Beam forming optimization method and device for communication perception integrated system

Abstract

The invention discloses a beam forming optimization method and device for a communication perception integrated system. The method aims at a scene of differentiated demands of communication and sensing services, and aims to meet the communication rate demand of a ground user and improve the sensing performance. The beam forming optimization method for the integrated system is designed by adopting theories of continuous convex approximation, positive semi-definite relaxation, first-order Taylor extension, sequential rank-one constraint relaxation and the like. According to the method provided by the invention, the sensing gain can be maximized, the communication rate requirement of the ground user can be ensured, a resource management scheme meeting the differentiated service requirement of the communication and sensing integrated system can be obtained, and the network transmission performance can be obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless transmission network technology, and in particular to a beamforming optimization method and device for a communication-awareness integrated system. Background Art

[0002] Today, the new generation of electronic and communication technologies represented by 5G networks and millimeter wave radars are accelerating the transformation of the economy and society towards digitalization, networking, and intelligence. As a result, a large number of new intelligent applications have emerged, such as unmanned driving, smart industry, and smart transportation. Their communication requirements for high bandwidth, low latency, transmission reliability, and perception requirements for high precision and high resolution are becoming more and more stringent. However, the early wireless communication and radar perception technologies showed a split and independent development mode. This independent system design has problems such as high hardware cost, low spectrum utilization, resource waste, and limited performance improvement, which cannot meet the multi-dimensional performance requirements of B5G / 6G intelligent applications. This has spawned the research on the network architecture and related technologies of interawareness integration. The key idea of ​​ISAC is to integrate communication and perception functions in a system by sharing time-frequency-power-hardware resources. By using a unified signal processing framework, spectrum, and hardware platform, the interawareness integration (ISAC) technology can improve spectrum and energy efficiency, thereby solving the problems of spectrum congestion and resource waste, while reducing hardware and signaling costs. Therefore, ISAC technology is of great significance for supporting various applications to access wireless networks and meet their high-quality wireless communication and high-precision sensing requirements.

[0003] In addition, with the increasing number of ground terminals such as self-driving cars and intelligent robots, inter-user interference has become an important factor that inhibits communication performance. Therefore, some scholars have proposed the rate split multiple access (RSMA) technology, which can achieve robust interference management and communication enhancement. The basic idea is that the information stream at the transmitter is selectively encoded into a public information stream and an independent private information stream through a linear precoding rate splitting technique. The public information stream is decoded by all receivers, while the private information stream needs to be independently encoded and decoded by the corresponding receiver using continuous interference cancellation. In this way, the RSMA scheme can alleviate the challenges brought by the scarcity of wireless resources and the demand for multi-user communication, and improve the performance of communication systems including ISAC. On the other hand, perceived performance is also another important indicator of the ISAC system, but it may be limited by severe path loss attenuation. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a beamforming optimization method and device for a synaesthesia integration system based on rate splitting multiple access. Aiming at the application scenarios with differentiated service requirements for communication and perception, in order to meet the service requirements of communication users as much as possible and improve the perception performance, the present invention designs beamforming for synaesthesia integration based on rate splitting multiple access technology, and adopts continuous convex approximation (SCA), semi-positive definite relaxation, first-order Taylor expansion and sequential rank-one constraint relaxation algorithms to solve the optimal public rate allocation, beamforming vectors and reflection and transmission coefficients of reconfigurable smart surfaces. The algorithm proposed in the present invention can convert the original non-convex optimization problem into a convex optimization problem, and ensure that the optimal solution can be approached when solving the problem.

[0005] To achieve the above object, the present invention provides the following technical solutions: The first aspect of the present invention is a beamforming optimization method for a communication-awareness integrated system, the method comprising the following steps: (1) Obtain the network status information of the current synaesthesia integrated system, including the location of the current communication user and the communication rate requirement, the direction and location of the sensing target, the direction and location of the scatterer interference source, and the height and plane position of the reconfigurable intelligent surface; (2) According to the network status information in step (1), the channel gains between the base station and the communication user and the base station and the target receiving end, the channel gains between the base station and the reconfigurable smart surface and the reconfigurable smart surface and the communication user and the sensing target are calculated, and the array direction vector between the base station transmitting end and the target receiving end is calculated; (3) According to the signal rate requirements of ground users, the base station uses rate splitting multiple access technology to split and reconstruct the information flow into public information and private information corresponding to each user, and serves the communication users and perception targets; (4) Based on the channel gains between the nodes in steps (2) and (3), the reflection and transmission coefficients of the reconfigurable smart surface, and the information splitting and reconstruction at the base station, determine the communication rate expression for decoding public information by the ground user, the communication rate expression for decoding private information, and the expression for the signal-to-interference-noise ratio of the target reflected echo signal; (5) Model an optimization problem and then jointly adjust the beamforming of the base station, the public information rate allocation, and the reflection coefficient and transmission coefficient of the reconfigurable smart surface to obtain a resource allocation solution that meets the communication and perception performance of the synaesthesia integrated system; (6) Generate an initial feasible solution set based on the calculation in step (2), calculate the initial feasible solution with the introduction of relaxation variables, and decompose the modeled optimization problem into two optimization sub-problems; then use Dinkelbach's Transform, semi-positive definite relaxation, variable substitution, first-order Taylor expansion and sequential rank-one relaxation algorithm to iteratively solve the two optimization sub-problems; when the iterative solution of the two sub-problems reaches the set maximum number of iterations or reaches the convergence threshold of the algorithm, output the final optimization results of the base station beamforming, public rate allocation and reflection and transmission coefficients of the reconfigurable smart surface.

