Multi-user communication system resource optimization method and system based on cascaded RIS and RSMA

Abstract

The invention discloses a multi-user communication system resource optimization method and system based on cascade RIS and RSMA, and belongs to the field of wireless communication. The method comprises the following steps: S1, constructing an initial model; s2, setting an initial iteration parameter; s3, under the condition that a base station beam forming vector, common rate allocation, a transmission matrix and a reflection matrix of the RIS1 and a transmission matrix and a reflection matrix of the RIS2 are given, the initial model is optimized, and the optimal beam forming vector and common rate allocation, the optimal transmission matrix and the reflection matrix of the RIS1 and the optimal transmission matrix and the reflection matrix of the RIS2 are obtained; and S4, substituting the optimal beam forming vector and common rate allocation, the transmission matrix and the reflection matrix of the optimal RIS1 and the transmission matrix and the reflection matrix of the optimal RIS2 into S3, updating the iteration frequency variable until the iteration termination precision is reached, terminating the iteration, and obtaining the 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 multi-user communication system resource optimization method and system based on cascaded RIS and RSMA. Background Art

[0002] RSMA (Rate Split Multiple Access) is an advanced multiple access technology that helps multiple users share the same spectrum resource by splitting their data into public and private parts while reducing interference between users. RSMA excels in multi-user interference management and spectrum efficiency, and has attracted research interest in ultra-dense networks and heterogeneous networks. RIS (Intelligent Reflection Surface) is a surface that can intelligently adjust the phase of the reflected signal, with the characteristics of low cost and low energy consumption. It can overcome the path loss problem in traditional wireless communications by reflecting and enhancing the signal path. RIS has a wide range of application scenarios, including signal coverage enhancement, energy efficiency improvement, physical layer security, etc. RSMA is effective in dealing with strong interference, but when the channel conditions are extremely bad (such as severe obstruction or deep fading), relying solely on RSMA may not solve all problems. For this reason, a resource optimization method for multi-user communication systems based on cascaded RIS and RSMA is proposed. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a multi-user communication system resource optimization method and system based on cascaded RIS and RSMA, which solves the problems in the prior art.

[0004] The purpose of the present invention can be achieved through the following technical solutions: The multi-user communication system resource optimization method based on cascaded RIS and RSMA includes the following steps: S1, with the goal of maximizing the total rate of users, construct the initial model of the multi-user communication system of cascaded RIS and RSMA; S2, set the iteration number variable, iteration termination accuracy, and give the initial base station beamforming vector , Initial Public Rate Allocation , the initial RIS1 transmission matrix and the reflection matrix and the initial RIS2 transmission matrix and the reflection matrix ; S3, given the base station beamforming vector , Public Rate Allocation , RIS1 transmission matrix and the reflection matrix And the transmission matrix of RIS2 and the reflection matrix Under the condition of , the initial model of the multi-user communication system with cascaded RIS and RSMA is optimized to obtain the optimal beamforming vector and Public Rate Allocation , the transmission matrix of the optimal RIS1 and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix ; S4, the optimal beamforming vector and Public Rate Allocation , the transmission matrix of the optimal RIS1 and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix Substitute into S3 and update the iteration number variable, and iterate until the iteration termination accuracy is reached. The iteration terminates and the optimal solution for the total user rate is obtained.

[0005] Furthermore, the multi-user communication system of cascaded RIS and RSMA includes: a base station BS, K near users , L remote user clusters , M and two intelligent reflective surfaces RIS1 and RIS2; The base station BS sends the superimposed signal of all users to nearby users and the intelligent reflective surface RIS1, which then reflects the received signal to the nearest user And transmit the signal to the remote user RIS1 then reflects the received signal to RIS2, which then reflects the signal to the remote user. And transmit the signal to the remote user .

