STAR-RIS and RSMA-based communication and inductance integrated system resource optimization method and system

Abstract

The invention discloses an STAR-RIS and RSMA-based resource optimization method and system for a communication and inductance integrated system, and belongs to the field of wireless communication. The optimization method comprises the following steps: S1, constructing a system initial model; s2, setting an iteration frequency variable, and giving an initial radar detection signal covariance matrix, initial power distribution, an initial decoding sequence and a transmission matrix and a reflection matrix of an initial STAR-RIS; s3, on the premise that power distribution, a radar detection signal covariance matrix, a decoding sequence and a transmission matrix and a reflection matrix of STAR-RIS are given, optimizing the system initial model to obtain optimal power distribution, an optimal covariance matrix, an optimal decoding sequence, an optimal transmission matrix and an optimal reflection matrix; and S4, substituting the obtained optimal parameter into S3 based on a BCD method, updating an iteration frequency variable until the iteration frequency is reached, ending iteration, and obtaining an optimal solution of the total rate of the user.

Description

Technical Field

[0001] The invention belongs to the field of wireless communications, and in particular relates to a synaesthesia integration system resource optimization method and system based on STAR-RIS and RSMA. Background Art

[0002] With the rapid development of wireless communication technology, people have higher and higher requirements on the performance of communication systems. While meeting the needs of high-speed and large-capacity communication, achieving the integration of communication and perception has become a hot topic in current research. The integrated sensing and communication (ISAC) system can effectively utilize spectrum resources and improve the overall performance of the system. In the ISAC system, how to further improve the spectrum efficiency and energy efficiency of the system is a key issue that needs to be solved urgently.

[0003] RSMA (rate splitting multiple access) is a non-orthogonal multiple access technology. By splitting the user's message into multiple sub-messages for transmission, it can effectively improve the spectrum efficiency of the system. As an extension of NOMA (non-orthogonal multiple access) technology, RSMA has greater flexibility and overcomes the limitations of NOMA in decoding complexity and interference management. At the same time, the emergence of reconfigurable smart surface (RIS) technology provides a new way to optimize the wireless communication environment. Among them, STAR-RIS, as a new type of RIS, has a higher degree of freedom and can realize signal reflection and transmission at the same time, which provides the possibility of further improving system performance. Most of the existing research is based on NOMA and RIS technology for resource optimization of synaesthesia integrated systems. However, the above methods have certain limitations in interference management and degrees of freedom.

[0004] Therefore, the use of RSMA and STAR-RIS technology in communication and perception systems can further improve system performance. For this purpose, a resource optimization method for synaesthesia integrated system based on STAR-RIS and RSMA is proposed. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a synaesthesia integrated system resource optimization method and system based on STAR-RIS and RSMA, which solves the problems in the prior art.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The resource optimization method of the synaesthesia integration system based on STAR-RIS and RSMA includes the following steps: S1, construct the initial model of synaesthesia integration system based on STAR-RIS and RSMA; S2, set the iteration number variable and give the initial radar detection signal covariance matrix , Initial power allocation , initial decoding order and the initial STAR-RIS transmission matrix and the reflection matrix ; S3, at a given power distribution , radar detection signal covariance matrix , decoding order and the STAR-RIS transmission matrix and the reflection matrix Under the premise of optimizing the initial model of the synaesthesia integration system based on STAR-RIS and RSMA, the optimal power distribution is obtained. , the optimal covariance matrix , optimal decoding order And the optimal transmission matrix and the reflection matrix ; S4, based on the BCD method to allocate the optimal power , the optimal covariance matrix , optimal decoding order And the optimal transmission matrix and the reflection matrix Substitute into S3 and update the iteration number variable, and iterate until the iteration number is reached. The iteration terminates and the optimal solution for the total user rate is obtained.

