Resource Optimization Method and System for Multi-User Communication System Based on Cascade RIS and RSMA

Abstract

The present invention discloses a resource optimization method and system for a multi-user communication system based on cascaded RIS and RSMA, belonging to the field of wireless communication; the method includes: S1, constructing an initial model; S2, setting initial iteration parameters; S3, optimizing the initial model under the given base station beamforming vector, common rate allocation, transmission matrix and reflection matrix of RIS1, and transmission matrix and reflection matrix of RIS2 to obtain the optimal beamforming vector and common rate allocation, the optimal transmission matrix and reflection matrix of RIS1, and the optimal transmission matrix and reflection matrix of RIS2; S4, substituting the optimal beamforming vector and common rate allocation, the optimal transmission matrix and reflection matrix of RIS1, and the optimal transmission matrix and reflection matrix of RIS2 into S3, and updating the iteration number variable until the iteration termination accuracy is reached, the iteration terminates, and the optimal solution of the total user rate is obtained.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication, and particularly relates to a resource optimization method and system for a multi-user communication system based on cascaded RIS and RSMA. Background Art

[0002] RSMA (Rate-Splitting Multiple Access) is an advanced multiple access technology that helps multiple users share the same spectrum resource while reducing interference between users by splitting the data of each user into common and private parts. RSMA performs well in multi-user interference management and spectral efficiency, and has attracted research interest especially in ultra-dense networks and heterogeneous networks. RIS (Reconfigurable Intelligent Surface) is a surface that can intelligently adjust the phase of the reflected signal, featuring low cost and low power consumption. It can overcome the path loss problem in traditional wireless communication by reflecting and enhancing the signal path. The application scenarios of RIS are extensive, including signal coverage enhancement, energy efficiency improvement, physical layer security, etc. While RSMA is effective in dealing with strong interference, when the channel condition is extremely poor (such as severe blockage or deep fading), relying solely on RSMA may not be able to solve all problems. Therefore, a resource optimization method for a multi-user communication system based on cascaded RIS and RSMA is proposed. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a resource optimization method and system for a multi-user communication 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:

[0005] A resource optimization method for a multi-user communication system based on cascaded RIS and RSMA includes the following steps:

[0006] S1, aiming at the maximum total rate of users, construct an initial model of a multi-user communication system with cascaded RIS and RSMA;

[0007] S2, set the iteration number variable and the iteration termination accuracy, and give the initial base station beamforming vector , the initial common rate allocation , the initial transmission matrix and reflection matrix of the first RIS, as well as the initial transmission matrix and reflection matrix of the second RIS;

[0008] S3, at the given base station beamforming vector , common rate allocation , the transmission matrix and reflection matrix and the transmission matrix of RIS2 and the reflection matrix Optimize the initial model of the cascaded RIS and RSMA multi - user communication system to obtain the optimal beamforming vector and the common rate allocation as well as the transmission matrix of the optimal RIS1 and the reflection matrix and the transmission matrix of the optimal RIS2 and the reflection matrix ;

[0009] S4. Substitute the optimal beamforming vector and the common rate allocation as well as the transmission matrix of the optimal RIS1 and the reflection matrix and the transmission matrix of the optimal RIS2 and the reflection matrix into S3, update the iteration - number variable, and perform iterations until the iteration termination accuracy is reached. Then the iteration terminates, and the optimal solution of the total user rate is obtained.

[0010] Furthermore, the multi - user communication system with cascaded RIS and RSMA includes: a base station BS, K near - users , L far - user clusters , M as well as two reconfigurable intelligent surfaces RIS1 and RIS2;

[0011] The base station BS sends the superimposed signals of all users to the near - users and the reconfigurable intelligent surface RIS1. The reconfigurable intelligent surface RIS1 then reflects the received signals to the near - users and transmits the signals to the far - users . The reconfigurable intelligent surface RIS1 then reflects the received signals to the reconfigurable intelligent surface RIS2, and then the reconfigurable intelligent surface RIS2 reflects the signals to the far - users and transmits the signals to the far - users .

