WO2023011075A1

WO2023011075A1 - Method and apparatus for determining channel statistical covariance

Info

Publication number: WO2023011075A1
Application number: PCT/CN2022/103404
Authority: WO
Inventors: 孟鑫; 严冠文; 蓝瑞宁; 秦晨翔; 杨烨
Original assignee: 华为技术有限公司
Priority date: 2021-08-02
Filing date: 2022-07-01
Publication date: 2023-02-09
Also published as: CN115701695A

Abstract

The present application relates to the technical field of communications, and provides a method and apparatus for determining a channel statistical covariance to accurately determine a statistical covariance of a channel. A network device performs channel estimation on the basis of the received uplink reference signal to obtain an uplink channel estimation matrix. Then, the uplink channel estimation matrix is transformed on the basis of a first transformation matrix to obtain a first channel estimation matrix, and the first transformation matrix is related to an uplink channel. A first statistical average energy corresponding to the first channel estimation matrix is determined, wherein the first statistical average energy is obtained by performing statistical averaging on energy corresponding to some or all elements in the first channel estimation matrix. Next, a first power spectrum is determined on the basis of the first statistical average energy, wherein a mapping relationship exists between the first statistical average energy and the first power spectrum. A statistical covariance matrix of a downlink channel is determined on the basis of the first power spectrum and a second transformation matrix. The second transformation matrix is related to the downlink channel.

Description

A method and device for determining channel statistical covariance

Cross References to Related Applications

This application claims the priority of the Chinese patent application with the application number 202110879764.2 and the application title "A Method and Device for Determining Channel Statistical Covariance" submitted to the China Patent Office on August 2, 2021, the entire contents of which are incorporated by reference in this application.

technical field

The embodiments of the present application relate to fields such as communications, and in particular to a method and device for determining channel statistical covariance.

Background technique

A multi-antenna system configures multiple transceiver antennas on a device (such as a network device) to increase system capacity by exploring and utilizing spatial dimension resources. A key factor for improving the downlink capacity of a multi-antenna system is to obtain more accurate channel state information (CSI) at the transmitting end.

In order to acquire (acquisition can also be interpreted as estimating) channel state information CSI more accurately, statistical information of the channel, especially statistical covariance information of the channel may be used for acquisition.

Based on this, how to determine the statistical covariance of the channel is a technical problem to be solved.

Contents of the invention

Embodiments of the present application provide a method and device for determining channel statistical covariance, so as to accurately determine channel statistical covariance.

In the first aspect, a method for determining channel statistical covariance is provided, and the execution subject of the method may be a network device, or may be a component applied to the network device, such as a chip, a processor, and the like. The following description is made by taking the execution subject as an example of a network device. The network device performs channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix. Then, the network device transforms the uplink channel estimation matrix based on the first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to the uplink channel. Then, the network device determines the first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is: performing statistics on the energy corresponding to some or all elements in the first channel estimation matrix get average. Next, the network device determines a first power spectrum based on the first statistical average energy, where a mapping relationship exists between the first statistical average energy and the first power spectrum. Next, the network device determines a statistical covariance matrix of the downlink channel based on the first power spectrum and the second transformation matrix; the second transformation matrix is a matrix related to the downlink channel.

In the first aspect, the uplink channel estimation matrix is transformed, and the average energy of the transformed matrix is counted, and then the power spectrum is determined based on the average energy. Then, based on the determined power spectrum, the statistical covariance matrix of the downlink channel is obtained. The method is simple and can accurately determine the statistical covariance of the channel.

In a possible implementation, the network device may also send data and/or reference signals based on the statistical covariance matrix of the downlink channel.

In a possible implementation, each element in the first power spectrum is a non-negative real value. This can ensure that the determined statistical covariance matrix of the downlink channel is positive semi-definite, and can improve the accuracy of the determined statistical covariance matrix of the downlink channel.

In a possible implementation, the first transformation matrix is any of the following: the first discrete cosine transform DCT matrix, the first Hadamard transform matrix, the first discrete Fourier DFT matrix, the first oversampled discrete Fourier transform Lie DFT matrix. The second transform matrix is any one of the following: a second discrete cosine transform DCT matrix, a second Hadamard transform matrix, a second discrete Fourier DFT matrix, and a second oversampled discrete Fourier DFT matrix.

The types of the first transformation matrix and the second transformation matrix may be the same, for example, both are discrete cosine transform DCT matrices, or both are Hadamard transformation matrices, and the contents of the first transformation matrix and the second transformation matrix may be the same, or May be different. Alternatively, the types of the first transformation matrix and the second transformation matrix may be different, for example, the first transformation matrix is a discrete cosine transform DCT matrix, and the second transformation matrix is a Hadamard transformation matrix.

In a possible implementation, the first transformation matrix is obtained based on at least one of the following matrices: a first space domain transformation matrix, a first frequency domain transformation matrix, and a first time domain transformation matrix. The second transformation matrix is obtained based on at least one of the following matrices: a second space domain transformation matrix, a second frequency domain transformation matrix, and a second time domain transformation matrix.

The matrix types used to obtain the first transformation matrix and the second transformation matrix are the same, for example, both are space-domain transformation matrices, or both are time-domain transformation matrices. The content of the matrices used to obtain the first transformation matrix and the second transformation matrix may be the same or different. Or, the matrix types used to obtain the first transformation matrix and the second transformation matrix are different, for example, the first transformation matrix is obtained based on the space domain transformation matrix, and the second transformation matrix is obtained based on the time domain transformation matrix.

The determination method of the present application can be applied to the scene of calculating the statistical covariance of one or more items in the space domain, the frequency domain, and the time domain, and is easy to popularize.

In a possible implementation, the mapping relationship between the first statistical average energy and the first power spectrum satisfies the following formula: Tω=φ, where ω is the first power spectrum, and φ is the The first statistical average energy, T is a mapping matrix, and T is related to the first transformation matrix.

In the second aspect, a method for determining channel statistical covariance is provided, and the execution body of the method may be a terminal device, or may be a component applied to the terminal device, such as a chip, a processor, and the like. The following description is made by taking the execution subject as a terminal device as an example. The terminal device performs channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix. Then, the terminal device transforms the downlink channel estimation matrix based on the third transformation matrix to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to the downlink channel. Then, the terminal device determines the second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is: performing statistics on the energy corresponding to some or all elements in the second channel estimation matrix get average. Next, the terminal device determines a second power spectrum based on the second statistical average energy, where a mapping relationship exists between the second statistical average energy and the second power spectrum. Next, the terminal device determines the statistical covariance matrix of the uplink channel based on the second power spectrum and the fourth transformation matrix; the fourth transformation matrix is a matrix related to the uplink channel.

In a possible implementation, the terminal device may also send data and/or reference signals based on the statistical covariance matrix of the uplink channel.

In a possible implementation, each element in the second power spectrum is a non-negative real value.

In a possible implementation, the third transformation matrix is any of the following: the third discrete cosine transform DCT matrix, the third Hadamard transform matrix, the third discrete Fourier DFT matrix, the third oversampled discrete Fourier Lie DFT matrix. The fourth transformation matrix is any one of the following: a fourth discrete cosine transform DCT matrix, a fourth Hadamard transform matrix, a fourth discrete Fourier DFT matrix, and a fourth oversampled discrete Fourier DFT matrix.

The types of the third transformation matrix and the fourth transformation matrix may be the same, for example, both are discrete cosine transform DCT matrices, or both are Hadamard transformation matrices, and the contents of the third transformation matrix and the fourth transformation matrix may be the same, or May be different. Alternatively, the types of the third transformation matrix and the fourth transformation matrix may be different, for example, the third transformation matrix is a discrete cosine transform DCT matrix, and the fourth transformation matrix is a Hadamard transformation matrix.

In a possible implementation, the third transformation matrix is obtained based on at least one of the following matrices: a third space domain transformation matrix, a third frequency domain transformation matrix, and a third time domain transformation matrix. The fourth transformation matrix is obtained based on at least one of the following matrices: a fourth space domain transformation matrix, a fourth frequency domain transformation matrix, and a fourth time domain transformation matrix.

The matrix types used to obtain the third transformation matrix and the fourth transformation matrix are the same, for example, both are space-domain transformation matrices, or both are time-domain transformation matrices. The content of the matrices used to obtain the third transformation matrix and the fourth transformation matrix may be the same or different. Or, the matrix types used to obtain the third transformation matrix and the fourth transformation matrix are different, for example, the third transformation matrix is obtained based on the space domain transformation matrix, and the fourth transformation matrix is obtained based on the time domain transformation matrix.

In a possible implementation, the mapping relationship between the second statistical average energy and the second power spectrum satisfies the following formula: Tω=φ, where ω is the second power spectrum, and φ is the The second statistical average energy, T is a mapping matrix, and T is related to the third transformation matrix.

In the third aspect, a communication device is provided, and the device has the function of realizing the above-mentioned first aspect and any possible implementation of the first aspect, or realizing the above-mentioned second aspect and any possible implementation of the second aspect Function. These functions may be implemented by hardware, or may be implemented by executing corresponding software through hardware. The hardware or software includes one or more functional modules corresponding to the above functions.

As an example, when the device has the function of realizing the above-mentioned first aspect and any possible implementation of the first aspect, the device includes:

an interface module, configured to receive an uplink reference signal;

A processing module, configured to perform channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix; transform the uplink channel estimation matrix based on a first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to the uplink channel; determine the first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is: corresponding to some or all elements in the first channel estimation matrix The energy is obtained by statistical averaging; based on the first statistical average energy, a first power spectrum is determined, wherein there is a mapping relationship between the first statistical average energy and the first power spectrum; based on the first power spectrum and a second transformation matrix for determining a statistical covariance matrix of the downlink channel; the second transformation matrix is a matrix related to the downlink channel.

Exemplarily, the interface module is further configured to send data and/or reference signals based on the statistical covariance matrix of the downlink channel.

As an example, when the device has the function of realizing the above second aspect and any possible implementation of the second aspect, the device includes:

an interface module, configured to receive a downlink reference signal;

A processing module, configured to perform channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix; transform the downlink channel estimation matrix based on a third transformation matrix to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to the downlink channel; determine the second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is: corresponding to some or all elements in the second channel estimation matrix The energy is obtained by statistical averaging; based on the second statistical average energy, a second power spectrum is determined, wherein there is a mapping relationship between the second statistical average energy and the second power spectrum; based on the second power spectrum and a fourth transformation matrix, determining a statistical covariance matrix of the uplink channel; the fourth transformation matrix is a matrix related to the uplink channel.

Exemplarily, the interface module is further configured to send data and/or reference signals based on the statistical covariance matrix of the uplink channel.

In a fourth aspect, a communication device is provided, including a processor, and optionally, a memory; the processor is coupled to the memory; the memory is used to store computer programs or instructions; the processor, A terminal for executing part or all of the computer programs or instructions in the memory, when the part or all of the computer programs or instructions are executed, for realizing the above first aspect and any possible implementation method of the first aspect The function of the device, or realize the second aspect and the function of the first network element in any possible implementation of the second aspect.

In a possible implementation, the apparatus may further include a transceiver, where the transceiver is configured to send a signal processed by the processor, or receive a signal input to the processor. The transceiver may perform the sending action or receiving action performed by the terminal device in the first aspect and any possible implementation of the first aspect; or, perform the second aspect and any possible implementation of the second aspect by the first network element send action or receive action.

In a fifth aspect, the present application provides a chip system, the chip system includes one or more processors (also referred to as processing circuits), and the electrical coupling between the processors and memories (also referred to as storage media) The memory may or may not be located in the chip system; the memory is used to store computer programs or instructions; the processor is used to execute part or all of the memory Computer programs or instructions, when part or all of the computer programs or instructions are executed, are used to realize the functions of the terminal device in the above-mentioned first aspect and any possible implementation method of the first aspect, or to realize the above-mentioned second aspect and The function of the first network element in any possible implementation of the second aspect.

In a possible implementation, the chip system may further include an input and output interface (also referred to as a communication interface), the input and output interface is used to output the signal processed by the processor, or receive an input to the signal to the processor. The input-output interface may perform the sending action or receiving action performed by the terminal device in the first aspect and any possible implementation of the first aspect; or, execute the second aspect and the first network element in any possible implementation of the second aspect The send action or receive action performed. Specifically, the output interface performs a sending action, and the input interface performs a receiving action.

In a possible implementation, the system-on-a-chip may consist of chips, or may include chips and other discrete devices.

In a sixth aspect, there is provided a computer-readable storage medium for storing a computer program, the computer program including instructions for realizing the functions in the first aspect and any possible implementation of the first aspect, or for realizing Instructions for the functions of the second aspect and any possible implementation of the second aspect.

Alternatively, a computer-readable storage medium is used to store a computer program, and when the computer program is executed by a computer, the computer can execute the first aspect and the terminal device in any possible implementation method of the first aspect. method, or execute the second aspect above and the method executed by the first network element in any possible implementation of the second aspect.