[0007] Furthermore, the synaesthesia integration system based on STAR-RIS and RSMA includes: single antenna users, a full-duplex base station and STAR-RIS, STAR-RIS includes The user transmits messages to the base station through STAR-RIS and direct connection in the uplink RSMA mode, and the base station detects a single radar target through STAR-RIS with a certain transmission power.

[0008] Furthermore, the initial model of the synaesthesia integration system based on STAR-RIS and RSMA is: in, is the covariance matrix of the base station radar detection signal. The total power of the signal can be obtained by the trace of its covariance matrix. To calculate, is the decoding order of the base station when receiving user messages, Power allocation for users, , are the transmission matrix and reflection matrix of STAR-RIS, For the The transmission rate when a user sends a message via the uplink RSMA, is the SINR of the radar target, is the threshold of radar target SINR; is the maximum available power of the base station; For the The power allocated to each user, The maximum power for each user; , For STAR-RIS The transmission amplitude and reflection amplitude of each transmission and reflection element; Defined as coming from All of the users The set of all possible decoding orders of a message; Indicates User's Sub-message Allocated power.

[0009] Further, The calculation formula is: in, For the User's The transmission rate of sub-messages, For the User's The SINR of each sub-message is For the The channel between a user and the base station, For the channels between users and STAR-RIS, and are the channels between STAR-RIS and the base station receiving antenna and transmitting antenna, respectively. represents the radar target amplitude variance, represents the received noise variance, is the self-interference of the base station, is the channel between STAR-RIS and the radar target, and Respectively represent the number of base station transmission antennas and receiving antennas, They are the channels of the base station transmission antenna, receiving antenna and radar target respectively; Indicates User's The decoding order of sub-messages is Indicates User's The decoding order of sub-messages is User's After the sub-message; Among them, the equivalent channel of radar detection target , base station equivalent interference They are defined as: .

[0010] Furthermore, in order to obtain the optimal power distribution , the optimal covariance matrix , the optimal transmission matrix and the reflection matrix When optimizing the initial model of the synaesthesia integration system based on STAR-RIS and RSMA, the method based on successive convex approximation and gradient descent is used.

[0011] Furthermore, in order to obtain the optimal decoding order When the greedy algorithm is used, the initial model of the synaesthesia integration system based on STAR-RIS and RSMA is optimized.

[0012] The integrated system resource optimization system based on STAR-RIS and RSMA includes: Model building module: construct the initial model of synaesthesia integration system based on STAR-RIS and RSMA; Parameter initialization module: Set the number of iterations and give the initial radar detection signal covariance matrix , Initial power allocation , initial decoding order and the initial STAR-RIS transmission matrix and the reflection matrix ; Optimization module: Given power allocation , radar detection signal covariance matrix , decoding order and the STAR-RIS transmission matrix and the reflection matrix Under the premise of optimizing the initial model of the synaesthesia integration system based on STAR-RIS and RSMA, the optimal power distribution is obtained. , the optimal covariance matrix , optimal decoding order And the optimal transmission matrix and the reflection matrix ; And, iterative module: optimal power allocation based on BCD method , the optimal covariance matrix , optimal decoding order And the optimal transmission matrix and the reflection matrix Substitute into the optimization module and update the iteration number variable, iterate until the iteration number is reached, the iteration terminates, and the optimal solution for the total user rate is obtained.

[0013] A computer storage medium stores a readable program, which can execute the above-mentioned synaesthesia integration system resource optimization method based on STAR-RIS and RSMA when the program is running.

[0014] An electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned synaesthesia integration system resource optimization method based on STAR-RIS and RSMA.

[0015] A computer program product includes computer instructions, wherein the computer instructions instruct a computing device to execute operations corresponding to the above-mentioned synaesthesia integration system resource optimization method based on STAR-RIS and RSMA.

[0016] Beneficial effects of the present invention: 1. Under the constraints of the maximum transmission power of the base station, the minimum transmission power of the user, and the minimum SINR of the radar target, the present invention studies the joint optimization of the covariance matrix, power allocation, decoding order of the radar detection signal transmitted by the base station, and the transmission matrix and reflection matrix of STAR-RIS to achieve the problem of maximizing the user rate, and proposes an iterative algorithm based on gradient descent, successive convex approximation and block coordinate descent.

