A method, system and medium for maximizing communication rate based on the MM algorithm

Abstract

The present invention discloses a method, system and medium for maximizing communication rate based on the MM algorithm, belonging to the field of wireless communication technology. The method includes: S1: In the multi-user system construction stage, a multiple-input single-output URLLC system is constructed with the assistance of intelligent reflecting surfaces; S2: In the stage of constructing the system sum rate maximization problem, according to the multiple-input single-output URLLC system, the packet decoding error probability of each single-antenna user, the precoding vector at the BS, and the distributed passive beamforming with discrete phase shifts are jointly optimized to obtain the system sum rate maximization problem; S3: In the stage of solving the system sum rate maximization problem, the MM algorithm is used to transform the objective function of the system sum rate maximization problem, and the AO algorithm is used to solve the optimization variables. Finally, the optimal solution of the system parameters is obtained through iteration. The complex optimization problem is simplified by the iterative approximation method, and the closed-form solution expression of the optimization variable sub-problem is effectively obtained.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular, to a method, system, and medium for maximizing communication rate based on the MM algorithm. Background Art

[0002] The performance optimization of wireless communication systems is one of the core issues in the modern communication field. Especially in a multi-user environment, how to effectively improve the total rate of the system, that is, the sum of the data transmission rates of all users, has become a research hotspot. In existing wireless communication systems, beamforming technology and precoding technology are widely used in signal transmission and reception to improve communication quality and rate. However, the optimization problems of these technologies often involve complex mathematical models and high-dimensional optimization variables, making the solution process extremely difficult.

[0003] Traditionally, common means to solve such optimization problems include convex optimization methods and heuristic algorithms. Although convex optimization methods can guarantee finding the global optimal solution, their computational complexity is relatively high and they are not applicable to communication systems with high real-time requirements. Heuristic algorithms such as genetic algorithms and particle swarm optimization, although having a relatively fast calculation speed, often can only obtain approximate solutions, and the quality of the solutions is greatly affected by parameter settings and initial conditions.

[0004] How to design efficient iterative algorithms, and how to handle problems of variable coupling and algorithm convergence, still remain technical problems to be urgently solved. In addition, some existing algorithms such as the SCA technology, although reducing the computational complexity to a certain extent, still have a large amount of calculation when dealing with large-scale optimization problems, which is not conducive to practical applications. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method, system, and medium for maximizing communication rate based on the MM algorithm.

[0006] The purpose of the present invention is achieved through the following technical solutions: In the first aspect of the present invention, there is provided: A method for maximizing communication rate based on the MM algorithm, characterized in that it includes the following steps:

[0007] S1: In the multi-user system construction stage, a multi-input single-output URLLC system is constructed with the assistance of an intelligent reflecting surface;

[0008] S2: In the system sum rate maximization problem construction stage, according to the multi-input single-output URLLC system, the packet decoding error probability of each single-antenna user, the precoding vector at the BS, and distributed passive beamforming with discrete phase shifts are jointly optimized to obtain the system sum rate maximization problem;

[0009] S3: In the stage of solving the system sum rate maximization problem, the MM algorithm is used to transform the objective function of the system sum rate maximization problem, and the AO algorithm is used to solve the optimization variables. Finally, the optimal solution of the system parameters is obtained through iteration.

[0010] Preferably, the multiple-input single-output URLLC system includes: K single-antenna users, N antenna base stations, R distributed intelligent reflecting surfaces (RISs), and each distributed intelligent reflecting surface (RIS) consists of M reflecting elements; S1: In the stage of constructing the multi-user system, the following steps are included:

[0011] Define to represent the set of single-antenna users, to represent the set of distributed intelligent reflecting surfaces (RISs), to represent the set of reflecting elements; The data transmission link from the antenna base station to the single-antenna user is composed of the base station-user direct link , the RIS-base station reflection link , and the RIS-user reflection link . Among them, represents a complex vector with N rows and 1 column; represents a complex vector with M rows and 1 column; represents a complex matrix with M rows and N columns;

[0012] Suppose the antenna base station uses linear precoding, and define the precoding vector of the antenna base station for the single-antenna user as , and the passive beamforming vector of the reflecting element is defined as:

[0013] Among them, represents the phase shift of the first reflecting element converted into a complex function, that is, the Euler transformation formula; represents the phase shift of the Mth reflecting element converted into a complex function; represents the imaginary unit; Define to represent the -th element of the vector s , and satisfy ,

[0014] At this time, the signal-to-noise ratio of the single-antenna user is defined as:

[0015] , where Denote the received Gaussian white noise intensity, symbol H Denote the conjugate transpose; Denote the set of single-antenna users All other single-antenna users except the single-antenna users in ; Denote the single-antenna user i 's precoding vector.

[0016] Preferably, after formulating the system sum rate maximization problem, the optimization problem P 1:

[0017]

[0018] Among them, the constraint C 1 represents the maximum transmit power of the system; P max Denote the actual maximum transmit power threshold; Constraint C 2 represents the discrete phase shift of the reconfigurable intelligent surface (RIS); Constraint C 3 represents the reliability of the single-antenna user; Is the value set of the discrete phase shift; Is the packet decoding error probability.