[0006] Furthermore, the step (2) includes the following sub-steps: (2.1) Base station to the The channel gain for each communication user is , the channel gain from the base station to the reconfigurable smart surface is , reconfigurable smart surface to the The channel gain for each user is , and represent the channel gains from the base station to the sensing target and from the reconfigurable smart surface to the sensing target respectively; (2.2) Reconfigurable smart surfaces components, , the reflection of each component With transmission coefficient Perform mathematical modeling: ;in represents the amplitude, Represents the phase shift of the component; according to the principle of energy conservation, it must satisfy ; (2.3) Assume that the direction of the perceived target is , the antenna array direction vector between the base station and the sensing target is: .

[0007] Furthermore, the step (3) is specifically as follows: Assume that the number of antennas of the base station is , the number of ground users is , ; The public information sent by the base station to the first communication user is , the corresponding beamforming vector is ; The private information part is represented as , the corresponding private beamforming vector is ; The sensing information transmitted by the base station is , and its corresponding beamforming vector is Generate an initial feasible beamforming solution based on the channel gain of the communication user and the sensing target, the available power of the base station and the user's communication rate requirements And the initial feasible solution of the reflection and transmission coefficients of the reconfigurable smart surface .

[0008] Furthermore, the step (4) specifically includes the following sub-steps: (4.1) Assume that the ground user is located in front of the reconfigurable smart surface STAR-RIS, receiving information directly from the base station and information reflected from the reconfigurable smart surface; let From the base station to the The channel gain of each user is Represents the transition from reconfigurable smart surfaces to The channel gain of each user is represents the channel gain from the base station to the reconfigurable smart surface, then all the information received by the user It is expressed as: ; in represents the reflectance coefficient of the reconfigurable smart surface, represents Gaussian white noise; (4.2) The direction of the perceived target is , then the antenna array steering vector between the base station and the sensing target is: ; (4.3) Assuming that the sensing target is located at the back of STAR-RIS, the synaesthesia signal of the base station first reaches the sensing target through a direct link and a transmission path assisted by the reconfigurable smart surface; then the sensing target reflects the received information to the base station along the same path; at the same time, the interference sources around the sensing target will also receive the base station information and reflect the received information to the base station, so the echo signal received at the base station is It is expressed as: ; in represents the transmission coefficient matrix of the reconfigurable smart surface, , represents the amplitude of the perceived target, which is determined by the path loss and radar cross section; and They represent the channel gains from the base station to the target and from STAR-RIS to the target respectively; (4.4) Based on rate splitting multiple access technology, users decode public information The achievable rate is expressed as: ; (4.5) The ground user then eliminates the public message from the received information through continuous interference cancellation (SIC) to ensure that the private message can be decoded independently without interference from the public information; therefore, the user decodes its designated private message The achievable rates are as follows: ; (4.6) In order to ensure that all ground users can successfully decode the public information, the user's achievable rate should exceed the actual allocated public data rate; therefore, in order for each user to accurately decode the public information, the following constraints must be met, namely: ; in Indicates The actual public rate allocated to each user; (4.7) The signal-to-interference-to-noise ratio of the target reflected echo signal received by the base station is: ; in , , is the noise power at the base station receiving antenna.

[0009] Furthermore, the step (5) includes the following sub-steps: (5.1) Based on the user's communication rate requirements, the base station's power constraints, and the energy conservation constraints of the reconfigurable smart surface elements, the optimization problem is modeled with the goal of maximizing the signal-to-interference-noise ratio of the sensed echo signal; (5.2) By simultaneously optimizing the base station’s transmit beamforming vector , Reconfigurable smart surface reflection and transmission coefficient matrix and the common information rate allocation vector Obtain a resource allocation plan that meets the communication and perception requirements of the synaesthesia integration system; (5.3) The base station models the optimization problem with the optimization goal of maximizing the signal-to-interference-noise ratio of the perceived echo signal.

