Precoding and power distribution joint optimization method, system and product

Abstract

The embodiment of the invention provides a joint optimization method, system and product of precoding and power distribution, and the method comprises the steps: building a joint optimization objective function of a precoding matrix and power distribution according to a rate function; converting the joint optimization objective function from a non-convexity function to a DC function; constructing a corresponding linear approximation DC function based on the DC function; performing iterative optimization on the linear approximation DC function through a DC planning algorithm, and determining a target optimal solution of iterative optimization; and configuring a precoding matrix and power allocation of each user in the communication system based on the target optimal solution. The invention aims to carry out joint optimization on a precoding matrix and power distribution so as to improve the performance of a frequency domain time reversal multi-user communication system.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a joint optimization method, system and product for precoding and power allocation. Background Art

[0002] Time Reversal (TR) is a technology that uses the multipath propagation characteristics of wireless channels to improve the transmission performance of the system. When TR is applied to wireless communication systems, the transmitter uses the time-reversed and conjugated version of the channel impulse response (CIR) as a preprocessing filter, and the transmitted signal must be processed by the preprocessing filter before being sent into the channel. After TR preprocessing and multipath channel transmission, the energy of the signal will be concentrated in the desired time domain and space domain, that is, the time-space focusing effect. This space-time focusing characteristic is a significant feature of TR transmission in a multipath environment, which means that TR can reduce inter-symbol interference (ISI) and inter-user interference (IUI), which are the main factors affecting the quality of service (QoS) and system capacity of users in wireless communication systems. In a multi-user system, due to the different path channel characteristics of different users, TR transmission can use multipath as a way to distinguish different users, and thus TR can be introduced into a multiple access system.

[0003] In the time-reversal OFDM communication system, the conventional precoding matrix is ​​the conjugate of the frequency domain channel matrix, which is not optimal for a multi-user system. The precoding matrix and power allocation of a user not only affect its own rate, but also determine the interference to other users, thus affecting the system and rate. Summary of the invention

[0004] In view of this, the present application provides a method, system and product for joint optimization of precoding and power allocation, aiming to jointly optimize the precoding matrix and power allocation to improve the performance of a frequency-domain time-reversal multi-user communication system.

[0005] In a first aspect, the present application provides a joint optimization method for precoding and power allocation, the method comprising:

[0006] According to the rate function, a joint optimization objective function of the precoding matrix and power allocation is established, and the joint optimization objective function is:

[0007] Where R represents the system and rate, P represents the total power transmitted by the system, and the rate function is Rk is the rate of user k, x k is the data flow vector sent to user k, Represents data flow vector x k The conjugate transpose of H k is the frequency domain channel matrix of the multipath channel between the transmitter and user k, Representation Matrix H k The conjugate transpose of N represents the N×N dimensional identity matrix, G k is the precoding matrix of user k, is the precoding matrix G k The conjugate transpose of Τr( S k ) determines the transmission power of user k, S is the expansion matrix, S H is the conjugate transpose of S, is the noise variance;

[0008] Converting the joint optimization objective function from a non-convex function to a DC function;

[0009] Based on the DC function, construct a corresponding linear approximate DC function;

[0010] Iteratively optimize the linear approximate DC function by using a DC planning algorithm to determine the target optimal solution of the iterative optimization;

[0011] The precoding matrix and power allocation of each user in the communication system are configured based on the target optimal solution.

[0012] Optionally, converting the joint optimization objective function from a non-convex function to a DC function includes:

[0013] Based on the first function and the rate function, a corresponding target rate function is constructed, where the target rate function is:

[0014]

[0015] Among them, the first function is I is a matrix with diagonal 1;

[0016] It means to decompose the first function in the target rate function into matrix determinant.

[0017] It represents the matrix determinant decomposition of the second function in the target rate function;

[0018] The matrix determinants in the target rate function are decomposed into convex functions, respectively. The expression of the convex function is:

[0019]

[0020] Among them, H A =[S H H1,S H H2,...,S H H K ]; P =diag{ S 1, S 2,..., S K};

[0021] H Bk =[S H H1,S H H2,...0 N×N ,...,S H H K ];

[0022] Based on the maximized system and rate function in the joint optimization objective function and the convex function, a first DC function corresponding to the maximized system and rate function is determined, and the first DC function expression is:

[0023]

[0024] Among them, y1( P )=Kf( P ) is a convex function, is a convex function;

[0025] A DC function is determined based on the first DC function and a power allocation constraint function in the joint optimization objective function.

[0026] Optionally, based on the DC function, constructing a corresponding linear approximate DC function includes:

[0027] Perform a first-order Taylor expansion on the second function in the first DC function at the initial matrix to obtain a linear approximation function of the second function. The expression of the linear approximation function is:

[0028]

[0029] in, is a convex function g k ( P) is a linear approximation function at the initial matrix, is the initial matrix;

[0030] Based on the linear approximation function of the second function, a first linear approximation DC function of the first DC function is constructed:

[0031]

[0032] Based on the first linear approximate DC function and the power allocation constraint function in the joint optimization objective function, a corresponding linear approximate DC function is determined.

[0033] Optionally, iteratively optimizing the linear approximate DC function by a DC planning algorithm to determine a target optimal solution of the iterative optimization includes:

[0034] The optimal solution that maximizes the linear approximate DC function in each round of iterative optimization is determined by using a convex optimization tool;

[0035] Determine whether the result corresponding to the current iterative optimization round meets the preset convergence condition;

[0036] When the result corresponding to the current iterative optimization round meets the preset convergence condition, the optimal solution obtained in the current iterative optimization round is determined as the target optimal solution.

[0037] Optionally, in the initial iterative optimization round, the initial matrix is ​​a preset initial matrix; in subsequent iterative optimization rounds, the initial matrix in the current iterative optimization round is the optimal solution obtained in the iterative optimization round before the current iterative optimization round.