In a seventh aspect, a computer program product is provided, and the computer program product includes: computer program code, when the computer program code is run on a computer, the computer is made to execute the above-mentioned first aspect and any possible method of the first aspect. The method performed by the terminal device during implementation, or the method performed by the first network element in any possible implementation of the second aspect and the second aspect.

In an eighth aspect, a communication system is provided, and the communication system includes a terminal device performing the above-mentioned first aspect and any possible implementation method of the first aspect, and a terminal device performing the above-mentioned second aspect and any possible implementation method of the second aspect. The first network element in the implemented method.

For the technical effects of the above third to eighth aspects, reference may be made to the descriptions in the first to second aspects, and repeated descriptions will not be repeated.

Description of drawings

FIG. 1 is an architecture diagram of a communication system provided in an embodiment of the present application;

FIG. 2 is a schematic flow diagram of communication based on the statistical covariance of the downlink channel provided in the embodiment of the present application;

FIG. 3 is a schematic diagram of a process for determining statistical covariance of a downlink channel provided in an embodiment of the present application;

FIG. 4 is a schematic diagram of another process for determining the statistical covariance of the downlink channel provided in the embodiment of the present application;

FIG. 5 is a schematic flow diagram of communication based on the statistical covariance of the uplink channel provided in the embodiment of the present application;

FIG. 6 is a schematic diagram of a process for determining the statistical covariance of an uplink channel provided in an embodiment of the present application;

FIG. 7 is a structural diagram of a communication device provided in an embodiment of the present application;

FIG. 8 is a structural diagram of another communication device provided in the embodiment of the present application.

Detailed ways

In order to facilitate understanding of the technical solutions of the embodiments of the present application, the system architecture of the method provided by the embodiments of the present application will be briefly described below. It can be understood that the system architecture described in the embodiments of the present application is for more clearly illustrating the technical solutions of the embodiments of the present application, and does not constitute a limitation on the technical solutions provided by the embodiments of the present application.

The technical solutions of the embodiments of the present application can be applied to various communication systems, such as satellite communication systems and traditional mobile communication systems. Wherein, the satellite communication system may be integrated with a traditional mobile communication system (ie, a ground communication system). Communication systems such as: wireless local area network (wireless local area network, WLAN) communication system, wireless fidelity (wireless fidelity, WiFi) system, long term evolution (long term evolution, LTE) system, LTE frequency division duplex (frequency division duplex, FDD) ) system, LTE time division duplex (time division duplex, TDD), fifth generation (5th generation, 5G) system or new radio (new radio, NR), sixth generation (6th generation, 6G) system, and other future Communication systems, etc., also support communication systems that integrate multiple wireless technologies. For example, they can also be applied to non-terrestrial networks such as unmanned aerial vehicles, satellite communication systems, and high altitude platform station (HAPS) communications. NTN) is a system that integrates terrestrial mobile communication networks.

FIG. 1 is a schematic structural diagram of a communication system 1000 applied in an embodiment of the present application. As shown in FIG. 1 , the communication system includes a radio access network 100 and a core network 200 , and optionally, the communication system 1000 may also include the Internet 300 .

Wherein, the radio access network 100 may include at least one radio access network device (such as 110a and 110b in FIG. 1 ), and may also include at least one terminal (such as 120a-120j in FIG. 1 ). The terminal is connected to the wireless access network device in a wireless manner, and the wireless access network device is connected to the core network in a wireless or wired manner. The core network equipment and the wireless access network equipment can be independent and different physical equipment, or the functions of the core network equipment and the logical functions of the wireless access network equipment can be integrated on the same physical equipment, or it can be a physical equipment It integrates some functions of core network equipment and some functions of wireless access network equipment. Terminals and wireless access network devices may be connected to each other in a wired or wireless manner. FIG. 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 FIG. 1 .

The radio access network equipment can be a base station (base station), an evolved base station (evolved NodeB, eNodeB), a transmission reception point (transmission reception point, TRP), and the next generation in the fifth generation (5th generation, 5G) mobile communication system Base station (next generation NodeB, gNB), the next generation base station in the sixth generation (6th generation, 6G) mobile communication system, the base station in the future mobile communication system or the access node in the WiFi system, etc.; it can also complete the base station part A functional module or unit, for example, can be a centralized unit (central unit, CU) or a distributed unit (distributed unit, DU). The radio access network device may be a macro base station (such as 110a in Figure 1), a micro base station or an indoor station (such as 110b in Figure 1), or a relay node or a donor node. It can be understood that all or part of the functions of the radio access network device in this application may also be realized by software functions running on hardware, or by virtualization functions instantiated on a platform (such as a cloud platform). The embodiment of the present application does not limit the specific technology and specific equipment form adopted by the radio access network equipment. For ease of description, a base station is used as an example of a radio access network device for description below.

A terminal may also be called terminal equipment, user equipment (user equipment, UE), mobile station, mobile terminal, and so on. 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 ( internet of things, IOT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearables, smart transportation, smart city, etc. Terminals can be mobile phones, tablet computers, computers with wireless transceiver functions, wearable devices, vehicles, drones, helicopters, airplanes, ships, robots, robotic arms, smart home devices, etc. The embodiment of the present application does not limit the specific technology and specific device form adopted by the terminal.

Base stations and terminals can be fixed or mobile. Base stations and terminals can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; they can also be deployed on aircraft, balloons and artificial satellites in the air. The embodiments of the present application do not limit the application scenarios of the base station and the terminal.

The roles of the base station and the terminal can be relative. For example, the helicopter or UAV 120i in FIG. base station; however, for base station 110a, 120i is a terminal, that is, communication between 110a and 120i is performed through a wireless air interface protocol. Of course, communication between 110a and 120i may also be performed through an interface protocol between base stations. In this case, compared to 110a, 120i is also a base station. Therefore, both the base station and the terminal can be collectively referred to as a communication device, 110a and 110b in FIG. 1 can be referred to as a communication device with a base station function, and 120a-120j in FIG. 1 can be referred to as a communication device with a terminal function.

The communication between the base station and the terminal, between the base station and the base station, and between the terminal and the terminal can be carried out through the licensed spectrum, the communication can also be carried out through the unlicensed spectrum, and the communication can also be carried out through the licensed spectrum and the unlicensed spectrum at the same time; Communications may be performed on frequency spectrums below megahertz (gigahertz, GHz), or communications may be performed on frequency spectrums above 6 GHz, or communications may be performed using both frequency spectrums below 6 GHz and frequency spectrums above 6 GHz. The embodiments of the present application do not limit the frequency spectrum resources used for wireless communication.

In the embodiments of the present application, the functions of the base station may also be performed by modules (such as chips) in the base station, or may be performed by a control subsystem including the functions of the base station. The control subsystem including base station functions here may be the control center in the application scenarios of the above-mentioned terminals such as smart grid, industrial control, intelligent transportation, and smart city. The functions of the terminal may also be performed by a module (such as a chip or a modem) in the terminal, or may be performed by a device including the terminal function.

In this application, the base station sends a downlink signal or downlink information to the terminal, and the downlink information is carried on the downlink channel; the terminal sends an uplink signal or uplink information to the base station, and the uplink information is carried on the uplink channel.

In order to facilitate the understanding of the embodiments of the present application, the application scenarios of the present application are introduced next. The network architecture and business scenarios described in the embodiments of the present application are for the purpose of more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a reference to the implementation of this application. Those skilled in the art know that, with the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

A multi-antenna system usually configures multiple transceiver antennas on the network device side to increase system capacity by exploring and utilizing spatial dimension resources. A key factor for improving the downlink capacity of a multi-antenna system is to obtain more accurate downlink channel state information (CSI) at the network device side.

In a time division duplex (TDD) system, after channel calibration, the downlink channel state information CSI can be estimated from the uplink sounding reference signal (SRS) sent by the terminal device because of the reciprocity of the uplink and downlink channels. . If the channel of the time division duplex TDD system is not calibrated or the calibration error is large, the uplink and downlink equivalent baseband channels between the network equipment and the terminal equipment are not reciprocal, and the downlink channel state information CSI needs to be fed back from the terminal equipment to the network equipment.

Frequency division duplex (frequency division duplex, FDD) system does not have channel reciprocity due to the difference between uplink and downlink frequency points (such as uplink 2.1G and downlink 3.5G), and downlink channel state information CSI can only be sent to network equipment through terminal equipment. feedback.

In the downlink channel state information CSI feedback process, in an example, the network device sends a downlink channel state information reference signal (channel state information reference signal, CSI-RS). The terminal device estimates the downlink channel based on the received downlink CSI-RS, then selects the codebook index that best matches the downlink channel from the predefined codebook set, and then feeds back the selected codebook index to the network through the uplink channel equipment. Limited by the uplink feedback overhead, the terminal device uses a finite state codebook to quantize the real channel, and there is an inevitable quantization error between the codebook and the real channel, which will limit the acquisition of network equipment (acquisition can also be called estimation) Accuracy of downlink channel state information CSI.

In order to obtain the downlink channel state information CSI more accurately, statistical information of the downlink channel, especially statistical covariance information of the downlink channel may be used for acquisition.

As shown in FIG. 2 , a schematic flow chart of communicating based on the statistical covariance of the downlink channel is introduced.

Some or all (one or more) antennas in the terminal device send an uplink reference signal (for example, SRS) to the network device. The network device performs channel estimation based on the received uplink reference signal, and estimates an uplink channel estimation matrix of a channel between each transmitting antenna of the terminal device and the network device. The uplink channel estimation matrix may be a matrix or a vector (a vector is a one-dimensional matrix).

Then, the network device determines the statistical covariance matrix of the downlink channel based on the uplink channel estimation matrix.

The statistical covariance matrix of the downlink channel can be used for downlink pilot weighting, and then downlink reference signals are sent. In addition, the statistical covariance matrix of the downlink channel can be used for single-user/multi-user precoding, and then downlink data is sent.

Next, in order to facilitate the understanding of the embodiments of the present application, some terms of the embodiments of the present application are explained below, so as to facilitate the understanding of those skilled in the art.

1) The power spectrum represents the physical characteristics of the channel. The first power spectrum hereinafter may be one or more combinations of angle power spectrum, time delay power spectrum, and Doppler power spectrum. The second power spectrum hereinafter may be one or more combinations of angle power spectrum, delay power spectrum, and Doppler power spectrum.

The angular power spectrum describes the distribution relationship of channel power with spatial angle, for example, the X axis represents the angle, and the Y axis represents the channel power.

The delay power spectrum describes the distribution of channel power with delay.

The Doppler power spectrum describes the distribution of channel power with Doppler frequency.

2) Transpose: The new matrix obtained by exchanging the rows and columns of the matrix A is called the transposed matrix A ^T , which is usually represented by "right corner mark T". A is an m*n matrix, and the transposed matrix A ^T is an n*m matrix.

For example,

3) Conjugate transposition generally refers to a mathematical transformation performed by an m*n matrix A, wherein any element a _ij in the matrix A belongs to the field C of complex numbers.

The symbol of the conjugate transposition corresponds to the ordinary transpose "right corner mark T", and the "H right corner mark" is usually used to represent the conjugate transpose. The matrix A ^H after the conjugate transpose is called the conjugate transpose matrix of A , A ^H is n*m type. For example, first take the conjugate of each element a _ij in A to get b _ij (the product of two mutually conjugate complex numbers is equal to the square of the modulus of this complex number, and the conjugation is usually represented by "*right corner mark"), and the new The obtained new m*n matrix composed of b _ij is denoted as matrix B, B=A*; then the ordinary transposition of matrix B is performed to obtain B ^T , which is the conjugate transposition matrix of A: B ^T =A ^H .

4), vec(·) means vectorization operation.

5), Kronecker product (Kronecker product) is an operation between two matrices of any size, expressed as

For example, A is a matrix of m*n, B is a matrix of p*q,

is a block matrix of mp*nq. For example:

6), Hadamard (Hadamard) product, represented by ⊙, the Hadamard product (Hadamard) product of matrix A and B is the product of the two corresponding positions, and the number of rows and columns of the two matrices are the same, for example, two m *n matrix multiplication.

7), diag(ω), used to construct a diagonal matrix, put the column vector ω on the diagonal, and the square matrix whose elements are not all 0 on the diagonal, for example

8), statistical covariance matrix, defined as: the statistical average of the autocorrelation matrix of random matrix (this matrix can be column vector).

For example, the statistical covariance matrix of the uplink/downlink channel can be obtained by calculating the autocorrelation matrix for the uplink/downlink channel estimation matrix, and multiple uplink channel estimation matrices can obtain multiple autocorrelation matrices, and average a large number of autocorrelation matrices get.

Autocorrelation matrix: This matrix is multiplied by the conjugate transpose of this matrix, for example, the autocorrelation matrix of matrix A is A*A ^H .

9) The frequency (frequency) refers to the transmission (eg sending) frequency of the wireless signal, for example, 1850MHz, 1910MHz.

Bandwidth (bandwidth) refers to a frequency bandwidth, for example, 20MHz, 40MHz. For example, the bandwidth between the frequency 1870MHz and the frequency 1890MHz is 20MHz.