[0012] Furthermore, the initial model of the multi - user communication system with cascaded RIS and RSMA is:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020] Among them, , is the beamforming vector of the entire system, which contains the beamforming parts of the common information and the private information of each user. is the beamforming vector of the common information. , and are the beamforming vectors of the private information of the near user , the far user and the far user respectively; , represents the resource allocation vector for different users in the system, where , and represent the common rate parts allocated to the near user , the far user and the far user respectively; represents the minimum rate that can be achieved by broadcasting the common information in the system. , and are the decoding rates of the near user , the far user and the far user for the common information respectively; and are the reflection matrix and transmission matrix of the RIS respectively, where , , and are the reflection matrix of RIS1, the transmission matrix of RIS1, the reflection matrix of RIS2, and the transmission matrix of RIS2 respectively. j represents the imaginary unit, and diag() represents the diagonalization operation on the vector. i is the module index, representing different RISs; is the total rate of all users. K is the total number of near users , L is the total number of far users , and M is the total number of far users ; represents the minimum rate requirement of each user, where represents the minimum rate requirement of the near user , represents the minimum rate requirement of the far user The minimum rate requirement, represents the minimum rate requirement of the far user ; represents the total rate that each user can actually achieve, where represents the total rate that the near user can actually achieve, represents the total rate that the far user can actually achieve, represents the total rate that the far user can actually achieve; is the maximum transmit power of the base station; represents the reflection phase angle of RIS1, representing the reflection phase value of the nth phase unit of RIS1, represents the transmission phase angle of RIS1, representing the transmission phase value of the nth phase unit of RIS1, represents the reflection phase angle of RIS2, representing the reflection phase value of the nth phase unit of RIS2, represents the transmission phase angle of RIS2, representing the transmission phase value of the nth phase unit of RIS2.

[0021] Furthermore, the total rate of the user is calculated as:

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031] where is the signal-to-noise ratio for the user to decode the common stream ; is the signal-to-noise ratio for the near user to decode the private stream ; is the signal-to-noise ratio for the far user to decode the private stream ; For the far user Decode the private stream Signal-to-noise ratio; and Are the channel gains from the BS to the near user and respectively; 、 and Are respectively to the near user 、far user and Channel gains; and Are respectively to the far user and far user Channel gains, and all the above channels follow Rayleigh fading; Represents the received noise variance, where Represents the received noise variance of the near user ; Represents the received noise variance of the far user ; Represents the received noise variance of the far user ; T represents the number of antennas of the base station, and N represents the number of cells of the RIS; Represents the decoding rate of the user for the common information, where 、 and Are respectively the decoding rates of the near user 、far user and far user for the common information; Represents the decoding rate of the user for the private information, where 、 and Are respectively the decoding rates of the near user 、far user and far user for the private information.

[0032] Furthermore, when obtaining the optimal beamforming vector and the common rate allocation , optimize the initial model of the multi-user communication system with cascaded RIS and RSMA based on convex second-order cone programming.

[0033] When obtaining the transmission matrix and the reflection matrix of the optimal RIS1, as well as the transmission matrix and the reflection matrix When, based on the rank-one constrained DC format and the successive convex approximation method, optimize the initial model of the multi-user communication system with cascaded RIS and RSMA.

[0034] A multi-user communication system resource optimization system based on cascaded RIS and RSMA, including:

[0035] Model construction module: With the goal of maximizing the total rate of users, construct the initial model of the multi-user communication system with cascaded RIS and RSMA;

[0036] Parameter setting module: Set the iteration number variable, the iteration termination accuracy, and give the initial base station beamforming vector , the initial common rate allocation , the initial transmission matrix of RIS1 and the reflection matrix as well as the initial transmission matrix of RIS2 and the reflection matrix ;

[0037] Optimization module: Given the base station beamforming vector , the common rate allocation , the transmission matrix of RIS1 and the reflection matrix as well as the transmission matrix of RIS2 and the reflection matrix , optimize the initial model of the multi-user communication system with cascaded RIS and RSMA to obtain the optimal beamforming vector and the common rate allocation , the optimal transmission matrix of RIS1 and the reflection matrix as well as the optimal transmission matrix of RIS2 and the reflection matrix ;

[0038] And, the iteration module: Substitute the optimal beamforming vector and the common rate allocation , the optimal transmission matrix of RIS1 and the reflection matrix as well as the optimal transmission matrix of RIS2 and the reflection matrix into the optimization module, update the iteration number variable, and perform iteration until the iteration termination accuracy is reached, then the iteration terminates and the optimal solution of the total user rate is obtained.

[0039] A computer storage medium stores a readable program that can execute the above-mentioned multi-user communication system resource optimization method based on cascaded RIS and RSMA when the program runs.

[0040] An electronic device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0041] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations corresponding to the above-mentioned resource optimization method for a multi-user communication system based on cascaded RIS and RSMA.

[0042] A computer program product, comprising computer instructions, and the computer instructions direct a computing device to execute the operations corresponding to the above-mentioned resource optimization method for a multi-user communication system based on cascaded RIS and RSMA.

