides a communication interface or means for communication via an internal bus or via an external transmission medium.

[0098] The processor manages the bus and general processing, including executing software stored on a computer-readable medium. When executed by the processor, this software causes the processing system to perform the various functions described below for any particular device. Functions that can be implemented by the processor, memory, and computer-readable medium include: encoding, decoding, rate matching, rate dematching, scrambling, descrambling, modulation, demodulation, layer mapping, fast Fourier transform (FFT), inverse fast Fourier transform (IFFT), inverse discrete Fourier transform (IDFT), precoding, resource element (RE) mapping, channel equalization, RE demapping, digital beamforming (BF), adding a cyclic prefix (CP), removing CP, and so on.

[0099] In this application, "sending information" can be understood as one device sending information to another device, or it can also be understood as one logical module within a device sending information to another logical module. For example, "access network device sending information" can be understood as the access network device sending information to another device (such as a terminal), or it can be understood as logical module 1 in the access network device sending information to logical module 2 in the access network device.

[0100] In this application, "receiving information" can be understood as one device receiving information from another device, or it can also be understood as a logical module within a device receiving information from another logical module. For example, "access network device receiving information" can be understood as the access network device receiving information from another device (such as a terminal), or it can be understood as logical module 1 in the access network device receiving information from logical module 2 in the access network device.

[0101] In this application, phrases such as "sending information to... (e.g., a terminal)" or related illustrations in the accompanying drawings can be understood as indicating that the destination of the information is a terminal. This can include sending information directly or indirectly to a terminal. Similarly, phrases such as "receiving information from... (e.g., a terminal)," "receiving information from... (e.g., a terminal)," or "receiving information sent by (e.g., a terminal)," or related illustrations in the accompanying drawings, can be understood as indicating that the source of the information is a terminal. This can include receiving information directly or indirectly from a terminal. Information may undergo necessary processing between the source and destination, such as format changes, but the destination can understand the valid information from the source. Similar expressions in this application can be interpreted similarly and will not be elaborated further here.

[0102] It is understood that the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0103] In this embodiment, taking a matrix with dimension m×n as an example, it means that the matrix has m rows and n columns, where m and n are integers greater than or equal to 1. Furthermore, if m = 1 or n = 1, the matrix can also be called a vector. The superscript H indicates the conjugate transpose, such as A... H Represents the conjugate transpose of matrix (or vector) A. Superscript -1 To represent the inverse of a matrix, e.g., A -1 Represents the inverse of matrix (or vector) A. Superscript This represents a pseudo-inverse operation, such as This represents the pseudo-inverse of matrix (or vector) A. The superscript * indicates conjugate, such as A0. * This represents the conjugate matrix of matrix (or vector) A. For the sake of brevity, explanations of identical or similar cases will be omitted in the following text.

[0104] In this embodiment, the statistical covariance matrix is ​​the statistical average of the autocorrelation matrices of the random matrices. The autocorrelation matrix is ​​the product of a matrix and its conjugate transpose; for example, the autocorrelation matrix of matrix A is A*A. HA statistical covariance matrix is ​​generally a square matrix, meaning it has the same number of rows and columns. In this application, for ease of description, it is referred to as a statistical covariance matrix, and this name does not limit the scope of protection of this application. For example, a statistical covariance matrix can also be called a matrix, a covariance matrix, an autocorrelation matrix, or a covariance matrix associated with the channel matrix, etc.

[0105] In a TDD system, access network devices can utilize the reciprocity of uplink and downlink channels to perform channel estimation by receiving the uplink reference signal, thereby obtaining channel state information. As shown in Figure 4, the following explanation uses the uplink reference signal (SRS) as an example to illustrate the specific process by which access network devices obtain channel state information based on the SRS.

[0106] S401. The access network device sends configuration information to the terminal, wherein the configuration information is used to indicate the relevant configuration parameters of SRS.

[0107] S402. The terminal sends SRS to the access network equipment based on the configuration information.

[0108] S403. The access network device performs channel estimation based on the received SRS to obtain channel state information.

[0109] Furthermore, the access network equipment can also determine the precoding to be used for transmitting data to the terminal based on channel estimation information.

[0110] In high-frequency bands, severe channel fading significantly reduces the signal-to-noise ratio (SNR) of the SRS, impacting its channel estimation performance. Consequently, SRS-based channel estimation suffers from severely compromised performance. Since the downlink signal power transmitted by access network equipment is typically higher than the uplink signal power transmitted by the terminal, the downlink SNR is higher than the uplink SNR. The terminal can measure the downlink reference signal and calculate the channel covariance matrix, feeding this matrix back to the base station to assist in SRS channel estimation and reduce SRS interference noise.

[0111] For example, the terminal can feed back the covariance matrix R to the access network equipment. The access network equipment then estimates the minimum mean squared error (MMSE) of the SRS based on the covariance matrix R, which helps reduce SRS interference noise. The access network equipment determines the channel matrix H based on the covariance matrix R and the SRS measurement results. filter Satisfy: H filter =R(R+σI) -1 H SRS R=USU s

[0112] Among them, H SRSLet I be the channel matrix obtained based on SRS measurements, where I is the identity matrix and σ is the noise power. The noise power can be a preset value or obtained through measurement.