[0010] Specifically, the expression of the modeling optimization problem is as follows: ; Where C1 is the transmit power constraint, is the maximum available power of the base station; C2 constraint indicates that the actual public rate allocated to all ground users shall not exceed the public data rate achievable by each user; C3 indicates the minimum communication rate requirement of each user, where It is C5 indicates that due to the passive characteristics of the reconfigurable smart surface STAR-RIS, the amplitude response of all elements is limited by the principle of energy conservation; C6 represents the range of reflection coefficient and transmission coefficient of the reconfigurable smart surface elements.

[0011] Furthermore, the step (6) includes the following sub-steps: (6.1) generating an initial feasible solution set according to the calculation in step (2), wherein the initial feasible solution set includes: a public beamforming vector transmitted by the base station, a private beamforming vector corresponding to each user, a beamforming vector used for sensing, and a reconfigurable smart surface reflection and transmission coefficient matrix; (6.2) Base station beamforming vector And reconfigurable smart surface reflection and transmission coefficient matrix The optimization objective and constraints C2 and C3 are multi-coupled, so the optimization problem modeled is is non-convex; a two-layer iterative algorithm based on continuous convex approximation is designed to solve the optimization problem by using Dinkelbach's transform, semi-positive definite relaxation, variable substitution, first-order Taylor expansion and sequential rank-one constraint relaxation; the two-layer iterative algorithm based on continuous convex approximation is specifically as follows: first, the original optimization problem is It can be divided into two sub-problems, namely and ; For the first sub-problem , given the initial values ​​of the reflection and transmission coefficient matrices of the reconfigurable smart surface , Dinkelbach's transform, semi-positive relaxation, first-order Taylor expansion and variable substitution are used to solve the first sub-problem, and the optimization results of the base station beamforming vector and public rate allocation are obtained; secondly, according to the solution The optimization results are obtained, and the second sub-problem is solved using semi-positive definite relaxation and sequential rank-one constraint relaxation methods, and the optimization results of the reflection and transmission coefficient matrices of the reconfigurable smart surface are obtained; finally, the optimal solution to the original problem is obtained by iteratively solving these two sub-problems.

[0012] The second aspect of the present invention is a beamforming optimization device for a communication-awareness integrated system, the device comprising the following modules: Acquisition of network information module: Acquisition of network status information of the current synaesthesia integrated system, including the location of the current communication user and the communication rate requirement, the direction and location of the sensing target, the direction and location of the scatterer interference source, and the height and plane position of the reconfigurable intelligent surface; Channel gain calculation module: according to the network status information of the network information acquisition module, the channel gain between the base station and the communication user and the base station and the target receiving end, the channel gain between the base station and the reconfigurable intelligent surface and the reconfigurable intelligent surface and the communication user and the sensing target are calculated, and the array direction vector between the base station transmitting end and the target receiving end is calculated; Information reconstruction module: According to the signal rate requirements of ground users, the base station uses rate splitting multiple access technology to split and reconstruct the information flow into public information and private information corresponding to each user, and serves the communication users and perception targets; Decoding perception module: Based on the channel gain between each node in the channel gain module and the information reconstruction module, the reflection and transmission coefficients of the reconfigurable intelligent surface, and the information splitting and reconstruction at the base station, the communication rate expression for decoding public information by the ground user, the communication rate expression for decoding private information, and the expression for the signal-to-interference-to-noise ratio of the reflected echo signal of the perceived target are determined; Modeling and Optimization Module: Model an optimization problem, and then jointly adjust the beamforming of the base station, the public information rate allocation, and the reflection coefficient and transmission coefficient of the reconfigurable smart surface to obtain a resource allocation solution that meets the communication and perception performance of the synaesthesia integrated system; Iterative output module: Generates the initial feasible solution set according to the calculation in the modeling optimization module, calculates the initial feasible solution of the introduction of relaxation variables, and decomposes the modeling optimization problem into two optimization sub-problems; then uses Dinkelbach's Transform, semi-positive definite relaxation, variable substitution, first-order Taylor expansion and sequential rank-one relaxation algorithm to iteratively solve the two optimization sub-problems; when the iterative solution of the two sub-problems reaches the set maximum number of iterations or reaches the convergence threshold of the algorithm, the final optimization results of the base station beamforming, public rate allocation and reflection and transmission coefficients of the reconfigurable smart surface are output.

[0013] A third aspect of the present invention: an electronic device, comprising: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the beamforming optimization method for a communication-awareness integrated system.

[0014] The fourth aspect of the present invention: a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of a beamforming optimization method for a communication-awareness integrated system.

