An optimization method, system, apparatus and storage medium of joint antenna selection and precoding

Abstract

The application discloses an optimization method, system and device of joint antenna selection and precoding and a storage medium, and belongs to the technical field of wireless communication. The method comprises the following steps: acquiring communication data of a base station to a user in a target XL-MIMO communication system; calculating a receiving signal of the user based on the communication data; constructing a joint optimization model with the maximum sum rate as an optimization target and an antenna selection matrix and a hybrid precoding matrix at the base station as optimization variables based on the receiving signal; decomposing a solution problem of the joint optimization model into sub-problems by using an alternating direction method; alternately solving the sub-problems to obtain an optimization result; and obtaining the antenna selection matrix and the hybrid precoding matrix at the base station according to the optimization result. The application converts the antenna selection problem from integer programming into a matrix sparsity optimization problem by using the alternating direction method, effectively solves the constraint optimization problem of the key variable, and improves the spectrum efficiency of the communication system.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to an optimization method, system, device, and storage medium for joint antenna selection and precoding. Background Technology

[0002] Extremely large scale multiple input multiple output (XL-MIMO) technology is a promising technology for sixth-generation mobile networks, achieving high spectral efficiency by deploying thousands of antennas. However, each antenna requires a dedicated radio frequency (RF) chain, leading to high hardware costs and high power consumption. Furthermore, due to the near-field propagation and spatial non-stationarity of XL-MIMO, users can only see a portion of the antenna array, resulting in some antennas contributing less to system performance, and activating these antennas increases the system load. To address this cost constraint, antenna selection can be used, where only a small portion of the antennas are active each coherent time. This reduces the number of required RF chains, thereby reducing overall RF costs without significantly degrading performance.

[0003] To achieve a favorable balance between system performance and hardware complexity, various optimization methods for joint antenna selection and precoding have been proposed in related research. Current research has proposed a simple greedy algorithm based on submodularity and monotonicity in massive MIMO (mMIMO) downlink systems to maximize rate capacity under antenna selection constraints. While this algorithm is relatively simple to implement and its performance is close to exhaustive search, its computational complexity is relatively high, rendering it impractical with a large number of antennas. In the downlink, an algorithm based on alternating optimization has also been developed. This algorithm considers determining the selected subset of receiving antennas solely based on channel characteristics, i.e., the amplitude of channel gain, and then optimizes beamforming according to the maximum ratio transmission criterion to maximize the power of the received signal. This algorithm is relatively simple, but its over-reliance on channel gain amplitude limits its application in complex communication environments. Currently, most studies on joint antenna selection and precoding matrix design employ alternating optimization. However, alternating optimization can only guarantee monotonically convergent but is almost destined to fall into local optima. Furthermore, if the coupling between variable blocks is strong, slow convergence will occur. Therefore, it is only suitable for small-scale scenarios with weak variable coupling and tolerance for local optima. Moreover, the above studies mainly focus on MIMO and mMIMO scenarios and do not consider joint antenna selection and precoding design in XL-MIMO scenarios. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide an optimization method, system, device and storage medium for joint antenna selection and precoding, which effectively solves the problem of constrained optimization of key variables and improves the spectral efficiency of communication systems.

[0005] This invention provides the following technical solution:

[0006] Firstly, an optimization method for joint antenna selection and precoding is provided, including:

[0007] Acquire communication data from the base station to the user in the target XL-MIMO communication system;

[0008] The user's received signal is calculated based on the communication data;

[0009] Based on the received signals of the user, a joint optimization model is constructed with the maximum sum rate as the optimization objective and the antenna selection matrix and the hybrid precoding matrix at the base station as optimization variables.

[0010] The joint optimization model is decomposed into sub-problems using the alternating direction method;

[0011] The subproblems are solved alternately to obtain the optimized result;

[0012] The antenna selection matrix and the hybrid precoding matrix at the base station are obtained based on the optimization results.