[0017] 2. The previously proposed STAR-RIS-assisted RSMA integrated sensing and communication system framework did not consider the situation where uplink users transmit messages through RSMA. The framework proposed in the present invention can adapt to special application scenarios more flexibly and conveniently, and improve user rates. The present invention considers multiple uplink users transmitting messages to the base station through STAR-RIS and direct connection with a certain transmission power, with the goal of maximizing the user rate under the constraints of the maximum transmission power constraint of the base station, the minimum transmission power constraint of the user, and the minimum SINR of the radar target. The covariance matrix, power allocation, decoding order of the radar detection signal transmitted by the base station, and the transmission matrix and reflection matrix of STAR-RIS are jointly optimized.

[0018] 3. Since the proposed optimization problem is non-convex and difficult to solve, the present invention solves four sub-problems by gradient descent, continuous convex approximation (SCA) and block coordinate descent (BCD); the five variables of the four sub-problems correspond to four blocks, namely, the covariance matrix, power allocation, decoding order of the radar detection signal transmitted by the base station, and the transmission matrix and reflection matrix of STAR-RIS; while keeping other block variables unchanged, the four sub-problems are solved by gradient descent and SCA methods, and the optimization variables of the four sub-problems are optimized alternately; and the solution obtained in each iteration will be used as the input of the next iteration.

[0019] 4. The convergence of the gradient descent method, continuous convex approximation technology (SCA) and block coordinate descent method (BCD) algorithms proposed in the present invention can be guaranteed, and the required complexity is low. The simulation results show that the user rate in different scenarios is significantly improved by using the proposed algorithm compared with existing schemes, such as non-orthogonal multiple access (NOMA) scheme, time division multiple access (TDMA) scheme and rate split multiple access (RSMA) scheme without power allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 The structure diagram of the synaesthesia integration system based on STAR-RIS and RSMA of the present invention; Figure 2 It is a flow chart of the resource optimization method of the synaesthesia integrated system based on STAR-RIS and RSMA of the present invention; Figure 3It is a curve diagram showing the relationship between the scheme proposed in the present invention, the non-orthogonal multiple access scheme, the time division multiple access scheme and the scheme without power allocation (WP-RSMA) under rate division multiple access. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Example 1 like Figure 1 As shown in the figure, the synaesthesia integrated system based on STAR-RIS and RSMA includes: base station (BS), STAR-RIS and single antenna users, the base station is a full-duplex base station, STAR-RIS includes transmission and reflection elements; and the uplink user transmits messages to the base station through STAR-RIS and direct connection at a certain transmission power, and the base station detects a single radar target through STAR-RIS at a certain transmission power; STAR-RIS is divided into a transmission area and a reflection area. Uplink users are distributed in the reflection area of ​​STAR-RIS. Each user's message is divided into two sub-messages. The signal transmitted by a user is , For the User's Sub message, , For the The power allocated to each user, Indicates User's Sub-message The allocated power; the radar target is located in the transmission area of ​​STAR-RIS; is the STAR-RIS transmission matrix, is the STAR-RIS reflection matrix, where: is the transmission phase shift matrix, is the reflection phase shift matrix, , They are STAR-RIS The transmission and reflection phases of each element, is the transmission amplitude matrix, is the reflection amplitude matrix, , They are STAR-RIS The transmission and reflection amplitude of each element.