[0113] However, when there are many antenna ports and a large bandwidth, the dimension of the covariance matrix is ​​large, and the feedback overhead of the covariance matrix is ​​also extremely large.

[0114] The communication method and apparatus will be further described below with reference to the accompanying drawings. It is understood that in the embodiment shown in Figure 5, the execution subjects for the interactive illustration are described using the access network device and the terminal as examples. However, this application does not limit the execution subjects for the interactive illustration. For example, the method executed by the access network device in this application can also be implemented by a module (e.g., circuit, chip, or chip system) applied to the access network device, or a logic node, logic module, or software capable of implementing all or part of the access network device's functions; the method executed by the terminal in this application can also be implemented by a communication / processing module applied to the terminal, or a circuit or chip responsible for communication / processing functions (such as a modem chip (also known as a baseband chip), or a SoC chip containing a modem core, or a SIP chip, or a GPU). The first communication device can correspond to the terminal described below, and the second communication device can correspond to the access network device described below.

[0115] As shown in Figure 5, this application provides a communication method, which includes:

[0116] Step 500: The access network device sends at least one downlink reference signal. Correspondingly, the terminal receives at least one downlink reference signal.

[0117] For example, the access network device may periodically or non-periodically transmit downlink reference signals, and the terminal receives the downlink reference signals from the access network device. For example, the downlink reference signal may be a channel state information reference signal (CSI-RS) or other downlink reference signals. This application does not limit this, and the following description only uses CSI-RS as an example of the downlink reference signal.

[0118] Step 510: The terminal determines the first vector based on the channel matrix corresponding to at least one downlink reference signal.

[0119] For example, the channel matrix corresponding to at least one downlink reference signal can be understood as a channel matrix corresponding to each downlink reference signal, thus there is a one-to-one correspondence between at least one downlink reference signal and at least one channel matrix. For instance, suppose a terminal receives M downlink reference signals, and based on the M downlink reference signals, M channel matrices can be determined, where the M downlink reference signals correspond one-to-one with the M channel matrices, and M is an integer.

[0120] The first vector is used by the access network device to determine the statistical covariance matrix, or the first vector is used by the access network device to recover or obtain the statistical covariance matrix.

[0121] The statistical covariance matrix is ​​a covariance matrix obtained based on the channel matrices corresponding to at least one downlink reference signal. In other words, the statistical covariance matrix is ​​the statistical result of at least one covariance matrix (or autocorrelation matrix), wherein at least one covariance matrix corresponds one-to-one with the channel matrix corresponding to at least one downlink reference signal, and the product of each channel matrix and its conjugate transpose is the covariance matrix corresponding to that channel matrix.

[0122] For example, each channel matrix has a dimension of N*1, and the statistical covariance matrix has a dimension of N*N, where N is a positive integer. The value of N can be determined based on the number of ports for the downlink reference signal and / or the number of frequency domain resources used to transmit the downlink reference signal. Frequency domain resources can be replaced by frequency domain units or frequency domain resource units, etc., and this application does not limit this. The number of frequency domain resources can be the number of subbands, the number of resource blocks (RBs), or the number of resource elements (REs). For example, if the number of ports for CSI-RS is 32 and the number of RBs corresponding to CSI-RS is 16, then N = 32 * 16 = 512.

[0123] In one possible implementation, the terminal can determine the statistical covariance matrix based on the channel matrix corresponding to at least one downlink reference signal, and further determine the first vector based on the statistical covariance matrix.

[0124] For example, the terminal determines the channel matrix corresponding to the received downlink reference signal based on the received downlink reference signal, and determines the corresponding covariance matrix (or autocorrelation matrix) based on the channel matrix. Further, the terminal can calculate a statistical covariance matrix based on one or more obtained covariance matrices (or autocorrelation matrices), and determine a first vector based on the statistical covariance matrix.

[0125] The following section first explains how the terminal obtains the statistical covariance matrix.

[0126] For example, suppose the terminal receives M downlink reference signals and determines M channel matrices based on the M downlink reference signals, as well as the covariance matrices (i.e., M covariance matrices or M autocorrelation matrices) corresponding to the M channel matrices, where M is a positive integer.

[0127] The i-th channel matrix is ​​any one of the M channel matrices, denoted as H. i H i H For H i The conjugate transpose of the i-th channel matrix is ​​given by H. i *H i H , where i is a positive integer and i≤M.

[0128] Furthermore, the terminal determines the statistical covariance matrix based on the covariance matrices corresponding to the M channel matrices, wherein the statistical covariance matrix satisfies either condition 1 or condition 2.

[0129] Condition 1:

[0130] Among them, R hh This represents the statistical covariance matrix.

