A method and device for determining channel statistical covariance

Abstract

The present application relates to the field of communication technology, and provides a method and device for determining the statistical covariance of a channel, which are used to accurately determine the statistical covariance of a channel. The network device performs channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix. Then, the uplink channel estimation matrix is ​​transformed based on a first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is ​​related to the uplink channel. Determine the 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 of the elements in the first channel estimation matrix. Next, based on the first statistical average energy, determine the first power spectrum, wherein there is a mapping relationship between the first statistical 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 ​​related to the downlink channel.

Description

Technical Field

[0001] 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 Art

[0002] A multi-antenna system configures multiple transmit and receive antennas on a device (such as a network device) to improve system capacity by exploring and utilizing spatial resources. A key factor in improving the downlink capacity of a multi-antenna system is to obtain more accurate channel state information (CSI) at the transmitter.

[0003] In order to more accurately acquire (acquisition may also be understood as estimation) the channel state information CSI, statistical information of the channel, especially statistical covariance information of the channel, may be used for acquisition.

[0004] Based on this, how to determine the statistical covariance of the channel is a technical problem that needs to be solved. Summary of the invention

[0005] The embodiments of the present application provide a method and apparatus for determining the statistical covariance of a channel, so as to accurately determine the statistical covariance of a channel.

[0006] In the first aspect, a method for determining channel statistical covariance is provided, and the execution subject of the method can be a network device, or a component applied in the network device, such as a chip, a processor, etc. The following description is taken as an example that the execution subject is 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 a 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 obtained by statistically averaging the energies corresponding to some or all elements in the first channel estimation matrix. Next, the network device determines a first power spectrum based on the first statistical average energy, wherein there is a mapping relationship between the first statistical average energy and the first power spectrum. Next, the network device determines 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.

[0007] In the first aspect, the uplink channel estimation matrix is ​​transformed, and the average energy of the transformed matrix is ​​calculated, 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. This method is simple and can accurately determine the statistical covariance of the channel.

[0008] In a possible implementation, the network device may also send data and / or a reference signal based on the statistical covariance matrix of the downlink channel.

[0009] 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 semi-positive definite, and can improve the accuracy of the determined statistical covariance of the downlink channel.

[0010] In a possible implementation, the first transformation matrix is ​​any one of the following: a first discrete cosine transform DCT matrix, a first Hadamard transform matrix, a first discrete Fourier DFT matrix, and a first oversampled discrete Fourier DFT matrix. The second transformation 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.

[0011] 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 transform matrices, and the contents of the first transformation matrix and the second transformation matrix may be the same or 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 transform matrix.

[0012] In a possible implementation, the first transformation matrix is ​​obtained based on at least one of the following matrices: a first spatial 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 spatial domain transformation matrix, a second frequency domain transformation matrix, and a second time domain transformation matrix.

[0013] The matrix types used to obtain the first transformation matrix and the second transformation matrix are the same, for example, both are spatial domain transformation matrices, or both are time domain transformation matrices. The matrix contents used to obtain the first transformation matrix and the second transformation matrix can be the same or different. Alternatively, 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 spatial domain transformation matrix, and the second transformation matrix is ​​obtained based on the time domain transformation matrix.

[0014] The determination method of the present application can be applied to the scenario of obtaining the statistical covariance of one or more items in the spatial domain, frequency domain, and time domain, and can be easily generalized.

[0015] 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, φ is the first statistical average energy, T is a mapping matrix, and T is related to the first transformation matrix.

[0016] In the second aspect, a method for determining channel statistical covariance is provided, and the execution subject of the method can be a terminal device, or a component applied in the terminal device, such as a chip, a processor, etc. The following description is taken as an example that the execution subject is a terminal device. 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 a 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 obtained by statistically averaging the energy corresponding to some or all elements in the second channel estimation matrix. Next, the terminal device determines the second power spectrum based on the second statistical average energy, wherein there is a mapping relationship 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.

[0017] 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.

[0018] In a possible implementation, each element in the second power spectrum is a non-negative real value.

[0019] In a possible implementation, the third transformation matrix is ​​any one of the following: a third discrete cosine transform DCT matrix, a third Hadamard transform matrix, a third discrete Fourier DFT matrix, and a 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.

[0020] 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 transform matrices, and the contents of the third transformation matrix and the fourth transformation matrix may be the same or 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 transform matrix.

[0021] In a possible implementation, the third transformation matrix is ​​obtained based on at least one of the following matrices: a third spatial 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 spatial domain transformation matrix, a fourth frequency domain transformation matrix, and a fourth time domain transformation matrix.

[0022] The matrix types used to obtain the third transformation matrix and the fourth transformation matrix are the same, for example, both are spatial domain transformation matrices, or both are time domain transformation matrices. The matrix contents used to obtain the third transformation matrix and the fourth transformation matrix may be the same or different. Alternatively, 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 spatial domain transformation matrix, and the fourth transformation matrix is ​​obtained based on the time domain transformation matrix.

[0023] 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, φ is the second statistical average energy, T is a mapping matrix, and T is related to the third transformation matrix.

[0024] In a third aspect, a communication device is provided, wherein the device has the functions of implementing the first aspect and any possible implementation of the first aspect, or implementing the functions of the second aspect and any possible implementation of the second aspect. These functions can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more functional modules corresponding to the above functions.

[0025] For example, when the device has the function of implementing the first aspect and any possible implementation of the first aspect, the device includes:

[0026] An interface module, used for receiving an uplink reference signal;

[0027] A processing module, used to perform channel estimation based on a 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 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 of the elements in the first channel estimation matrix; based on the first statistical average energy, determine a first power spectrum, wherein a mapping relationship exists between the first statistical average energy and the first power spectrum; based on the first power spectrum and the second transformation matrix, determine a statistical covariance matrix of a downlink channel; the second transformation matrix is ​​a matrix related to the downlink channel.

[0028] As an example, the interface module is further used to send data and / or reference signals based on the statistical covariance matrix of the downlink channel.

[0029] For example, when the device has the function of implementing the second aspect and any possible implementation of the second aspect, the device includes:

[0030] An interface module, used for receiving a downlink reference signal;

[0031] A processing module, used to perform channel estimation based on a 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 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 of the elements in the second channel estimation matrix; based on the second statistical average energy, determine a second power spectrum, wherein a mapping relationship exists between the second statistical average energy and the second power spectrum; based on the second power spectrum and a fourth transformation matrix, determine a statistical covariance matrix of an uplink channel; the fourth transformation matrix is ​​a matrix related to the uplink channel.

[0032] As an example, the interface module is further used to send data and / or reference signals based on the statistical covariance matrix of the uplink channel.

[0033] In a fourth aspect, a communication device is provided, comprising a processor and, optionally, a memory; the processor and the memory are coupled; the memory is used to store computer programs or instructions; the processor is used 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, they are used to implement the functions of the terminal device in the method of the first aspect and any possible implementation of the first aspect, or to implement the functions of the first network element in the second aspect and any possible implementation of the second aspect.

[0034] In a possible implementation, the apparatus may further include a transceiver, the transceiver being configured to send a signal processed by the processor, or to receive a signal input to the processor. The transceiver may perform a sending action or a receiving action performed by the terminal device in the first aspect and any possible implementation of the first aspect; or perform a sending action or a receiving action performed by the first network element in the second aspect and any possible implementation of the second aspect.

[0035] In a fifth aspect, the present application provides a chip system, which includes one or more processors (also referred to as processing circuits), and the processors are electrically coupled to a memory (also referred to as a storage medium); the memory may be located in the chip system 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 computer programs or instructions in the memory, and when the part or all of the computer programs or instructions are executed, they are used to implement the functions of the terminal device in the method of the first aspect and any possible implementation of the first aspect, or to implement the functions of the first network element in the second aspect and any possible implementation of the second aspect.

[0036] In a possible implementation, the chip system may further include an input / output interface (also referred to as a communication interface), the input / output interface being used to output a signal processed by the processor, or to receive a signal input to the processor. The input / output interface may execute a sending action or a receiving action executed by a terminal device in the first aspect and any possible implementation of the first aspect; or, execute a sending action or a receiving action executed by a first network element in the second aspect and any possible implementation of the second aspect. Specifically, the output interface executes a sending action, and the input interface executes a receiving action.

[0037] In a possible implementation, the chip system may be composed of a chip, or may include a chip and other discrete devices.

[0038] In a sixth aspect, a computer-readable storage medium is provided for storing a computer program, wherein the computer program includes instructions for implementing the functions of the first aspect and any possible implementation of the first aspect, or instructions for implementing the functions of the second aspect and any possible implementation of the second aspect.

[0039] Alternatively, a computer-readable storage medium is used to store a computer program, which, when executed by a computer, can enable the computer to execute the method executed by the terminal device in the above-mentioned first aspect and any possible implementation of the first aspect, or execute the method executed by the first network element in the above-mentioned second aspect and any possible implementation of the second aspect.

[0040] In the seventh aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method executed by the terminal device in the first aspect and any possible implementation of the first aspect, or to execute the method executed by the first network element in the second aspect and any possible implementation of the second aspect.

[0041] In an eighth aspect, a communication system is provided, comprising a terminal device executing the method of the first aspect and any possible implementation of the first aspect and a first network element executing the method of the second aspect and any possible implementation of the second aspect.

[0042] The technical effects of the third to eighth aspects mentioned above can refer to the descriptions in the first to second aspects, and the repeated parts will not be repeated. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A communication system architecture diagram provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of a process of communicating based on the statistical covariance of a downlink channel provided in an embodiment of the present application;

[0045] Figure 3 A schematic diagram of a process for determining a statistical covariance of a downlink channel provided in an embodiment of the present application;

[0046] Figure 4 A schematic diagram of another process for determining the statistical covariance of a downlink channel provided in an embodiment of the present application;

[0047] Figure 5 A schematic diagram of a process of communicating based on the statistical covariance of an uplink channel provided in an embodiment of the present application;

[0048] Figure 6 A schematic diagram of a process for determining a statistical covariance of an uplink channel provided in an embodiment of the present application;

[0049] Figure 7 A structural diagram of a communication device provided in an embodiment of the present application;

[0050] Figure 8 This is a structural diagram of another communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] To facilitate understanding of the technical solution of the embodiment of the present application, the system architecture of the method provided by the embodiment of the present application is briefly described below. It is understandable that the system architecture described in the embodiment of the present application is to more clearly illustrate the technical solution of the embodiment of the present application, and does not constitute a limitation on the technical solution provided by the embodiment of the present application.

[0052] The technical solution of the embodiment of the present application can be applied to various communication systems, such as satellite communication systems and traditional mobile communication systems. Among them, the satellite communication system can be integrated with the traditional mobile communication system (i.e., ground communication system). Communication systems such as wireless local area network (WLAN) communication system, wireless fidelity (WiFi) system, long term evolution (LTE) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD), fifth generation (5G) system or new radio (NR), sixth generation (6G) system, and other future communication systems, etc., also support communication systems that integrate multiple wireless technologies, for example, it can also be applied to drones, satellite communication systems, high altitude platform station (HAPS) communication and other non-terrestrial networks (NTN) that integrate ground mobile communication networks.

[0053] Figure 1 FIG. 1 is a schematic diagram of the architecture of a communication system 1000 used in an embodiment of the present application. Figure 1 As shown, the communication system includes a wireless access network 100 and a core network 200 . Optionally, the communication system 1000 may also include the Internet 300 .