[0024] Example 2 like Figure 2 As shown, the synaesthesia integrated system resource optimization method based on STAR-RIS and RSMA includes the following steps: S1, construct the initial model of synaesthesia integration system based on STAR-RIS and RSMA; The initial model of the synaesthesia integration system based on STAR-RIS and RSMA is: in, is the covariance matrix of the base station radar detection signal. The total power of the signal can be obtained by the trace of its covariance matrix. To calculate, is the decoding order of the base station when receiving user messages, Power allocation for users, , are the transmission matrix and reflection matrix of STAR-RIS, For the The transmission rate when a user sends a message via the uplink RSMA, is the SINR of the radar target, is the threshold of radar target SINR; is the maximum available power of the base station; For the The power allocated to each user, The maximum power for each user; , For STAR-RIS The transmission amplitude and reflection amplitude of each transmission and reflection element; Defined as coming from All of the users The set of all possible decoding orders for a message. Indicates User's Sub-message Allocated power.

[0025] The calculation formula is: in, For the User's The transmission rate of sub-messages, For the User's The SINR of each sub-message is For the The channel between a user and the base station, For the channels between users and STAR-RIS, and are the channels between STAR-RIS and the base station receiving antenna and transmitting antenna, respectively. represents the radar target amplitude variance, represents the received noise variance, is the self-interference of the base station, is the channel between STAR-RIS and the radar target, and Respectively represent the number of base station transmission antennas and receiving antennas, are the channels of the base station transmission antenna, receiving antenna and radar target respectively. Among them, the equivalent channel of radar detection target , base station equivalent interference They are defined as: .

[0026] In addition, define the following expression: Limit the minimum SINR of the radar target, is the SINR of the radar target; Limit base station transmission power, is the maximum transmission power of the base station; Limit the power of each user, The maximum power for each user, is the number of users; Ensure that the transmission and reflection amplitudes of each STAR-RIS element are non-negative, Where is the number of components of STAR-RIS; Indicates the decoding order using a permutation Representation, arrangement Belongs to the collection ,gather Defined as coming from All of the users The set of all possible decoding orders of a message, Indicates User's Sub-message Allocated power.

[0027] S2, set the iteration number variable and give the initial radar detection signal covariance matrix , Initial power allocation , initial decoding order and the initial STAR-RIS transmission matrix and the reflection matrix ; S3, at a given power distribution , radar detection signal covariance matrix , decoding order and the STAR-RIS transmission matrix and the reflection matrix Based on the premise of successive convex approximation (SCA) method and gradient descent method, the initial model of the synaesthesia integration system based on STAR-RIS and RSMA is optimized to obtain the optimal power allocation ,and As input for the next iteration; Get the best power distribution The steps are: S31, given the covariance matrix of the variable radar detection signal , decoding order 、STAR-RIS transmission matrix With the reflection matrix Under the premise of , the optimization of power distribution is studied. At this time, the problem is transformed into: Since the SINR expression in the objective function is It is non-convex, so it is difficult to directly find the optimal solution to the problem. Here we consider using the successive convex approximation (SCA) method and gradient descent method to solve the problem. By linearizing the non-convex objective function and constraints, we gradually approach the original non-convex problem and obtain a convergent solution. By calculating the gradient of the objective function with respect to the variables and updating the variables in the direction of steepest descent, the algorithm can converge to a local optimal solution.

[0028] S32, first, initialize , assuming that the power allocation in the nth iteration is The first-order Taylor expansion of the objective function is performed at: about The first-order approximation of is: in, is the value of the current iteration point, about The gradient of is: Through this linear approximation, the original non-convex optimization problem is transformed into a new convex optimization problem: S33, after converting into a convex optimization problem, uses the gradient descent method to solve the problem. First, select the initial power allocation scheme , so that it satisfies all constraints. Set the step size and convergence threshold . At each iteration, the gradient of the objective function is calculated: Substitute the previously calculated partial derivatives into the above formula to get the value of the objective function gradient. Update power allocation: S34, after each update, ensure that the updated solution Satisfy the non-negativity constraint and power constraint, that is, , if the updated power is negative, set it to zero, if the total power allocation of a user exceeds , then its power is scaled proportionally so that the total power is exactly equal to If the norm of the gradient is less than a preset threshold, the algorithm converges.