[0015] The beneficial effects of the present invention are as follows: the present invention provides a beamforming optimization method and device for a synaesthesia integrated system based on rate splitting multiple access, which optimizes the configuration of system resources to improve resource utilization efficiency for application scenarios with differentiated requirements for communication and perception services. The algorithm has a fast convergence speed and can improve perception performance while ensuring communication performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0017] Figure 1 A model diagram of a synaesthesia integration system based on rate splitting multiple access provided by an embodiment of the present invention; Figure 2 A schematic diagram of an algorithm flow for solving a modeled optimization problem provided by the present invention; Figure 3 The public rates, private rates and corresponding private beamforming power allocation result diagrams of four terrestrial users are provided. DETAILED DESCRIPTION

[0018] like Figure 1 As shown in FIG. 1 , a model diagram of a synaesthesia integrated system based on rate splitting multiple access is provided in an example of the present invention; specifically, the base station is configured with The base station uses rate split multiple access technology to encode the base station signal into public information and private information, and transmits the information to The base station can also sense and detect targets through multi-antenna beamforming technology. In addition, in order to overcome the limitations of traditional smart reflective surfaces in half-space coverage, a reconfigurable smart surface with full-space coverage capability is used, which works in conjunction with the base station through a controller to enhance the signal.

[0019] The present invention discloses a beamforming optimization method and device for a communication-sensing integrated system, comprising the following steps: S1: Obtain the network status information of the current synaesthesia integrated system, including the location of the current communication user and its communication rate requirement, the direction and location of the sensing target, the location and direction of the scatterer interference source, and the height and plane position of the reconfigurable smart surface; S2: According to the information in S1, the channel gains between the base station and the communication user and the base station and the target receiving end, the channel gains between the base station and the reconfigurable smart surface and the reconfigurable smart surface and the communication user and the sensing target are calculated, and the array direction vector between the base station transmitting end and the target receiving end is calculated; S3: According to the communication rate requirements of ground users, the base station uses rate splitting multiple access technology to split and reconstruct the information flow into public information and private information corresponding to each user; S4: Based on the channel gain between each node in S2, the reflection and transmission coefficient of the reconfigurable intelligent surface, and the information splitting and reconstruction at the base station, determine the communication rate expression for decoding public information by the ground user, the communication rate expression for decoding private information, and the expression for the signal-to-interference-to-noise ratio of the target reflected echo signal; S5: Based on the user's communication rate requirements, the power constraints of the base station, and the energy conservation constraints of the reconfigurable smart surface elements, the optimization problem is modeled with the goal of maximizing the signal-to-interference-noise ratio of the perceived echo signal. By jointly adjusting the beamforming of the base station, the public information rate allocation, and the reflection coefficient and transmission coefficient of the reconfigurable smart surface, a resource allocation solution that meets the communication and perception performance of the synaesthesia integrated system is obtained. S6: Generate an initial feasible solution set according to the information in S2, wherein the initial feasible solution set includes: a public beamforming vector transmitted by the base station, a private beamforming vector corresponding to each user, a beamforming vector for sensing, and a reconstructable smart surface reflection and transmission coefficient matrix; S7: According to the initial feasible solution set in S6, the initial feasible solution with the slack variables introduced is calculated, and the modeled optimization problem is decomposed into two optimization sub-problems; S8: According to the splitting results in S7, the two sub-problems are iteratively solved using Dinkelbach's Transform, semi-positive definite relaxation, variable substitution, one-section Taylor expansion and sequential rank-one relaxation algorithm; S9: When the iterative solution of the two sub-problems reaches the set maximum number of iterations or reaches the convergence threshold of the algorithm, the final optimization results of the base station beamforming, public rate allocation, and reflection and transmission coefficients of the reconfigurable smart surface are output.

[0020] like Figure 2 As shown in FIG. 1 , a schematic diagram of an algorithm flow for solving the modeled optimization problem provided by the present invention is shown. First, since the reflection and transmission coefficient matrices of the reconfigurable smart surface are coupled with the base station transmit beamforming and the public rate allocation variables, the original problem is a non-convex optimization problem that is difficult to solve directly. Therefore, the original problem is It can be decomposed into two sub-problems, namely and First, the initial values ​​of the reflection and transmission coefficient matrices are given , apply Dinkelbach's Transform, semidefinite relaxation (SDR), first-order Taylor expansion and variable substitution to solve the first subproblem , and obtain the optimization results of base station transmit beamforming and public rate allocation. On the basis of the optimization results, semidefinite relaxation, primary-secondary optimization (MM) and sequential rank-one constraint relaxation (SROCR) methods are used to deal with the second subproblem and obtain the optimization results of the reflection and transmission coefficient matrices of the reconfigurable smart surface. Finally, the final optimization result of the original optimization problem is obtained by iteratively solving these two subproblems.

[0021] Preferably, in step S2, the channel gains between different nodes are calculated according to the information in S1, as follows: S21: Base station to the The channel gain for each communication user is , the channel gain from the base station to the reconfigurable smart surface is , reconfigurable smart surface to the The channel gain for each user is , and They represent the channel gains from the base station to the sensing target and from STAR-RIS to the sensing target respectively; S22: Reconfigurable smart surface components, , the reflection of each component With transmission coefficient Perform mathematical modeling: ; in represents the amplitude, Represents the phase shift of the component; according to the principle of energy conservation, it must satisfy .