Frequency band (band), from the frequency of 1850MHz to the frequency of 1890MHz can be regarded as a frequency band, or can be divided into multiple frequency bands.

The frequency points are numbers for fixed frequencies. For example, when the frequency interval is 20MHz, it is divided into three frequency bands from frequency 1850MHz to frequency 1890MHz: 1850MHz-1870MHz, 1870MHz-1890MHz, and 1890MHz-1910MHz. Each channel is numbered, for example, 1 , 2, 3, the numbers of these fixed frequencies are the frequency points.

10), L2 norm refers to the modular square sum of each element of the vector, and then find the square root.

11), positive semi-definite, a matrix is positive semi-definite means that the complex quadratic form f(x1,x2,...,xn) corresponding to the matrix is for any set of complex numbers c1,c2,... ., cn all have f(c1,c2,...,cn)>=0. Or, it means that the matrix A is a conjugate symmetric matrix, and for any non-zero vector x x ^H *A*x≥0, A is called a positive semi-definite matrix.

12). The dimension of the matrix introduced in this application refers to the number of rows and columns of the matrix. For example, when the dimension is A×B, it means that the number of rows of the matrix is A and the number of columns is B.

For example, the dimension M _H M _V ×1 means that the number of rows of the matrix is M _H M _V and the number of columns is 1.

For example, the dimension M _H M _V M _F ×1 means that the number of rows of the matrix is M _H M _V M _F and the number of columns is 1.

For example, the dimension M _H M _V M _F M _T ×1 means that the number of rows of the matrix is M _H M _V M _F M _T and the number of columns is 1.

For example, the dimension M _H ×M _H O _H means that the number of rows of the matrix is M _H and the number of columns is M _H O _H .

13), the uplink frequency point/uplink frequency and downlink frequency point/downlink frequency in this application can be the frequency point/frequency belonging to the same frequency band (such as 2.1G, 3.5G frequency band), or the frequency point/frequency belonging to different frequency bands Frequency (for example, the uplink frequency belongs to the 3.5G frequency band, and the downlink frequency belongs to the 2.1G frequency band).

14), "and/or" in this application describes the relationship between related objects, indicating that there may be three relationships, for example, A and/or B, which can mean: A exists alone, A and B exist simultaneously, and there exists alone B these three situations. The character "/" generally indicates that the contextual objects are an "or" relationship. A plurality referred to in this application refers to two or more than two. In addition, it should be understood that in the description of this application, words such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or imply order.

As shown in FIG. 3 , a schematic diagram of a process in which a network device determines statistical covariance of a downlink channel based on an uplink channel estimation matrix is introduced.

Step 301: The network device calculates the statistical covariance matrix of the uplink channel based on the uplink channel estimation matrix.

For example, the statistical covariance matrix is a spatial statistical covariance matrix.

For example, the statistical covariance matrix of the uplink channel can be obtained by calculating the autocorrelation matrix of the uplink channel estimation matrix, multiple uplink channel estimation matrices can obtain multiple autocorrelation matrices, and average a large number of autocorrelation matrices.

Step 302: The network device uses the statistical covariance matrix of the uplink channel to estimate the angular power spectrum of the channel.

The angular power spectrum represents the physical characteristics of the channel, and describes the distribution of channel energy with the spatial angle. It is generally believed that the angular power spectrum of the uplink and downlink is reciprocal.

Exemplarily, based on the mapping relationship between the (spatial) statistical covariance matrix of the uplink channel and the angular power spectrum, combined with the minimum L2 norm distance criterion, the angular power spectrum is estimated.

Step 303: Using the angular power spectrum of the channel, determine the statistical covariance matrix of the downlink channel.

This step utilizes the reciprocity of the angular power spectrums of the uplink channel and the downlink channel.

Exemplarily, the statistical covariance of the downlink channel is determined by using a transformation matrix corresponding to the angular power spectrum and the downlink channel (for example, an oversampled discrete Fourier transform (discrete fourier transform, DFT) matrix).

The oversampled DFT matrix is, for example, a spatially oversampled DFT matrix.

The solution shown in FIG. 3 utilizes the relationship between the (space) statistical covariance of the uplink channel and the angular power spectrum to obtain the (space) statistical covariance matrix of the downlink channel.

When estimating the angular power spectrum in step 302, non-negativity constraints are not considered, and the estimated angular power spectrum may contain negative elements and have sidelobe leakage, resulting in a decrease in the accuracy of the (spatial) statistical covariance of the determined downlink channel. In addition, the scheme shown in Figure 3 can only determine the spatial statistical covariance, but cannot determine the statistical covariance of other dimensions (such as time, frequency), or generalize from the spatial statistical covariance to two or three terms in space, frequency, and time. When the joint statistical covariance is used, the complexity is greatly increased and it is difficult to achieve.

Based on this, the present application proposes a new method for determining the statistical covariance of the channel. In the new method, the uplink channel estimation matrix is transformed, and the average energy of the transformed matrix is counted, and then the power spectrum is determined based on the average energy. Then, based on the determined power spectrum, the statistical covariance matrix of the downlink channel is obtained.

Compared with the example provided in Figure 3 above, in this example, the statistical average energy needs to be obtained (and can also be stored) instead of the statistical covariance of the uplink channel; the relationship between the statistical average energy and the power spectrum is used , to estimate the power spectrum, instead of using the relationship between the statistical covariance and the power spectrum to estimate the power spectrum. The estimation method of the present application is relatively simple, and can be applied to the scene of calculating statistical covariance of one or more items in the space domain, frequency domain, and time domain, and is easy to popularize.

Embodiment one:

As shown in FIG. 4 , it provides a schematic diagram of a method for a network device to determine statistical covariance of a downlink channel.

Step 401: The network device performs channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix.

Step 402: The network device transforms the uplink channel estimation matrix based on the first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to the uplink channel.

Mathematical explanation: used to transform the uplink channel estimation matrix from the space domain to the angle domain, and/or transform the uplink channel estimation matrix from the frequency domain to the delay domain, and/or transform the uplink channel estimation matrix The matrix transforms from the time domain to the Doppler domain.

Step 403: The network device determines the first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is: performing statistics on the energy corresponding to some or all elements in the first channel estimation matrix get average.

Step 404: The network device determines a first power spectrum based on the first statistical average energy, where there is a mapping relationship between the first statistical average energy and the first power spectrum.

Step 405: The network device determines a statistical covariance matrix of the downlink channel based on the first power spectrum and the second transformation matrix; the second transformation matrix is a matrix related to the downlink channel.

Subsequently, the network device can send data and/or reference signals based on the statistical covariance matrix of the downlink channel.

The method is simple, can accurately determine the statistical covariance of the channel, is more suitable for downlink channel characteristics, and is beneficial to performance improvement.

The parameters involved in the first embodiment are as follows:

Next, the related process of step 401: performing channel estimation based on the received uplink reference signal to obtain the uplink channel estimation matrix will be introduced.

The terminal device may periodically send the uplink reference signal, and the terminal device may use one or more transmitting antennas to send the uplink reference signal. The terminal device may send the uplink reference signal at a certain uplink frequency point. The network device receives the uplink reference signal from the terminal device, and the network device performs channel estimation based on the uplink reference signal to obtain an uplink channel estimation matrix.

When the network device determines the uplink channel estimation matrix, one or more factors of space (such as antenna), frequency (such as the frequency in the bandwidth corresponding to the frequency point), and time (such as period) may be considered.

In an optional example a, the network device considers space (antenna) factors to determine the uplink channel estimation matrix. For example, for each transmitting antenna of the terminal device, the network device determines the uplink channel estimation matrix corresponding to the transmitting antenna based on the received uplink reference signal from the transmitting antenna. That is, one transmit antenna corresponds to one uplink channel estimation matrix. If the terminal device uses multiple transmit antennas to transmit the uplink reference signal, multiple uplink channel estimation matrices can be determined.

In an optional example b, the network device determines the uplink channel estimation matrix considering the frequency factor. For example, the network device performs channel estimation on each resource block. In this case, the uplink channel estimation matrix is obtained by combining channel estimation matrices corresponding to multiple resource blocks RB. For example, the total number of resource blocks is M _F , and M _F is an integer greater than or equal to 1. The channel estimation matrix corresponding to the m _Fth resource block is

It can be understood that m _F ranges from 1 to M _F , and t is the time when the network device receives the uplink reference signal, or is related to the time when the uplink reference signal is received. Combine the channel estimation matrices of all RBs to obtain the uplink channel estimation matrix h _t .

When _MF is equal to 1,

Alternatively, when the frequency factor is not considered, the total number of resource blocks _MF can also be regarded as 1, then

When _MF is greater than 1, the uplink channel estimation matrix h _t can be the channel estimation matrix of all RBs

The combination. The uplink channel estimation matrix is taken as an example for illustration. For example, splicing the channel estimation matrices of all RBs into a column vector satisfies the following formula:

Among them, vec( ) represents a vectorized operation.

In an optional example c, the network device determines the uplink channel estimation matrix considering time and frequency factors. Regarding the time factor, for example, not only the currently determined uplink channel estimation matrix but also the historical uplink channel estimation matrix may be considered. For example, splicing the uplink channel estimation matrix at time t and the most recent M _T -1 historical moments into a column vector satisfies the following formula:

Wherein, h _t represents the uplink channel estimation matrix, and M _T represents the time window length for estimating the Doppler power spectrum.

In an example, a two-dimensional rectangular antenna array is configured in the network device, the number of horizontal antennas is M _H , and the number of vertical antennas is M _V .

The channel estimation matrix corresponding to the m _Fth resource block

The dimension of is, for example, M _H M _V ×1, and the channel estimation matrix is a column vector, corresponding to the arrangement of the antennas: first horizontally, then vertically. This dimension can also have other deformations, as long as the number of elements in the matrices of multiple deformed dimensions is the same. For example the dimension is M _H ×M _V , or the dimension is M _V ×M _H .

when

When , the dimension of the uplink channel estimation matrix h _t is, for example, M _H M _V ×1, and the uplink channel estimation matrix is a column vector. Either the dimension is M _H ×M _V , or the dimension is M _V ×M _H .

When _MF is greater than 1, the dimension of the uplink channel estimation matrix h _t is M _H M _V M _F ×1, and the uplink channel estimation matrix is a column vector, where _MF is the total number of resource blocks. This dimension can also have other deformations, as long as the number of elements in the matrices of multiple deformed dimensions is the same. For example, the dimension is M _H M _V ×M _F , or the dimension is M _H ×M _V M _F .

When _MF is greater than 1, the dimension of the uplink channel estimation matrix h _t is M _H M _V M _F M _T ×1, and the uplink channel estimation matrix is a column vector. This dimension can also have other deformations, as long as the number of elements in the matrices of multiple deformed dimensions is the same. For example, the dimension is M _H M _V ×M _F M _T , or the dimension is M _H ×M _V M _F M _T . Or the dimension is M _H M _V M _F × M _T .

Hereinafter, the uplink channel estimation matrix is taken as an example for illustration.

Next, step 402: transforming the uplink channel estimation matrix based on the first transformation matrix (there may be one or more first transformation matrices) to obtain the related process of the first channel estimation matrix is introduced.

When transforming the uplink channel estimation matrix, one or more first transformation matrices may be used.

The type of one or more first transformation matrices may be a discrete cosine transform (discrete cosine transform, DCT) matrix, or a Hadamard transform matrix, or a DFT matrix, or an oversampled DFT matrix. It should be noted that, for these types, the types of the multiple first transformation matrices are usually the same.

When the type of the first transformation matrix is a discrete cosine transformation DCT matrix, the first transformation matrix is referred to as a first discrete cosine transformation DCT matrix. When the type of the first transformation matrix is a Hadamard transformation matrix, the first transformation matrix is called a first Hadamard transformation matrix. When the type of the first transformation matrix is a discrete Fourier transform DFT matrix, the first transformation matrix is referred to as a first discrete Fourier transform DFT matrix. When the type of the first transformation matrix is an oversampled DFT matrix, the first transformation matrix is referred to as a first oversampled DFT matrix.

In addition, a first transformation matrix may be obtained based on any transformation matrix in a space domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. Then at least one first transformation matrix may be obtained based on at least one of the following types of matrices: a space domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The transformation matrix used to determine the space domain type of the first transformation matrix is called the first space domain transformation matrix, and the transformation matrix used to determine the frequency domain type of the first transformation matrix is called the first frequency domain transformation matrix. The transformation matrix used to determine the time-domain type of the first transformation matrix is called the first time-domain transformation matrix.

It can be understood that when the type of at least one first transformation matrix is a discrete cosine transform DCT matrix, and the at least one first transformation matrix is based on at least one type of space domain transformation matrix, frequency domain transformation matrix, and time domain transformation matrix When matrices are obtained, at least one first transformation matrix may be regarded as obtained based on at least one type of matrix obtained from space domain DCT matrix, frequency domain DCT matrix, and time domain DCT matrix. When the type of at least one first transformation matrix is an oversampled DFT matrix, and at least one first transformation matrix is obtained based on at least one type of matrix in a space domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, at least one The first transformation matrix can be regarded as being obtained based on at least one type of matrix among a space-domain oversampling DFT matrix, a frequency-domain oversampling DFT matrix, and a time-domain oversampling DFT matrix. Several other types of matrices are similar and will not be repeated here.