[0006] Furthermore, the initial model of the multi-user communication system of cascaded RIS and RSMA is: in, , is the beamforming vector of the entire system, which contains the beamforming part of public information and each user's private information. is the beamforming vector of public information, , and Respectively, the nearest users Remote users And remote users beamforming vectors for private information; , Represents the resource allocation vector for different users in the system, where , and Indicates that it is used to allocate to nearby users Remote users And remote users The public rate portion of Indicates the minimum rate at which public information can be broadcast in the system. , and Respectively, the nearest users Remote users And remote users The decoding rate of public information; and are the reflection matrix and transmission matrix of RIS respectively, where , , and They are the reflection matrix of RIS1, the transmission matrix of RIS1, the reflection matrix of RIS2 and the transmission matrix of RIS2, j represents the imaginary unit, diag() represents the diagonalization operation of the vector, i is the module index, representing different RIS; is the total rate of all users, K is the nearest user The total number of remote users The total number of remote users Total number of represents the minimum rate requirement of each user, where Indicates the nearest user The minimum rate requirement, Remote User The minimum rate requirement, Remote User Minimum rate requirement; represents the total rate that each user can actually achieve, where Indicates the nearest user The total rate that can actually be achieved is Remote User The total rate that can actually be achieved is Remote User The total rate that can actually be achieved; is the maximum transmit power of the base station; represents the reflection phase angle of RIS1, represents the reflection phase value of the nth phase unit of RIS1, represents the transmission phase angle of RIS1, represents the transmission phase value of the nth phase unit of RIS1, represents the reflection phase angle of RIS2, represents the reflection phase value of the nth phase unit of RIS2, It represents the transmission phase angle of RIS2 and the transmission phase value of the nth phase unit of RIS2.

[0007] Furthermore, the total rate of the user The calculation formula is: in, Decoding public streams for users signal-to-noise ratio; For nearby users Decoding private streams signal-to-noise ratio; For remote users Decoding private streams signal-to-noise ratio; For remote users Decoding private streams signal-to-noise ratio; and BS to nearby users and The channel gain of , and They are To the nearest user Remote users and The channel gain of and They are To remote users And remote users The channel gain of the above channels all obey Rayleigh fading; represents the received noise variance, where Indicates the nearest user Receive noise variance, Remote User Receive noise variance, Remote User Receive noise variance; T represents the number of antennas in the base station, and N represents the number of units in the RIS; represents the decoding rate of public information by users, where , and Respectively, the nearest users Remote users And remote users The decoding rate of public information; represents the rate at which users decode private information, where , and Respectively, the nearest users Remote users And remote users The rate at which private information is decoded.

[0008] Further, in obtaining the optimal beamforming vector and Public Rate Allocation When the initial model of the multi-user communication system of cascaded RIS and RSMA is optimized based on convex second-order cone programming.

[0009] In obtaining the optimal RIS1 transmission matrix and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix When the initial model of the multi-user communication system of cascaded RIS and RSMA is optimized based on the rank-one constrained DC format and the step-by-step convexification method.

[0010] A multi-user communication system resource optimization system based on cascaded RIS and RSMA, including: Model building module: with the goal of maximizing the total rate of users, build the initial model of the multi-user communication system of cascaded RIS and RSMA; Parameter setting module: set the number of iterations, the iteration termination accuracy, and give the initial base station beamforming vector , Initial Public Rate Allocation , the initial RIS1 transmission matrix and the reflection matrix and the initial RIS2 transmission matrix and the reflection matrix ; Optimization module: Given a base station beamforming vector , Public Rate Allocation , RIS1 transmission matrix and the reflection matrix And the transmission matrix of RIS2 and the reflection matrix Under the condition of , the initial model of the multi-user communication system with cascaded RIS and RSMA is optimized to obtain the optimal beamforming vector and Public Rate Allocation , the transmission matrix of the optimal RIS1 and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix ; And, iterative module: the optimal beamforming vector and Public Rate Allocation , the transmission matrix of the optimal RIS1 and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix Substitute into the optimization module and update the iteration number variable, and iterate until the iteration termination accuracy is reached. The iteration is terminated and the optimal solution for the total user rate is obtained.

[0011] A computer storage medium stores a readable program, which can execute the above-mentioned multi-user communication system resource optimization method based on cascaded RIS and RSMA when the program is running.