[0043] Advantages of the present invention:

[0044] 1. The multi-user communication system of the present invention includes a base station equipped with T antennas, K near users ( , ), two far user clusters (respectively and ), and two RIS intelligent reflecting surfaces (RIS1, RIS2) equipped with N component quantities. The base station can communicate directly with the near users . However, since it cannot communicate directly with the far users, RIS-assisted communication is considered. Moreover, due to the obstacle blocking between and , cannot act as a relay to forward signals and is not within the coverage of RIS1. Therefore, cascaded RIS is adopted to assist communication. The present invention studies the joint optimization of the beamforming vector, common rate allocation, and the transmission matrix and reflection matrix of RIS1 and RIS2 to solve the problem of maximizing the user rate, and proposes an alternating optimization algorithm based on convex second-order cone programming, rank-one constraint DC format, and successive convex approximation method.

[0045] 2. The present invention proposes a resource optimization method for a multi-user communication system based on cascaded RIS and RSMA. The previously proposed framework for RIS-assisted RSMA communication does not consider the problem of difficult signal reception for users in multi-obstacle areas. The method proposed by the present invention can more flexibly and conveniently adapt to special application scenarios and improve the user rate. The present invention considers that the base station sends RSMA signals to three user clusters through beamforming, and aims to maximize the user rate under the constraints of the maximum transmit power constraint of the base station and the minimum transmit power constraint of the users, and jointly optimizes the beamforming vector, common rate allocation, and the transmission matrix and reflection matrix of RIS1 and RIS2.

[0046] 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, DC format with rank-one constraint, and successive convex approximation method. The four variables of the three sub-problems respectively correspond to three blocks, namely the beamforming vector and common rate allocation, the transmission matrix and reflection matrix of RIS1, and the transmission matrix and reflection matrix of RIS2. While keeping the other block variables unchanged, the four sub-problems are solved by the 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 for the next iteration.

[0047] 4. The convergence of the algorithms of the convex second-order cone programming, DC format with rank-one constraint, and successive convex approximation method proposed by the present invention can be guaranteed, and the required complexity is relatively low. The simulation results show the user rates in different scenarios. Compared with the existing schemes, such as the non-orthogonal multiple access (NOMA) scheme, time division multiple access (TDMA) scheme, and the scheme without rate allocation optimization under rate splitting multiple access (RSMA), etc., the performance is significantly improved by using the proposed algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a schematic structural diagram of a multi-user communication system with cascaded RIS and RSMA of the present invention;

[0050] Figure 2 It is a flowchart of a resource optimization method for a multi-user communication system based on cascaded RIS and RSMA of the present invention;

[0051] Figure 3 It is a curve graph of the total system user rate versus the number of iterations for 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 splitting multiple access;

[0052] Figure 4 It is a curve graph of the relationship between 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 (FCR-RSMA) under rate splitting multiple access; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0054] Embodiment 1

[0055] As Figure 1 shown, a multi-user communication system with cascaded RIS and RSMA includes: a base station (BS), K near users ( , ), two far-user clusters ( and respectively) and two intelligent reflecting surfaces (RIS1, RIS2);

[0056] Among them, 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 , and the distances from the base station BS, the near user and the far user to the intelligent reflecting surface RIS1 are less than , and the distance from the far user to the intelligent reflecting surface RIS1 is greater than . The distances from the far users and to the intelligent reflecting surface RIS2 are less than , and the distances from the base station BS and the near user to the intelligent reflecting surface RIS2 are greater than . The base station BS can directly communicate with the near user . However, since it cannot directly communicate with the far users and , RIS-assisted communication is considered. Moreover, due to obstacles between the near user and the far user , the near user cannot act as a relay to forward signals, and the far user is not within the coverage range of the intelligent reflecting surface RIS1. Therefore, cascaded RIS is adopted to assist communication.

[0057] First, the base station BS sends the superimposed signals of all users to the near user and the intelligent reflecting surface RIS1, and the intelligent reflecting surface RIS1 then reflects the received signals to the near user and transmits the signals to the far user , RIS1 then reflects the received signal to the intelligent reflecting surface RIS2, and then RIS2 reflects the signal to the far user. And transmits the signal to the far user. . Among them The message of Is divided by RSMA into And , , where Refers to all users, including , And , Is the information shared by all users, Is the private information unique to each user. Subsequently, the common message , And Are combined into a common message , encoded into the common stream Using a codebook and shared by all users. The private information , And Is encoded using an independent codebook and transmitted through the private streams , And . In this scenario, the base station uses antenna beamforming to concentrate the energy and transmit it to the near user. Then the signal x Transmitted by the base station is:

[0058] ;

[0059] In the formula, Is the beamforming vector of the common information, , And Are the beamforming vectors of the private information of the near user , the far user And the far user respectively. K is the total number of users , L is the total number of users , and M is the total number of users .