[0054] The wireless access network 100 may include at least one wireless access network device (such as Figure 1 110a and 110b), and may further include at least one terminal (such as Figure 1 120a-120j in the figure). The terminal is connected to the wireless access network device by wireless means, and the wireless access network device is connected to the core network by wireless or wired means. The core network device and the wireless access network device can be independent and different physical devices, or the functions of the core network device and the logical functions of the wireless access network device can be integrated on the same physical device, or part of the functions of the core network device and part of the functions of the wireless access network device can be integrated on one physical device. Terminals and wireless access network devices can be connected to each other by wired or wireless means. Figure 1 This is just a schematic diagram. The communication system may also include other network devices, such as wireless relay devices and wireless backhaul devices. Figure 1 Not drawn in.

[0055] The wireless access network equipment can be a base station, an evolved NodeB (eNodeB), a transmission reception point (TRP), a next generation NodeB (gNB) in the fifth generation (5G) mobile communication system, a next generation base station in the sixth generation (6G) mobile communication system, a base station in a future mobile communication system, or an access node in a WiFi system, etc.; it can also be a module or unit that completes part of the functions of a base station, for example, a centralized unit (CU) or a distributed unit (DU). The wireless access network equipment can be a macro base station (such as Figure 1 110a), or a micro base station or an indoor station (such as Figure 1 110b), it can also be a relay node or a donor node, etc. It can be understood that all or part of the functions of the wireless access network device in this application can also be implemented by software functions running on hardware, or by virtualization functions instantiated on a platform (such as a cloud platform). The embodiments of this application do not limit the specific technology and specific device form adopted by the wireless access network device. For the convenience of description, the following description takes a base station as an example of a wireless access network device.

[0056] The terminal may also be referred to as a terminal device, user equipment (UE), mobile station, mobile terminal, etc. The terminal can be widely used in various scenarios, for example, device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IOT), virtual reality, augmented reality, industrial control, automatic driving, telemedicine, smart grid, smart furniture, smart office, smart wear, smart transportation, smart city, etc. The terminal may be a mobile phone, a tablet computer, a computer with wireless transceiver function, a wearable device, a vehicle, a drone, a helicopter, an airplane, a ship, a robot, a mechanical arm, a smart home device, etc. The embodiments of the present application do not limit the specific technology and specific device form adopted by the terminal.

[0057] Base stations and terminals can be fixed or movable. Base stations and terminals can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on aircraft, balloons, and artificial satellites in the air. The embodiments of this application do not limit the application scenarios of base stations and terminals.

[0058] The roles of the base station and the terminal can be relative, for example, Figure 1 The helicopter or drone 120i in the figure can be configured as a mobile base station. For the terminal 120j that accesses the wireless access network 100 through 120i, the terminal 120i is a base station; but for the base station 110a, 120i is a terminal, that is, 110a and 120i communicate through the wireless air interface protocol. Of course, 110a and 120i can also communicate through the interface protocol between base stations. In this case, relative to 110a, 120i is also a base station. Therefore, base stations and terminals can be collectively referred to as communication devices. Figure 1 110a and 110b in the figure may be referred to as communication devices having base station functions. Figure 1 120a-120j in the figure can be called communication devices with terminal functions.

[0059] Base stations and terminals, base stations and base stations, and terminals and terminals can communicate through authorized spectrum, unauthorized spectrum, or both; they can communicate through spectrum below 6 gigahertz (GHz), spectrum above 6 GHz, or spectrum below 6 GHz and spectrum above 6 GHz. The embodiments of the present application do not limit the spectrum resources used for wireless communication.

[0060] In the embodiments of the present application, the functions of the base station may also be performed by a module (such as a chip) in the base station, or by a control subsystem including the base station function. The control subsystem including the base station function here may be a control center in the application scenarios of the above-mentioned terminals such as smart grid, industrial control, smart transportation, smart city, etc. The functions of the terminal may also be performed by a module (such as a chip or a modem) in the terminal, or by a device including the terminal function.

[0061] In the present application, the base station sends a downlink signal or downlink information to the terminal, and the downlink information is carried on a downlink channel; the terminal sends an uplink signal or uplink information to the base station, and the uplink information is carried on an uplink channel.

[0062] To facilitate understanding of the embodiments of the present application, the application scenarios of the present application are introduced below. The network architecture and business scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Ordinary technicians in this field can 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.

[0063] Multi-antenna systems usually configure multiple transmit and receive antennas on the network equipment side to improve system capacity by exploring and utilizing spatial dimension resources. A key factor in improving the downlink capacity of multi-antenna systems is to obtain more accurate downlink channel state information (CSI) on the network equipment side.

[0064] In a time division duplex (TDD) system, after the channel is calibrated, the downlink channel state information CSI can be estimated through 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 device and the terminal device are not reciprocal, and the downlink channel state information CSI needs to be fed back by the terminal device to the network device.

[0065] The frequency division duplex (FDD) system does not have channel reciprocity due to the difference in uplink and downlink frequencies (e.g. uplink 2.1G, downlink 3.5G), and the downlink channel state information CSI can only be fed back to the network equipment through the terminal equipment.

[0066] In the downlink channel state information CSI feedback process, in one example, the network device sends a downlink channel state information reference signal (CSI-RS). The terminal device estimates the downlink channel based on the received downlink CSI-RS, and then selects the codebook index that best matches the downlink channel from a predefined codebook set, and then feeds back the selected codebook index to the network device through the uplink channel. Limited by the uplink feedback overhead, the terminal device uses a finite-state codebook to quantize the real channel. There is an inevitable quantization error between the codebook and the real channel, which limits the accuracy of the network device in acquiring (acquisition can also be called estimation) the downlink channel state information CSI.

[0067] 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 to obtain the CSI.

[0068] like Figure 2 As shown, a flow chart of communication based on statistical covariance of a downlink channel is introduced.

[0069] Some or all (one or more) antennas in the terminal device send an uplink reference signal (e.g., SRS) to the network device. The network device performs channel estimation based on the received uplink reference signal and estimates the uplink channel estimation matrix of the channel between each transmitting antenna of the terminal device and the network device. The uplink channel estimation matrix can be a matrix or a vector (a vector is a one-dimensional matrix).

[0070] Then, the network device determines the statistical covariance matrix of the downlink channel based on the uplink channel estimation matrix.

[0071] The statistical covariance matrix of the downlink channel can be used for downlink pilot weighting, and then send a downlink reference signal. In addition, the statistical covariance matrix of the downlink channel can be used for single-user / multi-user precoding, and then send downlink data.

[0072] To facilitate understanding of the embodiments of the present application, some terms used in the embodiments of the present application are explained below to facilitate understanding by those skilled in the art.

[0073] 1) Power spectrum, which represents the physical characteristics of the channel. The first power spectrum mentioned below can be one or more combinations of angle power spectrum, delay power spectrum, and Doppler power spectrum. The second power spectrum mentioned below can be one or more combinations of angle power spectrum, delay power spectrum, and Doppler power spectrum.

[0074] 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.

[0075] The delay power spectrum describes the distribution relationship of channel power with time delay.

[0076] The Doppler power spectrum describes the distribution relationship between channel power and Doppler frequency.

[0077] 2) Transpose: The new matrix obtained by swapping the rows and columns of matrix A is called the transposed matrix A. T , usually represented by "right subscript T". If A is an m*n matrix, then the transposed matrix A T It is an n*m matrix.

[0078] For example,

[0079] 3) Conjugate transpose generally refers to a mathematical transformation of an m*n matrix A, where any element a in the matrix A ij belongs to the complex number field C.

[0080] The symbol of the conjugate transpose corresponds to the normal transpose "right subscript T", usually "right subscript H" is used to represent the conjugate transpose. The matrix A after the conjugate transpose is H is called the conjugate transposed matrix of A, A H For example, first, each element a in A ij Take the conjugate and get b ij (The product of two mutually conjugate complex numbers is equal to the square of the complex number modulus. Conjugation is usually represented by "* right subscript"), and the new product obtained by b ij The new m*n matrix is ​​recorded as matrix B, B = A*; then perform a normal transposition on matrix B to obtain B T , which is the conjugate transposed matrix of A: B T =A H .

[0081] 4) vec(·) indicates vectorized operation.

[0082] 5) Kronecker product is an operation between two matrices of any size, expressed as For example, A is an m*n matrix, B is a p*q matrix, is a mp*nq block matrix. For example:

[0083]

[0084] 6) Hadamard product, represented by ⊙, the Hadamard product of matrices A and B is the product of the corresponding positions of the two matrices. The number of rows and columns of the two matrices is the same, for example, the multiplication of two m*n matrices.

[0085] 7) diag(ω) is used to construct a diagonal matrix, placing the column vector ω on the diagonal and all elements not on the diagonal are zero, for example

[0086] 8) Statistical covariance matrix, defined as: the statistical average of the autocorrelation matrix of a random matrix (which can be a column vector).

[0087] For example, the statistical covariance matrix of the uplink / downlink channel can be obtained by calculating the autocorrelation matrix of the uplink / downlink channel estimation matrix, and multiple uplink channel estimation matrices can obtain multiple autocorrelation matrices, which are obtained by averaging a large number of autocorrelation matrices.

[0088] 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 .

[0089] 9) Frequency refers to the transmission (eg sending) frequency of a wireless signal, for example, 1850 MHz, 1910 MHz.

[0090] Bandwidth refers to the frequency bandwidth, such as 20 MHz, 40 MHz. For example, the bandwidth between the frequency 1870 MHz and the frequency 1890 MHz is 20 MHz.

[0091] The frequency band from 1850 MHz to 1890 MHz can be regarded as one frequency band, or it can be divided into multiple frequency bands.

[0092] Frequency points are numbers given to fixed frequencies. For example, when the frequency interval is 20 MHz, the frequency range from 1850 MHz to 1890 MHz is divided into three frequency bands: 1850 MHz-1870 MHz, 1870 MHz-1890 MHz, and 1890 MHz-1910 MHz. Each channel is numbered, for example, 1, 2, and 3. The numbers of these fixed frequencies are the frequency points.

[0093] 10) The L2 norm refers to the sum of the squares of the moduli of each element of the vector, and then the square root is calculated.

[0094] 11) Semi-positive definite: A matrix is ​​semi-positive definite if the corresponding complex quadratic form f(x1,x2,...,xn) has f(c1,c2,...,cn)>=0 for any set of complex numbers c1,c2,...,cn that are not all zero. Or, it means that the matrix A is a conjugate symmetric matrix and has x for any non-zero vector x. H *A*x≥0, then A is called a semi-positive definite matrix.

[0095] 12) The dimension of a 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.

[0096] For example, dimension M H M V ×1, indicating that the number of rows in the matrix is ​​M H M V , the number of columns is 1.

[0097] For example, dimension M H M V M F ×1, indicating that the number of rows in the matrix is ​​M H M V M F, the number of columns is 1.

[0098] For example, dimension M H M V M F M T ×1, indicating that the number of rows in the matrix is ​​M H M V M F M T , the number of columns is 1.

[0099] For example, dimension M H ×M H O H , indicating that the number of rows of the matrix is ​​M H , the number of columns is M H O H .

[0100] 13) The uplink frequency / uplink frequency and the downlink frequency / downlink frequency in this application may be frequencies / frequencies belonging to the same frequency band (e.g., 2.1G, 3.5G frequency bands), or frequencies / frequencies belonging to different frequency bands (e.g., the uplink frequency belongs to the 3.5G frequency band, and the downlink frequency belongs to the 2.1G frequency band).

[0101] 14) The term “and / or” in this application describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone. The character “ / ” generally indicates that the objects associated with each other are in an “or” relationship. The term “plural” referred to in this application refers to two or more. In addition, it should be understood that in the description of this application, words such as “first” and “second” are only used to distinguish the purpose of description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order.

[0102] like Figure 3 As shown, a schematic diagram of a process in which a network device determines the statistical covariance of a downlink channel based on an uplink channel estimation matrix is ​​introduced.