[0012] 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 multi-user communication system resource optimization method based on cascaded RIS and RSMA.

[0013] A computer program product includes computer instructions, wherein the computer instructions instruct a computing device to execute operations corresponding to the above-mentioned multi-user communication system resource optimization method based on cascaded RIS and RSMA.

[0014] Beneficial effects of the present invention: 1. The multi-user communication system of the present invention comprises a base station equipped with T antennas, K near users ( , ), two distant user clusters (respectively and ) and two RIS smart reflectors (RIS1, RIS2) with N elements. They can communicate directly with each other, but since they cannot communicate directly with remote users, RIS-assisted communication is considered. and There are obstacles blocking the way. Unable to act as a relay to forward signals and It is not within the coverage of RIS1, so cascaded RIS is used to assist communication; the present invention studies the joint optimization of beamforming vectors, public rate allocation, and transmission matrix and reflection matrix of RIS1 and RIS2 to achieve the problem of maximizing user rate, and proposes an alternating optimization algorithm based on convex second-order cone programming, rank-one constrained DC format and gradual convexification method.

[0015] 2. The present invention proposes a resource optimization method for a multi-user communication system based on cascaded RIS and RSMA. The previously proposed RIS-assisted RSMA communication framework does not take into account the problem of user signal reception difficulties in multi-obstacle areas. The method proposed in the present invention can adapt to special application scenarios more flexibly and conveniently, and improve user rates. The present invention considers that the base station sends RSMA signals to three user clusters through beamforming, with the goal of maximizing the user rate under the constraints of the base station's maximum transmission power constraint and the user's minimum transmission power constraint, and jointly optimizes the beamforming vector, public rate allocation, and the transmission matrix and reflection matrix of RIS1 and RIS2.

[0016] 3. Since the proposed optimization problem is non-convex and difficult to solve, the present invention solves three sub-problems through convex second-order cone programming, rank-one constrained DC format and gradual convexification method; the four variables of the three sub-problems correspond to three blocks, namely, beamforming vector and common rate allocation, transmission matrix and reflection matrix of RIS1, transmission matrix and reflection matrix of RIS2; while keeping other block variables unchanged, the four sub-problems are solved through alternating optimization (AO) and SCA methods, and the optimization variables of the four sub-problems are alternately optimized; and the solution obtained in each iteration will be used as the input of the next iteration.

[0017] 4. The convergence of the algorithms of the convex second-order cone programming, rank-one constrained DC format and gradual convexification method 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 the non-orthogonal multiple access (NOMA) scheme, the time division multiple access (TDMA) scheme and the rate split multiple access (RSMA) scheme without rate allocation optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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.

[0019] Figure 1 It is a schematic diagram of the structure of a multi-user communication system cascading RIS and RSMA of the present invention; Figure 2 It is a flow chart of a multi-user communication system resource optimization method based on cascaded RIS and RSMA of the present invention; Figure 3 It is a curve diagram of the total rate of system users and the number of iterations of the scheme proposed by the present invention, the non-orthogonal multiple access scheme, the time division multiple access scheme and the scheme without rate allocation under rate division multiple access; Figure 4 It 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 rate allocation (FCR-RSMA) under rate division multiple access. DETAILED DESCRIPTION

[0020] 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.

[0021] Example 1 like Figure 1 As shown in the figure, the multi-user communication system of cascaded RIS and RSMA includes: a base station (BS), K nearby users ( , ), two remote user clusters (respectively and ) and two smart reflective surfaces (RIS1, RIS2); The base station BS is equipped with T antennas, all users are equipped with single antennas, and the number of units of RIS1 and RIS2 is N. The coverage radius of each RIS is , base station BS, near user And remote users The distance to the intelligent reflector RIS1 is less than , remote user The distance to the intelligent reflector RIS1 is greater than , remote user and The distance to the intelligent reflector RIS2 is less than , base station BS and nearby users The distance to the intelligent reflector RIS2 is greater than . Base station BS and nearby users They can communicate directly with each other, but due to the remote user and Direct communication is not possible, so RIS-assisted communication is considered, and then the user is close to And remote users There are obstacles blocking the user. Unable to act as a relay to forward signals and far from the user It is not within the coverage of the smart reflector RIS1, so a cascaded RIS is used to assist communication.