[0013] As an optional technical solution of the present invention, the communication data from the base station to the user includes a channel matrix, an antenna selection matrix, and the base station's transmission signal.

[0014] As an optional technical solution of the present invention, the calculation of the user's received signal based on the communication data is expressed as follows:

[0015] ;

[0016] in, Indicates the user's received signal. This indicates the base station's transmitted signal. This represents the hybrid precoding matrix at the base station. This represents additive white Gaussian noise in the channel. Indicates average noise power. Represents the identity matrix. Top bid This indicates the conjugate transpose. Expressing expectations, This represents the antenna selection matrix. The Middle element , Indicates the first The first transmitting antenna has been connected to the first On the root RF chain, Indicates the first The first transmitting antenna was not connected to the first On the root RF chain, The channel matrix from the base station to the user is represented as follows:

[0017] ;

[0018] in, This indicates the total number of receiving antennas for the user. This indicates the total number of transmitting antennas at the base station. This represents the average path loss between the base station and the user. Indicates the total number of paths. Indicates the first Complex gain of the path, Indicates the user's position The angle of arrival of the path, Indicates the corresponding number at the user's location. The far-field array response vector of each path, Indicates the base station The angle of arrival of the path, Indicates the near field number The length of the path, Indicates the corresponding number at the base station The near-field array response vector of each path, superscript This indicates the conjugate transpose.

[0019] As an optional technical solution of the present invention, the joint optimization model with the maximum sum rate as the optimization objective and the antenna selection matrix and the hybrid precoding matrix at the base station as optimization variables is expressed as follows:

[0020] ;

[0021] ;

[0022] in, Indicates the total number of RF chains in the base station. Representation and rate, Represents the determinant of a matrix. Represents the trace of a matrix. This indicates the maximum power budget satisfied by the hybrid pre-encoder. This indicates that by optimizing the antenna selection matrix... Hybrid precoding matrix at base station Obtain The maximum value;

[0023] The joint optimization model is transformed into Problem 1, expressed as:

[0024] .

[0025] As an optional technical solution of the present invention, the method of using alternating directions to decompose the solution problem of the joint optimization model into sub-problems includes:

[0026] Introducing the first variable Converting question one into question two, we can express it as follows:

[0027] ;

[0028] in, Representing a matrix Norm, This indicates that by optimizing the first variable... To obtain the maximum value;

[0029] Introducing a second variable Then the augmented Lagrange function of problem two can be expressed as:

[0030] ;

[0031] in, This represents the augmented Lagrange function. Indicates the second variable Add the dual variable to the cost function. This indicates the step size for the alternating direction method. Denotes the F-norm of a matrix;

[0032] Convert question two into question three, represented as:

[0033] ;

[0034] in, This indicates that by optimizing the first variable... Second variable To obtain the maximum value;

[0035] The augmented Lagrange function can be decomposed into the following subproblems by alternating variable updates:

[0036] ;

[0037] ;

[0038] ;

[0039] in, Represents the first variable's... The result of the second iteration The second variable represents the first The result of the second iteration The first variable represents the dual variable. The result of the second iteration The second variable represents the first The result of the second iteration The first variable represents the dual variable. The result of the second iteration Indicates the first variable when taking the maximum value. The value of , This indicates the second variable when the maximum value is reached. The value of .

[0040] As an optional technical solution of the present invention, the step of alternately solving the sub-problems to obtain the optimized result includes:

[0041] The second variable is fixed. The result of the second iteration Update the first variable's first... The result of this iteration transforms problem three into problem four, as follows:

[0042] ;

[0043] Problem 4 is transformed into an unconstrained problem on a spherical manifold, denoted as Problem 5:

[0044] ;

[0045] in, Represents a manifold, It is defined as;

[0046] Problem 5 is solved using the Riemann gradient descent method, yielding the first variable's... The result of the second iteration , represented as:

[0047] ;