[0038] Optionally, determining whether the result corresponding to the current iterative optimization round meets a preset convergence condition includes:

[0039] Determine the value of the convergence factor according to the optimal solution of the current iterative optimization round and the optimal solution of the previous iterative optimization round of the current iterative optimization round;

[0040] According to the relationship between the value of the convergence factor and the threshold of the convergence factor, it is determined whether the result corresponding to the current iterative optimization round meets the preset convergence condition.

[0041] Optionally, determining whether a result corresponding to the current iterative optimization round satisfies a preset convergence condition according to a relationship between the value of the convergence factor and a threshold of the convergence factor includes:

[0042] When the value of the convergence factor does not exceed the threshold of the convergence factor, determining that the result corresponding to the current iterative optimization round meets the preset convergence condition;

[0043] When the value of the convergence factor exceeds the threshold of the convergence factor, it is determined that the result corresponding to the current iterative optimization round does not meet the preset convergence condition.

[0044] Optionally, determining whether the result corresponding to the current iterative optimization round meets a preset convergence condition includes:

[0045] When the number of iterative optimizations corresponding to the current iterative optimization round reaches a preset number, determining that the result corresponding to the current iterative optimization round satisfies a preset convergence condition;

[0046] When the number of iterative optimizations corresponding to the current iterative optimization round does not reach a preset number, it is determined that the result corresponding to the current iterative optimization round does not satisfy a preset convergence condition.

[0047] Optionally, the method further includes: in a starting round of iterative optimization, setting the precoding matrix to a conventional time reversal precoding matrix, and setting the power allocation to an average allocation.

[0048] A second aspect of the present application provides a joint optimization system for precoding and power allocation, the system comprising:

[0049] The joint optimization objective function building module is used to establish the joint optimization objective function of the precoding matrix and the power allocation according to the rate function. The joint optimization objective function is

[0050] Where R represents the system and rate, P represents the total power transmitted by the system, and the rate function is R k is the rate of user k, x k is the data flow vector sent to user k, Represents data flow vector x k The conjugate transpose of H k is the frequency domain channel matrix of the multipath channel between the transmitter and user k, Representation Matrix H k The conjugate transpose of N represents the N×N dimensional identity matrix, G k is the precoding matrix of user k, is the precoding matrix G k The conjugate transpose of Τr( S k ) determines the transmission power of user k, S is the expansion matrix, S H is the conjugate transpose of S, is the noise variance;

[0051] A convexity conversion module, used for converting the joint optimization objective function from a non-convex function to a DC function;

[0052] A linear approximation construction module, used to construct a corresponding linear approximation DC function based on the DC function;

[0053] An iterative optimization module, used for iteratively optimizing the linear approximate DC function through a DC planning algorithm to determine a target optimal solution for the iterative optimization;

[0054] A configuration module is used to configure the precoding matrix and power allocation of each user in the communication system based on the target optimal solution.

[0055] The third aspect of the present application provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and running on the processor, wherein when the computer program is executed by the processor, the steps in the joint optimization method of precoding and power allocation as described in the first aspect of the present application are implemented.

[0056] The fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the joint optimization method of precoding and power allocation as described in the first aspect of the present application are implemented.

[0057] The joint optimization method of precoding and power allocation provided in this application has the following advantages:

[0058] The embodiment of the present application provides a joint optimization method for precoding and power allocation. First, according to the rate function, a joint optimization objective function of the precoding matrix and power allocation is established. The joint optimization objective function is: Where R represents the system and rate, P represents the total power transmitted by the system, and the rate function is R k is the rate of user k, x k is the data flow vector sent to user k, Represents data flow vector x k The conjugate transpose of H k is the frequency domain channel matrix of the multipath channel between the transmitter and user k, Representation Matrix H k The conjugate transpose of N represents the N×N dimensional identity matrix, Gk is the precoding matrix of user k, is the precoding matrix G k The conjugate transpose of Τr( S k ) determines the transmission power of user k, S is the expansion matrix, S H is the conjugate transpose of S, is the noise variance; converts the joint optimization objective function from a non-convex function to a DC function; based on the DC function, constructs a corresponding linear approximate DC function; iteratively optimizes the linear approximate DC function through a DC planning algorithm to determine the target optimal solution for iterative optimization; configures the precoding matrix and power allocation of each user in the communication system based on the target optimal solution. Therefore, the joint optimization method of precoding and power allocation provided by the present application is based on the establishment of a joint optimization objective function involving both the precoding matrix and the power allocation, and then converts it from a non-convex function to a DC function, and constructs a corresponding linear approximate DC function, and finally iteratively optimizes and solves the linear approximate DC function through a DC planning algorithm to obtain the target optimal solution for configuring the precoding matrix and power allocation of each user in the time-reversed OFDM communication system, so as to improve the performance of the frequency-domain time-reversal multi-user communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0060] Figure 1 A flowchart of a joint optimization method for precoding and power allocation is shown in one embodiment of the present application;

[0061] Figure 2 A structure of a time-reversal OFDM communication system to which a joint optimization method of precoding and power allocation is applied according to an embodiment of the present application;

[0062] Figure 3 A schematic diagram of a system and rate performance of joint optimization under different total powers in a joint optimization method of precoding and power allocation shown in one embodiment of the present application;

[0063] Figure 4 A schematic diagram of the system and rate performance of joint optimization at different iteration times in a joint optimization method for precoding and power allocation shown in one embodiment of the present application;

[0064] Figure 5 A schematic diagram of the system and rate performance of joint optimization under the corresponding number of paths for different numbers of users in a joint optimization method of precoding and power allocation shown in one embodiment of the present application;

[0065] Figure 6 Another schematic diagram of the system and rate performance of joint optimization under the corresponding number of paths for different numbers of users in a joint optimization method of precoding and power allocation shown in one embodiment of the present application;