[0103] Step 301: The network device calculates the statistical covariance matrix of the uplink channel based on the uplink channel estimation matrix.

[0104] For example, the statistical covariance matrix is ​​a spatial statistical covariance matrix.

[0105] 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 be used to obtain multiple autocorrelation matrices, which are obtained by averaging a large number of autocorrelation matrices.

[0106] Step 302: The network device estimates the angular power spectrum of the channel using the statistical covariance matrix of the uplink channel.

[0107] The angular power spectrum represents the physical characteristics of the channel and describes the distribution relationship of the channel energy with the spatial angle. It is usually believed that the angular power spectra of the uplink and downlink are reciprocal.

[0108] For example, 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.

[0109] Step 303: Determine the statistical covariance matrix of the downlink channel using the angular power spectrum of the channel.

[0110] For example, the statistical covariance matrix is ​​a spatial statistical covariance matrix.

[0111] This step utilizes the reciprocity of the angular power spectra of the uplink channel and the downlink channel.

[0112] For example, the statistical covariance of the downlink channel is determined by using the angular power spectrum and a transformation matrix (eg, an oversampled discrete Fourier transform (DFT) matrix) corresponding to the downlink channel.

[0113] The oversampled DFT matrix is, for example, a spatially oversampled DFT matrix.

[0114] Figure 3 The scheme shown utilizes the relationship between the (spatial) statistical covariance and the angular power spectrum of the uplink channel to obtain the (spatial) statistical covariance matrix of the downlink channel.

[0115] In step 302, when estimating the angular power spectrum, the non-negative constraint is 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, Figure 3 The scheme shown can only determine the spatial statistical covariance, but cannot determine the statistical covariance of other dimensions (such as time and frequency). Or when generalizing the spatial statistical covariance to the statistical covariance of two or three of space, frequency and time, the complexity is greatly increased and it is difficult to implement.

[0116] 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 ​​calculated, 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.

[0117] Relative to the above Figure 3In the example provided, it is necessary to obtain (and store) the statistical average energy instead of obtaining the statistical covariance of the uplink channel; the power spectrum is estimated by using the relationship between the statistical average energy and 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 scenario of obtaining the statistical covariance of one or more items in the spatial domain, frequency domain, and time domain, and is easy to generalize.

[0118] Embodiment 1:

[0119] like Figure 4 As shown, a schematic diagram of a method process for a network device to determine the statistical covariance of a downlink channel is provided.

[0120] Step 401: The network device performs channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix.

[0121] Step 402: The network device transforms 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.

[0122] Mathematical explanation: used to transform the uplink channel estimation matrix from the spatial 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 from the time domain to the Doppler domain.

[0123] Step 403: The network device determines 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 of the elements in the first channel estimation matrix.

[0124] Step 404: The network device determines a first power spectrum based on the first statistical average energy, wherein a mapping relationship exists between the first statistical average energy and the first power spectrum.

[0125] 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.

[0126] Subsequently, the network device may send data and / or reference signals based on the statistical covariance matrix of the downlink channel.

[0127] The method is simple and can accurately determine the statistical covariance of the channel, which is more suitable for matching the downlink channel characteristics and is conducive to performance improvement.

[0128] The parameters involved in this embodiment 1 are as follows:

[0129]

[0130]

[0131] Next, the relevant process of step 401: performing channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix is ​​introduced.

[0132] The terminal device may periodically send an uplink reference signal, and the terminal device may use one or more transmit antennas to send the uplink reference signal. The terminal device may use a certain uplink frequency to send the uplink reference signal. 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.

[0133] When determining the uplink channel estimation matrix, the network device may take into account one or more factors of space (eg, antenna), frequency (eg, frequency in the bandwidth corresponding to the frequency point), and time (eg, period).

[0134] In an optional example a, the network device considers the space (antenna) factor to determine the uplink channel estimation matrix. For example, the network device determines the uplink channel estimation matrix corresponding to each transmitting antenna of the terminal device based on the uplink reference signal received from the transmitting antenna. That is, one transmitting antenna corresponds to one uplink channel estimation matrix. If the terminal device uses multiple transmitting antennas to send uplink reference signals, multiple uplink channel estimation matrices can be determined.

[0135] 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 the channel estimation matrices corresponding to multiple resource blocks RB. For example, the total number of resource blocks is M F , M F is an integer greater than or equal to 1. F The channel estimation matrix corresponding to the resource blocks is: Understandably, m F The value range is 1 to M F , 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. The channel estimation matrices of all RBs are combined to obtain the uplink channel estimation matrix h t .

[0136] When M F When it is equal to 1, Alternatively, when the frequency factor is not considered, the total number of resource blocks M can also be F Assumed to be 1, then

[0137] When M F When it is greater than 1, the uplink channel estimation matrix h t It can be the channel estimation matrix of all RBs The above row channel estimation matrix is ​​a vector. For example, the channel estimation matrices of all RBs are concatenated into a column vector, satisfying the following formula:

[0138]

[0139] Among them, vec(·) represents a vectorized operation.

[0140] In an optional example c, the network device considers time and frequency factors to determine the uplink channel estimation matrix. Regarding the time factor, for example, not only the currently determined uplink channel estimation matrix but also the historical uplink channel estimation matrix can be considered. For example, the time t and the most recent M T -The uplink channel estimation matrix of 1 historical moment is concatenated into a column vector, satisfying the following formula:

[0141]

[0142] in, h t represents the uplink channel estimation matrix, M T Represents the time window length used to estimate the Doppler power spectrum.

[0143] In one example, a two-dimensional rectangular antenna array is configured in the network device, and the number of horizontal antennas is M. H , the number of vertical antennas is M V .

[0144] mth F The channel estimation matrix corresponding to the resource block The dimension is, for example, M H M V ×1, the channel estimation matrix is ​​a column vector, and the order of the corresponding antennas is: horizontal first, then vertical. This dimension can also have other deformations, as long as the number of elements in the matrix of multiple deformation dimensions is the same. For example, the dimension is M H ×M V , or the dimension is M V ×M H .

[0145] when When the uplink channel estimation matrix h t The dimension is, for example, M H M V ×1, the uplink channel estimation matrix is ​​a column vector. Or the dimension is MH ×M V , or the dimension is M V ×M H .

[0146] When M F When it is greater than 1, the uplink channel estimation matrix h t The dimension is M H M V M F ×1, the uplink channel estimation matrix is ​​a column vector, where M F 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 deformation 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 .

[0147] When M F When greater than 1, the uplink channel estimation matrix h t The dimension is M H M V M F M T ×1, 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 deformation 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 .

[0148] The following description will be made by taking the upper row channel estimation matrix as a column vector as an example.

[0149] Next, the relevant process of step 402: transforming the uplink channel estimation matrix based on the first transformation matrix (the first transformation matrix may be one or more) to obtain the first channel estimation matrix is ​​introduced.

[0150] When transforming the uplink channel estimation matrix, the first transformation matrix used may be one or more.

[0151] The type of one or more first transform matrices may be a discrete cosine transform (DCT) matrix, a Hadamard transform matrix, a DFT matrix, or an oversampled DFT matrix. It should be noted that for these types, the types of the multiple first transform matrices are usually the same.

[0152] When the type of the first transformation matrix is ​​a discrete cosine transform DCT matrix, the first transformation matrix is ​​referred to as a first discrete cosine transform DCT matrix. When the type of the first transformation matrix is ​​a Hadamard transform matrix, the first transformation matrix is ​​referred to as a first Hadamard transform 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.

[0153] In addition, a first transformation matrix can be obtained based on any transformation matrix of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. Then at least one first transformation matrix can be obtained based on at least one of the following types of matrices: a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The transformation matrix of the spatial domain type used to determine the first transformation matrix is ​​called a first spatial domain transformation matrix, the transformation matrix of the frequency domain type used to determine the first transformation matrix is ​​called a first frequency domain transformation matrix, and the transformation matrix of the time domain type used to determine the first transformation matrix is ​​called a first time domain transformation matrix.

[0154] It can be understood that when the type of at least one first transformation matrix is ​​a discrete cosine transform DCT matrix, and at least one first transformation matrix is ​​obtained based on at least one type of matrix among a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, at least one first transformation matrix can be regarded as being obtained based on at least one type of matrix among a spatial domain DCT matrix, a frequency domain DCT matrix, and a 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 among a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, at least one first transformation matrix can be regarded as being obtained based on at least one type of matrix among a spatial domain oversampled DFT matrix, a frequency domain oversampled DFT matrix, and a time domain oversampled DFT matrix. Several other types of matrices are similar and will not be repeated.

[0155] Optionally, the space domain matrix can also be divided into a space domain horizontal matrix and a space domain vertical matrix.

[0156] The first transformation matrix obtained based on the spatial domain horizontal matrix is ​​denoted as F H, the dimension is M H ×M H O H , O H Indicates the oversampling multiple at the spatial domain level, M H It is understood that the dimension can have other variations, for example, the dimension is M H O H ×M H .

[0157] The first transformation matrix obtained based on the spatial domain vertical matrix is ​​denoted as F V , the dimension is M V ×M V O V , O V Indicates the vertical oversampling multiple in the spatial domain, M V It is understood that the dimension can have other variations, for example, the dimension is M V O V ×M V .

[0158] The first transformation matrix obtained based on the frequency domain matrix is ​​denoted as F F , the dimension is M F ×M F O F , O F Indicates the oversampling multiple in the frequency domain, M F It is understood that the dimension may have other variations, for example, the dimension is M F O F ×M F .

[0159] The first transformation matrix obtained based on the time domain matrix is ​​denoted as FT, and the dimension is, for example, M T ×M T O T , O T Indicates the oversampling multiple in the time domain, M T It is understood that the dimension can have other deformations, for example, the dimension is M T O T ×M T .

[0160] In addition, when there is no oversampling, for example, the DCT matrix, the DFT matrix, and the Hadamard transform matrix do not have an oversampling operation, in this case, O H , O V , O F , O T Both can be 1.

[0161] It should be noted 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 following also introduces a second transformation matrix (for example, ), the second transformation matrix corresponds to the downlink and is used to determine the statistical covariance matrix of the downlink channel.

[0162] Optional, F H , F V , F F , F T Each matrix in satisfies the following conditions: the L2 norm of each column of the matrix is ​​1. It can be understood that the L2 norm of a column vector means that the square root of the sum of the squares of the elements in the column vector is equal to 1.

[0163] For example, F H , F V , F F , F T The following formula is satisfied. Let F represent a matrix of dimension M×MO, whose mth row and nth column element is:

[0164]

[0165] Wherein, m is an integer from 1 to M, and n is an integer from 1 to M*O. Wherein, M may correspond to M described above. H 、M V 、M F 、M T ; O can correspond to O introduced above H , O V , O F , O T For example, applying this formula to the generator matrix F H When M in the formula is M H , O in this formula is O H . F V , F F , F T Similar, I will not introduce them one by one.

[0166] When the uplink channel estimation matrix is ​​transformed based on the first transformation matrix to obtain the first channel estimation matrix:

[0167] For example, one or more first transformation matrices are multiplied by an uplink channel estimation matrix to obtain a first channel estimation matrix.

[0168] For example, based on one or more first transformation matrices and Kronecker shift One or more algorithms such as transposition, conjugate transposition, etc. are used to obtain a first channel estimation matrix.

[0169] For example, the Kronecker shift of multiple first transformation matrices Multiply by the uplink channel estimation matrix to obtain a first channel estimation matrix.

[0170] 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.

[0171] In an optional example a, the first channel estimation matrix satisfies the following formula:

[0172]

[0173] For example, F H Dimension is M H ×M H O H , F V Dimension is M V ×M V O V , F F Dimension is M F ×M F O F , F T Dimension is M T ×M T O T , h t The dimension is M H M V M F M T ×1, g t The dimension is M H M V M F M T O H O V O F O T ×1.