[0131] Condition 2:

[0132] in, This represents the statistical covariance matrix obtained based on the covariance matrices corresponding to the M channel matrices, where... This represents the statistical covariance matrix obtained based on the covariance matrices corresponding to the M-1 channel matrices. The terminal receives downlink reference signals sequentially at M time points, receiving a total of M downlink reference signals. Therefore, the covariance matrices corresponding to the M channel matrices refer to the covariance matrices corresponding to the M time points, while the covariance matrices corresponding to the M-1 channel matrices refer to the covariance matrices corresponding to the first M-1 time points out of the M time points. H M Let H be the channel matrix corresponding to the Mth time point. M H For H M The conjugate transpose of the matrix, where a and b are preset weight coefficients, a + b = 1, and a and b can be empirical values. For example, a = 0.9, b = 0.1, or a = 0.95, b = 0.05.

[0133] It is understood that conditions 1 and 2 below are merely examples and are not intended to limit this application. The terminal may also use other methods to determine the statistical covariance matrix based on the channel matrices corresponding to the M downlink reference signals.

[0134] For example, if the access network device sends a CSI-RS every 20ms, the terminal can determine the corresponding channel matrix and covariance matrix based on each received CSI-RS. If the access network device is configured to have the terminal report a first vector every 240ms, i.e., M=12, then the terminal can determine 12 channel matrices and their corresponding covariance matrices based on the 12 CSI-RS received within 240ms. Ultimately, the statistical covariance matrix determined by the terminal based on the covariance matrices of the 12 channel matrices satisfies... or

[0135] After obtaining the statistical covariance matrix, the terminal can determine the first vector based on the statistical covariance matrix.

[0136] For example, the first vector is the result of the statistical covariance after the discrete fourier transform (DFT).

[0137] In one possible implementation, the first vector is determined based on a matrix of a first type and a statistical covariance matrix. Alternatively, the first vector and the matrix of the first type are used by the access network device to determine the statistical covariance matrix.

[0138] In this system, the first type of matrix is ​​determined based on K DFT matrices, where K is the oversampling factor. The first and second DFT matrices are any two DFT matrices from the K DFT matrices. The product of the conjugate transpose of the first and second DFT matrices is not an identity matrix, while the product of any DFT matrix from the K DFT matrices and its own conjugate transpose is an identity matrix. Each DFT matrix has a dimension of N*N.

[0139] For example, the K DFT matrices can be determined by element-wise product of an initial DFT matrix and K different vectors, or the K DFT matrices can include an initial DFT matrix and K-1 other DFT matrices obtained by transforming the initial DFT matrix.

[0140] For example, K DFT matrices can be determined by element-wise product of an initial DFT matrix and K distinct column vectors. Here, the nth column vector in the initial DFT matrix can be represented as:

[0141] Where 1≤n≤N, and n is a positive integer.

[0142] The k-th column vector among K distinct column vectors is represented as:

[0143] Where 1≤k≤K, and k is a positive integer.

[0144] The first type of matrix is ​​composed of K DFT matrices concatenated horizontally. The nth column vector in the kth DFT matrix can be represented as...

[0145] in,

[0146] For example, the terminal may determine the matrix of the first type in, but not limited to, the following two ways:

[0147] Method 1: Concatenate K DFT matrices horizontally, and the dimension of the matrix of the first type will be N*KN.

[0148] Method 2: By vertically concatenating the K DFT matrices, the dimension of the matrix of the first type is KN*N.

[0149] The following explanation uses a matrix of type N*KN as an example. Alternatively, if the matrix of type KN*N has a dimension of KN*N, the matrix of type KN in the following conditions can be replaced by the transpose of the matrix of type KN.

[0150] It is understandable that the determination method of the first type of matrix and the determination method of the K DFT matrices can be predefined by the protocol, or the access network device can configure the terminal in advance to ensure that the terminal and the access network device have a consistent understanding of the first type of matrix.

[0151] The following example illustrates how the terminal determines the first vector:

[0152] Example 1: The first vector satisfies: s = diag(D) H R hh D);

[0153] Where s is the first vector, diag(A) represents the vector consisting of the diagonal elements of matrix A, D is a matrix of the first type, and R hh To calculate the covariance matrix, D H It is the conjugate transpose of a matrix of type I.

[0154] Furthermore, the i-th element s in the first vector i satisfy:

[0155] Among them, D i Let i be the i-th column vector in D. D H The i-th row vector in the array is 1≤i≤KN, where i is a positive integer.

[0156] Example 2: The first vector satisfies:

[0157] Where s is the first vector, diag(A) represents the vector consisting of the diagonal elements of matrix A, D is a matrix of the first type, and R hh To calculate the covariance matrix, It is the pseudo-inverse of a matrix of the first type. It is the conjugate transpose of the pseudo-inverse of a matrix of type I.

[0158] Furthermore, the i-th element s in the first vector i satisfy:

[0159] in, for The i-th row vector in for The i-th column vector in the array, 1≤i≤KN, where i is a positive integer.

