A precoding transmission method, system and device for a cell-free multi-antenna system

Abstract

The application relates to a precoding transmission method, system and device of a cell-free multi-antenna system. The method comprises the following steps: obtaining channel spatial covariance information of the system and noise power of the channel; calculating a set of transmission characteristic matrices of the system according to the channel spatial covariance information, and iteratively optimizing a set of optimal power allocation vectors of the system according to the channel spatial covariance information and the noise power; calculating a precoding matrix of each access point based on the set of transmission characteristic matrices and the set of optimal power allocation vectors; and transmitting data by each access point in combination with user data and the obtained precoding matrix. The system and the device are used for executing the above method. The application can significantly reduce system measurement and feedback overhead, and can maximize the average transmission rate of system information under the condition that the transmission power of each access point of the transmission end is limited.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital communication, and particularly relates to a precoding transmission method, system and device of a cell-free multi-antenna system. BACKGROUND

[0002] Due to the denser deployment of base stations in the 5G communication system than in the 3G and 4G systems, the inter-cell user interference is more serious, which seriously restricts the capacity of the system. In recent years, a cell-free multi-input multi-output (MIMO) communication network architecture has been proposed by researchers to solve the problem of inter-cell user (especially edge user) interference. Unlike the traditional network architecture, this architecture is user-centric and has no obvious cell boundary. The system reduces or eliminates the inter-user interference by deploying a large number of small access points (APs) and cooperating between the access points.

[0003] The design of the transmitting end of the cell-free MIMO system is very important for improving the system performance, which has attracted extensive attention from researchers. For the forward link of the cell-free massive MIMO network, a modified conjugate beamforming scheme is proposed in IEEE Wireless Communications Letters (“Modified conjugate beamforming for cell-free massive MIMO”, IEEE Wireless Communications Letters, 2019, 8(2): 616-619). This scheme eliminates the user self-interference, and although it does not use the forward pilot signal, its performance is close to the performance that can be achieved using the pilot. For the downlink of the cell-free millimeter wave massive multi-antenna system, the energy efficiency of the system is studied in IEEE Communications Letters (“Max-min optimal beamforming for cell-free massive MIMO”, IEEE Communications Letters, 2020, 24(10): 2344-2348). The problem is modeled as a non-convex energy efficiency optimization problem. This paper divides the problem into two sub-problems of hybrid precoding design and power allocation, and proposes an algorithm based on fractional programming, non-cooperative game, etc. for solving the problem.

[0004] The design of the sending end usually needs channel state information (CSI). The CSI can be generally divided into two categories: instant CSI and statistical CSI. At present, the research on the design of the sending end using instant CSI is more, and the related design using statistical CSI is less, and there is still a large research space. If the channel is fast fading change, the frequent measurement and feedback of the instant CSI to the channel information will inevitably bring a great burden to the system feedback link, therefore, how to obtain the channel state information in the case of reducing the system burden to complete the data transmission is a technical problem to be solved by the person skilled in the art. SUMMARY

[0005] In order to solve the problems existing in the prior art, the present application aims at providing a precoding transmission method, system and device of a cell-free multi-antenna system. The present application can significantly reduce the system measurement and feedback overhead, and can maximize the average transmission rate of the system information under the condition that the transmission power of each access point of the sending end is limited.

[0006] The precoding transmission method of the cell-free multi-antenna system provided by the present application comprises the following steps:

[0007] S01, obtaining the channel spatial covariance information of the system and the noise power of the channel;

[0008] S02, calculating the sending characteristic matrix set of the system according to the channel spatial covariance information, and iteratively optimizing to obtain the optimal power allocation vector set of the system according to the channel spatial covariance information and the noise power;

[0009] S03, calculating the precoding matrix of each access point based on the sending characteristic matrix set and the optimal power allocation vector set;

[0010] S04, each access point transmits data in combination with the user data and the obtained precoding matrix.