[0022] S23: Assume that the direction of the perceived target is , the antenna array direction vector between the base station and the sensing target is: ; In the step, S3 generates an initial feasible solution set based on the information of S2. The specific sub-steps are as follows: S3: Assume the number of antennas of the base station is , the number of ground users is , ; The public information sent by the base station to the communication user is , the corresponding beamforming vector is ; The private information part is represented as , the corresponding private beamforming vector is ; The sensing information transmitted by the base station is , and its corresponding beamforming vector is The private information part is represented as , the corresponding private beamforming vector is ; The sensing information transmitted by the base station is , and its corresponding beamforming vector is According to the channel gain of the communication user and the sensing target, the available power of the base station and the user's communication rate requirement, the initial feasible solution of beamforming is generated. And the initial feasible solution of the reflection and transmission coefficients of the reconfigurable smart surface ; In the step S4, modeling optimization problems are performed according to the system communication and perception service requirements. The specific sub-steps are as follows: S41: Assuming that the user is located in front of STAR-RIS, the user receives information simultaneously through the direct link of the base station and the reflection link of the reconfigurable smart surface. The achievable rate of the user decoding public information based on rate splitting multiple access technology is expressed as: ; S42: The achievable rate at which the user decodes its private information based on rate splitting multiple access technology is expressed as: ; S43: In order to ensure that all ground users can successfully and accurately detect their designated information from the public message, the achievable public rate of each user should exceed the actual allocated public data rate, that is, the following constraints must be met: ; S44: The signal-to-interference-noise ratio of the echo signal reflected by the perceived target as the optimization target is: ; in , .

[0023] S45: Based on the user's communication rate requirements, the power constraints of the base station, and the energy conservation constraints of the reconfigurable smart surface elements, the base station takes maximizing the signal-to-interference-to-noise ratio of the perceived echo signal as the optimization goal. The modeling optimization problem is as follows: ; S5: In the step S5, the modeled optimization problem is decomposed into two optimization sub-problems, and the decomposition steps are as follows: S51: Given the reflection and transmission coefficient matrices of the reconfigurable smart surface elements, based on the initial feasible solution set and the initial feasible solution of the slack variables, using semi-positive definite relaxation, continuous convex approximation, and sequential constraint relaxation algorithm for rank-one constraint problems, solve the sub-problems of optimal beamforming and public rate allocation for base stations; S52: Based on the optimal beamforming and public rate allocation optimization results obtained in S51, the sub-problem about the optimal reconfigurable smart surface reflection and transmission coefficients is solved using semi-positive definite relaxation and optimization-minimization method; S6: According to the splitting results in S51 and S52, the specific steps for solving the two sub-problems are as follows: S61: Introducing slack variables , auxiliary variables , , Convert public rates to Convex form, that is: ; ; S62: Introducing slack variables , the private rate Converted to convex form, that is: ; ; S63: Introducing slack variables The objective optimization function is converted into a convex form as follows: ; in , .

[0024] S64: Based on the initial feasible solution set in S3, obtain the initial feasible solution of the slack variables , and .

[0025] S65: Based on the initial feasible solution of the relaxation variables and the initial feasible solution of the reflection and transmission coefficient matrix of the reconfigurable smart surface, solve the optimization sub-problems about beamforming and relaxation variables and obtain the optimization results ; S66: Based on the optimization results obtained , solve the optimization sub-problem about the reflection and transmission coefficient matrix of the reconfigurable smart surface, and get the optimization result ; S67: According to the results of the two optimization problems obtained by solving, the initial feasible solution set and the slack variable feasible solution are updated, that is, , , .

[0026] S7: Solve the two optimization sub-problems by continuously iterating steps S64, S65, S66, and S67 until the set maximum number of iterations is reached or the convergence threshold of the algorithm is reached, and the optimal solution of the optimization problem is output.

[0027] Figure 3 The signal-to-interference-noise ratio of the perceived echo signal achievable based on the proposed overall iterative algorithm is demonstrated, as well as the convergence of the algorithm under different rate thresholds Rth and different base station powers.