[0160] In Examples 1 and 2 above, the first vector comprises the diagonal elements of a first diagonal matrix, wherein the first diagonal matrix is ​​determined based on a matrix of the first type and a statistical covariance matrix. In Example 1, the first diagonal matrix is ​​D. H R hh D, in Example 2, the first diagonal matrix is Where D has a dimension of N*KN, R hh The dimension is N*N, D H If the dimension of the matrix is ​​KN*N, then the dimension of the first diagonal matrix is ​​KN*KN. Taking the diagonal elements of this matrix, we can obtain the first vector s, which has a dimension of KN*1 or 1*KN, where K < N. It is evident that by feeding back the first vector (i.e., the diagonal elements of the first diagonal matrix), the feedback overhead can be greatly reduced, and resource utilization efficiency can be improved.

[0161] In Example 2 above, since the terminal performs a pseudo-inverse operation during the process of determining the first vector, the number of zero elements in the first vector determined in Example 2 can be greater than the number of zero elements in the first vector determined in Example 1, thereby further reducing the feedback overhead.

[0162] Example 3: The first vector satisfies: s=(D * ⊙D) H vec(R hh );

[0163] Where s is the first vector, R hh To calculate the covariance matrix, vec(R) hh ) represents an N×N dimension R hhConvert to a vector of dimension N*1, where D is a matrix of type I. * Let be the conjugate matrix of a matrix of type I, and ⊙ denotes the Khatri rao product operation.

[0164] Example 4: The first vector satisfies:

[0165] Where s is the first vector, R hh To calculate the covariance matrix, vec(R) hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of type I. * Let be the conjugate matrix of a matrix of type I, and ⊙ denotes the Khatri rao product operation.

[0166] In Examples 3 and 4 above, the terminal first determines the Khatri rao product based on the matrix of the first type, and then determines the first vector by combining it with the statistical covariance matrix. * The dimension of D is NN*KN, (D * ⊙D) H The dimension is KN*NN. The dimension is KN*NN, vec(R) hh If the dimension of the first vector is N*1, then the dimension of the second vector is KN*1, where K < N. It is evident that compared to the feedback statistical covariance matrix (which has an N*N dimension), using the feedback first vector (which has a KN*1 dimension) can significantly reduce feedback overhead and improve resource utilization efficiency.

[0167] In Example 4 above, since the terminal performs a pseudo-inverse operation during the process of determining the first vector, the number of zero elements in the first vector determined in Example 4 is greater than the number of zero elements in the first vector determined in Example 3, thereby further reducing the feedback overhead.

[0168] Step 520: The terminal sends first information, which indicates a first vector. Correspondingly, the access network device receives the first information.

[0169] For example, the access network device can pre-configure the period for the terminal to report the first vector. Alternatively, before the terminal sends the first information to the access network device, the access network device can send a request message to the terminal, which requests the terminal to report the first vector.

[0170] For example, the first information can be carried via uplink control information (UCI), a MAC CE message, or an RRC message. The request message can be downlink control information (DCI), a MAC CE message, or an RRC message.

[0171] It is understandable that the terminal may only report the non-zero elements in the first vector, without reporting the zero elements. Here, a zero element can be understood as an element with a magnitude close to zero. For example, the first information indicates the index of the non-zero element in the first vector and the magnitude of that non-zero element.

[0172] In one example, the first piece of information could directly include the index of the non-zero element and the magnitude of the non-zero element.

[0173] In another example, the first information may include a bitmap of length KN bits, where each of the KN bits corresponds one-to-one with one of the KN elements in the first vector. For example, the elements corresponding to bits with a value of 0 in the bitmap are zero elements, and the elements corresponding to bits with a value of 1 are non-zero elements, or the elements corresponding to bits with a value of 1 are zero elements, and the elements corresponding to bits with a value of 0 are non-zero elements. The first information also includes the magnitudes of the non-zero elements in the first vector.

[0174] At this point, the first information indicates the first vector, which can also be understood as the first information indicating the non-zero elements in the first vector.

[0175] In this case, referring to Example 1 above, assume that there are L non-zero elements in the first vector, where L is a positive integer and L≤KN.

[0176] The l-th non-zero element t l satisfy:

[0177] Among them, D l Let L be the column vector in D corresponding to the l-th non-zero element. D corresponding to the l-th non-zero element H Row vectors in t, 1≤l≤L, where l is a positive integer, t l It belongs to the first vector s.

[0178] For example, assuming KN = 1024, the first vector contains 1024 elements, including 200 zero elements and 824 non-zero elements, i.e., L = 824. Assuming the first element of the first vector is zero, the second element is non-zero, the third element is non-zero, the fourth element is zero, the fifth element is non-zero, ..., the 1024th element is non-zero, then the first non-zero element is the second element of the first vector, the second non-zero element is the third element, the third non-zero element is the fifth element, ..., the 824th non-zero element is the 1024th element. The column vector corresponding to the first non-zero element in D is the second column vector in D, the column vector corresponding to the second non-zero element in D is the third column vector in D, the column vector corresponding to the third non-zero element in D is the fifth column vector in D, and so on, until the column vector corresponding to the 824th non-zero element in D is the 1024th column vector in D. The column vector corresponding to the first non-zero element in D... H The column vector in is D H The second column vector in the array, the second non-zero element corresponding to D H The column vector in is D H The third column vector in the array, the third non-zero element corresponding to D H The column vector in is D H The 5th column vector, ..., the 824th non-zero element corresponding to D H The column vector in is D H The 1024th column vector in the array.