[0011] Preferably, in step S02, the calculation of the sending characteristic matrix set of the system is specifically:

[0012] The system comprises M access points and one user end, the access point sequence number i=1~M, the access point i is equipped with N T,i root sending antennas, the user end is equipped with N R root sending antennas, and the channel spatial covariance information of the system is denoted as The noise power of the channel is denoted as Wherein, Θ R,i represents the receiving spatial covariance matrix of the access point i to the user end channel, and Θ T,i represents the sending spatial covariance matrix of the access point i to the user end channel.

[0013] According to the channel spatial covariance information To transmit spatial covariance matrix Θ T,i Eigenvalue decomposition is performed:

[0014]

[0015] Wherein, represents the transmission characteristic matrix of the i-th access point in the system, represents The conjugate transpose operation of.

[0016] Preferably, in step S02, the optimal power allocation vector set of the system is obtained by iterative optimization according to the channel spatial covariance information and the noise power, which specifically includes the following steps:

[0017] S0201, initialize the iteration index n=0, the components of the auxiliary parameter μ And the iteration step size t>0, wherein, [μ1 … μ M ] T The matrix transpose of the set [μ1 … μ M ] is represented, and the initial value of the power allocation vector set is calculated according to the power allocation algorithm, and the power allocation algorithm includes the following steps:

[0018] a01, initialize the loop index k=0 and the search step size 0<S<1, initialize the set B={b ij ,i=1~M,j=1~4}, denoted as And satisfy the constraint Wherein, represents the maximum transmission power of the access point i;

[0019] a02, calculate the search direction set under the current iteration

[0020]

[0021] Wherein:

[0022]

[0023] [M] ll represents the (l,l) element in the matrix M, l=4(i-1)+j;

[0024] is a 4M-dimensional column vector, defined as The matrix transpose of the set ;

[0025] blkdg(.) denotes the block diagonalization operation of a matrix, E(.) denotes the expectation of the channel matrix H, and diag(.) denotes the diagonalization operation. Represents a matrix The conjugate transpose operation;

[0026] H = [H1 H2 ... H] M ], H i This represents the channel matrix from access point i to the user terminal.

[0027] in,

[0028] i = 1 ~ M,

[0029]

[0030] g i H represents the large-scale fading factor. ω,i Represents a dimension of N R ×N T,i A complex Gaussian random matrix, each element is counted independently and follows a complex Gaussian distribution with mean 0 and variance 1;

[0031] a03, Based on the obtained search direction set Update set B:

[0032]

[0033] a04, if satisfied If a preset threshold is set, the optimal solution for set B is obtained, denoted as . If the condition is not met, the loop index k is increased, and the process returns to step a02;

[0034] a05. Calculate the power allocation vector set corresponding to the optimal solution of set B. As the initial value of the power allocation vector set:

[0035]

[0036] S202, Update the auxiliary parameter μ:

[0037]

[0038] in, P represents the optimal solution of set B obtained by the power allocation algorithm in the nth call. i Max Represents the set of power distribution vectors The maximum value of the elements in;

[0039] S203, updating the auxiliary parameter according to the updated auxiliary parameter calculating the updated power allocation vector set as the iterative value of the power allocation vector set by using the power allocation algorithm;

[0040] S204, judging whether convergence is achieved based on the initial value and the iterative value of the obtained power allocation vector set, and outputting the iterative value of the power allocation vector set as the optimal power allocation vector set if convergence is achieved, denoted as and ending the loop; if convergence is not achieved, updating the iterative index n and returning to step S202.

[0041] Preferably, the step S03 specifically comprises:

[0042] calculating the precoding matrix W i of each access point.

[0043]

[0044] constructing a precoding matrix set {W i} based on the precoding matrix W i of each access point.

[0045] Preferably, the step S04 comprises the following steps:

[0046] each access point multiplies the user data by the respective precoding matrix W i and synchronously transmits the data.

[0047] A precoding transmission system of the present application is used to execute the method as described above, comprising:

[0048] a measurement module for acquiring the channel spatial covariance information of the system and the noise power of the channel;

[0049] an iterative optimization module for calculating the transmission eigenmatrix set of the system according to the channel spatial covariance information, and iteratively optimizing to obtain the optimal power allocation vector set of the system according to the channel spatial covariance information and the noise power;

[0050] a precoding matrix calculation module for calculating the precoding matrix of each access point based on the transmission eigenmatrix set and the optimal power allocation vector set;

[0051] a transmission module for controlling each access point to transmit data in combination with the user data and the obtained precoding matrix.