[0066] Figure 7 A schematic diagram of a joint optimization system for precoding and power allocation is shown as an embodiment of the present application. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0068] Here, the technical idea of ​​a joint optimization method for precoding and power allocation provided in this application is first explained. In a time-reversal OFDM communication system, the conventional precoding matrix is ​​the conjugate of the frequency domain channel matrix, which is not optimal for a multi-user system. If the precoding matrix is ​​optimized according to a certain performance indicator of the multi-user system, the system performance can be improved. Since the system and rate are related to power allocation and precoding, optimizing power allocation alone cannot obtain the best system performance. In order to further improve the system and rate performance, this application proposes the joint optimization method of precoding and power allocation of this application for the joint optimization problem of precoding matrix and power allocation with the goal of maximizing the system and rate in the time-reversed OFDM multi-user communication system. First, the joint optimization objective function (i.e., mathematical model) of the joint optimization problem of power allocation and precoding matrix is ​​given. By decomposing the matrix determinant, the joint optimization objective function of the non-convex optimization problem is approximated as the difference between two convex functions (i.e., forming a DC function Difference of Convex), and then the DC planning algorithm is used to solve the optimization problem. Finally, the simulation analysis of the system and rate of the time-reversed OFDM communication system after joint optimization shows that compared with optimizing only power allocation, it has better performance. The method of this application comprehensively considers the impact of precoding matrix and power allocation on system performance, reduces interference between users, can effectively improve the system and rate, is better than the system that only optimizes the power allocation of user signals, and is better than the conventional time-reversal system without precoding matrix and power allocation.

[0069] refer to Figure 1 , Figure 1 The following is a flowchart of a joint optimization method for precoding and power allocation according to an embodiment of the present application. Figure 1 As shown, the method includes:

[0070] Step S1: According to the rate function, a joint optimization objective function of the precoding matrix and power allocation is established. The joint optimization objective function is

[0071] Where R represents the system and rate, P represents the total power transmitted by the system, and the rate function is R k is the rate of user k, x k is the data flow vector sent to user k, Represents data flow vector x k The conjugate transpose of H k is the frequency domain channel matrix of the multipath channel between the transmitter and user k, Representation Matrix H k The conjugate transpose of N represents the N×N dimensional identity matrix, G k is the precoding matrix of user k, is the precoding matrix G k The conjugate transpose of Τr( S k ) determines the transmission power of user k, S is the expansion matrix, S H is the conjugate transpose of S, is the noise variance.

[0072] In this embodiment, a joint optimization method of precoding and power allocation provided by the present application is applied to a time-reversal OFDM (Orthogonal Frequency Division Multiplexing) multi-user communication system (hereinafter collectively referred to as a communication system for ease of description), such as Figure 2 As shown, Figure 2 This is a model diagram of a time-reversed OFDM multi-user communication system. The system has K users, and both the transmitter and the receiver are equipped with a single antenna. The transmitter sends independent data streams to K users at the same time. The data stream vector sent to user k is recorded as x k , k=1,2,...,K.x k M data symbols x k,m Composition, m = 1, 2, ..., M, that is x k =[x k,1 ,x k,2 ,...,x k,M ] T , and E[x k,m 2 ]=1. Multiple users occupy the same time-frequency resources when transmitting, realizing signal multiplexing transmission. First, the data symbols are expanded by a matrix of size Q×M S Spreading, the purpose of spreading is to allocate data symbols to OFDM subcarriers, Q is the number of subcarriers in the OFDM system, and Q = DM. Here D is the rate decay factor (BOF), which is equivalent to the upsampling factor in the time domain TR system, and M is the number of data symbols. After spreading, each data symbol is allocated to D different subcarriers at intervals of M. S It is composed of D M×M diagonal matrices connected in series, and its diagonal elements are equally likely to have values This is to avoid allocating the same data symbols to different OFDM subcarriers, thereby causing a high peak-to-average power ratio, and to ensure that the signal power is not changed during the addition and expansion process, that is, to meet S H S = I M The expanded symbols are combined with the precoding matrix before transmission. G k Multiplication is performed for precoding, which is equivalent to filtering the signal sent in the time domain TR system through a time reversal prefilter. The precoded symbols are transformed into time domain signals through inverse Fourier transform, and then sent out after adding a cyclic prefix. The frequency domain channel matrix of the multipath channel between the transmitter and user k is H k , H k is a diagonal matrix whose diagonal elements are channel coefficients, which are also the discrete Fourier transform of the channel impulse response. The frequency domain symbol of the channel output is the frequency domain symbol sent and the frequency domain channel matrix H The product of k. The v in the figure k =[v k (1),v k (2),...,v k (Q)] T is the frequency domain additive white Gaussian noise sequence, is the noise variance. Here G k There is no requirement to normalize this, so it will affect the transmit power.

[0073] In this embodiment, for the communication system, the user's rate function is obtained, and then based on the rate function, a joint optimization objective function for the precoding matrix and power allocation is established. The expression of the joint optimization objective function is:

[0074]

[0075] Where R represents the system sum rate of the communication system, P represents the total system transmission power of the communication system, and the rate function is R k is the rate of user k, x k is the data flow vector sent to user k, Represents data flow vector x k The conjugate transpose of H k is the frequency domain channel matrix of the multipath channel between the transmitter and user k, Representation Matrix H k The conjugate transpose of N represents the N×N dimensional identity matrix, G k is the precoding matrix of user k, is the precoding matrix G k The conjugate transpose of Τr( S k ) determines the transmission power of user k, S is the expansion matrix, S H is the conjugate transpose of S, is the noise variance.

[0076] Step S2: Convert the joint optimization objective function from a non-convex function to a DC function.

[0077] In this embodiment, for the system and rate formula involved in the joint optimization objective function created in step S1, the precoding matrix and power allocation of user k not only determine the rate of user k itself, but also determine the interference to other users. The precoding matrix and power allocation of one user will affect the rates of all users. The optimization problem corresponding to the joint optimization objective function is a non-convex optimization problem, which is difficult to solve. This application approximates the joint optimization objective function of the non-convex optimization problem as a DC function (Difference of Convex Functions) of the difference between two convex functions by decomposing the matrix determinant, and then uses DC planning to solve the transformed optimization problem.

[0078] Step S3: Based on the DC function, construct a corresponding linear approximate DC function.