[0179] Based on Example 2 above, assume that there are L non-zero elements in the first vector, where L is a positive integer and L≤KN.

[0180] The l-th non-zero element t l satisfy:

[0181] in, The corresponding element of the l-th non-zero element row vectors in The corresponding element of the l-th non-zero element In the column vector, 1≤l≤L, where l is a positive integer, t l It belongs to the first vector s.

[0182] Step 530: The access network device determines the statistical covariance matrix based on the first vector.

[0183] The following example illustrates how access network devices determine the statistical covariance matrix based on the first vector:

[0184] Example A: The statistical covariance matrix satisfies:

[0185] Where s is the first vector, Diag(s) denotes a diagonal matrix with s as its diagonal elements, D is a matrix of type I, and R hh To calculate the covariance matrix, It is the pseudo-inverse of a matrix of the first type. It is the conjugate transpose of the pseudo-inverse of a matrix of type I.

[0186] Alternatively, the statistical covariance matrix satisfies:

[0187] Among them, s i Let i be the i-th element in the first vector. for The i-th row vector in for The i-th column vector in the array, 1≤i≤KN, where i is a positive integer.

[0188] Furthermore, if the first information indicates the index and magnitude of the non-zero elements in the first vector, assume there are L non-zero elements in the first vector, where L is a positive integer and L ≤ KN. The statistical covariance matrix satisfies:

[0189] Among them, t l For the l-th non-zero element, The corresponding element of the l-th non-zero element row vectors in The corresponding element of the l-th non-zero element In the column vector, 1≤l≤L, where l is a positive integer, t l It belongs to the first vector s.

[0190] Example B: The statistical covariance matrix satisfies: R hh =D*Diag(s)*D H ;

[0191] Where s is the first vector, Diag(s) denotes a diagonal matrix with s as its diagonal elements, D is a matrix of type I, and R hh To calculate the covariance matrix, D H It is the conjugate transpose of a matrix of type I.

[0192] Alternatively, the statistical covariance matrix satisfies:

[0193] Among them, s i D is the i-th element in the first vector. i Let i be the i-th column vector in D. DH The i-th row vector in the array, 1≤i≤KN, where i is a positive integer.

[0194] Furthermore, if the first information indicates the index and magnitude of the non-zero elements in the first vector, assume there are L non-zero elements in the first vector, where L is a positive integer and L ≤ KN. The statistical covariance matrix satisfies:

[0195] Among them, t l For the l-th non-zero element, D l Let L be the column vector in D corresponding to the l-th non-zero element. D corresponding to the l-th non-zero element H Row vectors in t, 1≤l≤L, where l is a positive integer, t l It belongs to the first vector s.

[0196] Using the examples A and B above, the access network device first determines the first diagonal matrix based on the first vector, and then determines the statistical covariance matrix by combining it with the matrix of the first type.

[0197] Example C: The statistical covariance matrix satisfies:

[0198] Where s is the first vector, R hh To calculate the covariance matrix, vec(R) hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of type I. * Let be the conjugate matrix of a matrix of type I, and ⊙ denotes the Khatri rao product operation.

[0199] Example D: The statistical covariance matrix satisfies: vec(R) hh )=(D * ⊙D)s;

[0200] Where s is the first vector, R hh To calculate the covariance matrix, vec(R) hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of type I. * Let be the conjugate matrix of a matrix of type I, and ⊙ denotes the Khatri rao product operation.

[0201] Using examples C and D above, the access network device first determines the Khatri rao product based on the matrix of the first type, and then determines the statistical covariance matrix by combining it with the first vector.

[0202] In some possible embodiments, Example 1 in step 510 corresponds to Example A in step 530, that is, the terminal sends the first vector determined by Example 1, and the access network device determines the statistical covariance matrix by Example A. Similarly, Example 2 in step 510 corresponds to Example B in step 530, Example 3 in step 510 corresponds to Example C in step 530, and Example 4 in step 510 corresponds to Example D in step 530.

[0203] In one possible implementation, before the terminal sends the first information, the terminal sends a second information, which instructs the access network device to perform a pseudo-inverse operation when determining the statistical covariance matrix. Alternatively, the second information instructs the access network device whether to perform a pseudo-inverse operation when determining the statistical covariance matrix. Accordingly, the access network device receives the second information and determines the covariance matrix based on the second information.

[0204] For example, if the second information instructs the access network device to perform a pseudo-inverse operation when determining the statistical covariance matrix, then the terminal uses Example 1 above to determine the first vector, and the access network device uses Example A above to determine the covariance matrix.

[0205] For example, if the second information instructs the access network device to perform a pseudo-inverse operation when determining the statistical covariance matrix, then the terminal uses Example 3 above to determine the first vector, and the access network device uses Example C above to determine the covariance matrix.

[0206] In another possible implementation, before sending the first information, the terminal sends third information indicating that the terminal supports the first capability, which includes performing a pseudo-inverse operation on a matrix. Alternatively, the third information indicates whether the terminal supports the first capability. Accordingly, the access network device receives the third information and determines the covariance matrix based on it.