[0028] like Figure 3 As shown in , the algorithm converges after several iterations. In addition, it can be seen that in the scenario where the base station power is high and the ground user rate threshold is low, the achievable perceived echo signal signal to noise ratio is higher. Specifically, when = 4, ; and when = 8 o'clock, The reason is as follows: when the rate threshold of each user is relatively low, the base station will use more power for target sensing, and the communication user has a higher tolerance for interference from the sensing signal. In this case, , and , the two will be more closely aligned to provide more perception degrees of freedom (DoF) and maximize the signal-to-interference-noise ratio of the perceived echo signal. When the power is higher, most of the power is used to form information signals to meet higher communication rate requirements, resulting in a decrease in perceived performance. Therefore, the optimization result of the private rate is consistent with the corresponding power allocation optimization result. When the public information flow can meet the communication rate requirements of the ground user, no private information flow will be allocated to it, and the corresponding private beamforming power is 0. In addition, when the user's communication rate requirements are higher, more power will be allocated to the private information flow to meet the communication rate requirements, resulting in an increase in the private rate of all users.

[0029] The present invention mainly targets application scenarios with differentiated service requirements for communication and perception. In order to meet the service requirements of communication users as much as possible and improve the perception performance, the present invention designs beamforming for synaesthesia integration based on rate splitting multiple access technology, and adopts an algorithm based on continuous convex approximation, semi-positive definite relaxation, first-order approximation and sequential quadratic constraint programming to solve the optimal rate splitting, beamforming vector and reflection and transmission coefficients of reconfigurable smart surfaces.

[0030] In addition, the present invention also provides a beamforming optimization device for a communication-awareness integrated system, the device comprising the following modules: Acquisition of network information module: Acquisition of network status information of the current synaesthesia integrated system, including the location of the current communication user and the communication rate requirement, the direction and location of the sensing target, the direction and location of the scatterer interference source, and the height and plane position of the reconfigurable intelligent surface; Channel gain calculation module: according to the network status information of the network information acquisition module, the channel gain between the base station and the communication user and the base station and the target receiving end, the channel gain between the base station and the reconfigurable intelligent surface and the reconfigurable intelligent surface and the communication user and the sensing target are calculated, and the array direction vector between the base station transmitting end and the target receiving end is calculated; Information reconstruction module: According to the signal rate requirements of ground users, the base station uses rate splitting multiple access technology to split and reconstruct the information flow into public information and private information corresponding to each user, and serves the communication users and perception targets; Decoding perception module: Based on the channel gain between each node in the channel gain module and the information reconstruction module, the reflection and transmission coefficients of the reconfigurable intelligent surface, and the information splitting and reconstruction at the base station, the communication rate expression for decoding public information by ground users, the communication rate expression for decoding private information, and the expression for the signal-to-interference-to-noise ratio of the reflected echo signal of the perceived target are determined; Modeling and optimization module: Model an optimization problem, and then jointly adjust the beamforming of the base station, the public information rate allocation, and the reflection coefficient and transmission coefficient of the reconfigurable smart surface to obtain a resource allocation solution that meets the communication and perception performance of the synaesthesia integrated system; Iterative output module: Generates the initial feasible solution set according to the calculation in the modeling optimization module, calculates the initial feasible solution of the introduction of relaxation variables, and decomposes the modeling optimization problem into two optimization sub-problems; then uses Dinkelbach's Transform, semi-positive definite relaxation, variable substitution, first-order Taylor expansion and sequential rank-one relaxation algorithm to iteratively solve the two optimization sub-problems; when the iterative solution of the two sub-problems reaches the set maximum number of iterations or reaches the convergence threshold of the algorithm, the final optimization results of the base station beamforming, public rate allocation and reflection and transmission coefficients of the reconfigurable smart surface are output.

[0031] The present invention further provides an electronic device, comprising: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the beamforming optimization method for a communication-awareness integrated system.

[0032] A computer-readable storage medium is provided, on which computer instructions are stored. When the instructions are executed by a processor, the steps of the beamforming optimization method for a communication-awareness integrated system are implemented.

[0033] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A beamforming optimization method for a communication-sensing integrated system, characterized in that: The method comprises the following steps: (1) Obtain the network status information of the current synaesthesia integrated system, including the location of the current communication user and the communication rate requirement, the direction and location of the sensing target, the direction and location of the scatterer interference source, and the height and plane position of the reconfigurable intelligent surface; (2) According to the network status information in step (1), the channel gains between the base station and the communication user and the base station and the target receiving end, the channel gains between the base station and the reconfigurable smart surface and the reconfigurable smart surface and the communication user and the sensing target are calculated, and the array direction vector between the base station transmitting end and the target receiving end is calculated; (3) According to the signal rate requirements of ground users, the base station uses rate splitting multiple access technology to split and reconstruct the information flow into public information and private information corresponding to each user, and serves the communication users and perception targets; (4) Based on the channel gains between the nodes in steps (2) and (3), the reflection and transmission coefficients of the reconfigurable smart surface, and the information splitting and reconstruction at the base station, determine the communication rate expression for decoding public information by the ground user, the communication rate expression for decoding private information, and the expression for the signal-to-interference-noise ratio of the target reflected echo signal; (5) Model an optimization problem and then jointly adjust the beamforming of the base station, the public information rate allocation, and the reflection coefficient and transmission coefficient of the reconfigurable smart surface to obtain a resource allocation solution that meets the communication and perception performance of the synaesthesia integrated system; (6) Generate an initial feasible solution set based on the calculation in step (2), calculate the initial feasible solution with the introduction of relaxation variables, and decompose the modeled optimization problem into two optimization sub-problems; then use Dinkelbach's Transform, semi-positive definite relaxation, variable substitution, first-order Taylor expansion and sequential rank-one relaxation algorithm to iteratively solve the two optimization sub-problems; when the iterative solution of the two sub-problems reaches the set maximum number of iterations or reaches the convergence threshold of the algorithm, output the final optimization results of the base station beamforming, public rate allocation and reflection and transmission coefficients of the reconfigurable smart surface.