[0022] First, the base station BS sends the superimposed signal of all users to the nearest user and the intelligent reflective surface RIS1, which then reflects the received signal to the nearest user And transmit the signal to the remote user RIS1 then reflects the received signal to the intelligent reflector RIS2, which then reflects the signal to the remote user. And transmit the signal to the remote user .in News Share through RSMA and , ,in Refers to all users, including , and , Information shared by all users, Private information specific to each user, followed by public information , and Combine into a common message , using the codebook to encode it into a common stream and shared by all users. Use independent codebooks for private information , and Encode and pass it through a private stream , and In this scenario, the base station uses antenna beamforming to concentrate energy on nearby users, so the signal transmitted by the base station xfor: ; In the formula, is the beamforming vector of public information, , and Respectively, the nearest users Remote users And remote users The beamforming vector of private information, K is the user The total number of users The total number of users The total quantity.

[0023] Here the reflection phase matrix of RIS is defined as , the transmission phase matrix is , and are respectively represented as the phase shift of the nth element of the i-th RIS.

[0024] Example 2 Based on the multi-user communication system of cascading RIS and RSMA mentioned in Example 1, in this embodiment, a multi-user communication system resource optimization method based on cascading RIS and RSMA is proposed, such as Figure 2 As shown, the following steps are included: S1, with the goal of maximizing the total rate of users, construct the initial model of the multi-user communication system of cascaded RIS and RSMA; The initial model of the multi-user communication system of cascaded RIS and RSMA is: in, , is the beamforming vector of the entire system, which contains the beamforming part of public information and each user's private information. is the beamforming vector of public information, , and Respectively, the nearest users Remote users And remote users beamforming vectors for private information; , Represents the resource allocation vector for different users in the system, where , and Indicates that it is used to allocate to nearby users Remote users And remote users The public rate portion of Indicates the minimum rate at which public information can be broadcast in the system. , and Respectively, the nearest users Remote users And remote users The decoding rate of public information; and are the reflection matrix and transmission matrix of RIS respectively, where , , and They are the reflection matrix of RIS1, the transmission matrix of RIS1, the reflection matrix of RIS2 and the transmission matrix of RIS2, j represents the imaginary unit, diag() represents the diagonalization operation of the vector, i is the module index, representing different RIS; is the total rate of all users, K is the nearest user The total number of remote users The total number of remote users Total number of represents the minimum rate requirement of each user, where Indicates the nearest user The minimum rate requirement, Remote User The minimum rate requirement, Remote User Minimum rate requirement; represents the total rate that each user can actually achieve, where Indicates the nearest user The total rate that can actually be achieved is Remote User The total rate that can actually be achieved is Remote User The total rate that can actually be achieved; is the maximum transmit power of the base station; represents the reflection phase angle of RIS1, represents the reflection phase value of the nth phase unit of RIS1, represents the transmission phase angle of RIS1, represents the transmission phase value of the nth phase unit of RIS1, represents the reflection phase angle of RIS2, represents the reflection phase value of the nth phase unit of RIS2, It represents the transmission phase angle of RIS2 and the transmission phase value of the nth phase unit of RIS2.

[0025] Total rate of users The calculation formula is: in, Decoding public streams for users signal-to-noise ratio; For nearby users Decoding private streams signal-to-noise ratio; For remote users Decoding private streams signal-to-noise ratio; For remote users Decoding private streams signal-to-noise ratio; and BS to nearby users and The channel gain of , and They are To the nearest user Remote users and The channel gain of and They are To remote users And remote users The channel gain of the above channels all obey Rayleigh fading; represents the received noise variance, where Indicates the nearest user Receive noise variance, Remote User Receive noise variance, Remote User Receive noise variance; T represents the number of antennas in the base station, and N represents the number of units in the RIS; represents the decoding rate of public information by users, where , and Respectively, the nearest users Remote users And remote users The decoding rate of public information; represents the rate at which users decode private information, where , and Respectively, the nearest users Remote users And remote users The rate at which private information is decoded.