[0079] In this embodiment, in order to improve the efficiency of the subsequent DC planning algorithm solution, a corresponding linear approximate DC function is first constructed based on the DC function obtained in step S3, and then the linear approximate DC function is iteratively optimized by the DC planning algorithm.

[0080] Step S4: Iteratively optimize the linear approximate DC function using a DC planning algorithm to determine the target optimal solution of the iterative optimization.

[0081] In this embodiment, the linear approximate DC function constructed by step S3 is subjected to multiple rounds of iterative optimization by the DC planning algorithm until the optimal solution obtained satisfies a pre-set specific condition, the iterative optimization is terminated, and the optimal solution of the last iterative optimization is determined as the final target optimal solution.

[0082] Step S5: configuring the precoding matrix and power allocation of each user in the communication system based on the target optimal solution.

[0083] In this embodiment, the precoding matrix and power allocation of each user in the communication system are configured based on the final target optimal solution obtained in step S4, which can ensure that the communication system has a better system and rate.

[0084] The embodiment of the present application provides a joint optimization method for precoding and power allocation. First, according to the rate function, a joint optimization objective function of the precoding matrix and power allocation is established. The joint optimization objective function is: Where R represents the system and rate, P represents the total power transmitted by the system, and the rate function is R k is the rate of user k, x k is the data flow vector sent to user k, Represents data flow vector x k The conjugate transpose of H k is the frequency domain channel matrix of the multipath channel between the transmitter and user k, Representation Matrix H k The conjugate transpose of N represents the N×N dimensional identity matrix, G k is the precoding matrix of user k, is the precoding matrix G k The conjugate transpose of Τr( S k ) determines the transmission power of user k, S is the expansion matrix, S H is the conjugate transpose of S, is the noise variance; converts the joint optimization objective function from a non-convex function to a DC function; based on the DC function, constructs a corresponding linear approximate DC function; iteratively optimizes the linear approximate DC function through a DC planning algorithm to determine the target optimal solution for iterative optimization; configures the precoding matrix and power allocation of each user in the communication system based on the target optimal solution. Therefore, the joint optimization method of precoding and power allocation provided by the present application is based on the establishment of a joint optimization objective function involving both the precoding matrix and the power allocation, and then converts it from a non-convex function to a DC function, and constructs a corresponding linear approximate DC function, and finally iteratively optimizes and solves the linear approximate DC function through a DC planning algorithm to obtain the target optimal solution for configuring the precoding matrix and power allocation of each user in the time-reversed OFDM communication system, so as to improve the performance of the frequency-domain time-reversal multi-user communication system.

[0085] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides a joint optimization method for precoding and power allocation. In the joint optimization method for precoding and power allocation, step S2 may include steps S21 to S24:

[0086] Step S21: Based on the first function and the rate function, a corresponding target rate function is constructed, wherein the target rate function is

[0087]

[0088] Among them, the first function is I is a matrix with diagonal 1;

[0089] It means to decompose the first function in the target rate function into matrix determinant.

[0090] It represents the matrix determinant decomposition of the second function in the target rate function.

[0091] In this embodiment, according to the expression of the first function and the rate function of the user, the rate function of the user can be rewritten as a target rate function, and the expression of the target rate function is:

[0092]

[0093] In this embodiment, It represents the matrix determinant decomposition of the first function in the target rate function.

[0094] Indicates the matrix determinant decomposition of the second function in the target rate function

[0095] In this embodiment, the expression of the first function is: Where I is a matrix with diagonal 1.

[0096] Step S22: Decompose each matrix determinant in the target rate function into an upward convex function, where the expression of the upward convex function is: in,

[0097] H A =[S H H1,S H H2,...,S H H K ]; P =diag{ S 1, S 2,..., S K};

[0098] H Bk =[ S H H 1, S H H 2,...0 N×N ,..., S H H K ].

[0099] In this embodiment, for the target rate function constructed based on the user's rate function in step S21, the two matrix determinants in the target rate function are decomposed into a single convex function. S 1, S 2,..., S K ) matrix determinant decomposition, the corresponding convex function constructed is For g k ( S 1, S 2,..., S K ) matrix determinant decomposition, the corresponding convex function constructed is in,

[0100] H A =[S H H1,S H H2,...,SH H K ]; P =diag{ S 1, S 2,..., S K};

[0101] H Bk =[ S H H 1, S H H 2,...0 N×N ,..., S H H K ].

[0102] Step S23: Based on the maximized system and rate function in the joint optimization objective function and the convex function, determine the first DC function corresponding to the maximized system and rate function, the first DC function expression is: in,

[0103] y1( P )=Kf( P ) is a convex function, is a convex function.

[0104] In this embodiment, the joint optimization objective function includes two parts, one is the maximum system and rate function, and the other is the power allocation constraint function for the user. Based on the maximum system and rate function in the joint optimization objective function and the two convex functions constructed by step S22, the present application constructs a first DC function corresponding to the maximum system and rate function. The expression of the first DC function is: Among them, y1( P )=Kf( P ) is a convex function, is a convex function.

[0105] Step S24: determining a DC function based on the first DC function and the power allocation constraint function in the joint optimization objective function.

[0106] In this embodiment, after the first DC function corresponding to the maximized system and rate function is obtained in step S23, a DC function is formed by the first DC function and the power allocation constraint function in the joint optimization objective function. The expression of the DC function is:

[0107]

[0108] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides a joint optimization method for precoding and power allocation. In the joint optimization method for precoding and power allocation, step S3 may include steps S31 to S33:

[0109] Step S31: Perform a first-order Taylor expansion on the second function in the first DC function at the initial matrix to obtain a linear approximation function of the second function. The expression of the linear approximation function is: in, is a convex function g k ( P ) is a linear approximation function at the initial matrix, is the initial matrix.

[0110] In this embodiment, the second function y2( P ) performs a first-order Taylor expansion at the initial matrix. After completing the first-order Taylor expansion, the linear approximation function of the second function is obtained. The expression of the linear approximation function of the second function is: in, is a convex function g k ( P ) is a linear approximation function at the initial matrix, is the initial matrix.