[0048] in, Indicates the first Search direction in the next iteration This represents the shrinkage operator. Indicates the step size. Represents the first variable's... The result of the next iteration;

[0049] Based on the first variable, the The result of the second iteration Update the second variable's first... The result of the second iteration Convert question three into question six, represented as:

[0050] ;

[0051] in, This indicates that by optimizing the second variable To obtain the maximum value;

[0052] Solving problem six using the hard threshold operator yields the second variable's... The result of the iteration is represented as:

[0053] ;

[0054] in, The second variable represents the first The result of the iteration is the first OK, express The OK, , express The The result of the second iteration The OK, express All The Large elements;

[0055] Update the dual variable of the first The result of the second iteration , represented as:

[0056] ;

[0057] After reaching the preset maximum number of iterations, the final iteration results of the first variable, the second variable, and the dual variable are used as the optimization results.

[0058] Secondly, an optimization system for joint antenna selection and precoding is provided to implement the optimization method for joint antenna selection and precoding described in the first aspect, the system comprising:

[0059] The data acquisition module is used to acquire communication data from the base station to the user in the target XL-MIMO communication system;

[0060] The calculation module is used to calculate the user's received signal based on communication data;

[0061] The model building module is used to construct a joint optimization model based on the user's received signal, with the maximum sum rate as the optimization objective and the antenna selection matrix and the hybrid precoding matrix at the base station as optimization variables.

[0062] The decomposition module is used to decompose the solution problem of the joint optimization model into subproblems using the alternating direction method;

[0063] The solution module is used to solve the sub-problems alternately to obtain the optimized results;

[0064] The result acquisition module is used to obtain the antenna selection matrix and the hybrid precoding matrix at the base station based on the optimization results.

[0065] Thirdly, an optimization apparatus for joint antenna selection and precoding is provided, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the optimization method for joint antenna selection and precoding described in the first aspect.

[0066] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the optimization method for joint antenna selection and precoding described in the first aspect.

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] The present invention provides an optimization method for joint antenna selection and precoding, which uses the alternating direction method to transform the antenna selection problem from an integer programming problem into a matrix sparsity optimization problem, effectively solving the constraint optimization problem of key variables and improving the spectral efficiency of the communication system. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating the optimization method for joint antenna selection and precoding in an embodiment of the present invention.

[0070] Figure 2 This is a schematic diagram illustrating the convergence behavior of the method under different parameters in an embodiment of the present invention;

[0071] Figure 3 This is a schematic diagram comparing the performance of the method in this invention with other benchmark methods under different numbers of active transmitting antennas in this embodiment of the invention;

[0072] Figure 4 This is a schematic diagram comparing the performance of the method in this invention with other benchmark methods under different signal-to-noise ratio environments.

[0073] Figure 5 This is a schematic diagram comparing the performance of the method in this embodiment of the invention with other benchmark methods under different transmission powers. Detailed Implementation

[0074] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0075] Example 1

[0076] This embodiment 1 provides an optimization method for joint antenna selection and precoding, considering an XL-MIMO downlink communication system, in which a base station equipped with a very large-scale antenna array directs antennas to a network of devices with different antenna configurations. The base station is equipped with a single-user transmit coded signal via a root receiving antenna. root transmitting antenna and Root RF chain, and .like Figure 1 As shown, the specific steps include the following:

[0077] Step 1: Obtain communication data from the base station to the user in the target XL-MIMO communication system.

[0078] In this embodiment, the communication data from the base station to the user includes the channel matrix, antenna selection matrix, and the base station's transmitted signal.

[0079] Step 2: Calculate the user's received signal based on the communication data.