[0207] For example, if the third information indicates that the terminal supports the first capability, then the terminal uses Example 2 above to determine the first vector, and the access network device uses Example B above to determine the covariance matrix.

[0208] For example, if the third information indicates that the terminal supports the first capability, then the terminal uses Example 4 above to determine the first vector, and the access network device uses Example D above to determine the covariance matrix.

[0209] In another possible implementation, before sending the first information, the terminal sends a fourth information. This fourth information indicates a statistical covariance determination mode or a first vector determination mode; the name of this application is not limited. For example, the fourth information may include 1 bit. When the value of this 1 bit is 1, the fourth information indicates that the terminal does not perform pseudo-inverse budgeting, and the access network device performs pseudo-inverse budgeting; when the value of this 1 bit is 0, the fourth information indicates that the terminal performs pseudo-inverse budgeting, and the access network device does not perform pseudo-inverse budgeting.

[0210] Furthermore, in some possible embodiments, the terminal may also transmit an uplink reference signal. After step 530, the access network device can determine the channel state information based on the uplink reference signal and the statistical covariance matrix. For example, the uplink reference signal here can be an SRS (Statistical Reference Signal). The determination of channel state information by the access network device based on the uplink reference signal and the statistical covariance matrix can be found in the aforementioned related content and will not be repeated here.

[0211] Using the above method, by feeding back the first vector, the access network device can be assisted in determining the statistical covariance matrix, and the signaling overhead of feeding back the first vector is smaller than that of directly feeding back the statistical covariance matrix. Furthermore, because the signal-to-noise ratio (SNR) of the uplink reference signal is low (e.g., the transmission power of the uplink reference signal is low), the accuracy (or reliability) of the channel state information determined by the access network device based solely on the uplink reference signal is not high. However, the SNR of the downlink reference signal is higher than that of the uplink reference signal (e.g., the transmission power of the downlink reference signal is greater than that of the uplink reference signal), and the statistical covariance matrix is ​​obtained based on the downlink reference signal. By combining the statistical covariance matrix with the uplink reference signal to jointly determine the channel state information, the access network device can improve the accuracy (or reliability) of the channel state information, thereby effectively improving communication quality.

[0212] It is understood that, in order to achieve the functions in the above embodiments, each communication device (e.g., a terminal or access network device) includes hardware structures and / or software modules corresponding to perform each function. Those skilled in the art should readily recognize that, based on the units and method steps of the various examples described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0213] Figures 6 and 7 are schematic diagrams of possible communication devices provided in embodiments of this application. These communication devices can be used to implement the functions of the various communication devices in the above method embodiments, and thus can also achieve the beneficial effects of the above method embodiments.

[0214] As shown in Figure 6, the communication device 600 includes a processing unit 610 and a transceiver unit 620.

[0215] When the communication device 600 is used to implement the function of the terminal in the method embodiment shown in FIG5 above:

[0216] The transceiver unit 620 is used to receive at least one downlink reference signal; the processing unit 610 is used to determine a first vector based on the channel matrix corresponding to the at least one downlink reference signal, the first vector being used by the second communication device to determine a statistical covariance matrix, the statistical covariance matrix being a covariance matrix obtained based on the channel matrix corresponding to the at least one downlink reference signal, wherein the dimension of the first vector is KN*1, the dimension of the statistical covariance matrix is ​​N*N, K is an oversampling factor, N and K are positive integers, K<N, and the value of N is determined based on the number of ports of the downlink reference signal and / or the amount of frequency domain resources used to transmit the downlink reference signal; the transceiver unit 620 is used to send first information, the first information indicating the first vector.

[0217] In one possible design, the transceiver unit 620 is also configured to send a second message before sending the first message, the second message instructing the second communication device to perform a pseudo-inverse operation when determining the statistical covariance matrix.

[0218] In one possible design, the transceiver unit 620 is further configured to send third information before sending the first information, the third information indicating that the first communication device supports a first capability, the first capability including performing pseudo-inverse operations on a matrix.

[0219] In one possible design, the transceiver unit 620 is also used to transmit an uplink reference signal.

[0220] When the communication device 600 is used to implement the function of the access network device in the method embodiment shown in FIG5 above:

[0221] The transceiver unit 620 is configured to transmit at least one downlink reference signal; receive first information, the first information being used to indicate a first vector, the first vector being used by the second communication device to determine a statistical covariance matrix, the statistical covariance matrix being a covariance matrix obtained based on the channel matrices corresponding to the at least one downlink reference signal respectively, wherein the dimension of the first vector is KN*1, the dimension of the statistical covariance matrix is ​​N*N, K is an oversampling factor, N and K are positive integers, K<N, and the value of N is determined according to the number of ports of the downlink reference signal and / or the amount of frequency domain resources used to transmit the downlink reference signal; and a processing unit 610 is configured to determine the statistical covariance matrix based on the first vector.

[0222] In one possible design, the transceiver unit 620 is also configured to receive second information before receiving the first information, the second information instructing the second communication device to perform a pseudo-inverse operation when determining the statistical covariance matrix.