Optionally, the space domain matrix can also be divided into a space domain horizontal matrix and a space domain vertical matrix.

The first transformation matrix obtained based on the spatial domain horizontal matrix is denoted as F _H , and its dimension is, for example, M _H ×M _H _OH , where _OH represents the oversampling multiple of the spatial domain level, and M _H represents the number of horizontal antennas. It can be understood that the dimension can have other deformations, for example, the dimension is M _H O _H ×M _H .

The first transformation matrix obtained based on the space-domain vertical matrix is denoted as F _V , and its dimension is, for example, M _V ×M _V O _V , where O _V represents the vertical oversampling multiple of the space domain, and M _V represents the number of vertical antennas. It can be understood that the dimension can have other deformations, for example, the dimension is M _V O _V ×M _V .

The first transformation matrix obtained based on the frequency domain matrix is denoted as _FF , and its dimension is, for example, M _F ×M _F O _F , where _OF represents the oversampling multiple in the frequency domain, and _MF represents the total number of resource blocks. It can be understood that the dimension can have other deformations, for example, the dimension is M _F O _F ×M _F .

The first transformation matrix obtained based on the time-domain matrix is denoted as F _T , and its dimension is, for example, M _T × M _T O _T , where O _T represents the oversampling multiple in the time domain, and M _T represents the time used to estimate the Doppler power spectrum window length. It can be understood that the dimension can have other deformations, for example, the dimension is M _T O _T ×M _T .

In addition, when there is no oversampling, for example, DCT matrix, DFT matrix, and Hadamard transform matrix do not have oversampling operations. In this case, _OH , O _V , _OF , and O _T can all be 1.

It should be noted that the first transformation matrix (for example, F _H , F _V , F _F , F _T ) introduced here is used to transform the uplink channel estimation matrix, and the first transformation matrix corresponds to the uplink. Also introduced below is a second transformation matrix (e.g.,

), the second transformation matrix corresponds to the downlink, and is used to determine the statistical covariance matrix of the downlink channel.

Optionally, each matrix in F _H , F _V , F _F , and F _T satisfies the following condition: the L2 norm of each column of the matrix is 1, which can be understood as the L2 norm of a column vector refers to the The sum of the squares of the elements in the column vector is equal to 1 when taken as the square root.

Exemplarily, F _H , F _V , F _F , and F _T satisfy the following formulas. Let F denote a matrix with dimension M×MO, whose m row and n column elements are:

Wherein, the value of m is an integer from 1 to M, and the value of n is an integer from 1 to M*0. Wherein, M may correspond to M _H , M _V , _MF , _MT introduced above; O may correspond to OH _, O _V , _OF , _OT introduced above. For example, when the formula is applied to the generator matrix F _H , M in the formula is M _H , and O in the formula is _OH . F _V , F _F , and _FT are similar and will not be introduced one by one.

Transforming the uplink channel estimation matrix based on the first transformation matrix to obtain the first channel estimation matrix:

For example: one or more first transformation matrices are multiplied by the uplink channel estimation matrix to obtain the first channel estimation matrix.

For example, based on one or more first transformation matrices, and the Kronecker product

One or more algorithms such as transposition, conjugate transposition, etc., to obtain the first channel estimation matrix.

For example, the Kronecker product of multiple first transformation matrices

Multiply the uplink channel estimation matrix to obtain the first channel estimation matrix.

For example, the Kronecker product of multiple first transformation matrices

The conjugate transpose of the obtained matrix is multiplied by the uplink channel estimation matrix to obtain the first channel estimation matrix.

In an optional example a, the first channel estimation matrix satisfies the following formula:

For example, the dimension of F _H is M _H ×M _H O _H , the dimension of F _V is M _V ×M _V O _V , the dimension of F _F is M _F ×M _F O _F , and the dimension of F _T is M _T ×M _T O _T , The dimension of h _t is M _H M _V M _F M _T ×1, and the dimension of g _t is M _H M _V M _F M _T O _H O _V O _F O _T ×1.

In an optional example b, the first channel estimation matrix satisfies the following formula:

For example, the dimension of F _H is M _H ×M _H O _H , the dimension of F _V is M _V ×M _V O _V , the dimension of F _F is M _F ×M _F O _F , and the dimension of h _t is M _H M _V M _F ×1 , the g _t dimension is M _H M _V M _F O _H O _V O _F ×1.

In an optional example c, the first channel estimation matrix satisfies the following formula:

For example, the dimension of F _H is M _H ×M _H O _H , the dimension of F _V is M _V ×M _V O _V , the dimension of h _t is M _H M _V ×1,

The dimension of is M _H M _V O _H O _V ×1. in,

Optionally, this application can also take the Kronecker product of multiple matrices in the four matrices F _H , F _V , F _F , and F _T

Considered as the first transformation matrix. For example, the first transformation matrix is

or the first transformation matrix is

Optionally, this application can also regard the conjugate transposition matrix of the matrix obtained by the Kronecker product of multiple matrices among the four matrices F _H , F _V , F _F , and F _T as the first transformation matrix, for example, the first transformation matrix is

or the first transformation matrix is

where H represents the conjugate transpose.

Transform the uplink channel estimation matrix. If the channel estimation matrix after transformation is sparse (for example, a vector of 100*1, only 10 elements have relatively large values after transformation, and other values are close to 0. Elements close to 0 can be filtered division) can be regarded as compressing the row channel estimation matrix, which can reduce storage overhead.

Next, a related process of step 403: determining (one or more) first statistical average energies corresponding to the first channel estimation matrix will be introduced.

The first statistical average energy is obtained by statistically averaging the energies respectively corresponding to some or all elements in one or more first channel estimation matrices. For example, energy corresponding to some or all elements in the first channel estimation matrix may be determined based on calculation methods such as Hadamard product ⊙, conjugate (·) ^*, etc.; energy of elements may be statistically averaged based on expected E.

The multiple first uplink channel estimation matrices here may be obtained based on one or more factors such as multiple transmitting antennas, multiple frequencies, and multiple periods. For example, one first uplink channel estimation matrix corresponds to one transmitting antenna, and one first uplink channel estimation matrix corresponds to multiple transmitting antennas. For example, one frequency corresponds to one first uplink channel estimation matrix, and multiple frequencies correspond to one first uplink channel estimation matrix. For example, one uplink channel estimation matrix is determined in one period, and multiple uplink channel estimation matrices are determined in multiple periods.

In an optional example, the first statistical average energy satisfies the following formula:

Among them, g _t is the first channel estimation matrix, φ represents the first statistical average energy, E represents the expectation, which can be obtained by statistically averaging one or more first channel estimation matrices, ⊙ represents the Hadamard product, which is used to represent The product of the corresponding positions of the two matrices, ( ) ^* represents conjugation, taking the conjugation of each element a _ij in the matrix g _t to obtain b _ij (the product of two mutually conjugate complex numbers is equal to the square of the complex modulus, The conjugate is usually represented by "*right corner mark"), and the newly obtained new matrix composed of b _ij is recorded as the matrix

Understandably, g _t can also be replaced by

When statistical averaging is performed, statistical averaging may be performed for one or more factors such as different times, different transmitting antennas, and different frequencies. One transmit antenna of the terminal equipment corresponds to one first channel estimation matrix, and one transmit antenna corresponds to one

yes

It is obtained by performing statistical averaging on different transmit antennas of the terminal equipment in time, for example, for multiple data obtained at different times and on different transmit antennas

Perform statistical averaging. For example, for multiple data acquired at different times and frequencies

Perform statistical averaging.

In an example, the first channel estimation matrix is a column vector, for example, the dimension of g _t is M _H M _V M _F M _T O _H O _V O _F O _T ×1, or M _H M _V M _F ×1 , or M _H M _V O _H O _V ×1. Correspondingly, the first statistical average energy is a column vector, and the dimension of the first statistical average energy is, for example, M _H M _V M _F M _T O _H O _V O _F O _T ×1, or M _H M _V M _F ×1 , or M _H M _V O _H O _V ×1.

Next, a related process of step 404: determining the first power spectrum based on the first statistical average energy is introduced.

There is a mapping relationship between the first statistical average energy and the first power spectrum, and the mapping relationship satisfies the following formula:

Tω=φ,

Wherein, ω is the first power spectrum, φ is the first statistical average energy, T is a mapping matrix, and T is related to the first transformation matrix.

An optional example, the first power spectrum is a column vector.

It can be understood that the first channel estimation matrix is obtained based on which matrices among F _H , F _V , _FF , and _FT , and the mapping matrix T is also obtained based on these matrices. In addition, the first power spectrum also represents a corresponding power spectrum, and the first power spectrum may be one or a combination of angle power spectrum, delay power spectrum, and Doppler power spectrum. Wherein, the angle power spectrum corresponds to the space domain, the delay power spectrum corresponds to the frequency domain, and the Doppler power spectrum corresponds to the time domain.

For example, when the first transformation matrix is obtained based on a space domain matrix (eg F _H , F _V ), the first power spectrum is an angular power spectrum.

For example, when the first transformation matrix is obtained based on a frequency domain matrix (such as _FF ), the first power spectrum is a delay power spectrum.

For example, when the first transformation matrix is obtained based on a time-domain matrix (such as _FT ), the first power spectrum is a Doppler power spectrum.

For example, when the first transformation matrix is obtained based on a space domain matrix or a frequency domain matrix (such as F _H , F _V , F _F ), the first power spectrum is a combination of an angle power spectrum and a delay power spectrum.

For example, when the first transformation matrix is obtained based on a space domain matrix, a frequency domain matrix, and a time domain matrix (such as F _H , F _V , F _F , F _T ), the first power spectrum is the angle power spectrum, the delay power spectrum and Combination of Doppler power spectra.

When the first power spectrum is a combined power spectrum of angle power spectrum, delay power spectrum, and Doppler power spectrum, the subsequently determined statistical covariance matrix of the downlink channel is a joint statistical covariance matrix of space, frequency, and time.

An optional example, each element in the first power spectrum is a non-negative real value. In this way, the determined statistical covariance matrix of the downlink channel can be guaranteed to be positive semi-definite, and the precision of the determined statistical covariance of the downlink channel can be improved. This can solve the problem that the power spectrum cannot be guaranteed to be non-negative in FIG. 3 .

The mapping matrix T is related to the first transformation matrix, for example, the mapping matrix T is related to one or more matrices among F _H , F _V , F _F , and F _T . For example, the mapping matrix T is based on one or more matrices in F _H , F _V , F _F , F _T , and based on conjugate, conjugate transpose, Hadamard product ⊙, Kronecker product

One or more of the algorithms are determined.

An optional example a,

An optional example b,

An optional example c,

An optional example,

In this application, the minimum L2 norm distance criterion, or the minimum KL divergence criterion, or the minimum L0 norm criterion can be used to determine the first power spectrum based on the first statistical average energy (for example, based on T and φ, estimate ω).

In the three examples provided below, the constraints

Indicates that every element in ω is non-negative.

In an example 1, when the minimum L2 norm distance criterion is used, it can be modeled as the following optimization problem:

st

The above optimization problem is a standard non-negative least square (NNLS) problem, which can be solved using the existing NNLS algorithm.

In an example 2, when the minimum KL divergence criterion is used, it can be modeled as the following optimization problem:

st

in order to avoid

The constraints of , let λ represent the root mean square of each element of ω, that is, ω=λ⊙λ, we can get:

Deriving the objective function on λ, and setting the derivative to zero, we can get:

T ^T q⊙λ-T ^T 1⊙λ=0;

Among them, 1 represents a column vector in which all elements are 1, and the dimension of the column vector is, for example, M _H M _V M _F M _T O _H O _V O _F O _T ×1, or M _H M _V M _F O _H O _V O _F ×1, or M _H M _V O _H O _V ×1 or other dimensions. and,

Therefore, the following iterative process is constructed:

for n=0:N _Iter

if n==0

else

end

Among them, for can be understood as "loop execution", and the value of n is 0 to N _Iter . N _Iter represents the number of iterations,

Represents the pseudo-inverse, max(a,b) represents the maximum value of a and b. output after iteration

In an example 3, when the minimum L0 norm criterion is used, it can be modeled as the following optimization problem:

s.t.Tω=φ

It can be solved using the matching pursuit (MP) algorithm, and the specific process is as follows:

Step 1: Find the largest term in φ

Record its corresponding position n _max and add it to the recovered angle-delay Doppler power spectrum ω:

The initial value of ω is the zero vector.

Step 2: Subtract φ from

If an element of φ is less than zero after subtraction and cancellation, it is set to zero:

Step 3: Perform power correction on each element of ω found before:

Step 4: Repeat the above three steps until the largest element in φ is smaller than the preset threshold or the number of cycles reaches the preset maximum value. The power threshold is generally set to the first found

1%, the maximum number of cycles is generally set to 50, and can also be other values, such as 40, or 30, or 60, etc.