[0111] Step S32: constructing a first linear approximate DC function of the first DC function based on the linear approximate function of the second function:

[0112]

[0113] In this embodiment, after the linear approximation function of the second function in the first DC function is obtained in step S31, a first linear approximation DC function of the first DC function is constructed based on the linear approximation function of the second function. The expression of the first linear approximation DC function is:

[0114] Step S33: Determine a corresponding linear approximate DC function based on the first linear approximate DC function and the power allocation constraint function in the joint optimization objective function.

[0115] In this embodiment, after the first linear approximate DC function of the first DC function is obtained in step S32, the first DC function in the DC function is replaced with the first linear approximate DC function of the first DC function to obtain a linear approximate DC function of the DC function, and the linear approximate DC function expression of the DC function is:

[0116] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides a joint optimization method for precoding and power allocation. In the joint optimization method for precoding and power allocation, step S4 may include steps S41 to S43:

[0117] Step S41: Determine the optimal solution that maximizes the linear approximate DC function in each round of iterative optimization by using a convex optimization tool.

[0118] In this embodiment, for the linear approximate DC function obtained in step S3 The linear approximate DC function is optimized and solved by multiple rounds of iterative optimization using the cvx convex optimization tool. In each round of iterative optimization, the power allocation constraint function is used to constrain the power allocation value of the user. In fact, each round of iterative optimization solves the problem of making the system and rate The optimal solution for maximizing the parameter Ψ P opt .

[0119] Step S42: Determine whether the result corresponding to the current iterative optimization round meets the preset convergence condition.

[0120] In this embodiment, after obtaining a solution result in each round of iterative optimization, it is determined whether the obtained solution result satisfies a preset convergence condition set in advance.

[0121] Step S43: when the result corresponding to the current iterative optimization round meets the preset convergence condition, determine the optimal solution obtained in the current iterative optimization round as the target optimal solution.

[0122] In this embodiment, if the solution obtained in a round of iterative optimization meets the preset convergence condition set in advance, the iterative optimization is terminated, and the system and rate are used in the solution obtained in the round of iterative optimization. The optimal solution for maximizing the parameter Ψ P opt Determine as the final optimal solution of the target, the optimal solution of the target is P * Then the optimal solution of this objective is P * Configure precoding matrices and power allocation for users in the communication system.

[0123] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides a joint optimization method for precoding and power allocation. In the joint optimization method for precoding and power allocation, in the initial iterative optimization round, the initial matrix is ​​a preset initial matrix; in subsequent iterative optimization rounds, the initial matrix in the current iterative optimization round is the optimal solution obtained in the previous iterative optimization round of the current iterative optimization round.

[0124] In this embodiment, an initial matrix will be involved in each round of iterative optimization solution. This application will preset an initial matrix. Represents the initial matrix, and the preset initial matrix will participate in the first-order Taylor expansion in the first iterative optimization solution. In the subsequent iterative optimization process, when solving the linear approximate function in the current round, the system and rate obtained in the previous round will be used. The optimal solution for maximizing the parameter Ψ P opt As the initial matrix required for solving the current round of iterative optimization.

[0125] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides a joint optimization method for precoding and power allocation. In the joint optimization method for precoding and power allocation, determining whether the result corresponding to the current iterative optimization round meets the preset convergence condition includes: determining the value of the convergence factor according to the optimal solution of the current iterative optimization round and the optimal solution of the previous iterative optimization round of the current iterative optimization round; determining whether the result corresponding to the current iterative optimization round meets the preset convergence condition according to the relationship between the value of the convergence factor and the threshold of the convergence factor.

[0126] In this embodiment, an optional implementation method for determining whether the result corresponding to the current iterative optimization round meets the preset convergence condition is: by bringing the optimal solution of the current iterative optimization round and the optimal solution of the previous iterative optimization round into the target algorithm for calculation, the value of the convergence factor of the current iterative optimization round is obtained. The expression of the target algorithm is: in, represents the optimal solution of the current iterative optimization round, is the optimal solution of the previous iteration optimization round of the current iteration optimization round, and De represents the value of the convergence factor. After obtaining the value of the convergence factor of the current iteration optimization round, it is compared with the threshold of the convergence factor, and based on the relationship between the two, it is determined whether the result corresponding to the current iteration optimization round meets the preset convergence condition. Among them, the threshold of the convergence factor can be set according to the actual application scenario, and is not specifically limited here.

[0127] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides a joint optimization method for precoding and power allocation. In the joint optimization method for precoding and power allocation, whether the result corresponding to the current iterative optimization round meets the preset convergence condition is determined based on the relationship between the value of the convergence factor and the threshold of the convergence factor, including: when the value of the convergence factor does not exceed the threshold of the convergence factor, determining that the result corresponding to the current iterative optimization round meets the preset convergence condition; when the value of the convergence factor exceeds the threshold of the convergence factor, determining that the result corresponding to the current iterative optimization round does not meet the preset convergence condition.

[0128] In this embodiment, if the value of the convergence factor corresponding to the current iterative optimization round does not exceed the threshold of the convergence factor, it is determined that the result corresponding to the current iterative optimization round meets the preset convergence condition, and the iterative optimization is terminated at this time, and the optimal solution obtained in the current iterative optimization round is determined as the final target optimal solution; if the value of the convergence factor corresponding to the current iterative optimization round exceeds the threshold of the convergence factor, it is determined that the result corresponding to the current iterative optimization round does not meet the preset convergence condition, and a new round of iterative optimization is continued.

[0129] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides a joint optimization method for precoding and power allocation. In the joint optimization method for precoding and power allocation, determining whether the result corresponding to the current iterative optimization round meets the preset convergence condition includes: when the number of iterative optimizations corresponding to the current iterative optimization round reaches the preset number, determining that the result corresponding to the current iterative optimization round meets the preset convergence condition; when the number of iterative optimizations corresponding to the current iterative optimization round does not reach the preset number, determining that the result corresponding to the current iterative optimization round does not meet the preset convergence condition.