[0026] formula To ensure that system performance is determined by the worst link (or user) rate, Indicates the minimum capacity or rate of the system; formula Total capacity of the system At least the sum of the capacity requirements of each user must be met; formula Make sure the capacity is not negative; formula Limit the rate of each user At least the predefined minimum rate ; formula The total power of all signals in the system is limited to not exceed the maximum allowed power ; formula The phase angle of the phase matrix is ​​constrained, keeping the signal amplitude constant and allowing optimization through phase control.

[0027] S2, set the iteration number variable, iteration termination accuracy, and give the initial base station beamforming vector , Initial Public Rate Allocation , the initial RIS1 transmission matrix and the reflection matrix and the initial RIS2 transmission matrix and the reflection matrix ; S3, given the base station beamforming vector , Public Rate Allocation , RIS1 transmission matrix and the reflection matrix And the transmission matrix of RIS2 and the reflection matrix In the case of , based on convex second-order cone programming (SOCP), the initial model of the multi-user communication system with cascaded RIS and RSMA is optimized to obtain the optimal beamforming vector and Public Rate Allocation ,and and As input for the next iteration; Obtaining the optimal beamforming vector and power distribution The steps are: S31, given the transmission matrix of RIS1 and the reflection matrix And the transmission matrix of RIS2 and the reflection matrix In the case of , the optimization of beamforming and power allocation is studied, and the problem is transformed into: S32, due to middle, (1) ; (2) However, equation (2) is non-convex, so a new slack variable is introduced To represent the interference plus noise term of the private flow. Therefore, formula (2) can be transformed into: ; (3) ; (4) The same applies to users and This is also true, namely: ; (5) ; (6) S33, similarly, for , ; (7) ; (8) Introducing a new slack variable to represent the interference plus noise term of the common flow. Therefore: ; (9) ; (10) S34, there are still non-convex terms in equations (3) and (9), and Follow the general form ,in represents a complex set, represents the set of positive real numbers. To this end, the lower bound concave approximation is used to approximate the function f(u, v) at the points u (n), v(n) so as to solve the non-convex terms in an iterative manner. The non-convex terms in equations (3) and (9) can be approximated as follows: S35, using the above approximation, the subproblem can be transformed into: As you can see, the subproblem is a convex second-order cone problem (SOCP) and can be solved using any convex optimization solver such as YALIMP or CVX.

[0028] S4, given the base station beamforming vector , Public Rate Allocation , RIS1 transmission matrix and the reflection matrix And the transmission matrix of RIS2 and the reflection matrix In the case of rank-one constrained DC format and step-by-step convexification method, the initial model of the multi-user communication system with cascaded RIS and RSMA is optimized to obtain the transmission matrix of the optimal RIS1 and the reflection matrix , and and As input for the next iteration; Obtaining the optimal RIS1 transmission matrix and the reflection matrix The steps are: S41, next optimize the transmission matrix of RIS1 and the reflection matrix . Fixing other variables, the sub-problem can be expressed as: make , , , .

[0029] S42, for Decode private streams: ;but: ; (11) in , , , .

[0030] Similarly, for users Decode private streams: ; (12) in , , , .

[0031] For Users Decode private streams: ; (13) in , , , , .

[0032] S43, for The common decoding streams are: ; (14) For Users and The common decoding streams are: ; (15) S44, so the sub-problem can be transformed into: in, , , , t is the introduced auxiliary variable.

[0033] S45, since the first constraint is still non-convex, and applying the SDR method to solve the feasibility check problem in the AO method cannot guarantee the feasibility of a given solution. In order to solve the drawbacks of eliminating the rank-one constraint and to improve the performance degradation of the SDR technology, the DC format of the rank-one constraint is adopted. This format guarantees a feasible solution to the phase shift optimization problem. ,in and denote the nuclear norm and spectral norm of V respectively. Therefore, the subproblem can be restated as: When the objective of the problem is zero, a rank-one feasible solution can be obtained. Note that due to is a convex function, so the problem is still a non-convex optimization problem. Therefore, SCA can be applied to solve this problem in an iterative manner. In particular, by linearizing the convex terms, all that remains is to solve the following optimization problem.