[0060] Here, the reflection phase matrix of the RIS is defined as , and the transmission phase matrix is , And respectively represent the phase shifts of the nth element of the ith RIS.

[0061] Embodiment 2

[0062] Based on the multi-user communication system with cascaded RIS and RSMA proposed in Embodiment 1, in this embodiment, a resource optimization method for the multi-user communication system with cascaded RIS and RSMA is proposed. As Figure 2 shown, it includes the following steps:

[0063] S1. With the goal of maximizing the total rate of users, construct an initial model of the multi-user communication system with cascaded RIS and RSMA;

[0064] The initial model of the multi-user communication system with cascaded RIS and RSMA is:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] Among them, , is the beamforming vector of the entire system, which contains the beamforming parts of the common information and the private information of each user. is the beamforming vector of the common information. , and are the beamforming vectors of the private information of the near user , the far user and the far user respectively; , represents the resource allocation vector for different users in the system, where , and represent the common rate parts allocated to the near user , the far user and the far user respectively; represents the minimum rate that can be achieved by broadcasting common information in the system. , and are the decoding rates of the near user , the far user and the far user for the common information respectively; and are the reflection matrix and transmission matrix of the RIS, respectively, where 、 、 and are the reflection matrix of RIS1, the transmission matrix of RIS1, the reflection matrix of RIS2, and the transmission matrix of RIS2, respectively. j represents the imaginary unit, diag() represents the diagonalization operation on a vector, and i is the module index, representing different RISs; is the total rate of all users, K is the total number of near users and L is the total number of far users and M is the total number of far users ; represents the minimum rate requirement of each user, where represents the minimum rate requirement of near user , represents the minimum rate requirement of far user , represents the minimum rate requirement of far user ; represents the total rate that each user can actually achieve, where represents the total rate that near user can actually achieve, represents the total rate that far user can actually achieve, represents the total rate that far user can actually achieve; is the maximum transmit power of the base station; represents the reflection phase angle of RIS1, representing the reflection phase value of the nth phase unit of RIS1, represents the transmission phase angle of RIS1, representing the transmission phase value of the nth phase unit of RIS1, represents the reflection phase angle of RIS2, representing the reflection phase value of the nth phase unit of RIS2, represents the transmission phase angle of RIS2, representing the transmission phase value of the nth phase unit of RIS2.

[0073] The total rate of the user is calculated as:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] wherein, is the signal-to-noise ratio for the user to decode the common stream ; is the signal-to-noise ratio for the near user to decode the private stream ; is the signal-to-noise ratio for the far user to decode the private stream ; is the signal-to-noise ratio for the far user to decode the private stream ; and are the channel gains from the BS to the near user and respectively; , and are the channel gains from to the near user , the far user and respectively; and are the channel gains from to the far user and the far user respectively, and all the above channels follow Rayleigh fading; represents the received noise variance, where represents the received noise variance of the near user , represents the received noise variance of the far user , represents the received noise variance of the far user ; T represents the number of antennas of the base station, and N represents the number of units of the RIS; represents the decoding rate of the user for the common information, where , and are the decoding rates of the near user , the far user and the far user for the common information respectively; denotes the decoding rate of the private information for the user, where , and are the decoding rates of the private information for the near user , far user and far user respectively.

[0084] The formula is to ensure that the system performance is determined by the rate of the worst link (or user), denotes the minimum capacity or rate of the system;

[0085] The formula The total capacity of the system should at least meet the sum of the capacity requirements of each user;

[0086] The formula ensures that the capacity is not negative;

[0087] The formula limits the rate of each user to at least meet the predefined minimum rate ;

[0088] The formula limits the total power of all signals in the system not to exceed the maximum allowable power ;

[0089] The formula limits the phase angle of the phase matrix, keeps the signal amplitude constant, and allows optimization through phase control.

[0090] S2, set the iteration number variable, iteration termination accuracy, and given the initial base station beamforming vector , initial common rate allocation , initial transmission matrix of RIS1 and reflection matrix as well as the initial transmission matrix of RIS2 and reflection matrix ;

[0091] S3, in the case of the given base station beamforming vector , common rate allocation , transmission matrix of RIS1 and reflection matrix as well as the transmission matrix of RIS2 and reflection matrix , based on convex second-order cone programming (SOCP), optimize the initial model of the cascaded RIS and RSMA multi-user communication system, and obtain the optimal beamforming vector and common rate allocation , and and as the input for the next iteration;

[0092] Obtain the optimal beamforming vector and power allocation The steps are as follows:

[0093] S31. Given the transmission matrix and reflection matrix of RIS1, as well as the transmission matrix and reflection matrix of RIS2, study the optimization of beamforming and power allocation. At this time, the problem is transformed into:

[0094]

[0095] S32. Since in , (1)

[0096] ; (2)

[0097] However, equation (2) is non-convex. Therefore, introduce a new slack variable to represent the interference plus noise term of the private stream. Thus, equation (2) can be transformed into:

[0098] ; (3)

[0099] ; (4)

[0100] Similarly, for users and it is the same, that is:

[0101] ; (5)

[0102] ; (6)

[0103] S33. Similarly, for ,

[0104] ; (7)

[0105] ; (8)

[0106] Introduce a new slack variable to represent the interference plus noise term of the common stream. Therefore:

[0107] ; (9)

[0108] ; (10)

[0109] In S34, there are still non-convex terms in Equation (3) and Equation (9). and Follow the general form , where represents the set of complex numbers, represents the set of positive real numbers. Therefore, a lower-bound concave approximation is used to approximate the function f(u, v) at points u(n) and v(n) in order to solve the non-convex terms iteratively. The non-convex terms in Equation (3) and Equation (9) can be approximated as follows:

[0110]

[0111] In S35, using the above approximation, the sub-problem can be transformed into:

[0112]

[0113] It can be seen that the sub-problem is a convex second-order cone problem (SOCP), and any convex optimization solver (such as YALIMP or CVX) can be used to solve it.

[0114] In S4, given the base station beamforming vector , the common rate allocation , the transmission matrix and the reflection matrix of RIS1, and the reflection matrix of RIS2, and the reflection matrix , and and as the input for the next iteration;

[0115] The steps to obtain the optimal transmission matrix and the reflection matrix of RIS1 are as follows:

[0116] In S41, next, optimize the transmission matrix and the reflection matrix of RIS1. Fixing other variables, the sub-problem at this time can be expressed as:

[0117]

[0118] Let , , , .

[0119] S42. For decoding the private stream, there is:

[0120] ; then: ; (11)

[0121] where , , , .

[0122] Similarly, for user decoding the private stream, there is:

[0123] ; (12)

[0124] where , , , .

[0125] For user decoding the private stream, there is:

[0126] ; (13)

[0127] where , , , , .

[0128] S43. For decoding the public stream, there is:

[0129] ; (14)

[0130] For users and decoding the public stream, there is:

[0131] ; (15)

[0132] S44. Therefore, the sub - problem can be transformed into:

[0133]

[0134] where, , , , t is an introduced auxiliary variable.

[0135] S45. Since the first constraint is still non-convex and applying the SDR method to solve the feasibility check problem in the AO method does not guarantee the feasibility of the given solution. To address the drawbacks brought about by eliminating the rank-one constraint and aiming at degrading to improve the performance of the SDR technique, the DC format with a rank-one constraint is adopted. This format guarantees a feasible solution to the phase shift optimization problem. Since , where and represent the nuclear norm and spectral norm of V respectively. Therefore, the sub-problem can be reformulated as:

[0136]

[0137] When the objective of this problem is zero, a rank-one feasible solution can be obtained. Note that since is a convex function, this problem is still a non-convex optimization problem. Therefore, SCA can be applied to solve this problem iteratively. In particular, by linearizing the convex term, all that remains is to solve the following optimization problem.

[0138]

[0139] where, is the solution obtained at iteration r−1, is the sub-gradient of the spectral norm at the point . denotes the inner product of, given the initial value , by iteratively solving the problem until the objective is zero, an exact first-order solution of the phase shift matrix can be guaranteed.

[0140] S46. A possible stopping criterion for the problem is given by , where is a sufficiently small constant. After obtaining V by solving the problem, it needs to be decomposed, and the Cholesky decomposition is used to recover the phase shift values of the RIS1 elements.

[0141] S5. Given the base station beamforming vector , the common rate allocation , the transmission matrix and reflection matrix of RIS1, and the transmission matrix and reflection matrix of RIS2, based on the DC format with a rank-one constraint and the sequential convex approximation method, optimize the initial model of the multi-user communication system with cascaded RIS and RSMA, obtain the optimal transmission matrix and reflection matrix of RIS2, and set and As the input for the next iteration;

[0142] Obtain the optimal transmission matrix of RIS2 and reflection matrix The steps are as follows:

[0143] S51. Next, optimize the transmission matrix of RIS2 and reflection matrix . Fix other variables. The sub-problem at this time can be expressed as:

[0144]

[0145] Let , , , .

[0146] S52. For user to decode the private stream, we have:

[0147] ; (16)

[0148] where , .

[0149] For user to decode the private stream, we have:

[0150] ; (17)

[0151] where , , .

[0152] S53. For users and to decode the common stream, we have:

[0153] ; (18)

[0154] S54. Therefore, the sub-problem can be transformed into:

[0155]

[0156] where, , , , are the introduced auxiliary variables.