[0080] The user's received signal is calculated based on communication data and represented as follows:

[0081] ;

[0082] in, Indicates the user's received signal. This indicates the base station's transmitted signal. This represents the hybrid precoding matrix at the base station. This represents additive white Gaussian noise in the channel. Indicates average noise power. Represents the identity matrix. Top bid This indicates the conjugate transpose. Expressing expectations, This represents the antenna selection matrix. The Middle element , Indicates the first The first transmitting antenna has been connected to the first On the root RF chain, Indicates the first The first transmitting antenna was not connected to the first On the root RF chain, The channel matrix from the base station to the user is represented as follows:

[0083] ;

[0084] in, This indicates the total number of receiving antennas for the user. This indicates the total number of transmitting antennas at the base station. This represents the average path loss between the base station and the user. Indicates the total number of paths. Indicates the first Complex gain of a path.

[0085] Indicates the corresponding number at the user's location. The far-field array response vector of each path, Indicates the corresponding number at the base station The near-field array response vector of each path, superscript This indicates the conjugate transpose.

[0086] and They are represented as follows:

[0087] ;

[0088] ;

[0089] in, Indicates the user's position The angle of arrival of the path, Indicates the base station The angle of arrival of the path, Indicates the near field number The length of the path, represents an imaginary number, Indicates the base station number The distance between the transmitting antenna and the user , Indicates the carrier wavelength. The superscript indicates the spacing between each antenna when they are evenly arranged. This indicates the conjugate transpose.

[0090] Step 3: Based on the user's received signal, construct a joint optimization model with the maximum sum rate as the optimization objective and the antenna selection matrix and the hybrid precoding matrix at the base station as optimization variables.

[0091] In this embodiment, the sum and rate are used as key metrics for quantifying the system throughput, and the joint optimization model is expressed as:

[0092] ;

[0093] ;

[0094] in, Indicates the total number of RF chains in the base station. Representation and rate, Represents the determinant of a matrix. Represents the trace of a matrix. This indicates the maximum power budget satisfied by the hybrid pre-encoder. This indicates that by optimizing the antenna selection matrix... Hybrid precoding matrix at base station Obtain The maximum value.

[0095] Furthermore, the joint optimization model is transformed into Problem 1, expressed as:

[0096] .

[0097] Step 4: Use the alternating direction method to decompose the optimization objective problem into subproblems corresponding to multiple variables.

[0098] Regarding the antenna selection matrix The discrete constraints reduce Problem 1 to an integer programming problem, which is an NP-hard problem. Therefore, the design problem in Problem 1 is challenging in its basic form, making the optimal design computationally difficult. Thus, this embodiment proposes a solution method based on the alternating direction method, where multiple variables are transformed from optimization variables:

[0099] Introducing the first variable Converting question one into question two, we can express it as follows:

[0100] ;

[0101] in, Representing a matrix Norm, This indicates that by optimizing the first variable... To obtain the maximum value.

[0102] Because question two involves nonconvex constraints Therefore, it is difficult to solve directly. To address the challenge posed by this non-convex constraint, the two constraints will be separated, and a second variable will be introduced. Then the augmented Lagrange function of problem two can be expressed as:

[0103] ;

[0104] in, This represents the augmented Lagrange function. Indicates the second variable Add the dual variable to the cost function. This indicates the step size for the alternating direction method. Let F be the norm of the matrix. This is because the antenna selection matrix... Each column contains only one non-zero element, and only The row contains non-zero elements. Therefore, we can obtain .

[0105] Convert question two into question three, represented as:

[0106] ;

[0107] in, This indicates that by optimizing the first variable... Second variable To obtain the maximum value.

[0108] The augmented Lagrange function can be decomposed into the following subproblems by alternately updating variables:

[0109] ;

[0110] ;

[0111] ;

[0112] in, Represents the first variable's... The result of the second iteration The second variable represents the first The result of the second iteration The first variable represents the dual variable. The result of the second iteration The second variable represents the first The result of the second iteration The first variable represents the dual variable. The result of the second iteration Indicates the first variable when taking the maximum value. The value of , This indicates the second variable when the maximum value is reached. The value of . In this embodiment, For initialization hour, .