[0223] In one possible design, the transceiver unit 620 is further configured to receive third information before receiving the first information, the third information indicating that the first communication device supports a first capability, the first capability including performing pseudo-inverse operations on a matrix.

[0224] In one possible design, the transceiver unit 620 is further configured to receive an uplink reference signal; and the processing unit 610 is further configured to determine channel state information based on the uplink reference signal and the statistical covariance matrix.

[0225] For some possible designs and beneficial effects of the communication device 600, please refer to the relevant content in the embodiment shown in Figure 5 above, which will not be repeated here.

[0226] As shown in Figure 7, the communication device 700 includes a processor 710 and an interface circuit 720. The processor 710 and the interface circuit 720 are coupled to each other. It is understood that the interface circuit 720 can be a transceiver or an input / output interface. Optionally, the communication device 700 may further include a memory 730 for storing instructions executed by the processor 710, or storing input data required by the processor 710 to execute instructions, or storing data generated after the processor 710 executes instructions.

[0227] When the communication device 700 is used to implement the above method embodiment, the processor 710 is used to implement the function of the processing unit 610, and the interface circuit 720 is used to implement the function of the transceiver unit 620.

[0228] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0229] This application provides another example of a device, the notification device including at least one processor and at least one memory, the at least one processor and the at least one memory coupled together, the at least one memory for storing instructions, which, when executed by the at least one processor, cause the communication device to perform the methods described in the above embodiments. Taking a communication device including a processor and a memory as an example, as shown in FIG7, the communication device 700 includes a processor 710 and a memory 730. The processor 710 and the memory 730 are coupled together, the memory 730 stores instructions, and when the instructions stored in the memory 730 are executed by the processor 710, the communication device 700 performs the methods performed by the various communication devices in the above embodiments.

[0230] The method steps in the embodiments of this application can be implemented in hardware or in software instructions executable by a processor. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. The storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in the aforementioned terminal or access network device. The processor and storage medium can also exist as discrete components in the terminal or access network device.

[0231] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both types of storage media.

[0232] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0233] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. In the textual description of this application, the character " / " generally indicates an "or" relationship between the preceding and following related objects; in the formulas of this application, the character " / " indicates a "division" relationship between the preceding and following related objects. "Including at least one of A, B, and C" can mean: including A; including B; including C; including A and B; including A and C; including B and C; including A, B, and C.

[0234] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the process numbers described above does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

Claims

1. A communication method, characterized in that, The method is applied to a first communication device, and the method includes: Receive at least one downlink reference signal; A first vector is determined based on the channel matrices corresponding to the at least one downlink reference signal. The first vector is used by the second communication device to determine a statistical covariance matrix. The statistical covariance matrix is ​​a covariance matrix obtained based on the channel matrices corresponding to the at least one downlink reference signal. The dimension of the first vector is KN*1, the dimension of the statistical covariance matrix is ​​N*N, K is the oversampling factor, N and K are positive integers, and K < N. Send a first message, which indicates the first vector.

2. The method as described in claim 1, characterized in that, The first vector and the matrix of the first type are used by the second communication device to determine the statistical covariance matrix; The matrix of the first type is determined based on K DFT matrices. The first DFT matrix and the second DFT matrix are any two DFT matrices among the K DFT matrices. The product of the conjugate transpose of the first DFT matrix and the second DFT matrix is ​​not an identity matrix. The product of any DFT matrix among the K DFT matrices and its own conjugate transpose is an identity matrix. The dimension of each DFT matrix is ​​N*N, and the dimension of the matrix of the first type is N*KN.

3. The method as described in claim 2, characterized in that, The statistical covariance matrix satisfies: Where s is the first vector, Diag(s) represents a diagonal matrix with s as its diagonal elements, D is a matrix of the first type, and R hh Let be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

4. The method as described in claim 2, characterized in that, The statistical covariance matrix satisfies: R hh =D*Diag(s)*D H ; Where s is the first vector, Diag(s) represents a diagonal matrix with s as its diagonal elements, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

5. The method as described in claim 2, characterized in that, The statistical covariance matrix satisfies: Among them, R hh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * It is the conjugate matrix of the first type of matrix. ⊙ represents the pseudo-inverse operation, and ⊙ represents the Khatri rao product operation.

6. The method as described in claim 2, characterized in that, The statistical covariance matrix satisfies: vec(R) hh )=(D * ⊙D)s; Among them, R hh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

7. The method according to any one of claims 2-6, characterized in that, The first vector satisfies: s = diag(D) H R hh D); Where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

8. The method according to any one of claims 2-6, characterized in that, The first vector satisfies: Where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hh Let be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

9. The method according to any one of claims 2-6, characterized in that, The first vector satisfies: s=(D * ⊙D) H vec(R hh ); Where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

10. The method according to any one of claims 2-6, characterized in that, The first vector satisfies: Where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denote the Khatri rao product operation. This indicates a pseudo-inverse operation.