[0029] S4, at a given power distribution Radar detection signal covariance matrix , decoding order and the STAR-RIS transmission matrix and the reflection matrix Based on the premise of successive convex approximation (SCA) method and gradient descent method, the initial model of the synaesthesia integration system based on STAR-RIS and RSMA is optimized to obtain the optimal covariance matrix ,and As input for the next iteration; Get the optimal covariance matrix The steps are: S41, next optimize the covariance matrix of the radar target . Fixing other variables, the optimization problem is transformed into: The SINR expression in the objective function is still non-convex, and the SINR constraint of the radar target is also non-convex, so it is difficult to directly find the optimal solution to the problem. Consider using the successive convex approximation (SCA) method and gradient descent method to solve the problem. The non-convex objective function and constraint conditions are linearized and approximated through the first-order Taylor expansion to gradually approach the original non-convex problem, thereby obtaining a convergent solution. By calculating the gradient of the objective function with respect to the variables and updating the variables in the direction of steepest descent, the algorithm can converge to a local optimal solution.

[0030] S42, however, the first-order Taylor expansion cannot ensure the effectiveness of the linear approximation. Therefore, the trust region method is used to limit the update range of the optimization variables so that the new optimization variables will not deviate too far from the current iteration point. First, initialize the covariance matrix : Set the trust region parameters: (like ). Set the trust region adjustment factor: (like ) (like ), set the convergence threshold , Step Length and the maximum number of iterations.

[0031] S43, due to The expression of is non-convex, and both the numerator and denominator of the constraint involve , assuming that the covariance matrix at the nth iteration is The first-order Taylor expansion of the objective function is performed at: about The first-order approximation of is: in, It is in The calculated value in the iteration value. The expression contains only the denominator ,therefore, about The gradient of is: Through this linear approximation, the original non-convex optimization problem is transformed into a new convex optimization problem: S44, the first-order Taylor expansion of the constraints is performed below: gradient The expression is: S45, Trust Region Constraints Update range: in, is the trust region radius of the current iteration. This constraint ensures that The update amplitude of will not be too large. Therefore, the complete statement of this sub-problem is: express is semi-positive definite. Set the step size . At each iteration, the gradient of the objective function is calculated: S46, substitute the previously calculated partial derivative into the above formula to obtain the value of the objective function gradient. Update : S47, solve the above optimization sub-problem, and get , update the objective function and radar SINR and check whether the constraints are satisfied: If the objective function is lifted and the constraint is: and , then the updated convergence domain is: And update the iteration point ; On the contrary, if the objective function is not improved or the constraints are not satisfied: or , then keep The updated convergence domain is unchanged: , and repeat this iteration until the objective function is improved and the constraints are satisfied: ; Iterate a specified number of times or meet the convergence region conditions: , terminate the iteration.

[0032] S5, at a given power distribution Radar detection signal covariance matrix , decoding order and the STAR-RIS transmission matrix and the reflection matrix Based on the premise of greedy algorithm, the initial model of the synaesthesia integration system based on STAR-RIS and RSMA is optimized to obtain the optimal decoding order. ,and As input for the next iteration; Get the best decoding order The steps are: S51, all possibilities of decoding order constitute a discrete set, so this problem belongs to integer programming and can be solved by optimal infinite search. Consider using a greedy algorithm to select the message that maximizes the target rate gain for decoding.

[0033] Initializing a Collection , including all possible decoding orders. In the initial state, no message is decoded, and the SINR of all messages is calculated according to the formula in the system model. For each user's sub-message, its current achievable rate is calculated. .

[0034] S52, select the message that makes the total rate increment the largest for priority decoding, after selecting the message, add the message to the current decoding order, remove the decoded message from the undecoded message set. Repeat the above process until all messages are decoded. When all messages are decoded, a decoding order is obtained. .

[0035] The computational complexity of the algorithm is relatively low, and at each step it is only necessary to select the message with the largest overall rate gain among the undecoded messages.