[0113] Step 5: Solve the above sub-problems alternately to obtain the optimization results for multiple variables.

[0114] The second variable is fixed. The result of the second iteration Update the first variable's first... The result of this iteration transforms problem three into problem four, as follows:

[0115] .

[0116] constraint Formed an embedded in Given a spherical manifold, the feasible set for problem four can be represented as... A manifold is a topological space that appears as a Euclidean space in the vicinity of each point. It is defined as follows.

[0117] Problem 4 is transformed into an unconstrained problem on a spherical manifold, denoted as Problem 5:

[0118] ;

[0119] in, Represents a manifold, It is defined as follows.

[0120] This is an unconstrained smooth convex optimization problem defined on a manifold, which can be solved directly using the smooth optimization problem on a manifold. Problem 5 is solved using the Riemann gradient descent method.

[0121] The Riemann gradient descent algorithm for solving Problem 5 is an iterative algorithm, and its first step is... The iteration process can be summarized as follows: assuming the nth iteration has been obtained... The result of the second iteration So, in order to obtain the next iteration point First, it is necessary to... Explore during the next iteration The tangent space is used to find the search direction. For manifolds any point on , exist The tangent space at the point is determined by and The combination of all tangent vectors is represented as , The cost function in question five Represented as , The Euclidean gradient is expressed as: .

[0122] Search direction By orthogonally projecting the Euclidean gradient onto Obtained in the tangent space. Through the projection operator. Get points Search direction at the location , is represented as:

[0123] ;

[0124] in, Indicates the first variable. The result of the second iteration Euclidean gradient The inner product of.

[0125] In the In the next iteration, after obtaining the search direction Then, use the shrinkage operator. Performing a shrinking operation allows the next iteration point to smoothly leave the initial point along a specified tangent direction while remaining on the sphere, thus enabling the updated point to be remapped back onto the manifold, yielding the first variable's... The result of the second iteration , is represented as:

[0126] ;

[0127] in, Indicates the first Search direction in the next iteration This represents the shrinkage operator. Indicates the step size. Represents the first variable's... The result of the next iteration.

[0128] Based on the first variable, the The result of the second iteration Update the second variable's first... The result of the second iteration Convert question three into question six, represented as:

[0129] ;

[0130] in, This indicates that by optimizing the second variable To obtain the maximum value.

[0131] Solving problem six using the hard threshold operator yields the second variable's... The result of the iteration is represented as:

[0132] ;

[0133] in, The second variable represents the first The result of the iteration is the first OK, express The OK, , express The The result of the second iteration The OK, express All The Large elements This is the total number of RF chains in the base station.

[0134] Update the dual variable of the first The result of the second iteration , is represented as:

[0135] .

[0136] After reaching the preset maximum number of iterations, the final iteration results of the first variable, the second variable, and the dual variable are used as the optimization results.

[0137] Step 6: Obtain the antenna selection matrix and the hybrid precoding matrix at the base station based on the optimization results of multiple variables.

[0138] Example 2

[0139] This embodiment 2, based on embodiment 1, compares the method of the present invention (Alternating Direction Method of Multipliers-Multi-Objective Optimization, ADMM-MO) with benchmark methods and provides simulation results. Benchmark methods include a subset selection algorithm based on alternating optimization, a stepwise refinement algorithm, and a penalized dual decomposition algorithm.

[0140] like Figure 2 The diagram shows the convergence behavior of this method under different parameters. Under different parameters and with varying rate values, the method rapidly converges to a stable value. Compared to benchmark methods, this method exhibits better convergence characteristics and can obtain a stable optimal solution within a finite number of iterations. Figure 3 The diagram shows a performance comparison between this method and other benchmark methods under different numbers of active transmit antennas. The performance increases with the number of base station RF chains and the data rate. Furthermore, the ADMM-MO proposed in this invention exhibits superior performance compared to the benchmark algorithm under the same conditions. Figure 4 The diagram shows a performance comparison between our method and other benchmark methods under different signal-to-noise ratio (SNR) environments. As the SNR increases, the summation rate of all methods increases, with ADMM-MO showing significantly better performance than the benchmark methods. Figure 5The results show a performance comparison between our method and other benchmark methods under different transmit powers. As the transmit power budget increases, the sum rate of all methods increases, and ADMM-MO significantly outperforms the benchmark methods. Simulation results verify the superiority of our method over other benchmark methods.