[0034] in, is the solution obtained at iteration r−1, is the spectral norm at the point The sub-gradient at . express The inner product of , by iteratively solving the problem until the target is zero, an exact first-order solution of the phase shift matrix can be guaranteed.

[0035] S46, a possible stopping criterion for the problem is given by Given, where is a sufficiently small constant. After obtaining V by solving the problem, it is necessary to decompose it and use Cholesky decomposition to recover the phase shift value of the RIS1 element.

[0036] S5, given the base station beamforming vector , Public Rate Allocation , RIS1 transmission matrix and the reflection matrix And the transmission matrix of RIS2 and the reflection matrix In the case of rank-one constrained DC format and step-by-step convexification method, the initial model of the multi-user communication system of cascaded RIS and RSMA is optimized to obtain the optimal RIS2 transmission matrix and the reflection matrix , and and As input for the next iteration; Obtaining the optimal RIS2 transmission matrix and the reflection matrix The steps are: S51, next optimize the transmission matrix of RIS2 and the reflection matrix . Fixing other variables, the sub-problem can be expressed as: make , , , .

[0037] S52, for users Decode private streams: ; (16) in , .

[0038] For Users Decode private streams: ; (17) in , , .

[0039] S53, for users and The common decoding streams are: ; (18) S54, so the sub-problem can be transformed into: in, , , , is the introduced auxiliary variable.

[0040] S55, as described in S4, the optimization problem is finally transformed into a sub-problem: in, is the solution obtained at iteration r−1, is the spectral norm at the point The sub-gradient at . express The inner product of , by iteratively solving the problem until the target is zero, an exact first-order solution of the phase shift matrix can be guaranteed.

[0041] S56, a possible stopping criterion for the problem is given by Given, where is a sufficiently small constant. By solving the problem, we can get After that, it needs to be decomposed and the phase shift value of RIS2 element is recovered by Cholesky decomposition.

[0042] S6, the optimal beamforming vector , Public Rate Allocation , RIS1 transmission matrix and the reflection matrix And the transmission matrix of RIS2 and the reflection matrix Substitute into S3 and update the iteration count variable , repeat S3 to S5 until the iteration termination accuracy is reached, the iteration is terminated, and the optimal solution of the total user rate of the initial model of the multi-user communication system of cascaded RIS and RSMA is obtained; 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.

[0043] Table 1 Simulation parameters like Figure 3 As shown in the figure, the convergence of the total user rate of RSMA, NOMA, TDMA and FCR-RSMA with the increase of the number of iterations is shown, among which FCR-RSMA refers to the average distribution of the public rate of each user and is not used as an optimization variable. As can be seen from the figure, the RSMA scheme has the highest total user rate. As the number of iterations increases, the rate rises rapidly and tends to stabilize, eventually reaching about 110 bits / second / Hz. The FCR-RSMA scheme is second, with a rate stabilized at about 85 bits / second / Hz. The RIS-NOMA scheme has a moderate rate and finally stabilizes at about 60 bits / second / Hz. The RIS-TDMA scheme has the worst performance, with a rate stabilized at about 40 bits / second / Hz. It can be concluded that RSMA technology has significant advantages in interference management and resource allocation, and can effectively improve system performance, especially when combined with RIS to further enhance channel quality, and performs best. Although FCR-RSMA also uses RSMA technology, its performance is slightly inferior due to incomplete optimization of resource allocation.

[0044] like Figure 4 As shown in the figure, the comparison of different numbers of RIS units shows that when the number of RIS units is [4, 8, 16, 32, 64], the more units there are, the higher the average user rate is. This is because increasing the number of units in RIS (Reconfigurable Smart Surface) can significantly enhance the reflection and regulation capabilities of wireless signals, improve the coverage and strength of signals, and thus increase the total rate of all users in the system. Compared with traditional TDMA (Time Division Multiple Access) and NOMA (Non-Orthogonal Multiple Access), RSMA achieves more flexible interference management and resource allocation by dividing user information into public and private parts, and can more efficiently adapt to the channel conditions of different users, improve spectrum utilization and system capacity, and thus provide better performance in a multi-user environment.