2. The beamforming optimization method for a communication-awareness integrated system according to claim 1, characterized in that: The step (2) includes the following sub-steps: (2.1) Base station to the The channel gain for each communication user is , the channel gain from the base station to the reconfigurable smart surface is , reconfigurable smart surface to the The channel gain for each user is , and represent the channel gains from the base station to the sensing target and from the reconfigurable smart surface to the sensing target respectively; (2.2) Reconfigurable smart surfaces components, , the reflection of each component With transmission coefficient Perform mathematical modeling: ;in represents the amplitude, Indicates the phase shift of the component; According to the principle of energy conservation, ; (2.3) Assume that the direction of the perceived target is , the antenna array direction vector between the base station and the sensing target is: .

3. The beamforming optimization method for a communication-sensing integrated system according to claim 1, characterized in that: The step (3) is specifically as follows: Assume that the number of antennas of the base station is , the number of ground users is , ; The public information sent by the base station to the first communication user is , the corresponding beamforming vector is ; The private information part is represented as , the corresponding private beamforming vector is ; The sensing information transmitted by the base station is , and its corresponding beamforming vector is Generate an initial feasible beamforming solution based on the channel gain of the communication user and the sensing target, the available power of the base station and the user's communication rate requirements And the initial feasible solution of the reflection and transmission coefficients of the reconfigurable smart surface .

4. The beamforming optimization method for a communication-sensing integrated system according to claim 1, characterized in that: The step (4) specifically includes the following sub-steps: (4.1) Assume that the ground user is located in front of the reconfigurable smart surface STAR-RIS, receiving information directly from the base station and information reflected from the reconfigurable smart surface; let From the base station to the The channel gain of each user is Represents the transition from reconfigurable smart surfaces to The channel gain of each user is represents the channel gain from the base station to the reconfigurable smart surface, then all the information received by the user It is expressed as: ； in represents the reflection coefficient of the reconfigurable smart surface, represents Gaussian white noise; (4.2) The direction of the perceived target is , then the antenna array steering vector between the base station and the sensing target is: ； (4.3) Assuming that the sensing target is located at the back of STAR-RIS, the synaesthesia signal of the base station first reaches the sensing target through a direct link and a transmission path assisted by the reconfigurable smart surface; The sensing target then reflects the received information back to the base station along the same path; at the same time, the interference sources around the sensing target will also receive the base station information and reflect the received information back to the base station, so the echo signal received at the base station It is expressed as: ； in represents the transmission coefficient matrix of the reconfigurable smart surface, , represents the amplitude of the perceived target, which is determined by the path loss and radar cross section; and They represent the channel gains from the base station to the target and from STAR-RIS to the target respectively; (4.4) Based on rate splitting multiple access technology, users decode public information The achievable rate is expressed as: ； (4.5) The ground user then eliminates the public message from the received information through continuous interference cancellation (SIC) to ensure that the private message can be decoded independently without interference from the public information; Therefore, the user decodes his specified private information The achievable rates are as follows: ； (4.6) In order to ensure that all ground users can successfully decode the public information, the user's achievable rate should exceed the actual allocated public data rate; therefore, in order for each user to accurately decode the public information, the following constraints must be met, namely: ; in Indicates The actual public rate allocated to each user; (4.7) The signal-to-interference-to-noise ratio of the target reflected echo signal received by the base station is: ； in , , is the noise power at the base station receiving antenna.

5. The beamforming optimization method for a communication-sensing integrated system according to claim 1, characterized in that: The step (5) comprises the following sub-steps: (5.1) Based on the user's communication rate requirements, the base station's power constraints, and the energy conservation constraints of the reconfigurable smart surface elements, the optimization problem is modeled with the goal of maximizing the signal-to-interference-noise ratio of the sensed echo signal; (5.2) By simultaneously optimizing the base station’s transmit beamforming vector , Reconfigurable smart surface reflection and transmission coefficient matrix and the common information rate allocation vector Obtain a resource allocation plan that meets the communication and perception requirements of the synaesthesia integration system; (5.3) The base station models the optimization problem with the optimization goal of maximizing the signal-to-interference-noise ratio of the perceived echo signal.