Next, the related process of step 405: determining the statistical covariance matrix of the downlink channel based on the first power spectrum and the second transformation matrix will be introduced.

When determining the statistical covariance matrix of the downlink channel, one or more second transformation matrices may be used.

The type of one or more second transform matrices may be a discrete cosine transform DCT matrix, or a Hadamard transform matrix, or a DFT matrix, or an oversampled DFT matrix. It should be noted that, for these types, the types of the multiple second transformation matrices are usually the same. For these types, the types of the first transformation matrix and the second transformation matrix may be the same or different.

When the type of the second transformation matrix is a discrete cosine transformation DCT matrix, the second transformation matrix is referred to as a second discrete cosine transformation DCT matrix. When the type of the second transformation matrix is a Hadamard transformation matrix, the second transformation matrix is called a second Hadamard transformation matrix. When the type of the second transformation matrix is a discrete Fourier transform DFT matrix, the second transformation matrix is referred to as a second discrete Fourier transform DFT matrix. When the type of the second transformation matrix is an oversampled DFT matrix, the second transformation matrix is called a second oversampled DFT matrix.

In addition, a second transformation matrix may be obtained based on any transformation matrix in a space domain transformation matrix, a frequency domain transformation matrix, or a time domain transformation matrix. Then at least one second transformation matrix may be obtained based on at least one of the following types of matrices: a space domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The transformation matrix used to determine the space domain type of the second transformation matrix is called a second space domain transformation matrix, and the transformation matrix used to determine the frequency domain type of the second transformation matrix is called a second frequency domain transformation matrix. The transformation matrix used to determine the time-domain type of the second transformation matrix is called the second time-domain transformation matrix.

It can be understood that when the type of at least one second transformation matrix is a discrete cosine transform DCT matrix, and the at least one second transformation matrix is based on at least one type of space domain transformation matrix, frequency domain transformation matrix, and time domain transformation matrix When the matrix is obtained, at least one second transformation matrix can be regarded as obtained based on at least one type of matrix obtained from space domain DCT matrix, frequency domain DCT matrix and time domain DCT matrix. When the type of at least one second transformation matrix is an oversampled DFT matrix, and at least one second transformation matrix is obtained based on at least one type of matrix in a space domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, at least one The second transformation matrix can be regarded as being obtained based on at least one type of matrix among a space-domain oversampling DFT matrix, a frequency-domain oversampling DFT matrix, and a time-domain oversampling DFT matrix. Several other types of matrices are similar and will not be repeated here.

For the type of matrix mentioned above, discrete cosine transform DCT matrix, or Hadamard transform matrix, or DFT matrix, or oversampled DFT matrix, the type of the second transformation matrix and the first transformation matrix can be the same or different, for example The first transformation matrix is a DCT matrix, and the second transformation matrix is a Hadamard transformation matrix. When the first transformation matrix and the second transformation matrix are of the same type, the specific content of the first transformation matrix and the second transformation matrix may be the same or different. It should be noted that for the three types of space domain, frequency domain, and time domain, the types of the first transformation matrix and the second transformation matrix are the same. For example, the first transformation matrix is obtained based on the space domain transformation matrix, and the second transformation matrix The matrix is also obtained based on the space domain transformation matrix, or the first transformation matrix is two, respectively obtained based on the frequency domain transformation matrix and the time domain transformation matrix, then the second transformation matrix is also two, respectively based on the frequency domain transformation matrix, time domain transformation matrix Domain transformation matrix is obtained.

The second transformation matrix obtained based on the spatial domain horizontal matrix is denoted as

Dimensions are M _H ×M _H _OH , O _H , indicating the oversampling multiple of the spatial domain level, and M _H indicating the number of horizontal antennas.

The second transformation matrix obtained based on the space-domain vertical matrix is denoted as

The dimension is M _V ×M _V O _V , where O _V represents the vertical oversampling multiple in the space domain, and M _V represents the number of vertical antennas.

The second transformation matrix obtained based on the frequency domain matrix is denoted as

The dimension is M _F ×M _F OF O _F , where _OF represents the vertical oversampling multiple in the spatial domain, and _MF represents the total number of resource blocks.

Denote the second transformation matrix obtained based on the time domain matrix as

The dimension is M _T ×M _T O _T , where O _T represents the vertical oversampling multiple in the spatial domain, and M _T represents the time window length for estimating the Doppler power spectrum.

Optionally, the number and dimensions of the second transformation matrix are the same as those of the first transformation matrix.

It is introduced above that the first transformation matrix (for example, F _H , F _V , F _F , F _T ) is used to transform the uplink channel estimation matrix, and the first transformation matrix corresponds to the uplink. The second transformation matrix (eg,

) corresponds to the downlink, and is used to determine the statistical covariance matrix of the downlink channel.

optional,

Each matrix in satisfies the following condition: the L2 norm of each column of the matrix is 1.

Exemplary,

satisfy the following formula. Let F denote a matrix with dimension M×MO, whose m row and n column elements are:

Wherein, the value of m is an integer from 1 to M, and the value of n is an integer from 1 to M*0. Wherein, M may correspond to M _H , M _V , _MF , _MT introduced above; O may correspond to OH _, O _V , _OF , _OT introduced above. For example, applying this formula to the generator matrix

When , M in the formula is M _H , and O in the formula is _OH .

Similar and will not be introduced one by one.

Based on the first power spectrum and the second transformation matrix, when determining the statistical covariance matrix of the downlink channel:

For example, based on one or more of the second transformation matrix, the first power spectrum, and the Kronecker product

Transpose, conjugate transpose, diag one or more algorithms to obtain the statistical covariance matrix of the downlink channel.

For example, the Kronecker product of multiple first transformation matrices

Multiplied by diag(ω), the statistical covariance matrix of the downlink channel is obtained.

Among them, ω is the first power spectrum, for example, the first power spectrum is a column vector, diag(ω) is used to indicate that the column vector is placed on the diagonal, for example

For another example, the Kronecker product of multiple first transformation matrices

A matrix is obtained, which is multiplied by diag(ω), and then multiplied by the conjugate transpose of the matrix to obtain the statistical covariance of the downlink channel.

In an optional example a, the statistical covariance matrix R of the downlink channel satisfies the following formula:

For example, the statistical covariance matrix is obtained according to transformation matrices respectively obtained based on the space domain, the frequency domain, and the time domain, and the statistical covariance may be called a joint spatial-frequency-time statistical covariance.

In an optional example b, the statistical covariance matrix of the downlink channel satisfies the following formula:

Exemplarily, the statistical covariance matrix is obtained based on transformation matrices obtained respectively in the space domain and the frequency domain, and the statistical covariance may be called a joint spatial-frequency statistical covariance.

In an optional example c, the statistical covariance matrix of the downlink channel satisfies the following formula:

Exemplarily, the statistical covariance matrix is obtained from a transformation matrix obtained based on the spatial domain, and the statistical covariance may be called spatial statistical covariance.

This application transforms the uplink channel estimation matrix through the first transformation matrix (such as DFT matrix/oversampling DFT matrix) in the space domain, frequency domain and time domain to obtain the first channel estimation matrix; based on the first channel estimation matrix, obtain The first statistical average energy; using the mapping relationship between the first statistical average energy and the angle, time delay, and Doppler power spectrum, the angle, time delay, and Doppler power spectrum are estimated; finally, based on the angle, time delay, and Doppler power spectrum The Doppler power spectrum and the second transformation matrix (such as DFT matrix/oversampling DFT matrix) corresponding to the downlink space domain, frequency domain, and time domain can reconstruct the space, frequency, and time joint statistical covariance of the downlink channel. Optionally, using the mapping relationship between the first statistical average energy and the angle, time delay, and Doppler power spectrum, combined with criteria under non-negative constraints (such as the minimum L2 norm distance criterion, or the minimum KL divergence criterion, or the minimum L0 norm criterion), and estimate the angle, time delay, and Doppler power spectrum.

Embodiment 1 above introduces a process in which a network device determines statistical covariance of a downlink channel so as to send downlink reference signals and/or downlink data. In another second embodiment of the present application, the terminal device also adopts a similar method to determine the statistical covariance of the uplink channel, so as to send the uplink reference signal and/or uplink data.

Embodiment two:

The process of the method shown in the second embodiment is similar to the process of the method shown in the first embodiment, and the uplink and downlink are reversed. Correspondingly, it is also possible to change the terminal device in Embodiment 1 into a network device, change the network device in Embodiment 1 into a terminal device, change the uplink channel estimation matrix in Embodiment 1 into a downlink channel estimation matrix, and change the In one, the statistical covariance matrix of the downlink channel is changed to the statistical covariance matrix of the uplink channel.

In addition, the names of some nouns can also be modified to distinguish them. For example, change the first transformation matrix to the third transformation matrix, and the third transformation matrix is related to the downlink channel; change the first channel estimation matrix to the second channel estimation matrix; change the first statistical average energy to the second statistical average energy , change the first power spectrum to the second power spectrum, change the second transformation matrix to the fourth transformation matrix, and the fourth transformation matrix is related to the uplink channel.

As shown in FIG. 5 , a communication process based on the statistical covariance of the uplink channel is introduced.

Some or all (one or more) antennas on the network device send downlink reference signals to the terminal device. The terminal device performs channel estimation based on the received downlink reference signal, and estimates a downlink channel estimation matrix of a channel between each transmitting antenna of the network device and the terminal device. The downlink channel estimation matrix may be a matrix or a vector (a vector is a one-dimensional matrix).

Then, the terminal device determines the statistical covariance matrix of the uplink channel based on the downlink channel estimation matrix.

The statistical covariance matrix of the uplink channel can be used for uplink pilot weighting, and then the uplink reference signal is sent. In addition, the statistical covariance matrix of the uplink channel can be used for single-user weight calculation, precoding, etc., and then uplink data is sent.

As shown in FIG. 6 , a schematic diagram of a method for determining statistical covariance of an uplink channel by a terminal device is provided.

Step 601: The terminal device performs channel estimation based on the received downlink reference signal, and obtains a downlink channel estimation matrix.

Step 602: The terminal device transforms the downlink channel estimation matrix based on the third transformation matrix to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to the downlink channel.

Step 603: The terminal device determines the second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is: performing statistics on the energy corresponding to some or all elements in the second channel estimation matrix get average.

Step 604: The terminal device determines a second power spectrum based on the second statistical average energy, where there is a mapping relationship between the second statistical average energy and the second power spectrum.

Step 605: The terminal device determines a statistical covariance matrix of the uplink channel based on the second power spectrum and the fourth transformation matrix; the fourth transformation matrix is a matrix related to the uplink channel.

Subsequently, the terminal device can send data and/or reference signals based on the statistical covariance matrix of the uplink channel.

The parameters involved in the second embodiment are as follows:

Regarding the above parameters, the differences from Embodiment 1 include: M _H , M _V represent the number of antennas in the terminal device, not the number of antennas in the network device, F _H , F _V , _FF , _FT correspond to the downlink,

Corresponds to uplink, h _t , h _t , R all correspond to downlink.

Next, step 601: the terminal device performs channel estimation based on the received downlink reference signal to obtain a related process of downlink channel estimation matrix is introduced.

The network device can periodically send the downlink reference signal, and the network device can use one or more transmitting antennas to send the downlink reference signal. The network device may send the downlink reference signal at a certain downlink frequency point. The terminal device receives the downlink reference signal from the network device, and the terminal device performs channel estimation based on the downlink reference signal to obtain a downlink channel estimation matrix.

When determining the downlink channel estimation matrix, the terminal device may consider one or more factors in space (such as antenna), frequency (such as the frequency in the bandwidth corresponding to the frequency point), and time (such as period).

In an optional example a, the terminal device considers space (antenna) factors to determine the downlink channel estimation matrix. For example, for each transmitting antenna of the network device, the terminal device determines the downlink channel estimation matrix corresponding to the transmitting antenna based on the received downlink reference signal from the transmitting antenna. That is, one transmit antenna corresponds to one downlink channel estimation matrix. If the network device uses multiple transmitting antennas to transmit downlink reference signals, multiple downlink channel estimation matrices can be determined.

In an optional example b, the terminal device determines the downlink channel estimation matrix considering the frequency factor. For example, the terminal device performs channel estimation on each resource block. In this case, the downlink channel estimation matrix is obtained by combining channel estimation matrices corresponding to multiple resource blocks RB. For example, the total number of resource blocks is M _F , and M _F is an integer greater than or equal to 1. The channel estimation matrix corresponding to the m _Fth resource block is

It can be understood that m _F ranges from 1 to M _F , and t is the time when the terminal device receives the downlink reference signal, or is related to the time when the downlink reference signal is received. Combine the channel estimation matrices of all RBs to obtain the downlink channel estimation matrix h _t .

When _MF is equal to 1,

When _MF is greater than 1, the downlink channel estimation matrix h _t can be the channel estimation matrix of all RBs

The combination. The following description is made by taking the downlink channel estimation matrix as a vector as an example. For example, splicing the channel estimation matrices of all RBs into a column vector satisfies the following formula:

Among them, vec( ) represents a vectorized operation.