[0141] Example 3

[0142] This embodiment 3 provides an optimization system for joint antenna selection and precoding, used to implement the optimization method for joint antenna selection and precoding in embodiment 1 above. The system includes:

[0143] The data acquisition module is used to acquire communication data from the base station to the user in the target XL-MIMO communication system;

[0144] The calculation module is used to calculate the user's received signal based on communication data;

[0145] The model building module is used to construct a joint optimization model based on the user's received signal, with the maximum sum rate as the optimization objective and the antenna selection matrix and the hybrid precoding matrix at the base station as optimization variables.

[0146] The decomposition module is used to decompose the joint optimization model into subproblems using the alternating direction method;

[0147] The solution module is used to solve the sub-problems alternately to obtain the optimized results;

[0148] The result acquisition module is used to obtain the antenna selection matrix and the hybrid precoding matrix at the base station based on the optimization results.

[0149] It is worth noting that the system embodiment corresponds to the above method embodiment. The implementation methods of the above method embodiments are all applicable to the system embodiment and can achieve the same or similar technical effects, so they will not be described in detail here.

[0150] Example 4

[0151] This embodiment 4 provides an optimization apparatus for joint antenna selection and precoding, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the optimization method for joint antenna selection and precoding described in embodiment 1.

[0152] Example 5

[0153] This embodiment 5 provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the joint antenna selection and precoding optimization method described in embodiment 1.

[0154] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0156] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0157] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0158] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An optimization method for joint antenna selection and precoding, characterized in that, include: Acquire communication data from the base station to the user in the target XL-MIMO communication system; The user's received signal is calculated based on the communication data; Based on the received signals of the user, a joint optimization model is constructed with the maximum sum rate as the optimization objective and the antenna selection matrix and the hybrid precoding matrix at the base station as optimization variables. The joint optimization model is decomposed into sub-problems using the alternating direction method; The subproblems are solved alternately to obtain the optimized result; Based on the optimization results, the antenna selection matrix and the hybrid precoding matrix at the base station are obtained; The calculation of the user's received signal based on the communication data is expressed as follows: ； in, Indicates the user's received signal. This indicates the base station's transmitted signal. This represents the hybrid precoding matrix at the base station. This represents additive white Gaussian noise in the channel. Indicates average noise power. Represents the identity matrix. Top bid This indicates the conjugate transpose. Expressing expectations, This represents the antenna selection matrix. The Middle element , Indicates the first The first transmitting antenna has been connected to the first On the root RF chain, Indicates the first The first transmitting antenna was not connected to the first On the root RF chain, The channel matrix from the base station to the user is represented as follows: ； in, This indicates the total number of receiving antennas for the user. This indicates the total number of transmitting antennas at the base station. This represents the average path loss between the base station and the user. Indicates the total number of paths. Indicates the first Complex gain of the path, Indicates the user's position The angle of arrival of the path, Indicates the corresponding number at the user's location. The far-field array response vector of each path, Indicates the base station The angle of arrival of the path, Indicates the near field number The length of the path, Indicates the corresponding number at the base station The near-field array response vector of each path, superscript Indicates conjugate transpose; The joint optimization model, which takes maximizing the sum rate as the optimization objective and uses the antenna selection matrix and the hybrid precoding matrix at the base station as optimization variables, is expressed as follows: ； ； in, Indicates the total number of RF chains in the base station. Representation and rate, Represents the determinant of a matrix. Represents the trace of a matrix. This indicates the maximum power budget satisfied by the hybrid pre-encoder. This indicates that by optimizing the antenna selection matrix... Hybrid precoding matrix at base station Obtain The maximum value; The joint optimization model is transformed into Problem 1, expressed as: 。 2. The optimization method for joint antenna selection and precoding according to claim 1, characterized in that, The communication data from the base station to the user includes the channel matrix, antenna selection matrix, and the base station's transmitted signals.