[0052] A computer device according to the present invention includes a processor and a memory connected by signals, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program executes the precoded transmission method as described above when loaded by the processor.

[0053] The present invention provides a computer-readable storage medium having stored thereon at least one instruction or at least one program, wherein the at least one instruction or at least one program, when loaded by a processor, executes the precoded transmission method as described above.

[0054] The precoding transmission method, system, and device for a cellular multi-antenna system described in this invention have the advantage of providing a precoding method for cellular MIMO transmission systems based on channel covariance and system signal-to-noise ratio feedback. This method can effectively improve the system's information transmission rate while meeting the system access point transmit power constraints. Compared to some existing transmission schemes, such as isotropic transmission and beamforming methods, this method achieves a higher information transmission rate. This invention also has the advantages of low feedback and low system overhead. Attached Figure Description

[0055] Figure 1 This is a flowchart of the steps of a precoding transmission method for a cellular multi-antenna system described in this embodiment;

[0056] Figure 2 This is a flowchart of the power allocation algorithm described in this embodiment;

[0057] Figure 3 This is a performance comparison chart of the method described in this embodiment with other methods;

[0058] Figure 4 This is a schematic diagram of the structure of the computer device described in this embodiment.

[0059] Figure labeling: 101 - Processor, 102 - Memory. Detailed Implementation

[0060] like Figure 1 As shown, the precoding transmission method for a cellular-free multi-antenna system according to the present invention is exemplarily applied to a single-user cellular-free MIMO system. The system includes M access points (APs) and one user terminal, with access point numbers i = 1 to M, and access point i equipped with N... T,i One transmitting antenna, and the user end is equipped with N R The root transmitting antenna, let the channel spatial domain covariance information of the system be denoted as . The noise power of the channel is Where, Θ R,iH T,i is the received spatial covariance matrix of the channel from access point i to the user terminal.

[0061] The access points are distributed and each access point is connected to a central processing unit (CPU). All access points jointly provide data transmission service for the user. The data received by the user can be represented as:

[0062]

[0063] wherein, represents the signal transmitted by the i-th access point, and the Gaussian white noise w satisfies distribution, represents the channel matrix between the i-th access point and the user terminal. Under the control of the central processing unit, the access points synchronously transmit data. In order to reduce the data overhead of the link, the access points do not share the transmitted data. The transmitted covariance matrix is defined as The overall transmitted covariance matrix Q of the system at this time can be represented as:

[0064] Q = blkdg({Q i}),

[0065] wherein, blkdg(·) represents the block diagonalization operation on the matrix, and the ergodic capacity of the system is represented as:

[0066]

[0067] The channel matrix H i above can be further represented as:

[0068]

[0069] wherein, g i represents a large-scale channel fading factor, Θ R,i represents the received spatial covariance matrix of the channel from access point i to the user terminal. T,i represents the transmitted spatial covariance matrix of the channel from access point i to the user terminal. H ω,i is a random matrix, and each element in the matrix is an independent distribution random variable, satisfying a complex Gaussian distribution with a mean of 0 and a variance of 1.

[0070] In order to facilitate performance comparison, the system signal-to-noise ratio is defined as follows:

[0071]

[0072] wherein, P i MaxThis represents the maximum transmit power of access point i.

[0073] Assume the transmitter has channel statistics: the channel spatial covariance information is... The noise power of the channel is The goal of this embodiment is to design the optimal transmission covariance matrix to maximize the traversal capacity of the system.

[0074] The operation at the transmitting end can be divided into two phases: a training phase and a data transmission phase. During the training phase, each access point transmits a pilot signal, and the user receiver measures its own channel matrix, calculates the transmit / receive covariance matrix, and feeds it back to each access point. Additionally, the receiver calculates and feeds back the channel's signal-to-noise ratio (SNR). Based on the received transmit / receive covariance matrix and SNR, each access point calculates its optimal precoding matrix set {W}. i Then, the system enters the data transmission phase: the access point multiplies the user data by its respective precoding matrix W. i After being coordinated and synchronized by the CPU, it is sent out.