[0157] S55. As described in S4, this optimization problem is finally transformed into a sub-problem:

[0158]

[0159] Among them, is the solution obtained at iteration r - 1, is the subgradient of the spectral norm at the point . denotes the inner product of, given the initial value , by iteratively solving the problem until the objective is zero, an exact first-order solution of the phase shift matrix can be guaranteed.

[0160] S56, a possible stopping criterion for the problem is given by , where is a sufficiently small constant. After obtaining by solving the problem, it needs to be decomposed, and the Cholesky decomposition is used to recover the phase shift values of the RIS2 elements.

[0161] S6, substituting the optimal beamforming vector , the common rate allocation , the transmission matrix of RIS1 and the reflection matrix as well as the transmission matrix and the reflection matrix of RIS2 into S3, and updating the iteration number variable , repeating S3 to S5 until the iteration termination accuracy is reached, the iteration terminates, and the optimal solution of the total rate of the users in the initial model of the multi-user communication system with cascaded RIS and RSMA is obtained;

[0162] Embodiment 3

[0163] In this embodiment, the solution of the present invention is described in detail in combination with a specific embodiment. MATLAB software is used to simulate the specific embodiment. The specific parameters are set as shown in Table 1.

[0164] Table 1 Simulation Parameter Table

[0165]

[0166] As Figure 3As shown, the convergence of the total user rate of RSMA, NOMA, TDMA, and FCR-RSMA with the increase in the number of iterations is presented. Here, FCR-RSMA means that the common rate of each user is evenly distributed and is not used as an optimization variable. It can be seen from the figure that the RSMA scheme has the highest total user rate. As the number of iterations increases, the rate rises rapidly and tends to be stable, finally reaching about 110 bits per second per hertz. The FCR-RSMA scheme comes second, with the rate stabilizing at about 85 bits per second per hertz. The RIS-NOMA scheme has a moderate rate, finally stabilizing at about 60 bits per second per hertz. The RIS-TDMA scheme has the worst performance, with the rate stabilizing at about 40 bits per second per hertz. From this, it can be obtained that the RSMA technology has significant advantages in interference management and resource allocation, can effectively improve the system performance. Especially when combined with RIS, it further enhances the channel quality and shows the best performance. Although FCR-RSMA also uses the RSMA technology, due to the incomplete optimization of resource allocation, its performance is slightly inferior.

[0167] As Figure 4 shown, by comparing different numbers of RIS units, it can be observed that among the values of the number of RIS units being [4, 8, 16, 32, 64], the more the number of units, the greater the average user rate. This is because increasing the number of RIS (reconfigurable intelligent surface) units can significantly enhance the reflection and regulation ability of wireless signals, improve the signal coverage and intensity, thereby increasing the total rate of all users in the system. Compared with traditional TDMA (time division multiple access) and NOMA (non-orthogonal multiple access), RSMA divides user information into common and private parts, realizes more flexible interference management and resource allocation, can more efficiently adapt to the channel conditions of different users, improve the spectrum utilization rate and system capacity, and thus provides better performance in a multi-user environment.

[0168] Embodiment 4

[0169] Based on the multi-user communication system resource optimization method based on cascaded RIS and RSMA proposed in Embodiment 2, in this embodiment, a multi-user communication system resource optimization system based on cascaded RIS and RSMA is proposed, specifically including:

[0170] Model construction module: Taking the maximum total rate of users as the goal, constructing an initial model of a multi-user communication system with cascaded RIS and RSMA;

[0171] Parameter setting module: Setting the number of iteration variables, iteration termination accuracy, and given the initial base station beamforming vector 、initial common rate allocation 、initial transmission matrix of RIS1 and reflection matrix as well as the initial transmission matrix of RIS2 and reflection matrix ;

[0172] Optimization module: Given the base station beamforming vector , common rate allocation , the transmission matrix of RIS1 and reflection matrix as well as the transmission matrix of RIS2 and reflection matrix , optimize the initial model of the multi-user communication system with cascaded RIS and RSMA to obtain the optimal beamforming vector and common rate allocation , the optimal transmission matrix of RIS1 and reflection matrix as well as the optimal transmission matrix of RIS2 and reflection matrix ;

[0173] And, iteration module: Substitute the optimal beamforming vector , the optimal common rate allocation , the optimal transmission matrix of RIS1 and reflection matrix as well as the optimal transmission matrix of RIS2 and reflection matrix into the optimization module and update the iteration count variable , and perform iterations until the iteration termination accuracy is reached, at which point the iteration terminates and the optimal solution of the total user rate is obtained.