[0130] In this embodiment, another optional implementation method for determining whether the result corresponding to the current iterative optimization round meets the preset convergence condition is: determine whether the number of iterative optimizations corresponding to the current iterative optimization round reaches the preset number, and if so, determine that the result corresponding to the current iterative optimization round meets the preset convergence condition, then terminate the iterative optimization, and determine the optimal solution obtained in the current iterative optimization round as the final target optimal solution. If the number of iterative optimizations corresponding to the current iterative optimization round does not reach the preset number, determine that the result corresponding to the current iterative optimization round does not meet the preset convergence condition, and then continue to perform a new round of iterative optimization.

[0131] In this embodiment, another optional implementation method for determining whether the result corresponding to the current iterative optimization round meets the preset convergence condition is: simultaneously determining the value of the convergence factor of the current iterative optimization round and the corresponding number of iterative optimizations, and when the value of the convergence factor of the current iterative optimization round does not exceed the threshold of the convergence factor and / or the number of iterative optimizations reaches the preset number, determining that the result corresponding to the current iterative optimization round meets the preset convergence condition, and then terminating the iterative optimization.

[0132] In combination with the above embodiments, in one implementation, the embodiment of the present application further provides a joint optimization method for precoding and power allocation. In the joint optimization method for precoding and power allocation, in the starting round of iterative optimization, the precoding matrix is ​​set to a conventional time reversal precoding matrix, and the power allocation is set to an average allocation.

[0133] In this embodiment, during the iterative optimization solution process, in the first iterative optimization round, the precoding matrix is ​​set to a conventional time reversal precoding matrix, and the power allocation of the user is set to an average allocation.

[0134] In this embodiment, the optimization matrix P The DC planning algorithm is shown in Table 1, where n represents the number of iterations and τ represents the threshold of the convergence factor.

[0135] Table 1

[0136]

[0137] In this embodiment, the performance of the joint optimization method for precoding and power allocation proposed in this application is verified by simulation, and the maximum number of iterations is set to n=50, and the threshold value of the convergence factor τ=1×10 -4 In the attached figure Figure 3 to Figure 6 The simulation results of the system and rate given in are the average values ​​of 10,000 sets of channel samples. In the simulation, the number of subcarriers of the OFDM system is Q = 1024; the channel is a Rayleigh fading channel, the number of paths is L = 10, and the total channel bandwidth is B = 15.36 MHz. Channel attenuation includes large-scale fading and small-scale fading. The coefficients of the channel impulse response are complex Gaussian random variables with zero mean and variance of Among them, σ T =10 / B is the root mean square delay of the path, T s =1 / B is the sampling period, η k is the large-scale fading coefficient of the channel of user k: Where c = 4 is the path loss index, η0 is the transmission loss at the reference distance, d0 = 10m is the reference distance, and d k is the distance between the transmitter and user k. In the simulation, η0=10 -5, the distance between the user and the transmitter is 50m. The channel noise power is 1×10 -10 W. The transmission power of each user signal is the same, that is, k=1,2,...,K, where P is the total power transmitted by the system. Figure 3 As shown, Figure 3 Shown are simulation results of the system sum and rate changing with increasing transmit power when the precoding matrix and power allocation are jointly optimized when the number of users K=4 and BOF D=2. The results of "conventional TR-OFDM" in the figure are the results when the conventional TR precoding matrix and average power allocation are used, the power allocation optimization is the result of optimizing the power allocation using the conventional power allocation method, and the joint optimization result is the result of the joint optimization of the precoding matrix and power allocation of the present application. It can be seen that the system sum and rate after the joint optimization of the precoding matrix and power allocation of the present application are significantly higher than the unoptimized system sum and rate, and also higher than the system sum and rate of only optimizing the power allocation, indicating that the joint optimization method of precoding and power allocation proposed in the present application has better performance than no optimization and only power allocation optimization. Figure 4 As shown, Figure 4 The figure shows the changes of the system and rate with the iterations in the optimization process when the number of users K = 4, BOF D = 2, the number of paths L = 10, and the transmission power is 30dBm, and the DC planning algorithm is used to jointly optimize the precoding matrix and power allocation. It can be seen from the figure that once the iteration starts, the system and rate will increase rapidly with the progress of the iteration, and converge after 4-5 iterations. This shows that the joint optimization method of precoding and power allocation proposed in this application has a faster convergence rate and better algorithm convergence performance. Figure 5 and Figure 6 As shown, Figure 5 and Figure 6 The figure shows how the system sum rate changes with the total transmit power under different numbers of users when BOF D=2 and the number of paths L is 10 and 20 respectively. It can be found that as the number of users increases, although it will bring more IUI, the system sum rate increases, indicating that the frequency domain TR precoding technology can significantly reduce the IUI in the multi-user system. Figure 5 and Figure 6 It can be seen that when the transmit power is low, the system sum rate is higher when the number of paths is large, and the increase in the sum rate is faster as the transmit power increases. This is because the more paths there are, the better the effect of distinguishing users through user multipath channel characteristics, and the better the effect of suppressing IUI through frequency domain TR preprocessing.

[0138] Based on the same inventive concept, an embodiment of the present application provides a joint optimization system for precoding and power allocation, such as Figure 7 As shown, the system 700 includes:

[0139] The joint optimization objective function building module 701 is used to establish a joint optimization objective function of the precoding matrix and power allocation according to the rate function. The joint optimization objective function is

[0140] Where R represents the system and rate, P represents the total power transmitted by the system, and the rate function is R k is the rate of user k, x k is the data flow vector sent to user k, Represents data flow vector x k The conjugate transpose of H k is the frequency domain channel matrix of the multipath channel between the transmitter and user k, Representation Matrix H k The conjugate transpose of N represents the N×N dimensional identity matrix, G k is the precoding matrix of user k, is the precoding matrix G k The conjugate transpose of Τr( S k ) determines the transmission power of user k, S is the expansion matrix, S H is the conjugate transpose of S, is the noise variance;

[0141] A convexity conversion module 702, used to convert the joint optimization objective function from a non-convex function to a DC function;

[0142] A linear approximation construction module 703, configured to construct a corresponding linear approximation DC function based on the DC function;

[0143] An iterative optimization module 704 is used to iteratively optimize the linear approximate DC function through a DC planning algorithm to determine a target optimal solution for the iterative optimization;

[0144] The configuration module 705 is used to configure the precoding matrix and power allocation of each user in the communication system based on the target optimal solution.