[0075] In this embodiment, the decellularized MIMO system consists of a single user terminal and M access points, with each access point having 4 transmit and receive antennas. All access points have the same maximum transmit power: P i Max =P1 Max ,i=1~M.

[0076] The spatial correlation matrix is ​​of exponential form, with a basis of ρ, and the element value at position (m,n) is represented by ρ. |m-n| .

[0077] Simulation settings ρ T,i =i / 10, ρ R,i =0.25, where ρ T,i and ρ R,i These are the transmitted spatial covariance matrix Θ T,i and the received spatial covariance matrix Θ R,i The base.

[0078] In addition, a large-scale fading factor is set. Noise power set to Signal-to-noise ratio (SNR) = 5 dB.

[0079] The method described in this embodiment includes the following steps:

[0080] S01. Obtain the channel spatial covariance information and channel noise power of the system; specifically, the system obtains the channel noise power through measurements over a period of time. and channel spatial covariance information

[0081] S02, calculating a set of transmit eigen-matrices of the system according to the channel spatial covariance information, and iteratively optimizing a set of optimal power allocation vectors of the system according to the channel spatial covariance information and a noise power;

[0082] The calculating of the set of transmit eigen-matrices of the system specifically comprises:

[0083] The system comprises M access points and one user terminal, the access point sequence number i = 1 ~ M, the access point i is equipped with N T,i root transmit antennas, and the user terminal is equipped with N R root receive antennas, and the channel spatial covariance information of the system is denoted as The noise power of the channel is denoted as Wherein, Θ R,i represents the receive spatial covariance matrix of the channel from the access point i to the user terminal, and Θ T,i represents the transmit spatial covariance matrix of the channel from the access point i to the user terminal.

[0084] According to the channel spatial covariance information The transmit spatial covariance matrix Θ T,i is subjected to eigenvalue decomposition:

[0085]

[0086] Wherein, represents the transmit eigen-matrix of the i-th access point in the system, represents the conjugate transpose operation of .

[0087] In this step, the iteratively optimizing a set of optimal power allocation vectors of the system according to the channel spatial covariance information and the noise power specifically comprises the following steps:

[0088] S0201, initializing the iteration index n = 0, the components of the auxiliary parameter μ and the iteration step size t > 0, wherein, [μ1 … μ M ] T represents the matrix transpose of the set [μ1 … μ M ], and the initial value of the power allocation vector set is calculated according to a power allocation algorithm, as shown in Figure 2 The power allocation algorithm comprises the following steps:

[0089] a01, initializing the loop index k = 0 and the search step size 0 < S < 1, initializing the set B = {b ij , i = 1 ~ M, j = 1 ~ 4}, denoted as and satisfying the constraint wherein, This represents the maximum transmit power of access point i;

[0090] a02. Calculate the set of search directions in the current iteration.

[0091]

[0092] in:

[0093]

[0094] [M] ll This indicates taking the (l,l) element from matrix M, where l = 4(i-1) + j;

[0095] A 4M-dimensional column vector is defined as follows: Represents a set Matrix transpose;

[0096] blkdg(.) denotes the block diagonalization operation of a matrix, E(.) denotes the expectation of the channel matrix H, and diag(.) denotes the diagonalization operation. Represents a matrix The conjugate transpose operation;

[0097] H = [H1 H2 … H] M ], H i This represents the channel matrix from access point i to the user terminal.

[0098] in,

[0099] i = 1 ~ M,

[0100]

[0101] g i H represents the large-scale fading factor. ω,i Represents a dimension of N R ×N T,i A complex Gaussian random matrix, each element is counted independently and follows a complex Gaussian distribution with mean 0 and variance 1;

[0102] a03, Based on the obtained search direction set Update set B:

[0103]

[0104] a04, if satisfied If a preset threshold is set, the optimal solution for set B is obtained, denoted as . If not, let the loop index k+1, and return to step a02;

[0105] a05, calculate the optimal solution of the set B corresponding power allocation vector set As the initial value of the power allocation vector set:

[0106]

[0107] S202, update the auxiliary parameter μ:

[0108]