[0174] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one Figure 1 one or more flows and / or blocks Figure 1 one or more blocks

[0175] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one Figure 1 one or more flows and / or blocksFigure 1 The functions specified in one or more boxes.

[0176] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes.

[0177] In the description of this specification, the descriptions with reference to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0178] The above has shown and described 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 by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and all these changes and improvements fall within the scope of the present invention claimed.

Claims

1. Resource optimization method for a multi-user communication system based on cascaded RIS and RSMA, characterized in that Including the following steps: S1. With the goal of maximizing the total rate of users, construct an initial model of a multi-user communication system with cascaded RIS and RSMA; S2, set the iteration number variable and the iteration termination precision, and give the initial base station beamforming vector , the initial common rate allocation , the initial transmission matrix of RIS1 and the reflection matrix and the initial transmission matrix of RIS2 and the reflection matrix ; S3. Given the base station beamforming vector , the common rate allocation , the transmission matrix and reflection matrix of RIS1, and the transmission matrix and reflection matrix of RIS2, optimize the initial model of the multi-user communication system with cascaded RIS and RSMA to obtain the optimal beamforming vector and common rate allocation , the optimal transmission matrix and reflection matrix of RIS1, and the optimal transmission matrix and reflection matrix ; S4, substitute the optimal beamforming vector and the common rate allocation , the transmission matrix of the optimal RIS1 and the reflection matrix as well as the transmission matrix of the optimal RIS2 and the reflection matrix into S3, update the iteration number variable, and perform iterations until the iteration termination accuracy is reached. The iteration terminates, and the optimal solution of the total user rate is obtained; The initial model of the multi-user communication system with cascaded RIS and RSMA is: Among them, , is the beamforming vector of the entire system, which contains the beamforming parts of the common information and the private information of each user, is the beamforming vector of the common information, , and are the beamforming vectors of the private information of the near user , the far user and the far user respectively; , where , and represent the common rate parts allocated to the near user , the far user and the far user ; represents the minimum rate that can be achieved for broadcasting common information in the system, , and are the decoding rates of the near user , the far user and the far user for the common information respectively; and are the reflection matrix and transmission matrix of the RIS respectively, where , , and are the reflection matrix of RIS1, the transmission matrix of RIS1, the reflection matrix of RIS2 and the transmission matrix of RIS2 respectively. j represents the imaginary unit, diag() represents the diagonalization operation on the vector, and i is the module index, representing different RISs; is the total rate of all users. K is the total number of near users , L is the total number of far users , and M is the total number of far users ; represents the minimum rate requirement of each user, where represents the minimum rate requirement of the near user , represents the minimum rate requirement of the far user , represents the minimum rate requirement of the far user ; represents the total rate that each user can actually achieve, where represents the total rate that the near user can actually achieve, represents the total rate that the far user The total rate that can actually be achieved Indicates the far user The total rate that can actually be achieved; Is the maximum transmit power of the base station; Indicates the reflection phase angle of RIS1, representing the reflection phase value of the nth phase unit of RIS1, Indicates the transmission phase angle of RIS1, representing the transmission phase value of the nth phase unit of RIS1, Indicates the reflection phase angle of RIS2, representing the reflection phase value of the nth phase unit of RIS2, Indicates the transmission phase angle of RIS2, representing the transmission phase value of the nth phase unit of RIS2; N represents the number of units of the RIS.

2. The resource optimization method for a multi-user communication system based on cascaded RIS and RSMA according to claim 1, wherein The multi-user communication system that cascades RIS and RSMA includes: a base station BS, K near users , L far-user clusters , M and two intelligent reflecting surfaces RIS1 and RIS2; The base station BS sends the superimposed signals of all users to the near user and the intelligent reflecting surface RIS1, and the intelligent reflecting surface RIS1 then reflects the received signal to the near user and transmits the signal to the far user , the intelligent reflecting surface RIS1 then reflects the received signal to the intelligent reflecting surface RIS2, and then the intelligent reflecting surface RIS2 reflects the signal to the far user and transmits the signal to the far user .

3. The resource optimization method for a multi-user communication system based on cascaded RIS and RSMA according to claim 2, wherein Total rate of the user The calculation formula is as follows: wherein, is the signal-to-noise ratio for the user to decode the common stream ; is the signal-to-noise ratio for the near user to decode the private stream ; is the signal-to-noise ratio for the far user to decode the private stream ; is the signal-to-noise ratio for the far user to decode the private stream ; and are the channel gains from the base station BS to the near user and respectively; , and are the channel gains from to the near user , the far user and respectively; and are the channel gains from to the far user and the far user respectively. All the above channels follow Rayleigh fading; represents the received noise variance, where represents the received noise variance of the near user , represents the received noise variance of the far user , represents the received noise variance of the far user ; T represents the number of antennas of the base station, and N represents the number of units of the RIS; represents the decoding rate of the user for the common information, where , and are the decoding rates of the near user , the far user and the far user for the common information respectively; represents the decoding rate of the user for the private information, where , and are the decoding rates of the near user , the far user and the far user for the private information respectively.