[0036] S6, at a given power distribution Radar detection signal covariance matrix , decoding order and the STAR-RIS transmission matrix and the reflection matrix Based on the premise of successive convex approximation (SCA) method and gradient descent method, the initial model of the synaesthesia integration system based on STAR-RIS and RSMA is optimized to obtain the optimal transmission matrix respectively. and the reflection matrix , and then and As input for the next iteration; Get the optimal transmission matrix and the reflection matrix The steps are: S61, This paper considers STAR-RIS working in UED mode, which means that each element of STAR-RIS can reflect and transmit the input signal at the same time, but the reflected and transmitted signals have different amplitudes and phases. Because the reflection and transmission coefficients of each element can be adjusted independently, this mode provides the highest degrees of freedom (DoFs).

[0037] The SINR expression in the objective function is still non-convex, and the SINR constraint of the radar target contains the variable , so the constraint is also non-convex, and it is difficult to directly find the optimal solution to the problem. Consider using the successive convex approximation (SCA) method and gradient descent method to solve the problem. The non-convex objective function and constraint conditions are linearly approximated through the first-order Taylor expansion to make the problem gradually approach the original non-convex problem, thereby obtaining a convergent solution. By calculating the gradient of the objective function with respect to the variable and updating the variable in the direction of steepest descent, the algorithm can converge to a local optimal solution. However, the first-order Taylor expansion cannot ensure the effectiveness of the linear approximation. Therefore, the trust region method is used to limit the update amplitude of the optimization variables so that the new optimization variables will not deviate too far from the current iteration point. Set the trust region parameters: (like ). Set the trust region adjustment factor: , , set the convergence threshold , Step Length and the maximum number of iterations.

[0038] First, fix and other variables, Optimize, the optimization problem at this time is: S62, perform first-order Taylor expansion on the objective function: about The first-order approximation of is: S63, among which, It is in The calculated value in the iteration The value of . about The gradient of is: in: Through this linear approximation, the original non-convex optimization problem is transformed into a new convex optimization problem. The first-order Taylor expansion of the constraints is performed as follows: gradient The expression is: S64, through the above linearized objective function and constraints, construct the convex optimization subproblem: S65, calculate the gradient of the objective function: Substitute the previously calculated partial derivative into the above formula to get the value of the objective function gradient. Update : S66, solve the above optimization sub-problem and get , update the objective function and radar SINR and check whether the constraints are satisfied: S67, if the objective function is lifted and constrained: and , then the updated convergence domain is: And update the iteration point ; On the contrary, if the objective function is not improved or the constraints are not satisfied: or , then keep The updated convergence domain is unchanged: , and repeat this iteration until the objective function is improved and the constraints are satisfied: ; Iterate a specified number of times or meet the convergence region conditions: , terminate the iteration and get the optimized .from Extract the amplitude from: S68, for variables Subproblem, optimization method is fixed and other variables, Optimize, the optimization problem at this time is: Set the trust region parameters: (like ). Set the trust region adjustment factor: , , set the convergence threshold , Step Length and the maximum number of iterations.

[0039] S69, perform first-order Taylor expansion on the objective function: about The first-order approximation of is: S610, among which, It is in The calculated value in the iteration For the convenience of calculation, about The gradient of is approximately: Through this linear approximation, the original non-convex optimization problem is transformed into a new convex optimization problem. The first-order Taylor expansion of the constraints is performed as follows: gradient The expression is: S611, construct the convex optimization subproblem through the above linearized objective function and constraints: S612, calculate the gradient of the objective function: Substitute the previously calculated partial derivative into the above formula to get the value of the objective function gradient. Update : S613, solve the above optimization sub-problem and obtain , update the objective function and radar SINR and check whether the constraints are satisfied: S614, if the objective function is lifted and constrained: and , then the updated convergence domain is: And update the iteration point ; On the contrary, if the objective function is not improved or the constraints are not satisfied: or , then keep The updated convergence domain is unchanged: , and repeat this iteration until the objective function is improved and the constraints are satisfied: ; Iterate a specified number of times or meet the convergence region conditions: , terminate the iteration and get the optimized .from Extract the amplitude from: After each iteration, the optimized objective function value is given.