6. The beamforming optimization method for a communication-sensing integrated system according to claim 5, characterized in that: The expression of the modeling optimization problem is as follows: ； Where C1 is the transmit power constraint, is the maximum available power of the base station; C2 constraint indicates that the actual public rate allocated to all ground users shall not exceed the public data rate achievable by each user; C3 indicates the minimum communication rate requirement of each user, where It is C5 indicates that due to the passive characteristics of the reconfigurable smart surface STAR-RIS, the amplitude response of all elements is limited by the principle of energy conservation; C6 represents the range of reflection coefficient and transmission coefficient of the reconfigurable smart surface elements.

7. The beamforming optimization method for a communication-sensing integrated system according to claim 1, characterized in that: The step (6) includes the following sub-steps: (6.1) generating an initial feasible solution set according to the calculation in step (2), wherein the initial feasible solution set includes: a public beamforming vector transmitted by the base station, a private beamforming vector corresponding to each user, a beamforming vector used for sensing, and a reconfigurable smart surface reflection and transmission coefficient matrix; (6.2) Base station beamforming vector And reconfigurable smart surface reflection and transmission coefficient matrix The optimization objective and constraints C2 and C3 are multi-coupled, so the optimization problem modeled is is non-convex; a two-layer iterative algorithm based on continuous convex approximation is designed to solve the optimization problem by using Dinkelbach's transform, semi-positive definite relaxation, variable substitution, first-order Taylor expansion and sequential rank-one constraint relaxation; the two-layer iterative algorithm based on continuous convex approximation is specifically as follows: first, the original optimization problem is It can be divided into two sub-problems, namely and ; For the first sub-problem , given the initial values ​​of the reflection and transmission coefficient matrices of the reconfigurable smart surface , Dinkelbach's transform, semi-positive relaxation, first-order Taylor expansion and variable substitution are used to solve the first sub-problem, and the optimization results of the base station beamforming vector and public rate allocation are obtained; secondly, according to the solution The optimization results are obtained, and the second sub-problem is solved using semi-positive definite relaxation and sequential rank-one constraint relaxation methods, and the optimization results of the reflection and transmission coefficient matrices of the reconfigurable smart surface are obtained; finally, the optimal solution to the original problem is obtained by iteratively solving these two sub-problems.

8. A beamforming optimization device for a communication-sensing integrated system, characterized in that: The device includes the following modules: Acquisition of network information module: Acquisition of network status information of the current synaesthesia integrated system, including the location of the current communication user and the communication rate requirement, the direction and location of the sensing target, the direction and location of the scatterer interference source, and the height and plane position of the reconfigurable intelligent surface; Channel gain calculation module: according to the network status information of the network information acquisition module, the channel gain between the base station and the communication user and the base station and the target receiving end, the channel gain between the base station and the reconfigurable intelligent surface and the reconfigurable intelligent surface and the communication user and the sensing target are calculated, and the array direction vector between the base station transmitting end and the target receiving end is calculated; Information reconstruction module: According to the signal rate requirements of ground users, the base station uses rate splitting multiple access technology to split and reconstruct the information flow into public information and private information corresponding to each user, and serves the communication users and perception targets; Decoding perception module: Based on the channel gain between each node in the channel gain module and the information reconstruction module, the reflection and transmission coefficients of the reconfigurable intelligent surface, and the information splitting and reconstruction at the base station, the communication rate expression for decoding public information by ground users, the communication rate expression for decoding private information, and the expression for the signal-to-interference-to-noise ratio of the reflected echo signal of the perceived target are determined; Modeling and optimization module: Model an optimization problem, and then jointly adjust the beamforming of the base station, the public information rate allocation, and the reflection coefficient and transmission coefficient of the reconfigurable smart surface to obtain a resource allocation solution that meets the communication and perception performance of the synaesthesia integrated system; Iterative output module: Generates the initial feasible solution set according to the calculation in the modeling optimization module, calculates the initial feasible solution of the introduction of relaxation variables, and decomposes the modeling optimization problem into two optimization sub-problems; then uses Dinkelbach's Transform, semi-positive definite relaxation, variable substitution, first-order Taylor expansion and sequential rank-one relaxation algorithm to iteratively solve the two optimization sub-problems; when the iterative solution of the two sub-problems reaches the set maximum number of iterations or reaches the convergence threshold of the algorithm, the final optimization results of the base station beamforming, public rate allocation and reflection and transmission coefficients of the reconfigurable smart surface are output.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a beamforming optimization method for a communication-awareness integrated system as described in any one of claims 1-7.

10. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by the processor, the steps of a beamforming optimization method for a communication-awareness integrated system as described in any one of claims 1 to 7 are implemented.

Images