3. The optimization method for joint antenna selection and precoding according to claim 1, characterized in that, The method of alternating directions is used to decompose the problem of solving the joint optimization model into sub-problems, including: Introducing the first variable Converting question one into question two, we can express it as follows: ； in, Representing a matrix Norm, This indicates that by optimizing the first variable... To obtain the maximum value; Introducing a second variable Then the augmented Lagrange function of problem two can be expressed as: ； in, This represents the augmented Lagrange function. Indicates the second variable Add the dual variable to the cost function. This indicates the step size for the alternating direction method. Denotes the F-norm of a matrix; Convert question two into question three, represented as: ； in, This indicates that by optimizing the first variable... Second variable To obtain the maximum value; The augmented Lagrange function can be decomposed into the following subproblems by alternating variable updates: ； ； ； in, Represents the first variable's... The result of the second iteration The second variable represents the first The result of the second iteration The first variable represents the dual variable. The result of the second iteration The second variable represents the first The result of the second iteration The first variable represents the dual variable. The result of the second iteration Indicates the first variable when taking the maximum value. The value of , This indicates the second variable when the maximum value is reached. The value of .

4. The optimization method for joint antenna selection and precoding according to claim 3, characterized in that, The process of alternately solving the sub-problems to obtain the optimized result includes: The second variable is fixed. The result of the second iteration Update the first variable's first... The result of this iteration transforms problem three into problem four, as follows: ； Problem 4 is transformed into an unconstrained problem on a spherical manifold, denoted as Problem 5: ； in, Represents a manifold, It is defined as; Problem 5 is solved using the Riemann gradient descent method, yielding the first variable's... The result of the second iteration , is represented as: ； in, Indicates the first Search direction in the next iteration This represents the shrinkage operator. Indicates the step size. Represents the first variable's... The result of the next iteration; Based on the first variable, the The result of the second iteration Update the second variable's first... The result of the second iteration Convert question three into question six, represented as: ； in, This indicates that by optimizing the second variable To obtain the maximum value; Solving problem six using the hard threshold operator yields the second variable's... The result of the iteration is represented as: ； in, The second variable represents the first The result of the iteration is the first OK, express The OK, , express The The result of the second iteration The OK, express All The Large elements; Update the dual variable of the first The result of the second iteration , is represented as: ； After reaching the preset maximum number of iterations, the final iteration results of the first variable, the second variable, and the dual variable are used as the optimization results.

5. An optimization system for joint antenna selection and precoding, characterized in that, The system is used to implement the optimization method for joint antenna selection and precoding as described in any one of claims 1-4, the system comprising: The data acquisition module is used to acquire communication data from the base station to the user in the target XL-MIMO communication system; The calculation module is used to calculate the user's received signal based on communication data; The model building module is used to construct a joint optimization model based on the user's received signal, with the maximum sum rate as the optimization objective and the antenna selection matrix and the hybrid precoding matrix at the base station as optimization variables. The decomposition module is used to decompose the solution problem of the joint optimization model into subproblems using the alternating direction method; The solution module is used to solve the sub-problems alternately to obtain the optimized results; The result acquisition module is used to obtain the antenna selection matrix and the hybrid precoding matrix at the base station based on the optimization results.

6. An optimization apparatus for joint antenna selection and precoding, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the optimization method for joint antenna selection and precoding as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the optimization method for joint antenna selection and precoding as described in any one of claims 1 to 4.

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