[0145] Optionally, the convexity conversion module 702 includes:

[0146] A target rate function construction module is used to construct a corresponding target rate function based on the first function and the rate function, wherein the target rate function is

[0147]

[0148] Among them, the first function is I is a matrix with diagonal 1;

[0149] It means to decompose the first function in the target rate function into matrix determinant.

[0150] It represents the matrix determinant decomposition of the second function in the target rate function;

[0151] The convex function construction module is used to decompose each matrix determinant in the target rate function into a convex function. The expression of the convex function is:

[0152] Among them, H A =[S H H1,S H H2,...,S H H K ]; P =diag{ S 1, S 2,..., S K};

[0153] H Bk =[S H H1,S H H2,...0 N×N ,...,S H H K ];

[0154] The first DC function determination module is used to determine the first DC function corresponding to the maximized system and rate function based on the maximized system and rate function in the joint optimization objective function and the convex function, wherein the first DC function expression is:

[0155]

[0156] Among them, y1( P )=Kf( P ) is a convex function, is a convex function;

[0157] A DC function determination module is used to determine a DC function based on the first DC function and a power allocation constraint function in the joint optimization objective function.

[0158] Optionally, the linear approximation building module 703 includes:

[0159] The first linear approximation building module is used to perform a first-order Taylor expansion on the second function in the first DC function at the initial matrix to obtain a linear approximation function of the second function, where the expression of the linear approximation function is:

[0160]

[0161] in, is a convex function g k ( P ) is a linear approximation function at the initial matrix, is the initial matrix;

[0162] A second linear approximation construction module is used to construct a first linear approximation DC function of the first DC function based on the linear approximation function of the second function:

[0163] The third linear approximation construction module is used to determine the corresponding linear approximation DC function based on the first linear approximation DC function and the power allocation constraint function in the joint optimization objective function.

[0164] Optionally, the iterative optimization module 704 includes:

[0165] An optimal solution determination module, used to determine the optimal solution that maximizes the linear approximate DC function in each round of iterative optimization by using a convex optimization tool;

[0166] A convergence determination module is used to determine whether the result corresponding to the current iterative optimization round meets the preset convergence condition;

[0167] The target optimal solution determination module is used to determine the optimal solution obtained in the current iterative optimization round as the target optimal solution when the result corresponding to the current iterative optimization round meets the preset convergence condition.

[0168] Optionally, the initial matrix involved in the system 700 is a preset initial matrix in the initial iterative optimization round; in subsequent iterative optimization rounds, the initial matrix in the current iterative optimization round is the optimal solution obtained in the iterative optimization round before the current iterative optimization round.

[0169] Optionally, a convergence determination module includes:

[0170] A convergence factor value determination module, used to determine the value of the convergence factor according to the optimal solution of the current iterative optimization round and the optimal solution of the previous iterative optimization round of the current iterative optimization round;

[0171] The convergence determination submodule is used to determine whether the result corresponding to the current iterative optimization round meets the preset convergence condition according to the relationship between the value of the convergence factor and the threshold of the convergence factor.

[0172] Optionally, a convergence determination submodule includes:

[0173] A first convergence determination submodule, configured to determine whether a result corresponding to a current iterative optimization round satisfies a preset convergence condition when the value of the convergence factor does not exceed a threshold value of the convergence factor;

[0174] The second convergence determination submodule is used to determine that the result corresponding to the current iterative optimization round does not meet the preset convergence condition when the value of the convergence factor exceeds the threshold of the convergence factor.

[0175] Optionally, a convergence determination module includes:

[0176] The iteration number convergence determination module is used to determine that the result corresponding to the current iterative optimization round meets the preset convergence condition when the number of iterative optimizations corresponding to the current iterative optimization round reaches the preset number; and to determine that the result corresponding to the current iterative optimization round does not meet the preset convergence condition when the number of iterative optimizations corresponding to the current iterative optimization round does not reach the preset number.

[0177] Optionally, in the system 700, in the initial round of iterative optimization, the precoding matrix is ​​set to a conventional time reversal precoding matrix, and the power allocation is set to an average allocation.

[0178] Based on the same inventive concept, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and running on the processor. When the computer program is executed by the processor, it implements the steps in a joint optimization method of precoding and power allocation as described in the first aspect of the present application.

[0179] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in a joint optimization method for precoding and power allocation as described in the first aspect of the present application are implemented.

[0180] As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0181] It should be noted that, for the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0182] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0183] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0184] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure one A process or multiple processes and / or boxes Figure one A device that provides the functions specified in a block or multiple blocks.

[0185] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure one A process or multiple processes and / or boxes Figure one A function specified in one or more boxes.

[0186] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure one A process or multiple processes and / or boxes Figure one A step that specifies a function in one or more boxes.

[0187] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional 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 that fall within the scope of the embodiments of the present application.

[0188] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0189] The above is a detailed introduction to the joint optimization method, system and product of precoding and power allocation provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A joint optimization method for precoding and power allocation, characterized in that: The method comprises: According to the rate function, a joint optimization objective function of the precoding matrix and power allocation is established, and the joint optimization objective function is: Where R represents the system and rate, P represents the total power transmitted by the system, and the rate function is R k is the rate of user k, x k is the data flow vector sent to user k, Represents data flow vector x k The conjugate transpose of H k is the frequency domain channel matrix of the multipath channel between the transmitter and user k, Denotes the matrix H k The conjugate transpose of N represents the N×N dimensional identity matrix, G k is the precoding matrix of user k, is the precoding matrix G k The conjugate transpose of Τr( Σ k ) determines the transmission power of user k, S is the expansion matrix, S H is the conjugate transpose of S, is the noise variance; Converting the joint optimization objective function from a non-convex function to a DC function; Based on the DC function, construct a corresponding linear approximate DC function; Iteratively optimize the linear approximate DC function by using a DC planning algorithm to determine the target optimal solution of the iterative optimization; The precoding matrix and power allocation of each user in the communication system are configured based on the target optimal solution.