4. The resource optimization method for a multi-user communication system based on cascaded RIS and RSMA according to claim 1, characterized in that When obtaining the optimal beamforming vector and common rate allocation Based on convex second-order cone programming, the initial model of the multi-user communication system with cascaded RIS and RSMA is optimized.

5. The resource optimization method for a multi-user communication system based on cascaded RIS and RSMA according to claim 1, wherein When obtaining the transmission matrix and reflection matrix of the optimal RIS1, and the transmission matrix and reflection matrix of the optimal RIS2, the initial model of the multi-user communication system with cascaded RIS and RSMA is optimized based on the rank-one constrained DC format and the successive convex approximation method.

6. A resource optimization system for a multi-user communication system based on cascaded RIS and RSMA, characterized in that, Including: Model construction module: With the goal of maximizing the total rate of users, construct an initial model of a multi-user communication system with cascaded RIS and RSMA; Parameter setting module: Set the iteration number variable, iteration termination accuracy, and give the initial base station beamforming vector , initial common rate allocation , initial transmission matrix of RIS1 and reflection matrix as well as the initial transmission matrix of RIS2 and reflection matrix ; Optimization module: Given the base station beamforming vector , common rate allocation , transmission matrix of RIS1 and reflection matrix as well as the transmission matrix of RIS2 and reflection matrix , optimize the initial model of the multi-user communication system with cascaded RIS and RSMA to obtain the optimal beamforming vector and common rate allocation , optimal transmission matrix of RIS1 and reflection matrix as well as the optimal transmission matrix of RIS2 and reflection matrix ; And, an iterative module: substituting the optimal beamforming vector and the common rate allocation , the transmission matrix of the optimal RIS1 and the reflection matrix as well as the transmission matrix of the optimal RIS2 and the reflection matrix into the optimization module, updating the iteration number variable, and performing iterations until the iteration termination accuracy is reached, at which point the iteration terminates and the optimal solution of the total user rate is obtained; The initial model of the multi-user communication system with cascaded RIS and RSMA is: Among them, , is the beamforming vector of the entire system, which contains the beamforming parts of the common information and the private information of each user, is the beamforming vector of the common information, , and are the beamforming vectors of the private information of the near user , the far user and the far user respectively; , where , and represent the common rate parts allocated to the near user , the far user and the far user ; represents the minimum rate that can be achieved by broadcasting common information in the system, , and are the decoding rates of the near user , the far user and the far user for the common information respectively; and are the reflection matrix and transmission matrix of the RIS respectively, where , , and are the reflection matrix of RIS1, the transmission matrix of RIS1, the reflection matrix of RIS2 and the transmission matrix of RIS2 respectively. j represents the imaginary unit, diag() represents the diagonalization operation on the vector, and i is the module index, representing different RISs; is the total rate of all users, K is the total number of near users , L is the total number of far users , and M is the total number of far users ; represents the minimum rate requirement of each user, where represents the minimum rate requirement of the near user , represents the minimum rate requirement of the far user , represents the minimum rate requirement of the far user ; represents the total rate that each user can actually achieve, where represents the total rate that the near user can actually achieve, represents the total rate that the far user The total rate that can actually be achieved Indicates the far user The total rate that can actually be achieved; Is the maximum transmit power of the base station; Indicates the reflection phase angle of RIS1, representing the reflection phase value of the nth phase unit of RIS1, Indicates the transmission phase angle of RIS1, representing the transmission phase value of the nth phase unit of RIS1, Indicates the reflection phase angle of RIS2, representing the reflection phase value of the nth phase unit of RIS2, Indicates the transmission phase angle of RIS2, representing the transmission phase value of the nth phase unit of RIS2; N represents the number of units of RIS.

7. A computer storage medium stores a readable program, characterized in that, When the program runs, it can execute the multi-user communication system resource optimization method based on cascaded RIS and RSMA described in any one of claims 1-5.

8. An electronic device, characterized in that, Including: A processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete communication with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations corresponding to the multi-user communication system resource optimization method based on cascaded RIS and RSMA described in any one of claims 1-5.

9. A computer program product comprising computer instructions, characterized in that, The computer instruction instructs the computing device to execute the operations corresponding to the multi-user communication system resource optimization method based on cascaded RIS and RSMA described in any one of claims 1-5.

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