[0040] S7, based on the BCD method to allocate the optimal power , the optimal covariance matrix , optimal decoding order And the optimal transmission matrix and the reflection matrix Substitute into S3 and update the iteration count variable , repeat S3 to S6 until the number of iterations is reached, the iteration is terminated, and the optimal solution for the total user rate is obtained.

[0041] Example 3 In this embodiment, the scheme of the present invention is described in detail in conjunction with a specific embodiment. The specific embodiment is simulated using MATLAB software. The specific parameters are set as shown in Table 1.

[0042] Table 1 Simulation parameters like Figure 3 As shown in the figure, the comparison of different numbers of users shows that the more users there are, the greater the total system rate is. This is because the system receives messages from more users, and the total user rate of the system increases. In addition, the total user rate of the scheme proposed in the present invention is greater than that of the NOMA scheme and the WP-RSMA scheme under different numbers of users, because the RSMA scheme can better allocate user power. The performance is better than that of the proposed NOMA scheme, because the RSMA scheme provides flexible interference management.

[0043] Example 4 In this embodiment, a synaesthesia integrated system resource optimization system based on STAR-RIS and RSMA is proposed, including: Model building module: construct the initial model of synaesthesia integration system based on STAR-RIS and RSMA; Parameter initialization module: Set the number of iterations and give the initial radar detection signal covariance matrix , Initial power allocation , initial decoding order and the initial STAR-RIS transmission matrix and the reflection matrix ; Optimization module: Given power allocation , radar detection signal covariance matrix , decoding order and the STAR-RIS transmission matrix and the reflection matrix Under the premise of optimizing the initial model of the synaesthesia integration system based on STAR-RIS and RSMA, the optimal power distribution is obtained. , the optimal covariance matrix , optimal decoding order And the optimal transmission matrix and the reflection matrix ; And, iterative module: optimal power allocation based on BCD method , the optimal covariance matrix , optimal decoding order And the optimal transmission matrix and the reflection matrix Substitute into the optimization module and update the iteration number variable, iterate until the iteration number is reached, the iteration terminates, and the optimal solution for the total user rate is obtained.

[0044] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0045] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0047] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A resource optimization method for the synaesthesia integrated system based on STAR-RIS and RSMA, characterized in that: The following steps are involved: S1, construct the initial model of synaesthesia integration system based on STAR-RIS and RSMA; S2, set the iteration number variable and give the initial radar detection signal covariance matrix , Initial power allocation , initial decoding order and the initial STAR-RIS transmission matrix and the reflection matrix ; S3, at a given power distribution , radar detection signal covariance matrix , decoding order and the STAR-RIS transmission matrix and the reflection matrix Under the premise of optimizing the initial model of the synaesthesia integration system based on STAR-RIS and RSMA, the optimal power distribution is obtained. , the optimal covariance matrix , optimal decoding order And the optimal transmission matrix and the reflection matrix ; S4, based on the BCD method to allocate the optimal power , the optimal covariance matrix , optimal decoding order And the optimal transmission matrix and the reflection matrix Substitute into S3 and update the iteration number variable, and iterate until the iteration number is reached. The iteration terminates and the optimal solution for the total user rate is obtained.

2. The method for optimizing synaesthesia integrated system resources based on STAR-RIS and RSMA according to claim 1, characterized in that: The synaesthesia integration system based on STAR-RIS and RSMA includes: single antenna users, a full-duplex base station and STAR-RIS, STAR-RIS includes The user transmits messages to the base station through STAR-RIS and direct connection in the uplink RSMA mode, and the base station detects a single radar target through STAR-RIS with a certain transmission power.