[0109] Wherein, Indicates the optimal solution of the set B calculated by the power allocation algorithm for the nth time, P i Max Indicates the power allocation vector set The maximum value of the elements in the set;

[0110] S203, according to the updated auxiliary parameter The updated power allocation vector set calculated by the power allocation algorithm is used as the iteration value of the power allocation vector set;

[0111] S204, based on the initial value and the iteration value of the obtained power allocation vector set, judge whether it converges or not, for example, the initial value is The iteration value is Based on the initial value And the iteration value Judge whether it satisfies E is a preset threshold, if it satisfies, it is judged to be convergent, and the iteration value of the power allocation vector set is output as the optimal power allocation vector set, denoted as And end the loop; if it does not satisfy, it is judged to be not convergent, the iteration index n is updated, and it returns to step S202.

[0112] S03, based on the transmission feature matrix set and the optimal power allocation vector set, calculate the precoding matrix of each access point, specifically:

[0113] Calculate the precoding matrix W i of each access point:

[0114]

[0115] Based on the precoding matrix W i of each access point, construct a precoding matrix set {W i}.

[0116] S04. Each access point, combining user data and the obtained precoding matrix, transmits data, specifically as follows:

[0117] Each access point multiplies the user data by its own precoding matrix W. i Data is transmitted synchronously.

[0118] Based on the obtained precoding matrix set {W i} Calculate the transmit covariance matrix Q for each access point. i :

[0119]

[0120] Based on the obtained transmission covariance matrix Q of each access point i Q i Substituting into the above formula for calculating the transmission covariance matrix Q, we can obtain the overall transmission covariance matrix Q of the system. Substituting the obtained overall transmission covariance matrix Q of the system into the above formula for calculating the traversal capacity C(Q), we can obtain the traversal capacity C(Q) of the system.

[0121] Calculation instructions:

[0122] 1) Given the number of access points M, what is the maximum transmit power P of each access point? i Max It can be obtained by reverse calculation from the system signal-to-noise ratio formula based on the preset SNR=5dB;

[0123] 2) When calculating expressions with the expectation operation E(.), several channel samples are first generated by MATLAB, and then substituted into the expression to calculate the average, replacing the expectation calculation operation. For example: in step a02, the 4M×4M matrix M is calculated (at this point, the value is known). (And the number of access points M are three parameters). This can be determined based on the channel matrix H. i The expression generates a set of 1000 channel samples: Then, based on the expression for the user's received receipt, 1000 channel matrices are combined: H (m) m = 1 to 1000. Finally, substituting these values ​​into the expression for matrix M, we obtain 1000 M matrices, and then average them to get the final result.

[0124] Figure 3 This graph compares the performance of the method in this embodiment with other methods in a 4-transmit, 4-receive non-cellular MIMO system. The comparison uses two benchmark algorithms.

[0125] 1) Equal power transmission method: All access points transmit data at maximum power and in an isotropic manner, i.e., set...

[0126] 2) Beamforming method: each access point employs a single beamforming method, which injects all power into the "strongest" direction in space.

[0127] It can be seen that the average capacity increases with the increase of the number of access points M, because the increase of M brings spatial diversity gain. When M is small (M = 1 or 2), the performance of the equal power transmission method is close to that of the method of the present application; but with the further increase of M, the average capacity cannot continue to increase, but can only reach a gradual level - about 7.6 bit / symbol. The beamforming method performs poorly when M is small, but its performance rises rapidly with the increase of M. In comprehensive comparison, the performance of the method of the present application is significantly better than the other two existing methods.

[0128] The present application provides a precoding method for a non-cellular MIMO transmission system based on channel covariance and system signal-to-noise ratio feedback. The method can effectively improve the system information transmission rate while meeting the system access point transmission power constraint. Compared with some existing transmission schemes such as isotropic transmission and beamforming method, the method achieves higher information transmission rate. The present application also has the advantages of small feedback amount and small system overhead.