In an optional example c, the terminal device determines the downlink channel estimation matrix considering time and frequency factors. Regarding the time factor, for example, not only the currently determined downlink channel estimation matrix but also the historical downlink channel estimation matrix may be considered. For example, splicing the downlink channel estimation matrix at time t and the most recent M _T -1 historical moments into a column vector satisfies the following formula:

Wherein, h _t represents the downlink channel estimation matrix, and M _T represents the time window length for estimating the Doppler power spectrum.

In an example, a two-dimensional rectangular antenna array is configured in the terminal device, the number of horizontal antennas is M _H , and the number of vertical antennas is M _V .

The channel estimation matrix corresponding to the m _Fth resource block

when

When , the dimension of the downlink channel estimation matrix h _t is, for example, M _H M _V ×1, and the downlink channel estimation matrix is a column vector. Either the dimension is M _H ×M _V , or the dimension is M _V ×M _H .

When _MF is greater than 1, the dimension of the downlink channel estimation matrix h _t is M _H M _V M _F ×1, and the downlink channel estimation matrix is a column vector, where _MF is the total number of resource blocks. This dimension can also have other deformations, as long as the number of elements in the matrices of multiple deformed dimensions is the same. For example, the dimension is M _H M _V ×M _F , or the dimension is M _H ×M _V M _F .

When _MF is greater than 1, the dimension of the downlink channel estimation matrix h _t is M _H M _V M _F M _T ×1, and the downlink channel estimation matrix is a column vector. This dimension can also have other deformations, as long as the number of elements in the matrices of multiple deformed dimensions is the same. For example, the dimension is M _H M _V ×M _F M _T , or the dimension is M _H ×M _V M _F M _T . Or the dimension is M _H M _V M _F × M _T .

Hereinafter, the downlink channel estimation matrix is taken as a column vector as an example for illustration.

Next, step 602: transforming the downlink channel estimation matrix based on the third transformation matrix (there may be one or more third transformation matrices) to obtain the related process of the second channel estimation matrix is introduced.

When transforming the downlink channel estimation matrix, one or more third transformation matrices may be used.

The type of one or more third transformation matrices may be a discrete cosine transform (discrete cosine transform, DCT) matrix, or a Hadamard transform matrix, or a DFT matrix, or an oversampled DFT matrix. It should be noted that, for these types, the types of the multiple third transformation matrices are usually the same.

When the type of the third transformation matrix is a discrete cosine transformation DCT matrix, the third transformation matrix is referred to as a third discrete cosine transformation DCT matrix. When the type of the third transformation matrix is a Hadamard transformation matrix, the third transformation matrix is called a third Hadamard transformation matrix. When the type of the third transformation matrix is a discrete Fourier transform DFT matrix, the third transformation matrix is referred to as a third discrete Fourier transform DFT matrix. When the type of the third transformation matrix is an oversampled DFT matrix, the third transformation matrix is referred to as a third oversampled DFT matrix.

In addition, a third transformation matrix may be obtained based on any transformation matrix in a space domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. Then at least one third transformation matrix may be obtained based on at least one of the following types of matrices: a space domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The transformation matrix used to determine the space domain type of the third transformation matrix is called the third space domain transformation matrix, and the transformation matrix used to determine the frequency domain type of the third transformation matrix is called the third frequency domain transformation matrix. The transformation matrix used to determine the time-domain type of the third transformation matrix is called a third time-domain transformation matrix.

It can be understood that when the type of at least one third transformation matrix is a discrete cosine transform DCT matrix, and at least one third transformation matrix is based on at least one type of space domain transformation matrix, frequency domain transformation matrix, and time domain transformation matrix When the matrix is obtained, at least one third transformation matrix may be regarded as obtained based on at least one type of matrix obtained from space domain DCT matrix, frequency domain DCT matrix and time domain DCT matrix. When the type of at least one third transformation matrix is an oversampled DFT matrix, and at least one third transformation matrix is obtained based on at least one type of matrix in a space domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, at least one The third transformation matrix can be regarded as being obtained based on at least one type of matrix among a space-domain oversampling DFT matrix, a frequency-domain oversampling DFT matrix, and a time-domain oversampling DFT matrix. Several other types of matrices are similar and will not be repeated here.

The third transformation matrix obtained based on the spatial domain horizontal matrix is denoted as F _H , and its dimension is, for example, M _H ×M _H _OH , where _OH represents the oversampling multiple of the spatial domain level, and M _H represents the number of horizontal antennas. It can be understood that the dimension can have other deformations, for example, the dimension is M _H O _H ×M _H .

The third transformation matrix obtained based on the space domain vertical matrix is denoted as F _V , and its dimension is, for example, M _V ×M _V O _V , where O _V represents the vertical oversampling multiple of the space domain, and M _V represents the number of vertical antennas. It can be understood that the dimension can have other deformations, for example, the dimension is M _V O _V ×M _V .

The third transformation matrix obtained based on the frequency domain matrix is denoted as _FF , and its dimension is, for example, M _F ×M _F O _F , where _OF represents the oversampling multiple in the frequency domain, and _MF represents the total number of resource blocks. It can be understood that the dimension can have other deformations, for example, the dimension is M _F O _F ×M _F .

The third transformation matrix obtained based on the time-domain matrix is denoted as F _T , and its dimension is, for example, M _T × M _T O _T , where O _T represents the oversampling multiple in the time domain, and M _T represents the time used to estimate the Doppler power spectrum window long. It can be understood that the dimension can have other deformations, for example, the dimension is M _T O _T ×M _T .

It should be noted that the third transformation matrix (for example, F _H , F _V , F _F , F _T ) introduced here is used to transform the downlink channel estimation matrix, and the third transformation matrix corresponds to the downlink. Also introduced below is a fourth transformation matrix (e.g.,

), the fourth transformation matrix corresponds to the uplink, and is used to determine the statistical covariance matrix of the uplink channel.

When the downlink channel estimation matrix is transformed based on the third transformation matrix to obtain the second channel estimation matrix:

For example: one or more third transformation matrices are multiplied by the downlink channel estimation matrix to obtain the second channel estimation matrix.

For example, based on one or more third transformation matrices, and the Kronecker product

One or more algorithms such as transposition, conjugate transposition, etc., to obtain the second channel estimation matrix.

For example, the Kronecker product of multiple third transformation matrices

Multiply the downlink channel estimation matrix to obtain the second channel estimation matrix.

For example, the Kronecker product of multiple third transformation matrices

The conjugate transpose of the obtained matrix is multiplied by the downlink channel estimation matrix to obtain the second channel estimation matrix.

In an optional example a, the second channel estimation matrix satisfies the following formula:

In an optional example b, the second channel estimation matrix satisfies the following formula:

In an optional example c, the second channel estimation matrix satisfies the following formula:

The dimension of is M _H M _V O _H O _V ×1. in,

Think of as the third transformation matrix. For example, the third transformation matrix is

Or the third transformation matrix is

Optionally, this application can also regard the conjugate transposition matrix of the matrix obtained by the Kronecker product of multiple matrices among the four matrices F _H , F _V , F _F , and F _T as the third transformation matrix, for example, the third transformation matrix is

Or the third transformation matrix is

where H represents the conjugate transpose.

Next, the related process of step 603: determining (one or more) second statistical average energies corresponding to the second channel estimation matrix will be introduced.

The second statistical average energy is obtained by statistically averaging the energies respectively corresponding to some or all elements in one or more second channel estimation matrices. For example, energy corresponding to some or all elements in the second channel estimation matrix may be determined based on calculation methods such as Hadamard product ⊙, conjugate (·) ^*, etc.; energy of elements may be statistically averaged based on expected E.

The multiple first downlink channel estimation matrices here may be obtained based on one or more factors such as multiple transmitting antennas, multiple frequencies, and multiple periods. For example, one first downlink channel estimation matrix corresponds to one transmitting antenna, and one first downlink channel estimation matrix corresponds to multiple transmitting antennas. For example, one frequency corresponds to one first downlink channel estimation matrix, and multiple frequencies correspond to one first downlink channel estimation matrix. For example, one downlink channel estimation matrix is determined in one period, and multiple downlink channel estimation matrices are determined in multiple periods.

In an optional example, the second statistical average energy satisfies the following formula:

Among them, g _t is the second channel estimation matrix, φ represents the second statistical average energy, E represents the expectation, which can be obtained by statistically averaging one or more second channel estimation matrices, ⊙ represents the Hadamard product, which is used to represent The product of the corresponding positions of the two matrices, ( ) ^* represents conjugation, taking the conjugation of each element a _ij in the matrix g _t to obtain b _ij (the product of two mutually conjugate complex numbers is equal to the square of the complex modulus, The conjugate is usually represented by "*right corner mark"), and the newly obtained new matrix composed of b _ij is recorded as the matrix

Understandably, g _t can also be replaced by

When statistical averaging is performed, statistical averaging may be performed for one or more factors such as different times, different transmitting antennas, and different frequencies. A transmit antenna of the network device corresponds to a second channel estimation matrix, and a transmit antenna corresponds to a

yes

It is obtained by performing statistical averaging on different transmitting antennas of network equipment in time, for example, for multiple data obtained at different times and different transmitting antennas

Perform statistical averaging.

In an example, the second channel estimation matrix is a column vector, for example, the dimension of g _t is M _H M _V M _F M _T O _H O _V O _F O _T ×1, or M _H M _V M _F ×1 , or M _H M _V O _H O _V ×1. Correspondingly, the second statistical average energy is a column vector, and the dimension of the second statistical average energy is, for example, M _H M _V M _F M _T O _H O _V O _F O _T ×1, or M _H M _V M _F ×1 , or M _H M _V O _H O _V ×1.

Next, a related process of step 604: determining the second power spectrum based on the second statistical average energy will be introduced.

There is a mapping relationship between the second statistical average energy and the second power spectrum, and the mapping relationship satisfies the following formula:

Tω=φ,

Wherein, ω is the second power spectrum, φ is the second statistical average energy, T is a mapping matrix, and T is related to the third transformation matrix.

An optional example, the second power spectrum is a column vector.

It can be understood that the second channel estimation matrix is obtained based on which matrices among F _H , F _V , _FF , and _FT , and the mapping matrix T is also obtained based on these matrices. In addition, the second power spectrum also represents a corresponding power spectrum, and the second power spectrum may be one or a combination of angle power spectrum, time delay power spectrum, and Doppler power spectrum. Wherein, the angle power spectrum corresponds to the space domain, the delay power spectrum corresponds to the frequency domain, and the Doppler power spectrum corresponds to the time domain.

For example, when the third transformation matrix is obtained based on a space domain matrix (eg F _H , F _V ), the second power spectrum is an angular power spectrum.

For example, when the third transformation matrix is obtained based on a frequency domain matrix (such as _FF ), the second power spectrum is a delay power spectrum.

For example, when the third transformation matrix is obtained based on a time-domain matrix (such as _FT ), the second power spectrum is a Doppler power spectrum.

For example, when the third transformation matrix is obtained based on a space domain matrix or a frequency domain matrix (such as F _H , F _V , F _F ), the second power spectrum is a combination of an angle power spectrum and a delay power spectrum.

For example, when the third transformation matrix is obtained based on a space domain matrix, a frequency domain matrix, and a time domain matrix (such as F _H , F _V , F _F , F _T ), the second power spectrum is an angle power spectrum, a delay power spectrum and Combination of Doppler power spectra.

When the second power spectrum is a combined power spectrum of angle power spectrum, delay power spectrum and Doppler power spectrum, the subsequently determined statistical covariance matrix of the uplink channel is a joint statistical covariance matrix of space, frequency and time.

An optional example, each element in the second power spectrum is a non-negative real value. In this way, the determined statistical covariance matrix of the uplink channel can be guaranteed to be positive semi-definite, and the precision of the determined statistical covariance of the uplink channel can be improved.

The mapping matrix T is related to the third transformation matrix, for example, the mapping matrix T is related to one or more matrices among F _H , F _V , F _F , and F _T . For example, the mapping matrix T is based on one or more matrices in F _H , F _V , F _F , F _T , and based on conjugate, conjugate transpose, Hadamard product ⊙, Kronecker product

One or more of the algorithms are determined.

An optional example a,

An optional example b,

An optional example c,

An optional example,

In the present application, the minimum L2 norm distance criterion, or the minimum KL divergence criterion, or the minimum L0 norm criterion can be used to determine the second power spectrum based on the second statistical average energy (for example, based on T and φ, estimate ω). This is the same as the process of determining the first power spectrum based on the first statistical average energy using the minimum L2 norm distance criterion, or the minimum KL divergence criterion, or the minimum L0 norm criterion in Embodiment 1, which can be referred to The description of Embodiment 1 will not be repeated here.

Next, step 605: a related process of determining the statistical covariance matrix of the uplink channel based on the second power spectrum and the fourth transformation matrix will be introduced.