[0174] In an optional example b, the first channel estimation matrix satisfies the following formula:

[0175]

[0176] For example, F H Dimension is M H ×M H O H , F V Dimension is MV ×M V O V , F F Dimension is M F ×M F O F ,h t The dimension is M H M V M F ×1, g t The dimension is M H M V M F O H O V O F ×1.

[0177] In an optional example c, the first channel estimation matrix satisfies the following formula:

[0178]

[0179] For example, F H Dimension is M H ×M H O H , F V Dimension is M V ×M V O V ,h t The dimension is M H M V ×1, The dimension is M H M V O H O V ×1. Among them,

[0180] Optionally, this application can also use F H , F V , F F , F T The Kronecker product of multiple matrices among these 4 matrices is considered as the first transformation matrix. For example, the first transformation matrix is Or the first transformation matrix is Or the first transformation matrix is

[0181] Optionally, this application can also use F H , F V , F F , F T The conjugate transposed matrix of the matrix obtained by the Kronecker product of multiple matrices among these four matrices is regarded as the first transformation matrix. For example, the first transformation matrix is Or the first transformation matrix is Or the first transformation matrix is Here, H represents the conjugate transpose.

[0182] The uplink channel estimation matrix is ​​transformed. If the transformed channel estimation matrix is ​​sparse (for example, a 100*1 vector, only 10 elements have relatively large values ​​after the transformation, and the other values ​​are close to 0, and the elements close to 0 can be filtered out), it can be regarded as compressing the row channel estimation matrix, which can reduce storage overhead.

[0183] Next, the relevant process of step 403: determining the first statistical average energy corresponding to (one or more) the first channel estimation matrix is ​​introduced.

[0184] The first statistical average energy is obtained by statistically averaging the energies corresponding to some or all elements in one or more first channel estimation matrices. For example, the first statistical average energy can be obtained based on the Hadamard product ⊙, the conjugate (·) * The energies corresponding to some or all elements in the first channel estimation matrix can be determined by calculation methods such as ; and the energies of the elements can be statistically averaged based on the expected E.

[0185] The multiple first uplink channel estimation matrices here can be obtained based on one or more factors such as multiple transmitting antennas, multiple frequencies, and multiple periods. For example, if one transmitting antenna corresponds to one first uplink channel estimation matrix, then multiple transmitting antennas correspond to one first uplink channel estimation matrix. For example, if one frequency corresponds to one first uplink channel estimation matrix, then multiple frequencies correspond to one first uplink channel estimation matrix. For example, one period determines one uplink channel estimation matrix, then multiple periods determine multiple uplink channel estimation matrices.

[0186] In an optional example, the first statistical average energy satisfies the following formula:

[0187]

[0188] Where gt is the first channel estimation matrix, φ represents the first statistical average energy, E represents the expectation, which can be obtained by taking the statistical average of one or more first channel estimation matrices, ⊙ represents the Hadamard product, which is used to represent the product of the corresponding positions of two matrices, (·) * Represents conjugate, and each element a in the matrix gt ij Take the conjugate and get b ij (The product of two mutually conjugate complex numbers is equal to the square of the complex number modulus. Conjugation is usually represented by "* right subscript"), and the new product obtained by b ij The new matrix is ​​denoted as matrix

[0189] Understandably, g t Can also be replaced by

[0190] When performing statistical averaging, the statistical averaging may be performed for one or more factors such as different time, different transmitting antennas, and different frequencies. One transmitting antenna of the terminal device corresponds to one first channel estimation matrix, and one transmitting antenna corresponds to one Can The statistical average is obtained by performing statistical averaging on different transmitting antennas of the terminal device in time. For example, multiple signals obtained at different times and different transmitting antennas are averaged. For example, multiple Perform statistical averaging.

[0191] In one example, the first channel estimation matrix is ​​a column vector, such as g t The dimension is M H M V M F MTO 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.

[0192] Next, the relevant process of step 404: determining the first power spectrum based on the first statistical average energy is introduced.

[0193] There is a mapping relationship between the first statistical average energy and the first power spectrum, and the mapping relationship satisfies the following formula:

[0194] Tω=φ,

[0195] Among them, ω is the first power spectrum, φ is the first statistical average energy, T is the mapping matrix, and T is related to the first transformation matrix.

[0196] In an optional example, the first power spectrum is a column vector.

[0197] It can be understood that the first channel estimation matrix is ​​based on F H , F V , F F , F T The mapping matrix T is also obtained based on these matrices. In addition, the first power spectrum also represents the corresponding power spectrum, and the first power spectrum can be a combination of one or more of the angle power spectrum, the delay power spectrum, and the Doppler power spectrum. Among them, the angle power spectrum corresponds to the spatial domain, the delay power spectrum corresponds to the frequency domain, and the Doppler power spectrum corresponds to the time domain.

[0198] For example, when the first transformation matrix is ​​based on a spatial domain matrix (e.g., F H , F V ) is obtained, the first power spectrum is the angular power spectrum.

[0199] For example, when the first transformation matrix is ​​based on a frequency domain matrix (eg, F F ) is obtained, the first power spectrum is the delayed power spectrum.

[0200] For example, when the first transformation matrix is ​​based on a time domain matrix (e.g., F T ) is obtained, the first power spectrum is the Doppler power spectrum.

[0201] For example, when the first transformation matrix is ​​based on a spatial domain matrix, a frequency domain matrix (e.g., F H , F V , F F ) is obtained, the first power spectrum is a combination of the angle power spectrum and the time delay power spectrum.

[0202] For example, when the first transformation matrix is ​​based on a spatial domain matrix, a frequency domain matrix, and a time domain matrix (eg, F H , F V , F F , F T ) is obtained, the first power spectrum is a combination of the angle power spectrum, the delay power spectrum and the Doppler power spectrum.

[0203] When the first power spectrum is a power spectrum of a combination of an angle power spectrum, a delay power spectrum, and a Doppler power spectrum, the statistical covariance matrix of the downlink channel subsequently determined is a joint statistical covariance matrix of space, frequency, and time.

[0204] In an optional example, each element in the first power spectrum is a non-negative real value. This can ensure that the statistical covariance matrix of the determined downlink channel is semi-positive definite, and can improve the accuracy of the statistical covariance of the determined downlink channel. This can solve Figure 3 The power spectrum cannot be guaranteed to be non-negative.

[0205] The mapping matrix T is related to the first transformation matrix. For example, the mapping matrix T is related to F H , F V , F F , F T For example, the mapping matrix T is based on F H , F V , F F , F T One or more matrices in , and conjugate, conjugate transpose, Hadamard product ⊙, Kronecker product is determined by one or more algorithms in .

[0206] In an optional example a,

[0207] In an alternative example b,

[0208] In an alternative example c,

[0209] An optional example,

[0210] An optional example,

[0211] An optional example,

[0212] An optional example,

[0213] In the present application, the minimum L2 norm distance criterion, the minimum KL divergence criterion, or the minimum L0 norm criterion may be used to determine the first power spectrum based on the first statistical average energy (eg, estimate ω based on T and φ).

[0214] In the three examples provided below, the constraint ω ≥ 0 means that every element in ω is non-negative.

[0215] In Example 1, when the minimum L2 norm distance criterion is adopted, it can be modeled as the following optimization problem:

[0216]

[0217] stω≥0

[0218] The above optimization problem is a standard non-negative least square (NNLS) problem, which can be solved using the existing NNLS algorithm.

[0219] In Example 2, when the minimum KL divergence criterion is used, it can be modeled as the following optimization problem:

[0220]

[0221] stω≥0

[0222] In order to avoid the constraint of ω≥0, let λ represent the root mean square of each element of ω, that is, ω=λ⊙λ, we can get:

[0223]

[0224] Taking the derivative of the objective function with respect to λ and setting the derivative to zero, we can obtain:

[0225] T T q⊙λ-T T 1⊙λ=0;

[0226] Among them, 1 represents a column vector whose elements are all 1. 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.

[0227]

[0228] Therefore, the following iterative process is constructed:

[0229] for n=0:N Iter

[0230] if n == 0

[0231]

[0232] else

[0233]

[0234]

[0235] end

[0236] end

[0237] 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, Indicates pseudo-inverse, max(a, b) indicates the maximum value of a and b. Output after iteration is completed

[0238] In Example 3, when the minimum L0 norm criterion is adopted, it can be modeled as the following optimization problem:

[0239]

[0240] stTω=φ

[0241] ω ≥ 0

[0242] The matching pursuit (MP) algorithm can be used to solve it. The specific process is as follows:

[0243] 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:

[0244]

[0245] The initial value of ω is the zero vector.

[0246] Step 2: Subtract φ If an element of φ is less than zero after the subtraction, set that element to zero:

[0247]

[0248] Step 3: Perform power correction on each element of ω found previously:

[0249]

[0250] Step 4: Repeat the above three steps until the largest element in φ is less than the preset threshold or the number of cycles reaches the preset maximum value. The power threshold is usually set to the first found 1%, the maximum number of cycles is generally set to 50, but can also be other values, such as 40, 30, or 60, etc.

[0251] Next, the relevant process of step 405: determining the statistical covariance matrix of the downlink channel based on the first power spectrum and the second transformation matrix is ​​introduced.

[0252] When determining the statistical covariance matrix of the downlink channel, the second transformation matrix used may be one or more.

[0253] The type of one or more second transformation matrices may be a discrete cosine transform DCT matrix, a Hadamard transform matrix, 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.

[0254] When the type of the second transformation matrix is ​​a discrete cosine transform DCT matrix, the second transformation matrix is ​​referred to as a second discrete cosine transform DCT matrix. When the type of the second transformation matrix is ​​a Hadamard transform matrix, the second transformation matrix is ​​referred to as a second Hadamard transform 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 ​​referred to as a second oversampled DFT matrix.

[0255] In addition, a second transformation matrix can be obtained based on any transformation matrix of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. Then at least one second transformation matrix can be obtained based on at least one of the following types of matrices: a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The transformation matrix of the spatial domain type used to determine the second transformation matrix is ​​called a second spatial domain transformation matrix, the transformation matrix of the frequency domain type used to determine the second transformation matrix is ​​called a second frequency domain transformation matrix, and the transformation matrix of the time domain type used to determine the second transformation matrix is ​​called a second time domain transformation matrix.

[0256] It can be understood that when the type of at least one second transformation matrix is ​​a discrete cosine transform DCT matrix, and at least one second transformation matrix is ​​obtained based on at least one type of matrix among a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, at least one second transformation matrix can be regarded as being obtained based on at least one type of matrix among a spatial domain DCT matrix, a frequency domain DCT matrix, and a 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 among a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, at least one second transformation matrix can be regarded as being obtained based on at least one type of matrix among a spatial domain oversampled DFT matrix, a frequency domain oversampled DFT matrix, and a time domain oversampled DFT matrix. Several other types of matrices are similar and will not be repeated.

[0257] Optionally, the space domain matrix can also be divided into a space domain horizontal matrix and a space domain vertical matrix.

[0258] For the types of matrices mentioned above, discrete cosine transform DCT matrix, or Hadamard transform matrix, or DFT matrix, or oversampled DFT matrix, the second transform matrix and the first transform matrix may be of the same type or different types, for example, the first transform matrix is ​​a DCT matrix, and the second transform matrix is ​​a Hadamard transform matrix. When the first transform matrix and the second transform matrix are of the same type, the specific contents of the first transform matrix and the second transform matrix may be the same or different. It should be noted that in the three types of spatial domain, frequency domain, and time domain, the types of the first transform matrix and the second transform matrix are the same. For example, if the first transform matrix is ​​obtained based on the spatial domain transform matrix, then the second transform matrix is ​​also obtained based on the spatial domain transform matrix, or if the first transform matrix is ​​two, which are obtained based on the frequency domain transform matrix and the time domain transform matrix respectively, then the second transform matrix is ​​also two, which are obtained based on the frequency domain transform matrix and the time domain transform matrix respectively.