2. A joint optimization method for precoding and power allocation according to claim 1, characterized in that: Converting the joint optimization objective function from a non-convex function to a DC function includes: Based on the first function and the rate function, a corresponding target rate function is constructed, where the target rate function is: Among them, the first function is I is a matrix with diagonal 1; It means to decompose the first function in the target rate function into matrix determinant. It represents the matrix determinant decomposition of the second function in the target rate function; The matrix determinants in the target rate function are decomposed into convex functions, respectively. The expression of the convex function is: in, H A =[ S H H 1, S H H 2,..., S H H K ]; Ψ =diag{ Σ 1, Σ 2,..., Σ K }; H Bk ＝[ S H H 1, S H H 2,...0 N×N ,..., S H H K ]； Based on the maximized system and rate function in the joint optimization objective function and the convex function, a first DC function corresponding to the maximized system and rate function is determined, and the first DC function expression is: Among them, y1(Ψ)=Kf(Ψ) is a convex function, is a convex function; A DC function is determined based on the first DC function and a power allocation constraint function in the joint optimization objective function.

3. The joint optimization method for precoding and power allocation according to claim 2, characterized in that: Based on the DC function, a corresponding linear approximate DC function is constructed, including: Perform a first-order Taylor expansion on the second function in the first DC function at the initial matrix to obtain a linear approximation function of the second function. The expression of the linear approximation function is: in, is a convex function g k ( Ψ ) is a linear approximation function at the initial matrix, is the initial matrix; Based on the linear approximation function of the second function, a first linear approximation DC function of the first DC function is constructed: Based on the first linear approximate DC function and the power allocation constraint function in the joint optimization objective function, a corresponding linear approximate DC function is determined.

4. The joint optimization method for precoding and power allocation according to claim 1, characterized in that: The linear approximate DC function is iteratively optimized by a DC planning algorithm to determine a target optimal solution for the iterative optimization, including: The optimal solution that maximizes the linear approximate DC function in each round of iterative optimization is determined by using a convex optimization tool; Determine whether the result corresponding to the current iterative optimization round meets the preset convergence condition; When the result corresponding to the current iterative optimization round meets the preset convergence condition, the optimal solution obtained in the current iterative optimization round is determined as the target optimal solution.

5. The joint optimization method of precoding and power allocation according to claim 4, characterized in that: In the initial iterative optimization round, the initial matrix is ​​a preset initial matrix; in subsequent iterative optimization rounds, the initial matrix in the current iterative optimization round is the optimal solution obtained in the previous iterative optimization round of the current iterative optimization round.

6. A joint optimization method for precoding and power allocation according to claim 4, characterized in that: Determine whether the result corresponding to the current iterative optimization round meets the preset convergence conditions, including: Determine the value of the convergence factor according to the optimal solution of the current iterative optimization round and the optimal solution of the previous iterative optimization round of the current iterative optimization round; According to the relationship between the value of the convergence factor and the threshold of the convergence factor, it is determined whether the result corresponding to the current iterative optimization round meets the preset convergence condition.

7. A joint optimization method for precoding and power allocation according to claim 6, characterized in that: Determining whether the result corresponding to the current iterative optimization round meets the preset convergence condition according to the relationship between the value of the convergence factor and the threshold of the convergence factor includes: When the value of the convergence factor does not exceed the threshold of the convergence factor, determining that the result corresponding to the current iterative optimization round meets the preset convergence condition; When the value of the convergence factor exceeds the threshold of the convergence factor, it is determined that the result corresponding to the current iterative optimization round does not meet the preset convergence condition.

8. A joint optimization method for precoding and power allocation according to claim 4, characterized in that: Determine whether the result corresponding to the current iterative optimization round meets the preset convergence conditions, including: When the number of iterative optimizations corresponding to the current iterative optimization round reaches a preset number, determining that the result corresponding to the current iterative optimization round satisfies a preset convergence condition; When the number of iterative optimizations corresponding to the current iterative optimization round does not reach a preset number, it is determined that the result corresponding to the current iterative optimization round does not satisfy a preset convergence condition.

9. A joint optimization method for precoding and power allocation according to claim 4, characterized in that: The method further includes: in a starting round of iterative optimization, setting the precoding matrix to a conventional time reversal precoding matrix, and setting the power allocation to an average allocation.

10. A joint optimization system for precoding and power allocation, characterized in that: The system comprises: The joint optimization objective function building module is used to establish the joint optimization objective function of the precoding matrix and the power allocation according to the rate function. The joint optimization objective function is Where R represents the system and rate, P represents the total power transmitted by the system, and the rate function is R k is the rate of user k, x k is the data flow vector sent to user k, Represents data flow vector x k The conjugate transpose of H k is the frequency domain channel matrix of the multipath channel between the transmitter and user k, Representation Matrix H k The conjugate transpose of N represents the N×N dimensional identity matrix, G k is the precoding matrix of user k, is the precoding matrix G k The conjugate transpose of Τr( Σ k ) determines the transmission power of user k, S is the expansion matrix, S H is the conjugate transpose of S, is the noise variance; A convexity conversion module, used for converting the joint optimization objective function from a non-convex function to a DC function; A linear approximation construction module, used to construct a corresponding linear approximation DC function based on the DC function; An iterative optimization module, used for iteratively optimizing the linear approximate DC function through a DC planning algorithm to determine a target optimal solution for the iterative optimization; A configuration module is used to configure the precoding matrix and power allocation of each user in the communication system based on the target optimal solution.

11. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and running on the processor, wherein when the computer program is executed by the processor, the steps in the joint optimization method for precoding and power allocation as claimed in claims 1 to 9 are implemented.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the joint optimization method for precoding and power allocation as described in claims 1 to 9 are implemented.

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