11. The method according to any one of claims 3, 5, 7, and 9, characterized in that, Before sending the first information, it also includes: A second message is sent, which instructs the second communication device to perform a pseudo-inverse operation when determining the statistical covariance matrix.

12. The method according to any one of claims 4, 6, 8, and 10, characterized in that, Before sending the first information, it also includes: Send a third message indicating that the first communication device supports a first capability, the first capability including performing pseudo-inverse operations on a matrix.

13. The method according to any one of claims 1-12, characterized in that, Also includes: Send an uplink reference signal.

14. A communication method, characterized in that, The method is applied to a second communication device, and the method includes: Send at least one downlink reference signal; The system receives first information, which is used to indicate a first vector. The first vector is used by the second communication device to determine a statistical covariance matrix. The statistical covariance matrix is ​​a covariance matrix obtained based on the channel matrices corresponding to the at least one downlink reference signal. The first vector has a dimension of KN*1, the statistical covariance matrix has a dimension of N*N, K is the oversampling factor, N and K are positive integers, and K < N. The statistical covariance matrix is ​​determined based on the first vector.

15. The method as described in claim 14, characterized in that, The first vector and the matrix of the first type are used by the second communication device to determine the statistical covariance matrix; The matrix of the first type is determined based on K DFT matrices. The first DFT matrix and the second DFT matrix are any two DFT matrices among the K DFT matrices. The product of the conjugate transpose of the first DFT matrix and the second DFT matrix is ​​not an identity matrix. The product of any DFT matrix among the K DFT matrices and its own conjugate transpose is an identity matrix. The dimension of each DFT matrix is ​​N*N, and the dimension of the matrix of the first type is N*KN.

16. The method as described in claim 15, characterized in that, The statistical covariance matrix satisfies: Where s is the first vector, Diag(s) represents a diagonal matrix with s as its diagonal elements, D is a matrix of the first type, and R hh Let be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

17. The method as described in claim 15, characterized in that, The statistical covariance matrix satisfies: R hh =D*Diag(s)*D H ; Where s is the first vector, Diag(s) represents a diagonal matrix with s as its diagonal elements, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

18. The method as described in claim 15, characterized in that, The statistical covariance matrix satisfies: Among them, R hh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * It is the conjugate matrix of the first type of matrix. ⊙ represents the pseudo-inverse operation, and ⊙ represents the Khatri rao product operation.

19. The method as described in claim 15, characterized in that, The statistical covariance matrix satisfies: vec(R hh )=(D * ⊙D)s; Among them, R hh Let be the statistical covariance matrix, s be the first vector, and vec(R) be the first vector. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

20. The method according to any one of claims 15-19, characterized in that, The first vector satisfies: s = diag(D) H R hh D); Where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hh Let D be the statistical covariance matrix. H It is the conjugate transpose of a matrix of the first type.

21. The method according to any one of claims 15-19, characterized in that, The first vector satisfies: Where s is the first vector, diag(A) represents the vector composed of the diagonal elements of matrix A, D is a matrix of the first type, and R hh Let be the statistical covariance matrix. It is the pseudo-inverse matrix of the first type of matrix. It is the conjugate transpose of the pseudo-inverse matrix of the matrix of the first type.

22. The method according to any one of claims 15-19, characterized in that, The first vector satisfies: s=(D * ⊙D) H vec(R hh ); Where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denotes the Khatri rao product operation.

23. The method according to any one of claims 15-19, characterized in that, The first vector satisfies: Where s is the first vector, R hh Let vec(R) be the statistical covariance matrix. hh ) represents an N×N dimension R hh Convert to a vector of dimension N*1, where D is a matrix of the first type. * Let be the conjugate matrix of the first type of matrix, and ⊙ denote the Khatri rao product operation. This indicates a pseudo-inverse operation.

24. The method according to any one of claims 16, 18, 20, and 22, characterized in that, Before receiving the first information, it also includes: The second information is received, which instructs the second communication device to perform a pseudo-inverse operation when determining the statistical covariance matrix.

25. The method according to any one of claims 17, 19, 21, and 23, characterized in that, Before receiving the first information, it also includes: The first communication device receives a third message indicating that it supports a first capability, which includes performing a pseudo-inverse operation on a matrix.

26. The method according to any one of claims 14-25, characterized in that, Also includes: Receive uplink reference signal; Channel state information is determined based on the uplink reference signal and the statistical covariance matrix.

27. A communication device, characterized in that, It includes units or modules for performing the method as described in any one of claims 1 to 13, or units or modules for performing the method as described in any one of claims 14 to 26.

28. A communication device, characterized in that, The communication device includes at least one processor; the at least one processor is configured to perform the method as described in any one of claims 1 to 13, or to perform the method as described in any one of claims 14 to 26.

29. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a computer program or instructions that, when executed, cause the method as described in any one of claims 1 to 13 to be performed, or cause the method as described in any one of claims 14 to 26 to be performed.

30. A computer program product, characterized in that, The computer program product includes a computer program or instructions that, when executed by a device, cause the device to perform the method as claimed in any one of claims 1 to 13, or cause the device to perform the method as claimed in any one of claims 14 to 26.

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