[0259] The second transformation matrix obtained based on the spatial domain horizontal matrix is ​​recorded as Dimension is M H ×M H O H , O H , represents the oversampling multiple at the spatial domain level, M H Indicates the number of horizontal antennas.

[0260] The second transformation matrix obtained based on the spatial domain vertical matrix is ​​recorded as Dimension is M V ×M V O V , O V Indicates the vertical oversampling multiple in the spatial domain, MV Indicates the number of vertical antennas.

[0261] The second transformation matrix obtained based on the frequency domain matrix is ​​recorded as Dimension is M F ×M F O F , O F Indicates the vertical oversampling multiple in the spatial domain, M F Indicates the total number of resource blocks.

[0262] The second transformation matrix obtained based on the time domain matrix is ​​recorded as Dimension is M T ×M T O T , O T Indicates the vertical oversampling multiple in the spatial domain, M T Represents the time window length used to estimate the Doppler power spectrum.

[0263] In addition, when there is no oversampling, for example, the DCT matrix, the DFT matrix, and the Hadamard transform matrix do not have an oversampling operation, in this case, O H , O V , O F , O T Both can be 1.

[0264] Optionally, the second transformation matrix has the same number and dimension as the first transformation matrix.

[0265] The first transformation matrix (eg, 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 (for example, ) corresponds to the downlink and is used to determine the statistical covariance matrix of the downlink channel.

[0266] Optional, Each matrix in satisfies the following condition: the L2 norm of each column of the matrix is ​​1.

[0267] For example, The following formula is satisfied. Let F represent a matrix of dimension M×MO, whose mth row and nth column element is:

[0268]

[0269] Wherein, m is an integer from 1 to M, and n is an integer from 1 to M*O. Wherein, M may correspond to M described above. H 、M V 、MF 、M T ; O can correspond to O introduced above H , O V , O F , O T For example, applying this formula to generate the matrix When M in the formula is M H , O in this formula is O H . Similar, I will not introduce them one by one.

[0270] When determining the statistical covariance matrix of the downlink channel based on the first power spectrum and the second transformation matrix:

[0271] For example, based on one or more second transformation matrices, the first power spectrum, and the Kronecker product One or more algorithms of transpose, conjugate transpose, and diag are used to obtain a statistical covariance matrix of the downlink channel.

[0272] For example, the Kronecker product of multiple first transformation matrices Multiply it by diag(ω) to get the statistical covariance matrix of the downlink channel.

[0273] Where ω is the first power spectrum. For example, the first power spectrum is a column vector, and diag(ω) is used to indicate that the column vector is placed on the diagonal. For example

[0274] For another example, the Kronecker product of multiple first transformation matrices A matrix is ​​obtained, the matrix is ​​multiplied by diag(ω), and then multiplied by the conjugate transpose of the matrix to obtain the statistical covariance of the downlink channel.

[0275] In an optional example a, the statistical covariance matrix R of the downlink channel satisfies the following formula:

[0276]

[0277] For example, the statistical covariance matrix is ​​obtained according to transformation matrices obtained based on the space domain, the frequency domain, and the time domain, respectively. The statistical covariance can be called a space-frequency-time joint statistical covariance.

[0278] In an optional example b, the statistical covariance matrix of the downlink channel satisfies the following formula:

[0279]

[0280] For example, the statistical covariance matrix is ​​obtained according to transformation matrices obtained based on the spatial domain and the frequency domain respectively, and the statistical covariance can be called a spatial-frequency joint statistical covariance.

[0281] In an optional example c, the statistical covariance matrix of the downlink channel satisfies the following formula:

[0282]

[0283] For example, the statistical covariance matrix is ​​obtained based on a transformation matrix obtained based on the spatial domain, and the statistical covariance can be called spatial statistical covariance.

[0284] The present application transforms the uplink channel estimation matrix through the first transformation matrix (e.g., DFT matrix / oversampled 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, the first statistical average energy is obtained; the mapping relationship between the first statistical average energy and the angle, time delay, and Doppler power spectrum is used to estimate the angle, time delay, and Doppler power spectrum; finally, based on the angle, time delay, Doppler power spectrum and the second transformation matrix (e.g., DFT matrix / oversampled DFT matrix) in the space domain, frequency domain, and time domain corresponding to the downlink, the spatial, frequency, and time joint statistical covariance of the downlink channel is reconstructed. Optionally, the mapping relationship between the first statistical average energy and the angle, time delay, and Doppler power spectrum is combined with the criteria under the non-negative constraint (e.g., the minimum L2 norm distance criterion, or the minimum KL divergence criterion, or the minimum L0 norm criterion) to estimate the angle, time delay, and Doppler power spectrum.

[0285] Relative to the above Figure 3 In the example provided, it is necessary to obtain (and store) the statistical average energy instead of obtaining the statistical covariance of the uplink channel; the power spectrum is estimated by using the relationship between the statistical average energy and 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 scenario of obtaining the statistical covariance of one or more items in the spatial domain, frequency domain, and time domain, and is easy to generalize.

[0286] The above embodiment 1 introduces the process of a network device determining the statistical covariance of a downlink channel to facilitate sending a downlink reference signal and / or downlink data. In another embodiment 2 of the present application, a terminal device also adopts a similar method to determine the statistical covariance of an uplink channel to facilitate sending an uplink reference signal and / or uplink data.

[0287] Embodiment 2:

[0288] 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. Accordingly, the terminal device in the first embodiment can be changed to a network device, the network device in the first embodiment can be changed to a terminal device, the uplink channel estimation matrix in the first embodiment can be changed to a downlink channel estimation matrix, and the statistical covariance matrix of the downlink channel in the first embodiment can be changed to a statistical covariance matrix of the uplink channel.

[0289] In addition, the names of some nouns may be modified to distinguish them. For example, the first transformation matrix is ​​changed to the third transformation matrix, which is related to the downlink channel; the first channel estimation matrix is ​​changed to the second channel estimation matrix; the first statistical average energy is changed to the second statistical average energy, the first power spectrum is changed to the second power spectrum, and the second transformation matrix is ​​changed to the fourth transformation matrix, which is related to the uplink channel.

[0290] like Figure 5 As shown, a communication process based on the statistical covariance of the uplink channel is introduced.

[0291] 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 the downlink channel estimation matrix of the channel between each transmitting antenna of the network device and the terminal device. The downlink channel estimation matrix can be a matrix or a vector (a vector is a one-dimensional matrix).

[0292] Then, the terminal device determines the statistical covariance matrix of the uplink channel based on the downlink channel estimation matrix.

[0293] The statistical covariance matrix of the uplink channel can be used for uplink pilot weighting, and then send uplink reference signals. In addition, the statistical covariance matrix of the uplink channel can be used for single-user weight calculation, precoding, etc., and then send uplink data.

[0294] like Figure 6 As shown, a schematic diagram of a method process for a terminal device to determine the statistical covariance of an uplink channel is provided.

[0295] Step 601: The terminal device performs channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix.

[0296] Step 602: The terminal device transforms 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.

[0297] Step 603: The terminal device determines 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 of the elements in the second channel estimation matrix.

[0298] Step 604: The terminal device determines a second power spectrum based on the second statistical average energy, wherein a mapping relationship exists between the second statistical average energy and the second power spectrum.

[0299] Step 605: 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.

[0300] Subsequently, the terminal device may send data and / or reference signals based on the statistical covariance matrix of the uplink channel.

[0301] The parameters involved in this embodiment 2 are as follows:

[0302]

[0303] Regarding the above parameters, the differences from the first embodiment include: H 、M V Indicates the number of antennas in the terminal device, not the number of antennas in the network device. H , F V , F F , F T Corresponding to the downlink, Corresponding to the upper line, h t , h t , R both correspond to downward movement.

[0304] Next, the relevant process of step 601: the terminal device performs channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix is ​​introduced.

[0305] The network device may periodically send a downlink reference signal, and the network device may use one or more transmitting antennas to send the downlink reference signal. The network device may use a certain downlink frequency to send the downlink reference signal. 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.

[0306] When determining the downlink channel estimation matrix, the terminal device may take into account one or more factors of space (eg, antenna), frequency (eg, frequency in the bandwidth corresponding to the frequency point), and time (eg, period).

[0307] In an optional example a, the terminal device considers the space (antenna) factor and determines the downlink channel estimation matrix. For example, the terminal device determines the downlink channel estimation matrix corresponding to each transmitting antenna of the network device based on the downlink reference signal received from the transmitting antenna. That is, one transmitting antenna corresponds to one downlink channel estimation matrix. If the network device uses multiple transmitting antennas to send downlink reference signals, multiple downlink channel estimation matrices can be determined.

[0308] 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 the channel estimation matrices corresponding to multiple resource blocks RB. For example, the total number of resource blocks is M F , M F is an integer greater than or equal to 1. F The channel estimation matrix corresponding to the resource blocks is: Understandably, m F The value range is 1 to M F , 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. The channel estimation matrices of all RBs are combined to obtain the downlink channel estimation matrix ht.

[0309] When M F When it is equal to 1, Alternatively, when the frequency factor is not considered, the total number of resource blocks M can also be F Assumed to be 1, then

[0310] When M F When it is greater than 1, the downlink channel estimation matrix h t It can be the channel estimation matrix of all RBs The following is an example of a row channel estimation matrix being a vector. For example, the channel estimation matrices of all RBs are concatenated into a column vector, satisfying the following formula:

[0311]

[0312] Among them, vec(·) represents a vectorized operation.

[0313] In an optional example c, the terminal device considers time and frequency factors to determine the downlink channel estimation matrix. Regarding the time factor, for example, not only the currently determined downlink channel estimation matrix is ​​considered, but also the historical downlink channel estimation matrix can be considered. For example, the downlink channel estimation matrix at time t and the most recent MT-1 historical moments are concatenated into a column vector that satisfies the following formula:

[0314]

[0315] in, h t represents the downlink channel estimation matrix, M T Represents the time window length used to estimate the Doppler power spectrum.

[0316] In one example, a two-dimensional rectangular antenna array is configured in the terminal device, and the number of horizontal antennas is M. H , the number of vertical antennas is M V .

[0317] mth F The channel estimation matrix corresponding to the resource block The dimension is, for example, M H M V ×1, the channel estimation matrix is ​​a column vector, and the order of the corresponding antennas is: horizontal first, then vertical. This dimension can also have other deformations, as long as the number of elements in the matrix of multiple deformation dimensions is the same. For example, the dimension is M H ×M V , or the dimension is M V ×M H .

[0318] when When the downlink channel estimation matrix h t The dimension is, for example, M H M V ×1, the downlink channel estimation matrix is ​​a column vector. Or the dimension is M H ×M V , or the dimension is M V ×M H .

[0319] When M F When it is greater than 1, the downlink channel estimation matrix h t The dimension is M H M V M F ×1, the downlink channel estimation matrix is ​​a column vector, where M F 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 deformation 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 .

[0320] When M F When greater than 1, the downlink channel estimation matrix h t The dimension is MH M V M F M T ×1, 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 deformation 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 .

[0321] The following description will be made by taking the row channel estimation matrix as a column vector as an example.

[0322] Next, the relevant process of step 602: transforming the downlink channel estimation matrix based on a third transformation matrix (the third transformation matrix may be one or more) to obtain a second channel estimation matrix is ​​introduced.