[0129] The embodiment also provides a precoding transmission system for performing the method as described above, comprising:

[0130] a measurement module for obtaining channel spatial covariance information of the system and noise power of the channel;

[0131] an iterative optimization module for calculating a set of transmission eigenmatrix of the system according to the channel spatial covariance information, and iteratively optimizing to obtain a set of optimal power allocation vectors of the system according to the channel spatial covariance information and the noise power;

[0132] a precoding matrix calculation module for calculating a precoding matrix of each access point based on the set of transmission eigenmatrix and the set of optimal power allocation vectors;

[0133] a transmission module for controlling each access point to transmit data in combination with user data and the obtained precoding matrix.

[0134] The system of the embodiment belongs to the same inventive concept as the method described above, and can be understood with reference to the description above, which will not be repeated here.

[0135] As Figure 4As shown, the embodiment further provides a computer device including a processor 101 and a memory 102 connected by a bus signal, and the memory 102 stores at least one instruction or at least one program, and the at least one instruction or the at least one program is executed by the processor 101 to perform the precoding transmission method as described above. The memory 102 can be used to store software programs and modules, and the processor 101 performs various functional applications by running the software programs and modules stored in the memory 102. The memory 102 can mainly include a program storage area and a data storage area, wherein the program storage area can store operating systems, application programs required for functions, etc.; the data storage area can store data created according to the use of the device, etc. In addition, the memory 102 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory 102 can also include a memory controller to provide the processor 101 with access to the memory 102.

[0136] The method embodiment provided by the embodiment of the application can be executed in a computer terminal, a server or a similar computing device, that is, the computer device can include the computer terminal, the server or the similar computing device. The internal structure of the computer device can include but is not limited to a processor, a network interface and a memory. The processor, the network interface and the memory in the computer device can be connected by a bus or other means.

[0137] The processor 101 (or CPU (Central Processing Unit)) is the computing core and control core of the computer device. The network interface can optionally include a standard wired interface, a wireless interface (such as WI-FI, a mobile communication interface, etc.). The memory 102 is a memory device in the computer device, used to store programs and data. It can be understood that the memory 102 here can be a high-speed RAM storage device, or a non-volatile memory device, for example, at least one disk storage device; optionally, it can also be at least one storage device located away from the aforementioned processor 101. The memory 102 provides a storage space that stores an operating system of the electronic device, which can include but is not limited to: a Windows system (an operating system), a Linux (an operating system), an Android (a mobile operating system) system, an IOS (a mobile operating system) system, etc., and the present application does not make any limitation thereto; and in the storage space, one or more instructions suitable for being loaded and executed by the processor 101 are also stored, and these instructions can be one or more computer programs (including program codes). In the embodiment of the present application, the processor 101 loads and executes one or more instructions stored in the memory 102 to implement the precoding transmission method described in the above method embodiment.

[0138] The embodiment of the present application also provides a computer readable storage medium having at least one instruction or at least one program stored thereon, which is loaded by the processor 101 to execute the precoding transmission method as described above. The computer readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiment of the present application is implemented.

[0139] According to the embodiment of the present application, the computer readable storage medium can be a non-volatile computer readable storage medium. For example, it can include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program, which can be used by or in conjunction with an instruction execution system, device or apparatus.

[0140] In the description of the application, it should be understood that the orientation words such as "front, back, upper, lower, left, right", "transverse, vertical, horizontal" and "top, bottom" and the like indicated orientation or position relationship are generally based on the orientation or position relationship shown in the drawings, only for the convenience of describing the application and simplifying the description, without making the opposite statement, these orientation words do not indicate and imply that the indicated device or element must have a particular orientation or be constructed and operated in a particular orientation, therefore it cannot be understood as a limitation on the protection scope of the application.

[0141] For those skilled in the art, according to the above described technical solutions and concepts, other various corresponding changes and transformations can be made, and all these changes and transformations should belong to the protection scope of the claims of the present application.