3. The method for optimizing synaesthesia integrated system resources based on STAR-RIS and RSMA according to claim 2, characterized in that: The initial model of the synaesthesia integration system based on STAR-RIS and RSMA is: in, is the covariance matrix of the base station radar detection signal. The total power of the signal can be obtained by the trace of its covariance matrix. To calculate, is the decoding order of the base station when receiving user messages, Power allocation for users, , are the transmission matrix and reflection matrix of STAR-RIS, For the The transmission rate when a user sends a message via the uplink RSMA, is the SINR of the radar target, is the threshold of radar target SINR; is the maximum available power of the base station; For the The power allocated to each user, The maximum power for each user; , For STAR-RIS The transmission amplitude and reflection amplitude of each transmission and reflection element; Defined as coming from All users The set of all possible decoding orders of a message; Indicates User's Sub-message Allocated power.

4. The method for optimizing synaesthesia integrated system resources based on STAR-RIS and RSMA according to claim 3, characterized in that: The calculation formula is: in, For the User's The transmission rate of sub-messages, For the User's The SINR of each sub-message is For the The channel between a user and the base station, For the channels between users and STAR-RIS, and are the channels between STAR-RIS and the base station receiving antenna and transmitting antenna, respectively. represents the radar target amplitude variance, represents the received noise variance, is the self-interference of the base station, is the channel between STAR-RIS and the radar target, and Respectively represent the number of base station transmission antennas and receiving antennas, They are the channels of the base station transmission antenna, receiving antenna and radar target respectively; Indicates User's The decoding order of sub-messages is Indicates User's The decoding order of sub-messages is User's After the sub-message; Among them, the equivalent channel of radar detection target , base station equivalent interference They are defined as: 。 5. The method for optimizing synaesthesia integrated system resources based on STAR-RIS and RSMA according to claim 1, characterized in that: To achieve the best power distribution , the optimal covariance matrix , the optimal transmission matrix and the reflection matrix When optimizing the initial model of the synaesthesia integration system based on STAR-RIS and RSMA, the method based on successive convex approximation and gradient descent is used.

6. The method for optimizing synaesthesia integrated system resources based on STAR-RIS and RSMA according to claim 1, characterized in that: To obtain the optimal decoding order When the greedy algorithm is used, the initial model of the synaesthesia integration system based on STAR-RIS and RSMA is optimized.

7. The synaesthesia integrated system resource optimization system based on STAR-RIS and RSMA is characterized by: include: Model building module: construct the initial model of synaesthesia integration system based on STAR-RIS and RSMA; Parameter initialization module: Set the number of iterations and give the initial radar detection signal covariance matrix , Initial power allocation , initial decoding order and the initial STAR-RIS transmission matrix and the reflection matrix ; Optimization module: Given power allocation , radar detection signal covariance matrix , decoding order and the STAR-RIS transmission matrix and the reflection matrix Under the premise of optimizing the initial model of the synaesthesia integration system based on STAR-RIS and RSMA, the optimal power distribution is obtained. , the optimal covariance matrix , optimal decoding order And the optimal transmission matrix and the reflection matrix ; And, iterative module: optimal power allocation based on BCD method , the optimal covariance matrix , optimal decoding order And the optimal transmission matrix and the reflection matrix Substitute into the optimization module and update the iteration number variable, iterate until the iteration number is reached, the iteration terminates, and the optimal solution for the total user rate is obtained.

8. A computer storage medium storing a readable program, characterized in that: When the program is running, the synaesthesia integration system resource optimization method based on STAR-RIS and RSMA described in any one of claims 1 to 6 can be executed.

9. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the synaesthesia integration system resource optimization method based on STAR-RIS and RSMA as described in any one of claims 1 to 6.

10. A computer program product comprising computer instructions, characterized in that: The computer instructions instruct the computing device to execute operations corresponding to the synaesthesia integration system resource optimization method based on STAR-RIS and RSMA as described in any one of claims 1 to 6.

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