When determining the statistical covariance matrix of the uplink channel, one or more fourth transformation matrices may be used.

The type of one or more fourth transform matrices may be a discrete cosine transform DCT matrix, or a Hadamard transform matrix, or a DFT matrix, or an oversampled DFT matrix. It should be noted that, for these types, the types of the multiple fourth transformation matrices are generally the same. For these types, the types of the third transformation matrix and the fourth transformation matrix may be the same or different.

When the type of the fourth transformation matrix is a discrete cosine transformation DCT matrix, the fourth transformation matrix is referred to as a fourth discrete cosine transformation DCT matrix. When the type of the fourth transformation matrix is a Hadamard transformation matrix, the fourth transformation matrix is called a fourth Hadamard transformation matrix. When the type of the fourth transformation matrix is a discrete Fourier transform DFT matrix, the fourth transformation matrix is referred to as a fourth discrete Fourier transform DFT matrix. When the type of the fourth transformation matrix is an oversampled DFT matrix, the fourth transformation matrix is called a fourth oversampled DFT matrix.

In addition, a fourth transformation matrix may be obtained based on any transformation matrix in a space domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. Then at least one fourth transformation matrix may be obtained based on at least one of the following types of matrices: a space domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The transformation matrix used to determine the space domain type of the fourth transformation matrix is called the fourth space domain transformation matrix, and the transformation matrix used to determine the frequency domain type of the fourth transformation matrix is called the fourth frequency domain transformation matrix. The transformation matrix used to determine the time-domain type of the fourth transformation matrix is called a fourth time-domain transformation matrix.

It can be understood that when the type of at least one fourth transformation matrix is a discrete cosine transform DCT matrix, and at least one fourth transformation matrix is based on at least one type of space domain transformation matrix, frequency domain transformation matrix, and time domain transformation matrix When the matrix is obtained, at least one fourth transformation matrix may be regarded as obtained based on at least one type of matrix obtained from space domain DCT matrix, frequency domain DCT matrix and time domain DCT matrix. When the type of at least one fourth transformation matrix is an oversampled DFT matrix, and at least one fourth transformation matrix is obtained based on at least one type of matrix in a space domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, at least one The fourth transformation matrix can be regarded as being obtained based on at least one type of matrix among a space-domain oversampling DFT matrix, a frequency-domain oversampling DFT matrix, and a time-domain oversampling DFT matrix. Several other types of matrices are similar and will not be repeated here.

For the type of matrix mentioned above, discrete cosine transform DCT matrix, or Hadamard transform matrix, or DFT matrix, or oversampling DFT matrix, the type of the fourth transformation matrix and the third transformation matrix can be the same or different, for example The third transformation matrix is a DCT matrix, and the fourth transformation matrix is a Hadamard transformation matrix. When the third transformation matrix and the fourth transformation matrix are of the same type, the specific content of the third transformation matrix and the fourth transformation matrix may be the same or different. It should be noted that for the three types of space domain, frequency domain, and time domain, the types of the third transformation matrix and the fourth transformation matrix are the same. For example, the third transformation matrix is obtained based on the space domain transformation matrix, and the fourth transformation The matrix is also obtained based on the space domain transformation matrix, or the third transformation matrix is two, respectively obtained based on the frequency domain transformation matrix and the time domain transformation matrix, then the fourth transformation matrix is also two, respectively based on the frequency domain transformation matrix, time domain transformation matrix Domain transformation matrix is obtained.

The fourth transformation matrix obtained based on the spatial domain horizontal matrix is denoted as

The fourth transformation matrix obtained based on the spatial domain vertical matrix is denoted as

The fourth transformation matrix obtained based on the frequency domain matrix is denoted as

The fourth transformation matrix obtained based on the time domain matrix is denoted as

Optionally, the number and dimensions of the fourth transformation matrix and the third transformation matrix are the same.

It is introduced above that the third transformation matrix (for example, F _H , F _V , F _F , F _T ) is used to transform the downlink channel estimation matrix, and the third transformation matrix corresponds to the downlink. A fourth transformation matrix (for example,

) corresponds to the uplink, and is used to determine the statistical covariance matrix of the uplink channel.

optional,

Exemplary,

When , M in the formula is M _H , and O in the formula is _OH .

Similar and will not be introduced one by one.

Based on the second power spectrum and the fourth transformation matrix, when determining the statistical covariance matrix of the uplink channel:

For example, based on one or more fourth transformation matrices, the second power spectrum, and the Kronecker product

Transpose, conjugate transpose, diag one or more algorithms to obtain the statistical covariance matrix of the uplink channel.

For example, the Kronecker product of multiple third transformation matrices

Multiplied by diag(ω), the statistical covariance matrix of the uplink channel is obtained.

Among them, ω is the second power spectrum, for example, the second power spectrum is a column vector, diag(ω) is used to indicate that the column vector is placed on the diagonal, for example

For another example, the Kronecker product of multiple third transformation matrices

A matrix is obtained, which is multiplied by diag(ω), and then multiplied by the conjugate transpose of the matrix to obtain the statistical covariance of the uplink channel.

In an optional example a, the statistical covariance matrix R of the uplink channel satisfies the following formula:

In an optional example b, the statistical covariance matrix of the uplink channel satisfies the following formula:

In an optional example c, the statistical covariance matrix of the uplink channel satisfies the following formula:

The present application transforms the downlink channel estimation matrix through the third transformation matrix (such as DFT matrix/oversampling DFT matrix) in the space domain, frequency domain, and time domain to obtain the second channel estimation matrix; based on the second channel estimation matrix, obtain The second statistical average energy; using the mapping relationship between the second statistical average energy and angle, time delay, and Doppler power spectrum, the angle, time delay, and Doppler power spectrum are estimated; finally, based on the angle, time delay, and Doppler power spectrum The Doppler power spectrum and the fourth transformation matrix (such as DFT matrix/oversampling DFT matrix) corresponding to the downlink space domain, frequency domain, and time domain can reconstruct the space, frequency, and time joint statistical covariance of the downlink channel. Optionally, using the mapping relationship between the second statistical average energy and the angle, time delay, and Doppler power spectrum, combined with criteria under non-negative constraints (such as the minimum L2 norm distance criterion, or the minimum KL divergence criterion, or the minimum L0 norm criterion), and estimate the angle, time delay, and Doppler power spectrum.

The estimation method of the present application is relatively simple, and can be applied to the scene of calculating statistical covariance of one or more items in the space domain, frequency domain, and time domain, and is easy to popularize.

The method in the embodiment of the present application is introduced above, and the device in the embodiment of the present application will be introduced in the following. The method and the device are based on the same technical concept. Since the principles of the method and the device to solve problems are similar, the implementation of the device and the method can be referred to each other, and the repetition will not be repeated.

The embodiment of the present application may divide the device into functional modules according to the above method example, for example, each function may be divided into each functional module, or two or more functions may be integrated into one module. These modules can be implemented not only in the form of hardware, but also in the form of software function modules. It should be noted that the division of the modules in the embodiment of the present application is schematic, and is only a logical function division, and there may be another division manner during specific implementation.

Based on the same technical concept as the above method, referring to FIG. 7 , a schematic structural diagram of a communication device 700 is provided. The device 700 may include: a processing module 710 , and optionally, an interface module 720 and a storage module 730 . The processing module 710 may be connected to the storage module 730 and the interface module 720 respectively, and the storage module 730 may also be connected to the interface module 720 .

In an example, the above-mentioned interface module 720 may also be separated and defined as a receiving module and a sending module.

In an example, the apparatus 700 may be a network device, or may be a chip or a functional unit applied in the network device. The apparatus 700 has any function of the network device in the above method, for example, the apparatus 700 can execute each step performed by the network device in the above method in FIG. 4 .

The interface module 720 can perform the receiving action and sending action performed by the network device in the above method embodiments.

The processing module 710 may execute other actions except the sending action and the receiving action among the actions performed by the network device in the above method embodiments.

In an example, the interface module 720 is configured to receive an uplink reference signal; the processing module 710 is configured to perform channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix; based on the first transformation matrix pair The uplink channel estimation matrix is transformed to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to the uplink channel; the first statistical average energy corresponding to the first channel estimation matrix is determined; the first The statistical average energy is obtained by statistically averaging the energies corresponding to some or all elements in the first channel estimation matrix; determining a first power spectrum based on the first statistical average energy, wherein the first statistical There is a mapping relationship between the average energy and the first power spectrum; based on the first power spectrum and the second transformation matrix, determine the statistical covariance matrix of the downlink channel; the second transformation matrix is a matrix related to the downlink channel .

In an example, the interface module 720 is further configured to send data and/or reference signals based on the statistical covariance matrix of the downlink channel.

In an example, the storage module 730 may store computer-executed instructions of the method executed by the network device, so that the processing module 710 and the interface module 720 execute the method executed by the network device in the above examples.

Exemplarily, the storage module may include one or more memories, and the memories may be devices used to store programs or data in one or more devices and circuits. The storage module may be a register, cache or RAM, etc., and the storage module may be integrated with the processing module. The storage module can be ROM or other types of static storage devices that can store static information and instructions, and the storage module can be independent from the processing module.

The transceiver module may be an input or output interface, a pin or a circuit, and the like.

In an example, the apparatus 700 may be a terminal device, or may be a chip or a functional unit applied in the terminal device. The apparatus 700 has any function of the terminal device in the above method, for example, the apparatus 700 can execute each step performed by the terminal device in the above method in FIG. 6 .

The interface module 720 can execute the receiving action and the sending action performed by the terminal device in the above method embodiments.

The processing module 710 may execute other actions except the sending action and the receiving action among the actions performed by the terminal device in the above method embodiments.

In an example, the interface module 720 is configured to receive a downlink reference signal; the processing module 710 is configured to perform channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix; based on the third transformation matrix pair The downlink channel estimation matrix is transformed to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to the downlink channel; the second statistical average energy corresponding to the second channel estimation matrix is determined; the second The statistical average energy is obtained by statistically averaging the energies corresponding to some or all elements in the second channel estimation matrix; determining a second power spectrum based on the second statistical average energy, wherein the second statistical There is a mapping relationship between the average energy and the second power spectrum; based on the second power spectrum and the fourth transformation matrix, determine the statistical covariance matrix of the uplink channel; the fourth transformation matrix is a matrix related to the uplink channel .

In an example, the interface module 720 is further configured to send data and/or reference signals based on the statistical covariance matrix of the uplink channel.

In an example, the storage module 730 may store computer-executed instructions of the method executed by the terminal device, so that the processing module 710 and the interface module 720 execute the method executed by the terminal device in the above examples.

The above describes the apparatus applied to network equipment and the apparatus applied to terminal equipment according to the embodiments of the present application. The following describes possible product forms of the apparatus applied to network equipment and the apparatus applied to terminal equipment. It should be understood that any form of product having the characteristics of the device applied to the network device described above in FIG. 7 and any form of product having the characteristics of the device applied to the terminal device fall within the scope of protection of the present application. It should also be understood that the following introduction is only an example, and should not limit the product form of the device applied to the network device in the embodiment of the present application, and the product form of the device applied to the terminal device is limited thereto.

As a possible product form, the device can be realized by a general bus architecture.

As shown in FIG. 8 , a schematic block diagram of a communication device 800 is provided.

The apparatus 800 may include: a processor 810 , and optionally, a transceiver 820 and a memory 830 . The transceiver 820 can be used to receive programs or instructions and transmit them to the processor 810, or the transceiver 820 can be used for the device 800 to communicate with other communication devices, such as interactive control signaling and/or business data etc. The transceiver 820 may be a code and/or data read/write transceiver, or the transceiver 820 may be a signal transmission transceiver between the processor and the transceiver. The processor 810 is electrically coupled to the memory 830 .

In an example, the apparatus 800 may be a network device, or may be a chip applied to the network device. It should be understood that the apparatus has any function of the network device in the above method, for example, the apparatus 800 can execute the various steps performed by the network device in the above method in FIG. 4 . Exemplarily, the memory 830 is used to store computer programs; the processor 810 can be used to call the computer programs or instructions stored in the memory 830 to execute the method performed by the network device in the above example, or through the The transceiver 820 executes the method executed by the network device in the above example.

In an example, the apparatus 800 may be a terminal device, or may be a chip applied to the terminal device. It should be understood that the apparatus has any function of the terminal device in the above method, for example, the apparatus 800 can execute the various steps performed by the terminal device in the above method in FIG. 6 . Exemplarily, the memory 830 is used to store computer programs; the processor 810 can be used to call the computer programs or instructions stored in the memory 830 to execute the method performed by the terminal device in the above example, or through the The transceiver 820 executes the method executed by the terminal device in the above examples.

The processing module 710 in FIG. 7 may be implemented by the processor 810 .

The interface module 720 in FIG. 7 may be implemented by the transceiver 820 . Alternatively, the transceiver 820 is divided into a receiver and a transmitter, the receiver performs the receiving function of the interface module, and the transmitter performs the sending function of the interface module.

The storage module 730 in FIG. 7 may be implemented by the memory 830 .