[0323] When transforming the downlink channel estimation matrix, the third transformation matrix used may be one or more.

[0324] The type of one or more third transform matrices may be a discrete cosine transform (DCT) matrix, a Hadamard transform matrix, a DFT matrix, or an oversampled DFT matrix. It should be noted that for these types, the types of the multiple third transform matrices are usually the same.

[0325] When the type of the third transformation matrix is ​​a discrete cosine transform DCT matrix, the third transformation matrix is ​​referred to as a third discrete cosine transform DCT matrix. When the type of the third transformation matrix is ​​a Hadamard transform matrix, the third transformation matrix is ​​referred to as a third Hadamard transform 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.

[0326] In addition, a third transformation matrix can be obtained based on any transformation matrix of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. Then at least one third transformation matrix can be obtained based on at least one of the following types of matrices: a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The transformation matrix of the spatial domain type used to determine the third transformation matrix is ​​called a third spatial domain transformation matrix, the transformation matrix of the frequency domain type used to determine the third transformation matrix is ​​called a third frequency domain transformation matrix, and the transformation matrix of the time domain type used to determine the third transformation matrix is ​​called a third time domain transformation matrix.

[0327] 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 ​​obtained based on at least one type of matrix among a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, at least one third transformation matrix can be regarded as being obtained based on at least one type of matrix among a spatial domain DCT matrix, a frequency domain DCT matrix, and a 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 among a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, at least one third transformation matrix can be regarded as being obtained based on at least one type of matrix among a spatial domain oversampled DFT matrix, a frequency domain oversampled DFT matrix, and a time domain oversampled DFT matrix. Several other types of matrices are similar and will not be repeated.

[0328] Optionally, the space domain matrix can also be divided into a space domain horizontal matrix and a space domain vertical matrix.

[0329] The third transformation matrix obtained based on the spatial domain horizontal matrix is ​​denoted as F H , the dimension is M H ×M H O H , O H Indicates the oversampling multiple at the spatial domain level, M H It is understood that the dimension can have other variations, for example, the dimension is M H O H ×M H .

[0330] The third transformation matrix obtained based on the spatial domain vertical matrix is ​​denoted as F V , the dimension is M V ×M V O V , O V Indicates the vertical oversampling multiple in the spatial domain, M VIt is understood that the dimension can have other variations, for example, the dimension is M V O V ×M V .

[0331] The third transformation matrix obtained based on the frequency domain matrix is ​​denoted as F F , the dimension is M F ×M F O F , O F Indicates the oversampling multiple in the frequency domain, M F It is understood that the dimension may have other variations, for example, the dimension is M F O F ×M F .

[0332] The third transformation matrix obtained based on the time domain matrix is ​​recorded as FT, and the dimension is, for example, M T ×M T O T , O T Indicates the oversampling multiple in the time domain, M T It is understood that the dimension can have other deformations, for example, the dimension is M T O T ×M T .

[0333] In addition, when there is no oversampling, for example, the DCT matrix, the DFT matrix, and the Hadamard transform matrix do not have an oversampling operation, in this case, O H , O V , O F , O T Both can be 1.

[0334] It should be noted 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. The fourth transformation matrix (for example, ), the fourth transformation matrix corresponds to the uplink and is used to determine the statistical covariance matrix of the uplink channel.

[0335] Optional, F H , F V , F F , F TEach matrix in satisfies the following conditions: the L2 norm of each column of the matrix is ​​1. It can be understood that the L2 norm of a column vector means that the square root of the sum of the squares of the elements in the column vector is equal to 1.

[0336] For example, F H , F V , F F , F T The following formula is satisfied. Let F represent a matrix of dimension M×MO, whose mth row and nth column element is:

[0337]

[0338] Wherein, m is an integer from 1 to M, and n is an integer from 1 to M*O. Wherein, M may correspond to M described above. H 、M V 、M F 、M T ; O can correspond to O introduced above H , O V , O F , O T For example, applying this formula to the generator matrix F H When M in the formula is M H , O in this formula is O H . F V , F F , F T Similar, I will not introduce them one by one.

[0339] When the downlink channel estimation matrix is ​​transformed based on the third transformation matrix to obtain a second channel estimation matrix:

[0340] For example, one or more third transformation matrices are multiplied by the downstream channel estimation matrix to obtain the second channel estimation matrix.

[0341] For example, based on one or more third transformation matrices and the Kronecker product One or more algorithms such as transpose, conjugate transpose, etc. are used to obtain a second channel estimation matrix.

[0342] For example, the Kronecker product of multiple third transformation matrices Multiply by the downlink channel estimation matrix to obtain the second channel estimation matrix.

[0343] 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.

[0344] In an optional example a, the second channel estimation matrix satisfies the following formula:

[0345]

[0346] For example, F H Dimension is M H ×M H O H , F V Dimension is M V ×M V O V , F F Dimension is M F ×M F O F , F T Dimension is M T ×M T O T , h t The dimension is M H M V M F M T ×1, g t The dimension is M H M V M F M T O H O V O F O T ×1.

[0347] In an optional example b, the second channel estimation matrix satisfies the following formula:

[0348]

[0349] For example, F H Dimension is M H ×M H O H , F V Dimension is M V ×M V O V , F F Dimension is M F ×M F O F ,h t The dimension is M H M V M F ×1, g t The dimension is M H M V M F O H O V O F ×1.

[0350] In an optional example c, the second channel estimation matrix satisfies the following formula:

[0351]

[0352] For example, F H Dimension is M H ×M H O H , F V Dimension is M V ×M V O V ,h t The dimension is M H M V ×1, The dimension is M H M V O H O V ×1. Among them,

[0353] Optionally, this application can also use F H , F V , F F , F T The Kronecker product of multiple matrices among these 4 matrices is considered as the third transformation matrix. For example, the third transformation matrix is Or the third transformation matrix is Or the third transformation matrix is

[0354] Optionally, this application can also use F H , F V , F F , F T The conjugate transposed matrix of the matrix obtained by the Kronecker product of multiple matrices among these four matrices is regarded as the third transformation matrix. For example, the third transformation matrix is Or the third transformation matrix is Or the third transformation matrix is Here, H represents the conjugate transpose.

[0355] Next, the relevant process of step 603: determining the second statistical average energy corresponding to (one or more) the second channel estimation matrix is ​​introduced.

[0356] The second statistical average energy is obtained by statistically averaging the energies corresponding to some or all elements in one or more second channel estimation matrices. For example, the second statistical average energy can be obtained based on the Hadamard product ⊙, the conjugate (·) *The energies corresponding to some or all elements in the second channel estimation matrix can be determined by calculation methods such as ; and the energies of the elements can be statistically averaged based on the expected E.

[0357] The multiple first downlink channel estimation matrices here can be obtained based on one or more factors such as multiple transmitting antennas, multiple frequencies, and multiple periods. For example, if one transmitting antenna corresponds to one first downlink channel estimation matrix, then multiple transmitting antennas correspond to one first downlink channel estimation matrix. For example, if one frequency corresponds to one first downlink channel estimation matrix, then multiple frequencies correspond to one first downlink channel estimation matrix. For example, one period determines one downlink channel estimation matrix, then multiple periods determine multiple downlink channel estimation matrices.

[0358] In an optional example, the second statistical average energy satisfies the following formula:

[0359]

[0360] Where gt is the second channel estimation matrix, φ represents the second statistical average energy, E represents the expectation, which can be obtained by taking the statistical average of 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 conjugate, and each element a in the matrix gt ij Take the conjugate and get b ij (The product of two mutually conjugate complex numbers is equal to the square of the complex number modulus. Conjugation is usually represented by "* right subscript"), and the new product obtained by b ij The new matrix composed is recorded as matrix

[0361] Understandably, g t Can also be replaced by

[0362] When performing statistical averaging, the statistical averaging may be performed for one or more factors such as different time, different transmitting antennas, and different frequencies. One transmitting antenna of the network device corresponds to one second channel estimation matrix, and one transmitting antenna corresponds to one second channel estimation matrix. Can The statistical average is obtained by performing statistical averaging on different transmitting antennas of the network device in time. For example, multiple signals obtained at different times and different transmitting antennas are averaged. For example, multiple Perform statistical averaging.

[0363] In one example, the second channel estimation matrix is ​​a column vector, such as g t The dimension is M H MV 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.

[0364] Next, the relevant process of step 604: determining the second power spectrum based on the second statistical average energy is introduced.

[0365] There is a mapping relationship between the second statistical average energy and the second power spectrum, and the mapping relationship satisfies the following formula:

[0366] Tω=φ,

[0367] Among them, ω is the second power spectrum, φ is the second statistical average energy, T is the mapping matrix, and T is related to the third transformation matrix.

[0368] In an optional example, the second power spectrum is a column vector.

[0369] It can be understood that the second channel estimation matrix is ​​based on F H , F V , F F , F T The mapping matrix T is also obtained based on these matrices. In addition, the second power spectrum also represents the corresponding power spectrum, and the second power spectrum can be a combination of one or more of the angle power spectrum, the delay power spectrum, and the Doppler power spectrum. Among them, the angle power spectrum corresponds to the spatial domain, the delay power spectrum corresponds to the frequency domain, and the Doppler power spectrum corresponds to the time domain.

[0370] For example, when the third transformation matrix is ​​based on the spatial domain matrix (such as F H , F V ) is obtained, the second power spectrum is the angular power spectrum.

[0371] For example, when the third transformation matrix is ​​based on the frequency domain matrix (eg, F F ) is obtained, the second power spectrum is the delayed power spectrum.

[0372] For example, when the third transformation matrix is ​​based on the time domain matrix (eg, F T ) is obtained, the second power spectrum is the Doppler power spectrum.

[0373] For example, when the third transformation matrix is ​​based on the spatial domain matrix, the frequency domain matrix (such as F H , F V , F F ) is obtained, the second power spectrum is a combination of the angle power spectrum and the time delay power spectrum.

[0374] For example, when the third transformation matrix is ​​based on the spatial domain matrix, the frequency domain matrix, and the time domain matrix (such as F H , F V , F F , F T ) is obtained, the second power spectrum is a combination of the angle power spectrum, the delay power spectrum and the Doppler power spectrum.

[0375] When the second power spectrum is a power spectrum of a combination of an angle power spectrum, a delay power spectrum, and a Doppler power spectrum, the statistical covariance matrix of the uplink channel subsequently determined is a joint statistical covariance matrix of space, frequency, and time.

[0376] In an optional example, each element in the second power spectrum is a non-negative real value. This can ensure that the determined statistical covariance matrix of the uplink channel is semi-positive definite, and can improve the accuracy of the determined statistical covariance of the uplink channel.

[0377] The mapping matrix T is related to the third transformation matrix. For example, the mapping matrix T is related to F H , F V , F F , F T For example, the mapping matrix T is based on F H , F V , F F , F T One or more matrices in , and conjugate, conjugate transpose, Hadamard product ⊙, Kronecker product is determined by one or more algorithms in .

[0378] In an optional example a,

[0379] In an alternative example b,

[0380] In an alternative example c,

[0381] An optional example,

[0382] An optional example,

[0383] An optional example,

[0384] An optional example,

[0385] In the present application, the minimum L2 norm distance criterion, 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 using the minimum L2 norm distance criterion, the minimum KL divergence criterion, or the minimum L0 norm criterion in the first embodiment to determine the first power spectrum based on the first statistical average energy. The description of the first embodiment can be referred to and will not be repeated.

[0386] Next, the relevant process of step 605: determining the statistical covariance matrix of the uplink channel based on the second power spectrum and the fourth transformation matrix is ​​introduced.

[0387] When determining the statistical covariance matrix of the uplink channel, the fourth transformation matrix used may be one or more.