Claims

1. A precoding transmission method for a cell-free multi-antenna system, characterized in that, The method comprises the following steps: S01, obtaining channel spatial covariance information of a system and noise power of a channel; S02, calculating a set of transmission characteristic matrices of the system according to the channel spatial covariance information, and iteratively optimizing a set of optimal power distribution vectors of the system according to the channel spatial covariance information and the noise power; S03, calculating a precoding matrix of each access point based on the set of transmission characteristic matrices and the set of optimal power distribution vectors; S04, transmitting data by each access point in combination with user data and the obtained precoding matrix; The calculation of the set of transmission characteristic matrices of the system specifically comprises: The computing system comprises M access points and one user terminal, the access point sequence number is , the access point is equipped with a root transmitting antenna, and the user terminal is equipped with a root transmitting antenna, the channel space covariance information of the system is denoted as , the noise power of the channel is denoted as , wherein denotes the receiving space covariance matrix of the access point to the user terminal channel, denotes the transmitting space covariance matrix of the access point to the user terminal channel. According to the channel spatial covariance information Eigenvalue decomposition is performed on the transmit spatial covariance matrix ​ ， wherein, denotes a transmission signature matrix of the denotes a conjugate transpose operation;​ In step S02, the iteratively optimizing of the set of optimal power distribution vectors of the system according to the channel spatial covariance information and the noise power specifically comprises the following steps: S0201、initialize an iteration index , an auxiliary parameter , a component of the auxiliary parameter , and an iteration step size wherein , denotes a matrix transpose of the set , an initial value is calculated with respect to a set of power allocation vectors according to a power allocation algorithm, the power allocation algorithm comprising the following steps: a01, initialize loop index and search step , initialize set , denoted by , and satisfying constraints wherein, denotes the maximum transmit power of the access point ; a02, compute the search direction set at the current iteration : ， Wherein, ， representing the matrix of elements l , l , ; For a vector v, define , denotes the matrix transpose of the set ; , , denotes a block diagonalization operation of a matrix, denotes a channel matrix expectation, denotes a diagonalization operation, denotes a conjugate transpose operation of a matrix ; , representing an access point channel matrix to the user end, The step S03 specifically comprises: ， ， denotes a large scale fading factor, denotes a complex Gaussian random matrix of size with each element statistically independent and following a complex Gaussian distribution with mean 0 and variance 1. a03. updating the set based on the resulting set of search directions :​ ， ， a04、if the condition is satisfied , , then the optimal solution of the set is obtained, denoted as ; if not, then the loop index is increased and the process returns to step a02; a05、computing the set corresponding to the optimal solution as the initial value of the set of power allocation vectors: ， ； S202, updating the auxiliary parameter : ， in, , Indicates the first The set obtained by the power allocation algorithm is called once. The optimal solution. Represents the set of power distribution vectors The maximum value of the elements in; S203、According to the updated auxiliary parameters , using the power allocation algorithm to obtain an updated power allocation vector set as the iteration value of the power allocation vector set. S204, judging whether to converge based on the initial value and the iterative value of the obtained power allocation vector set, outputting the iterative value of the power allocation vector set as the optimal power allocation vector set if converging, denoted as , and ending the loop; if not converging, updating the iterative index , and returning to step S202.

2. The precoding transmission method of claim 1, wherein Step S04 comprises the following steps: computing a precoding matrix for each access point : ， ， Precoding matrix based on individual access points Constructing a set of precoding matrices .

3. The precoding transmission method of claim 2, wherein Comprise: Each access point multiplies the user data by a respective precoding matrix , synchronously transmitting the data.

4. A precoding transmission system for carrying out the method according to any one of claims 1 to 3, characterized in that A measuring module for obtaining channel spatial covariance information of a system and noise power of a channel; An iteratively optimizing module for calculating a set of transmission characteristic matrices of the system according to the channel spatial covariance information, and iteratively optimizing a set of optimal power distribution vectors of the system according to the channel spatial covariance information and the noise power; A precoding matrix calculating module for calculating a precoding matrix of each access point based on the set of transmission characteristic matrices and the set of optimal power distribution vectors; A transmitting module for controlling each access point to transmit data in combination with user data and the obtained precoding matrix. The memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded by the processor to execute the precoding transmission method according to any one of claims 1-3.

5. A computer device comprising a processor and a memory connected by a signal, characterized in that, The at least one instruction or the at least one program is loaded by the processor to execute the precoding transmission method according to any one of claims 1-3.

6. A computer-readable storage medium having stored thereon, at least one instruction or at least one piece of program, characterized in that, ​

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