As a possible product form, the device may be implemented by a general-purpose processor (a general-purpose processor may also be referred to as a chip or system-on-a-chip).

In a possible implementation manner, the general-purpose processor implementing the device applied to the network device or the device of the terminal device includes: a processing circuit (the processing circuit may also be called a processor); optionally, further includes: The circuit is internally connected with an input and output interface for communication, and a storage medium (the storage medium may also be referred to as a memory), and the storage medium is used to store instructions executed by the processing circuit to execute the method executed by the network device or the terminal device in the above examples.

The processing module 710 in FIG. 7 may be implemented by a processing circuit.

The interface module 720 in FIG. 7 can be realized through an input and output interface. Alternatively, the input and output interfaces are divided into input interfaces and output interfaces, the input interface performs the receiving function of the interface module, and the output interface performs the sending function of the interface module.

The storage module 730 in FIG. 7 may be implemented by a storage medium.

As a possible product form, the device of the embodiment of the present application can also be realized using the following: one or more FPGAs (Field Programmable Gate Arrays), PLDs (Programmable Logic Devices), controllers, state machines, Any combination of gate logic, discrete hardware components, any other suitable circuitry, or circuitry capable of performing the various functions described throughout this application.

The embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a computer, the computer can be used to execute the above-mentioned method for determining the statistical covariance of a channel. Or in other words: the computer program includes instructions for implementing the above-mentioned method for determining the statistical covariance of a channel.

The embodiment of the present application also provides a computer program product, including: computer program code, when the computer program code is run on the computer, the computer can execute the method for determining the statistical covariance of the channel provided above.

An embodiment of the present application also provides a communication system, where the communication system includes: a terminal device and a network device that execute the above method for determining statistical covariance of a channel.

In addition, the processor mentioned in the embodiment of the present application may be a central processing unit (central processing unit, CPU), a baseband processor, and the baseband processor and the CPU may be integrated or separated, or may be a network processor (network processing unit). processor, NP) or a combination of CPU and NP. Processors may further include hardware chips or other general-purpose processors. The aforementioned hardware chip may be an application-specific integrated circuit (application-specific integrated circuit, ASIC), a programmable logic device (programmable logic device, PLD) or a combination thereof. The above PLD can be complex programmable logic device (complex programmable logic device, CPLD), field programmable logic gate array (field-programmable gate array, FPGA), general array logic (generic array logic, GAL) and other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc., or any combination thereof. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

The memory mentioned in the embodiments of the present application may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electronically programmable Erase Programmable Read-Only Memory (Electrically EPROM, EEPROM) or Flash. The volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (Static RAM, SRAM), Dynamic Random Access Memory (Dynamic RAM, DRAM), Synchronous Dynamic Random Access Memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (Synchlink DRAM, SLDRAM ) and Direct Memory Bus Random Access Memory (Direct Rambus RAM, DR RAM). It should be noted that the memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

The transceiver mentioned in the embodiment of the present application may include a separate transmitter and/or a separate receiver, or the transmitter and the receiver may be integrated. Transceivers can operate under the direction of corresponding processors. Optionally, the transmitter may correspond to the transmitter in the physical device, and the receiver may correspond to the receiver in the physical device.

Those of ordinary skill in the art can realize that, in combination with the various method steps and units described in the embodiments disclosed herein, they can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the possibility of hardware and software For interchangeability, in the above description, the steps and components of each embodiment have been generally described according to their functions. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those of ordinary skill in the art may implement the described functionality using different methods for each particular application, but such implementation should not be considered as exceeding the scope of the present application.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices or units, and may also be electrical, mechanical or other forms of connection.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present application.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of software products, and the computer software products are stored in a storage medium In, several instructions are included to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (read-only memory, ROM), random access memory (random access memory, RAM), magnetic disk or optical disc and other media that can store program codes. .

While preferred embodiments of the present application have been described, additional changes and modifications to these embodiments can be made by those skilled in the art once the basic inventive concept is appreciated. Therefore, the appended claims are intended to be construed to cover the preferred embodiment and all changes and modifications which fall within the scope of the application.

Apparently, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the scope of the embodiments of the present application. In this way, if the modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

A method for determining channel statistical covariance, characterized in that it is applied to network equipment, comprising:

performing channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix;

Transforming the uplink channel estimation matrix based on the first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to the uplink channel;

determining a first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is obtained by statistically averaging the energies corresponding to some or all elements in the first channel estimation matrix;

determining a first power spectrum based on the first statistical average energy, where there is a mapping relationship between the first statistical average energy and the first power spectrum;

Determine the statistical covariance matrix of the downlink channel based on the first power spectrum and the second transformation matrix; the second transformation matrix is a matrix related to the downlink channel.
The method of claim 1, further comprising:

Sending data and/or reference signals based on the statistical covariance matrix of the downlink channel.
The method according to claim 1 or 2, wherein each element in the first power spectrum is a non-negative real value.
The method according to any one of claims 1-3, wherein the first transform matrix is any of the following: the first discrete cosine transform DCT matrix, the first Hadamard transform matrix, the first discrete Fourier transform matrix Leaf DFT matrix, first oversampled discrete Fourier DFT matrix;

The second transform matrix is any one of the following: a second discrete cosine transform DCT matrix, a second Hadamard transform matrix, a second discrete Fourier DFT matrix, and a second oversampled discrete Fourier DFT matrix.
The method according to any one of claims 1-4, wherein the first transformation matrix is obtained based on at least one of the following matrices:

a first space domain transformation matrix, a first frequency domain transformation matrix, and a first time domain transformation matrix;

The second transformation matrix is obtained based on at least one of the following matrices:

A second space domain transformation matrix, a second frequency domain transformation matrix, and a second time domain transformation matrix.
The method according to any one of claims 1-5, wherein the mapping relationship between the first statistical average energy and the first power spectrum satisfies the following formula:

Tω=φ, where ω is the first power spectrum, φ is the first statistical average energy, T is a mapping matrix, and T is related to the first transformation matrix.
A method for determining channel statistical covariance, characterized in that it is applied to a terminal device, comprising:

performing channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix;

Transforming the downlink channel estimation matrix based on a third transformation matrix to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to the downlink channel;

Determining a second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is obtained by statistically averaging the energies corresponding to some or all elements in the second channel estimation matrix;

determining a second power spectrum based on the second statistical average energy, where there is a mapping relationship between the second statistical average energy and the second power spectrum;

Based on the second power spectrum and the fourth transformation matrix, determine the statistical covariance matrix of the uplink channel; the fourth transformation matrix is a matrix related to the uplink channel.
The method of claim 7, further comprising:

Sending data and/or reference signals based on the statistical covariance matrix of the uplink channel.
The method according to claim 7 or 8, wherein each element in the second power spectrum is a non-negative real value.
The method according to any one of claims 7-9, wherein the third transform matrix is any one of the following: the third discrete cosine transform DCT matrix, the third Hadamard transform matrix, the third discrete Fourier transform matrix Leaf DFT matrix, third oversampled discrete Fourier DFT matrix;

The fourth transformation matrix is any one of the following: a fourth discrete cosine transform DCT matrix, a fourth Hadamard transform matrix, a fourth discrete Fourier DFT matrix, and a fourth oversampled discrete Fourier DFT matrix.
The method according to any one of claims 7-10, wherein the third transformation matrix is obtained based on at least one of the following matrices:

a third space domain transformation matrix, a third frequency domain transformation matrix, and a third time domain transformation matrix;

The fourth transformation matrix is obtained based on at least one of the following matrices:

A fourth space domain transformation matrix, a fourth frequency domain transformation matrix, and a fourth time domain transformation matrix.
The method according to any one of claims 7-11, wherein the mapping relationship between the second statistical average energy and the second power spectrum satisfies the following formula:

Tω=φ, where ω is the second power spectrum, φ is the second statistical average energy, T is a mapping matrix, and T is related to the third transformation matrix.
A communication device, characterized by comprising:

an interface module, configured to receive an uplink reference signal;

A processing module, configured to perform channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix; transform the uplink channel estimation matrix based on a first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to the uplink channel; determine the first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is: corresponding to some or all elements in the first channel estimation matrix The energy is obtained by statistical averaging; based on the first statistical average energy, a first power spectrum is determined, wherein there is a mapping relationship between the first statistical average energy and the first power spectrum; based on the first power spectrum and a second transformation matrix for determining a statistical covariance matrix of the downlink channel; the second transformation matrix is a matrix related to the downlink channel.
The device according to claim 13, wherein the interface module is further configured to send data and/or reference signals based on the statistical covariance matrix of the downlink channel.
The apparatus according to claim 13 or 14, wherein each element in the first power spectrum is a non-negative real value.
The device according to any one of claims 13-15, wherein the first transform matrix is any one of the following: the first discrete cosine transform DCT matrix, the first Hadamard transform matrix, the first discrete Fourier transform matrix Leaf DFT matrix, first oversampled discrete Fourier DFT matrix;

The second transform matrix is any one of the following: a second discrete cosine transform DCT matrix, a second Hadamard transform matrix, a second discrete Fourier DFT matrix, and a second oversampled discrete Fourier DFT matrix.
The device according to any one of claims 13-16, wherein the first transformation matrix is obtained based on at least one of the following matrices:

a first space domain transformation matrix, a first frequency domain transformation matrix, and a first time domain transformation matrix;

The second transformation matrix is obtained based on at least one of the following matrices:

A second space domain transformation matrix, a second frequency domain transformation matrix, and a second time domain transformation matrix.
The device according to any one of claims 13-17, wherein the mapping relationship between the first statistical average energy and the first power spectrum satisfies the following formula:

Tω=φ, where ω is the first power spectrum, φ is the first statistical average energy, T is a mapping matrix, and T is related to the first transformation matrix.
A communication device, characterized by comprising:

an interface module, configured to receive a downlink reference signal;

A processing module, configured to perform channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix; transform the downlink channel estimation matrix based on a third transformation matrix to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to the downlink channel; determine the second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is: corresponding to some or all elements in the second channel estimation matrix The energy is obtained by statistical averaging; based on the second statistical average energy, a second power spectrum is determined, wherein there is a mapping relationship between the second statistical average energy and the second power spectrum; based on the second power spectrum and a fourth transformation matrix, determining a statistical covariance matrix of the uplink channel; the fourth transformation matrix is a matrix related to the uplink channel.
The device according to claim 19, wherein the interface module is further configured to send data and/or reference signals based on the statistical covariance matrix of the uplink channel.
The device according to claim 19 or 20, wherein each element in the second power spectrum is a non-negative real value.
The device according to any one of claims 19-21, wherein the third transform matrix is any one of the following: the third discrete cosine transform DCT matrix, the third Hadamard transform matrix, the third discrete Fourier transform matrix Leaf DFT matrix, third oversampled discrete Fourier DFT matrix;

The fourth transformation matrix is any one of the following: a fourth discrete cosine transform DCT matrix, a fourth Hadamard transform matrix, a fourth discrete Fourier DFT matrix, and a fourth oversampled discrete Fourier DFT matrix.
The device according to any one of claims 19-22, wherein the third transformation matrix is obtained based on at least one of the following matrices:

a third space domain transformation matrix, a third frequency domain transformation matrix, and a third time domain transformation matrix;

The fourth transformation matrix is obtained based on at least one of the following matrices:

A fourth space domain transformation matrix, a fourth frequency domain transformation matrix, and a fourth time domain transformation matrix.
The device according to any one of claims 19-23, wherein the mapping relationship between the second statistical average energy and the second power spectrum satisfies the following formula:

Tω=φ, where ω is the second power spectrum, φ is the second statistical average energy, T is a mapping matrix, and T is related to the third transformation matrix.
A communication device, characterized in that it includes a processor, and the processor is coupled to a memory;

said memory for storing computer programs or instructions;

The processor is configured to execute part or all of the computer programs or instructions in the memory, and when the part or all of the computer programs or instructions are executed, it is used to implement the method described in any one of claims 1-6. method, or used to implement the method according to any one of claims 7-12.
A communication device, characterized in that it includes a processor and a memory;

said memory for storing computer programs or instructions;

The processor is configured to execute part or all of the computer programs or instructions in the memory, and when the part or all of the computer programs or instructions are executed, it is used to implement the method described in any one of claims 1-6. method, or used to implement the method according to any one of claims 7-12.
A chip system, characterized in that the chip system includes: a processing circuit; the processing circuit is coupled to a storage medium;

The processing circuit is configured to execute part or all of the computer programs or instructions in the storage medium, and when the part or all of the computer programs or instructions are executed, it is used to implement any one of claims 1-6. method, or for realizing the method as described in any one of claims 7-12.
A computer-readable storage medium, characterized in that it is used to store a computer program, the computer program comprising instructions for implementing the method according to any one of claims 1-6, or implementing any one of claims 7-12 Directives for the methods described in Item .
A computer program product, characterized in that the computer program product comprises: computer program code, when the computer program code is run on the computer, the computer is made to execute the method according to any one of claims 1-6, Or execute the method as described in any one of claims 7-12.