[0388] The type of one or more fourth transform matrices may be a discrete cosine transform DCT matrix, a Hadamard transform matrix, a DFT matrix, or an oversampled DFT matrix. It should be noted that for these types, the types of multiple fourth transform matrices are usually the same. For these types, the types of the third transform matrix and the fourth transform matrix may be the same or different.

[0389] When the type of the fourth transformation matrix is ​​a discrete cosine transform DCT matrix, the fourth transformation matrix is ​​referred to as a fourth discrete cosine transform DCT matrix. When the type of the fourth transformation matrix is ​​a Hadamard transform matrix, the fourth transformation matrix is ​​referred to as a fourth Hadamard transform 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 ​​referred to as a fourth oversampled DFT matrix.

[0390] In addition, a fourth transformation matrix can be obtained based on any transformation matrix of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. Then at least one fourth transformation matrix can be obtained based on at least one of the following types of matrices: a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The transformation matrix of the spatial domain type used to determine the fourth transformation matrix is ​​called a fourth spatial domain transformation matrix, the transformation matrix of the frequency domain type used to determine the fourth transformation matrix is ​​called a fourth frequency domain transformation matrix, and the transformation matrix of the time domain type used to determine the fourth transformation matrix is ​​called a fourth time domain transformation matrix.

[0391] It can be understood that when the type of at least one fourth transform matrix is ​​a discrete cosine transform DCT matrix, and at least one fourth transform matrix is ​​obtained based on at least one type of matrix among a spatial domain transform matrix, a frequency domain transform matrix, and a time domain transform matrix, at least one fourth transform matrix can be regarded as being obtained based on at least one type of matrix among a spatial domain DCT matrix, a frequency domain DCT matrix, and a time domain DCT matrix. When the type of at least one fourth transform matrix is ​​an oversampled DFT matrix, and at least one fourth transform matrix is ​​obtained based on at least one type of matrix among a spatial domain transform matrix, a frequency domain transform matrix, and a time domain transform matrix, at least one fourth transform matrix can be regarded as being obtained based on at least one type of matrix among a spatial domain oversampled DFT matrix, a frequency domain oversampled DFT matrix, and a time domain oversampled DFT matrix. Several other types of matrices are similar and will not be repeated.

[0392] Optionally, the space domain matrix can also be divided into a space domain horizontal matrix and a space domain vertical matrix.

[0393] For the types of matrices mentioned above, discrete cosine transform DCT matrix, or Hadamard transform matrix, or DFT matrix, or oversampled DFT matrix, the fourth transform matrix and the third transform matrix may be of the same type or different types, for example, the third transform matrix is ​​a DCT matrix, and the fourth transform matrix is ​​a Hadamard transform matrix. When the third transform matrix and the fourth transform matrix are of the same type, the specific contents of the third transform matrix and the fourth transform matrix may be the same or different. It should be noted that the types of the third transform matrix and the fourth transform matrix are the same in the three types of spatial domain, frequency domain, and time domain. For example, the third transform matrix is ​​obtained based on the spatial domain transform matrix, and the fourth transform matrix is ​​also obtained based on the spatial domain transform matrix, or the third transform matrix is ​​2, which are obtained based on the frequency domain transform matrix and the time domain transform matrix respectively, and the fourth transform matrix is ​​also 2, which are obtained based on the frequency domain transform matrix and the time domain transform matrix respectively.

[0394] The fourth transformation matrix obtained based on the spatial domain horizontal matrix is ​​recorded as Dimension is MH ×M H O H , O H , represents the oversampling multiple at the spatial domain level, M H Indicates the number of horizontal antennas.

[0395] The fourth transformation matrix obtained based on the spatial domain vertical matrix is ​​recorded as Dimension is M V ×M V O V , O V Indicates the vertical oversampling multiple in the spatial domain, M V Indicates the number of vertical antennas.

[0396] The fourth transformation matrix obtained based on the frequency domain matrix is ​​recorded as Dimension is M F ×M F O F , O F Indicates the vertical oversampling multiple in the spatial domain, M F Indicates the total number of resource blocks.

[0397] The fourth transformation matrix obtained based on the time domain matrix is ​​recorded as Dimension is M T ×M T O T , O T Indicates the vertical oversampling multiple in the spatial domain, M T Represents the time window length used to estimate the Doppler power spectrum.

[0398] In addition, when there is no oversampling, for example, the DCT matrix, the DFT matrix, and the Hadamard transform matrix do not have an oversampling operation, in this case, O H , O V , O F , O T Both can be 1.

[0399] Optionally, the fourth transformation matrix has the same number and dimension as the third transformation matrix.

[0400] The third transformation matrix (eg, 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. The fourth transformation matrix (for example, ) corresponds to the uplink and is used to determine the statistical covariance matrix of the uplink channel.

[0401] Optional, Each matrix in satisfies the following condition: the L2 norm of each column of the matrix is ​​1.

[0402] For example, The following formula is satisfied. Let F represent a matrix of dimension M×MO, whose mth row and nth column element is:

[0403]

[0404] Wherein, m is an integer from 1 to M, and n is an integer from 1 to M*O. Wherein, M may correspond to M described above. H 、M V 、M F 、M T ; O can correspond to O introduced above H , O V , O F , O T For example, applying this formula to generate the matrix When M in the formula is M H , O in this formula is O H . Similar, I will not introduce them one by one.

[0405] When determining the statistical covariance matrix of the uplink channel based on the second power spectrum and the fourth transformation matrix:

[0406] For example, based on one or more fourth transformation matrices, the second power spectrum, and the Kronecker product One or more algorithms of transpose, conjugate transpose, and diag are used to obtain a statistical covariance matrix of the uplink channel.

[0407] For example, the Kronecker product of multiple third transformation matrices Multiply it by diag(ω) to get the statistical covariance matrix of the uplink channel.

[0408] Where ω is the second power spectrum. For example, the second power spectrum is a column vector, and diag(ω) is used to indicate that the column vector is placed on the diagonal. For example

[0409] For another example, the Kronecker product of multiple third transformation matrices A matrix is ​​obtained, the matrix is ​​multiplied by diag(ω), and then multiplied by the conjugate transpose of the matrix to obtain the statistical covariance of the uplink channel.

[0410] In an optional example a, the statistical covariance matrix R of the uplink channel satisfies the following formula:

[0411]

[0412] In an optional example b, the statistical covariance matrix of the uplink channel satisfies the following formula:

[0413]

[0414] In an optional example c, the statistical covariance matrix of the uplink channel satisfies the following formula:

[0415]

[0416] For example, the statistical covariance matrix is ​​obtained according to transformation matrices obtained based on the space domain, the frequency domain, and the time domain, respectively. The statistical covariance can be called a space-frequency-time joint statistical covariance.

[0417] In an optional example b, the statistical covariance matrix of the uplink channel satisfies the following formula:

[0418]

[0419] For example, the statistical covariance matrix is ​​obtained according to transformation matrices obtained based on the spatial domain and the frequency domain respectively, and the statistical covariance can be called a spatial-frequency joint statistical covariance.

[0420] In an optional example c, the statistical covariance matrix of the uplink channel satisfies the following formula:

[0421]

[0422] For example, the statistical covariance matrix is ​​obtained based on a transformation matrix obtained based on the spatial domain, and the statistical covariance can be called spatial statistical covariance.

[0423] The present application transforms the downlink channel estimation matrix through the third transformation matrix (e.g., DFT matrix / oversampled DFT matrix) of the spatial domain, frequency domain, and time domain to obtain a second channel estimation matrix; based on the second channel estimation matrix, obtain the second statistical average energy; use the mapping relationship between the second statistical average energy and the angle, delay, and Doppler power spectrum to estimate the angle, delay, and Doppler power spectrum; finally, based on the angle, delay, Doppler power spectrum and the fourth transformation matrix (e.g., DFT matrix / oversampled DFT matrix) of the spatial domain, frequency domain, and time domain corresponding to the downlink, reconstruct the spatial, frequency, and time joint statistical covariance of the downlink channel. Optionally, the mapping relationship between the second statistical average energy and the angle, delay, and Doppler power spectrum is combined with the criteria under the non-negative constraint (e.g., the minimum L2 norm distance criterion, or the minimum KL divergence criterion, or the minimum L0 norm criterion) to estimate the angle, delay, and Doppler power spectrum.

[0424] The estimation method of the present application is relatively simple, and can be applied to the scenario of obtaining the statistical covariance of one or more items in the spatial domain, frequency domain, and time domain, and is easy to generalize.

[0425] The above describes the method of the embodiment of the present application, and the following describes the device in the embodiment of the present application. The method and the device are based on the same technical concept. Since the principles of the method and the device to solve the problem are similar, the implementation of the device and the method can refer to each other, and the repeated parts will not be repeated.

[0426] The embodiment of the present application can divide the functional modules of the device according to the above method example. For example, each function can be divided into each functional module, or two or more functions can be integrated into one module. These modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in the specific implementation.

[0427] Based on the same technical concept as the above method, see Figure 7 , a schematic diagram of the structure of a communication device 700 is provided, and 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.

[0428] In an example, the above-mentioned interface module 720 can also be separated and defined as a receiving module and a sending module.

[0429] In an example, the apparatus 700 may be a network device, or a chip or a functional unit used in a network device. The apparatus 700 has any function of the network device in the above method. For example, the apparatus 700 can execute the above Figure 4 The method includes steps performed by a network device.

[0430] The interface module 720 can execute the receiving action and the sending action executed by the network device in the above method embodiment.

[0431] The processing module 710 may execute other actions, except for the sending action and the receiving action, among the actions executed by the network device in the above method embodiment.

[0432] In one example, the interface module 720 is used to receive an uplink reference signal; the processing module 710 is used to perform channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix; the uplink channel estimation matrix is ​​transformed 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; a first statistical average energy corresponding to the first channel estimation matrix is ​​determined; the first statistical average energy is obtained by statistically averaging the energies corresponding to some or all of the elements in the first channel estimation matrix; based on the first statistical average energy, a first power spectrum is determined, wherein a mapping relationship exists between the first statistical average energy and the first power spectrum; based on the first power spectrum and the second transformation matrix, a statistical covariance matrix of the downlink channel is determined; the second transformation matrix is ​​a matrix related to the downlink channel.

[0433] In one example, the interface module 720 is further used to send data and / or reference signals based on the statistical covariance matrix of the downlink channel.

[0434] In an example, the storage module 730 may store computer-executable instructions for 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 example.

[0435] For example, the storage module may include one or more memories, and the memory may be a device in one or more devices or circuits for storing programs or data. The storage module may be a register, a cache, or a RAM, etc., and the storage module may be integrated with the processing module. The storage module may be a ROM or other types of static storage devices that can store static information and instructions, and the storage module may be independent of the processing module.

[0436] The transceiver module may be an input or output interface, a pin or a circuit, etc.

[0437] In an example, the apparatus 700 may be a terminal device, or a chip or a functional unit applied to the terminal device. The apparatus 700 has any function of the terminal device in the above method. For example, the apparatus 700 can execute the above Figure 6 The steps in the method are performed by the terminal device.

[0438] The interface module 720 can execute the receiving action and the sending action executed by the terminal device in the above method embodiment.

[0439] The processing module 710 may execute other actions except the sending action and the receiving action among the actions executed by the terminal device in the above method embodiment.

[0440] In one example, the interface module 720 is used to receive a downlink reference signal; the processing module 710 is used to perform channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix; the downlink channel estimation matrix is ​​transformed 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; the second statistical average energy corresponding to the second channel estimation matrix is ​​determined; the second statistical average energy is obtained by statistically averaging the energies corresponding to some or all of the elements in the second channel estimation matrix; based on the second statistical average energy, a second power spectrum is determined, wherein a mapping relationship exists between the second statistical average energy and the second power spectrum; based on the second power spectrum and the fourth transformation matrix, a statistical covariance matrix of the uplink channel is determined; the fourth transformation matrix is ​​a matrix related to the uplink channel.