[0045] Example 4 Based on the multi-user communication system resource optimization method based on cascaded RIS and RSMA proposed in Example 2, in this embodiment, a multi-user communication system resource optimization system based on cascaded RIS and RSMA is proposed, which specifically includes: Model building module: with the goal of maximizing the total rate of users, build the initial model of the multi-user communication system of cascaded RIS and RSMA; Parameter setting module: set the number of iterations, the iteration termination accuracy, and give the initial base station beamforming vector , Initial Public Rate Allocation , the transmission matrix of the initial RIS1 and the reflection matrix and the transmission matrix of the initial RIS2 and the reflection matrix ; Optimization module: Given a base station beamforming vector , Public Rate Allocation , RIS1 transmission matrix and the reflection matrix And the transmission matrix of RIS2 and the reflection matrix Under the condition of , the initial model of the multi-user communication system with cascaded RIS and RSMA is optimized to obtain the optimal beamforming vector and Public Rate Allocation , the transmission matrix of the optimal RIS1 and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix ; And, iterative module: the optimal beamforming vector , optimal public rate allocation , the transmission matrix of the optimal RIS1 and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix Substitute into the optimization module and update the number of iterations variable , iterate until the iteration termination accuracy is reached, the iteration is terminated, and the optimal solution for the total user rate is obtained.

[0046] The present application 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 box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing 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.

[0047] 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.

[0048] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0049] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0050] 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 multi-user communication system resource optimization method based on cascaded RIS and RSMA, characterized in that: The following steps are involved: S1, with the goal of maximizing the total rate of users, construct the initial model of the multi-user communication system of cascaded RIS and RSMA; S2, set the iteration number variable, iteration termination accuracy, and give the initial base station beamforming vector , Initial Public Rate Allocation , the initial RIS1 transmission matrix and the reflection matrix and the initial RIS2 transmission matrix and the reflection matrix ; S3, given the base station beamforming vector , Public Rate Allocation , RIS1 transmission matrix and the reflection matrix And the transmission matrix of RIS2 and the reflection matrix Under the condition of , the initial model of the multi-user communication system with cascaded RIS and RSMA is optimized to obtain the optimal beamforming vector and Public Rate Allocation , the transmission matrix of the optimal RIS1 and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix ; S4, the optimal beamforming vector and Public Rate Allocation , the transmission matrix of the optimal RIS1 and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix Substitute into S3 and update the iteration number variable, and iterate until the iteration termination accuracy is reached. The iteration terminates and the optimal solution for the total user rate is obtained.

2. The multi-user communication system resource optimization method based on cascaded RIS and RSMA according to claim 1, characterized in that: The multi-user communication system of cascaded RIS and RSMA includes: a base station BS, K nearby users , L remote user clusters , M and two intelligent reflective surfaces RIS1 and RIS2; The base station BS sends the superimposed signal of all users to nearby users and the intelligent reflective surface RIS1, which then reflects the received signal to the nearest user And transmit the signal to the remote user RIS1 then reflects the received signal to RIS2, which then reflects the signal to the remote user. And transmit the signal to the remote user .