[0441] In one example, the interface module 720 is further used to send data and / or reference signals based on the statistical covariance matrix of the uplink channel.

[0442] In an example, the storage module 730 may store computer-executable instructions of a method executed by a terminal device, so that the processing module 710 and the interface module 720 execute the method executed by the terminal device in the above example.

[0443] For example, the storage module may include one or more memories, and the memory may be a device in one or more devices or circuits for storing programs or data. The storage module may be a register, a cache, or a RAM, etc., and the storage module may be integrated with the processing module. The storage module may be a ROM or other types of static storage devices that can store static information and instructions, and the storage module may be independent of the processing module.

[0444] The transceiver module may be an input or output interface, a pin or a circuit, etc.

[0445] The above describes the device applied to the network device and the device applied to the terminal device in the embodiment of the present application. The following describes the possible product forms of the device applied to the network device and the device applied to the terminal device. It should be understood that any device having the above Figure 7 Any form of product with the characteristics of the device applied to the network device and any form of product with the characteristics of the device applied to the terminal device fall within the protection scope of this 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 and the product form of the device applied to the terminal device in the embodiment of this application to only this.

[0446] As a possible product form, the device can be implemented by a general bus architecture.

[0447] like Figure 8 As shown, a schematic block diagram of a communication device 800 is provided.

[0448] The device 800 may include: a processor 810, and optionally, a transceiver 820 and a memory 830. The transceiver 820 may be used to receive a program or instruction and transmit it to the processor 810, or the transceiver 820 may be used for the device 800 to communicate and interact with other communication devices, such as interactive control signaling and / or business data. The transceiver 820 may be a code and / or data reading and writing transceiver, or the transceiver 820 may be a signal transmission transceiver between a processor and a transceiver. The processor 810 and the memory 830 are electrically coupled.

[0449] In one example, the apparatus 800 may be a network device or a chip used in a 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 above method. Figure 4 , and the steps performed by the network device in the method. For example, the memory 830 is used to store a computer program; the processor 810 can be used to call the computer program or instruction stored in the memory 830 to execute the method performed by the network device in the above example, or to execute the method performed by the network device in the above example through the transceiver 820.

[0450] In one example, the apparatus 800 may be a terminal device, or a chip used in a 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 above method. Figure 6 The steps in the method performed by the terminal device in the example are as follows. For example, the memory 830 is used to store a computer program; the processor 810 can be used to call the computer program or instruction stored in the memory 830 to execute the method performed by the terminal device in the above example, or to execute the method performed by the terminal device in the above example through the transceiver 820.

[0451] Figure 7 The processing module 710 in the embodiment can be implemented by the processor 810.

[0452] Figure 7 The interface module 720 in the embodiment can 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.

[0453] Figure 7 The storage module 730 in the embodiment can be implemented by the memory 830 .

[0454] 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 a chip system).

[0455] In one possible implementation, a general processor implemented in an apparatus for a network device or an apparatus for a terminal device includes: a processing circuit (a processing circuit may also be referred to as a processor); optionally, it also includes: an input / output interface connected to and communicating with the internal portion of the processing circuit, and a storage medium (a storage medium may also be referred to as a memory), wherein 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 example.

[0456] Figure 7 The processing module 710 in can be implemented by a processing circuit.

[0457] Figure 7 The interface module 720 in the embodiment can be implemented by an input / output interface. Alternatively, the input / output interface is divided into an input interface and an output interface, the input interface performs the receiving function of the interface module, and the output interface performs the sending function of the interface module.

[0458] Figure 7 The storage module 730 in can be implemented by a storage medium.

[0459] As a possible product form, the device of the embodiment of the present application can also be implemented using the following: one or more FPGAs (field programmable gate arrays), PLDs (programmable logic devices), controllers, state machines, gate logic, discrete hardware components, any other suitable circuits, or any combination of circuits capable of performing the various functions described throughout the present application.

[0460] The embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a computer, enables the computer to perform the above-mentioned method for determining the statistical covariance of a channel. In other words, the computer program includes instructions for implementing the above-mentioned method for determining the statistical covariance of a channel.

[0461] An embodiment of the present application further provides a computer program product, including: a computer program code, when the computer program code is executed on a computer, the computer can execute the method for determining the statistical covariance of a channel provided above.

[0462] An embodiment of the present application further provides a communication system, which includes: a terminal device and a network device that execute the above-mentioned method for determining the statistical covariance of a channel.

[0463] In addition, the processor mentioned in the embodiments of the present application may be a central processing unit (CPU), a baseband processor, the baseband processor and the CPU may be integrated together or separated, or may be a network processor (NP) or a combination of a CPU and a NP. The processor may further include a hardware chip or other general-purpose processor. The above-mentioned hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above-mentioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) and other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. or any combination thereof. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0464] The memory mentioned in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory (DR RAM). It should be noted that the memory described in this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0465] The transceiver mentioned in the embodiments of the present application may include a separate transmitter and / or a separate receiver, or the transmitter and the receiver may be integrated into one. The transceiver may operate under the instruction of the corresponding processor. Optionally, the transmitter may correspond to a transmitter in a physical device, and the receiver may correspond to a receiver in a physical device.

[0466] Those of ordinary skill in the art will appreciate that the various method steps and units described in the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the steps and components of each embodiment have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those of ordinary skill in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0467] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0468] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0469] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0470] If the integrated unit is implemented in the form of a software functional 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 a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0471] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0472] Obviously, 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. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for determining channel statistical covariance, characterized in that: Applied to network equipment, including: Perform channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix; Transforming 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 a first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is obtained by statistically averaging energies corresponding to some or all elements in the first channel estimation matrix; Based on the first statistical average energy, determining a first power spectrum, wherein a mapping relationship exists between the first statistical average energy and the first power spectrum; Based on the first power spectrum and the second transformation matrix, a statistical covariance matrix of the downlink channel is determined; the second transformation matrix is ​​a matrix related to the downlink channel.

2. The method according to claim 1, characterized in that Also includes: Data and / or a reference signal are sent based on the statistical covariance matrix of the downlink channel.

3. The method according to claim 1 or 2, characterized in that Each element in the first power spectrum is a non-negative real value.

4. The method according to claim 1 or 2, characterized in that: The first transformation matrix is ​​any one of the following: a first discrete cosine transform DCT matrix, a first Hadamard transform matrix, a first discrete Fourier DFT matrix, a first oversampled discrete Fourier DFT matrix; The second transformation 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.

5. The method according to claim 1 or 2, characterized in that: The first transformation matrix is ​​obtained based on at least one of the following matrices: A first spatial 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 spatial domain transformation matrix, a second frequency domain transformation matrix, and a second time domain transformation matrix.

6. The method according to claim 1 or 2, characterized in that: 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.

7. A method for determining channel statistical covariance, characterized in that: Applied to terminal equipment, including: Perform 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; Determine a second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is obtained by statistically averaging energies corresponding to some or all elements in the second channel estimation matrix; Based on the second statistical average energy, determining a second power spectrum, wherein a mapping relationship exists between the second statistical average energy and the second power spectrum; Based on the second power spectrum and the fourth transformation matrix, a statistical covariance matrix of the uplink channel is determined; the fourth transformation matrix is ​​a matrix related to the uplink channel.

8. The method according to claim 7, characterized in that Also includes: Data and / or a reference signal are sent based on the statistical covariance matrix of the uplink channel.

9. The method according to claim 7 or 8, characterized in that Each element in the second power spectrum is a non-negative real value.

10. The method according to claim 7 or 8, characterized in that The third transformation matrix is ​​any one of the following: a third discrete cosine transform DCT matrix, a third Hadamard transform matrix, a third discrete Fourier DFT matrix, and a 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.

11. The method according to claim 7 or 8, characterized in that 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 spatial domain transformation matrix, a fourth frequency domain transformation matrix, and a fourth time domain transformation matrix.

12. The method according to claim 7 or 8, characterized in that 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.

13. A communication device, characterized in that: include: An interface module, used for receiving 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; Transforming 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 a first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is obtained by statistically averaging energies corresponding to some or all elements in the first channel estimation matrix; Based on the first statistical average energy, determining a first power spectrum, wherein a mapping relationship exists between the first statistical average energy and the first power spectrum; Based on the first power spectrum and the second transformation matrix, a statistical covariance matrix of the downlink channel is determined; the second transformation matrix is ​​a matrix related to the downlink channel.

14. The device according to claim 13, characterized in that The interface module is further used to send data and / or reference signals based on the statistical covariance matrix of the downlink channel.

15. The device according to claim 13 or 14, characterized in that Each element in the first power spectrum is a non-negative real value.

16. The device according to claim 13 or 14, characterized in that The first transformation matrix is ​​any one of the following: a first discrete cosine transform DCT matrix, a first Hadamard transform matrix, a first discrete Fourier DFT matrix, a first oversampled discrete Fourier DFT matrix; The second transformation 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.

17. The device according to claim 13 or 14, characterized in that The first transformation matrix is ​​obtained based on at least one of the following matrices: A first spatial 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 spatial domain transformation matrix, a second frequency domain transformation matrix, and a second time domain transformation matrix.

18. The device according to claim 13 or 14, characterized in that 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.

19. A communication device, characterized in that: include: An interface module, used for receiving 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; 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; Determine a second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is obtained by statistically averaging energies corresponding to some or all elements in the second channel estimation matrix; Based on the second statistical average energy, determining a second power spectrum, wherein a mapping relationship exists between the second statistical average energy and the second power spectrum; Based on the second power spectrum and the fourth transformation matrix, a statistical covariance matrix of the uplink channel is determined; the fourth transformation matrix is ​​a matrix related to the uplink channel.

20. The device according to claim 19, characterized in that The interface module is further used to send data and / or reference signals based on the statistical covariance matrix of the uplink channel.

21. The device according to claim 19 or 20, characterized in that Each element in the second power spectrum is a non-negative real value.

22. The device according to claim 19 or 20, characterized in that The third transformation matrix is ​​any one of the following: a third discrete cosine transform DCT matrix, a third Hadamard transform matrix, a third discrete Fourier DFT matrix, and a 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.

23. The device according to claim 19 or 20, characterized in that 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 spatial domain transformation matrix, a fourth frequency domain transformation matrix, and a fourth time domain transformation matrix.

24. The device according to claim 19 or 20, characterized in that 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.

25. A communication device, characterized in that: comprising a processor coupled to a memory; The memory is used to store computer programs or instructions; The processor is used 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 according to any one of claims 1 to 6, or to implement the method according to any one of claims 7 to 12.

26. A communication device, characterized in that: including a processor and a memory; The memory is used to store computer programs or instructions; The processor is used 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 according to any one of claims 1 to 6, or to implement the method according to any one of claims 7 to 12.

27. A chip system, characterized in that: The chip system comprises: a processing circuit; the processing circuit is coupled to a storage medium; The processing circuit is used to execute part or all of the computer programs or instructions in the storage medium. When the part or all of the computer programs or instructions are executed, it is used to implement the method as described in any one of claims 1-6, or to implement the method as described in any one of claims 7-12.

28. A computer-readable storage medium, characterized in that: Used to store a computer program, wherein the computer program includes instructions for implementing the method according to any one of claims 1 to 6, or instructions for implementing the method according to any one of claims 7 to 12.

29. A computer program product, characterized in that The computer program product comprises: a computer program code, and when the computer program code is run on a computer, the computer executes the method according to any one of claims 1 to 6, or executes the method according to any one of claims 7 to 12.

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