3. The multi-user communication system resource optimization method based on cascaded RIS and RSMA according to claim 2, characterized in that: The initial model of the multi-user communication system cascaded RIS and RSMA is: in, , is the beamforming vector of the entire system, which contains the beamforming part of public information and each user's private information. is the beamforming vector of public information, , and Respectively, the nearest users Remote users And remote users beamforming vectors for private information; , Represents the resource allocation vector for different users in the system, where , and Indicates that it is used to allocate to nearby users Remote users And remote users The public rate portion of Indicates the minimum rate at which public information can be broadcast in the system. , and Respectively, the nearest users Remote users And remote users The decoding rate of public information; and are the reflection matrix and transmission matrix of RIS respectively, where , , and They are the reflection matrix of RIS1, the transmission matrix of RIS1, the reflection matrix of RIS2 and the transmission matrix of RIS2, j represents the imaginary unit, diag() represents the diagonalization operation of the vector, i is the module index, representing different RIS; is the total rate of all users, K is the nearest user The total number of remote users The total number of remote users Total number of represents the minimum rate requirement of each user, where Indicates the nearest user The minimum rate requirement, Remote User The minimum rate requirement, Remote User Minimum rate requirement; represents the total rate that each user can actually achieve, where Indicates the nearest user The total rate that can actually be achieved is Remote User The total rate that can actually be achieved is Remote User The total rate that can actually be achieved; is the maximum transmit power of the base station; represents the reflection phase angle of RIS1, represents the reflection phase value of the nth phase unit of RIS1, represents the transmission phase angle of RIS1, represents the transmission phase value of the nth phase unit of RIS1, represents the reflection phase angle of RIS2, represents the reflection phase value of the nth phase unit of RIS2, It represents the transmission phase angle of RIS2 and the transmission phase value of the nth phase unit of RIS2.

4. The multi-user communication system resource optimization method based on cascaded RIS and RSMA according to claim 3, characterized in that: Total rate of users The calculation formula is: in, Decoding public streams for users signal-to-noise ratio; For nearby users Decoding private streams signal-to-noise ratio; For remote users Decoding private streams signal-to-noise ratio; For remote users Decoding private streams signal-to-noise ratio; and Base station BS to nearby users and The channel gain of , and They are To the nearest user Remote users and The channel gain of and They are To remote users And remote users The channel gain of the above channels all obey Rayleigh fading; represents the received noise variance, where Indicates the nearest user Receive noise variance, Remote User Receive noise variance, Remote User Receive noise variance; T represents the number of antennas in the base station, and N represents the number of units in the RIS; represents the decoding rate of public information by users, where , and Respectively, the nearest users Remote users And remote users The decoding rate of public information; represents the rate at which users decode private information, where , and Respectively, the nearest users Remote users And remote users The rate at which private information is decoded.

5. The multi-user communication system resource optimization method based on cascaded RIS and RSMA according to claim 1, characterized in that: In obtaining the optimal beamforming vector and Public Rate Allocation When the initial model of the multi-user communication system of cascaded RIS and RSMA is optimized based on convex second-order cone programming.

6. The multi-user communication system resource optimization method based on cascaded RIS and RSMA according to claim 1, characterized in that: In obtaining the optimal RIS1 transmission matrix and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix When the initial model of the multi-user communication system of cascaded RIS and RSMA is optimized based on the rank-one constrained DC format and the step-by-step convexification method.

7. A multi-user communication system resource optimization system based on cascaded RIS and RSMA, characterized in that: include: Model building module: with the goal of maximizing the total rate of users, build the initial model of the multi-user communication system of cascaded RIS and RSMA; Parameter setting module: set the number of iterations, the iteration termination accuracy, and give the initial base station beamforming vector , Initial Public Rate Allocation , the initial RIS1 transmission matrix and the reflection matrix and the initial RIS2 transmission matrix and the reflection matrix ; Optimization module: Given a base station beamforming vector , Public Rate Allocation , RIS1 transmission matrix and the reflection matrix And the transmission matrix of RIS2 and the reflection matrix Under the condition of , the initial model of the multi-user communication system with cascaded RIS and RSMA is optimized to obtain the optimal beamforming vector and Public Rate Allocation , the transmission matrix of the optimal RIS1 and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix ; And, iterative module: the optimal beamforming vector and Public Rate Allocation , the transmission matrix of the optimal RIS1 and the reflection matrix And the optimal RIS2 transmission matrix and the reflection matrix Substitute into the optimization module and update the iteration number variable, and iterate until the iteration termination accuracy is reached. The iteration is terminated 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 multi-user communication system resource optimization method based on cascaded RIS and RSMA as 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 multi-user communication system resource optimization method based on cascaded 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 multi-user communication system resource optimization method based on cascaded RIS and RSMA as described in any one of claims 1-6.

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