[0019] Preferably, in the S3: system sum rate maximization problem solving stage, the following steps are further included:

[0020] Set the optimal reliability threshold of the finite block length transmission system , and the achievable transmission rate of the single-antenna user k is rewritten as:

[0021] In the formula, , Is the maximum reliability threshold of the finite block length transmission system, Is a constant, related to , Is the vector 's maximum value, Is the packet transmission block length;

[0022] According to the finite block length coding theory, the achievable transmission rate of the single-antenna user k is expressed as:

[0023] , where Denote the channel dispersion, Respectively denote the block length and packet transmission error probability of the single-antenna user, Denote the Gaussian Function inverse, defined as Among them,t represents the variable of the integral function; when , the achievable transmission rate of the single-antenna user k is further expressed as:

[0024] ;

[0025] reconstruction optimization problem P The lower bound of the objective function of 1 is obtained, and the convex lower bound of

[0026]

[0027]

[0028] where is the channel interference coefficient; is the interference power; f 0 is a constant representing a fixed frequency offset or noise floor; is the real part of the complex product, used for signal enhancement or phase adjustment; represents the combination of the channel response and the weight, used to optimize signal transmission; ; is the weight coefficient in channel estimation; (n) represents the nth iteration;

[0029] Fix the reflection unit of the fixed distributed intelligent reflecting surface RIS, and according to the convex lower bound of optimize the precoding vector P 5 of the single-antenna user to obtain the optimization sub-problem

[0030]

[0031] where ; represents the precoding vector of the ith antenna base station; the optimal solution of the optimization sub-problem P 5 is as follows:

[0032]

[0033] where is the Lagrangian multiplier; represents an N-order identity matrix; the optimal value of is defined as:

[0034] ;

[0035] Precoding vector for fixed single-antenna users , and optimize the reflection elements of the reconfigurable intelligent surface (RIS) according to formula (26). Optimize;

[0036] Formulate the desired signal power formula for single-antenna user k as:

[0037]

[0038] The reflection elements of the reconfigurable intelligent surface (RIS) Optimization subproblem P is as follows:

[0039]

[0040] ;

[0041] Use the SRA algorithm to find the suboptimal solution of optimization subproblem P 6, and keep the reflection elements of other reconfigurable intelligent surfaces (RISs) fixed , where , , , is the first specific angle when solving the optimization problem, is the second specific angle when solving the optimization problem;

[0042] Reformulate the optimization problem into a linear form with respect to . The objective function in formula (31) is written as:

[0043]

[0044]

[0045] where is in the form of a complex exponential, represents the phase angle, j represents the imaginary unit; is an element of a matrix or transformation matrix, and the subscript represents the row and column indices; is the angle related to the index; is the element on the diagonal of a matrix, and the subscript indicates that the row and column are the same;

[0046] Based on formulas (33) and (34), formulate the optimal value of as:

[0047]

[0048] Among them, is a parameter related to ; define the objective function of as formulated as follows:

[0049] ;

[0050] Initialize to a feasible value, set the maximum number of iterations , and obtain ; continuously calculate to obtain , and let ; continuously update , and let ; until the objective function of the system sum rate maximization problem converges or , obtain the optimal solution of the system parameters.

[0051] The second aspect of the present invention provides: A communication rate maximization solving system based on the MM algorithm, used to implement any of the above communication rate maximization solving methods based on the MM algorithm, including:

[0052] A multi-user system construction module, used to construct a multiple-input single-output URLLC system with the assistance of an intelligent reflecting surface;

[0053] A system sum rate maximization problem construction module, used to jointly optimize the packet decoding error probability of each single-antenna user, the precoding vector at the BS, and the distributed passive beamforming with discrete phase shifts according to the multiple-input single-output URLLC system to obtain the system sum rate maximization problem;

[0054] A system sum rate maximization problem solving module, used to transform the objective function of the system sum rate maximization problem using the MM algorithm, and solve the optimization variables through the AO algorithm, and finally obtain the optimal solution of the system parameters through iteration.

[0055] The third aspect of the present invention provides: A computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are loaded and executed by a processor, any of the above communication rate maximization solving methods based on the MM algorithm is implemented.

[0056] The beneficial effects of the present invention are:

[0057] 1) Use the Majorization-Minimization (MM) technique to solve the total rate maximization problem in a wireless communication system, simplify the complex optimization problem through an iterative approximation method, and effectively obtain the closed-form solution expression of the optimization variable sub-problem.

[0058] 2) The original objective function is innovatively reconstructed to obtain its convex lower bound form. This transformation not only makes the problem easier to handle but also ensures the mathematical rigor of the optimization process, laying a foundation for subsequent optimization steps.

[0059] 3) Through the alternating optimization (AO) method, by fixing the reflection unit parameters of the distributed intelligent reflecting surface (RIS) and the BS precoding vector respectively, the effective collaborative optimization of the two is achieved. This strategy significantly improves the performance of the wireless communication system.

[0060] 4) In the RIS passive beamforming optimization, the SRA algorithm is adopted to find a sub-optimal solution with low complexity and high efficiency, which is suitable for actual system deployment. Description of the Drawings

[0061] Figure 1 It is a flowchart of the solution method for maximizing the communication rate based on the MM algorithm;

[0062] Figure 2 It is a performance comparison diagram of four algorithms in the experimental comparison;

[0063] Figure 3 It is a performance comparison diagram of three algorithms at 10 dB and 30 dB in the experimental comparison;

[0064] Figure 4 It is a performance comparison diagram of four algorithms under different numbers of RISs in the experimental comparison;

[0065] Figure 5 It is a performance comparison diagram of three algorithms under the influence of BL in the experimental comparison. Detailed Implementation Manner

[0066] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0067] Refer to Figures 1 - 5 , the first aspect of the present invention provides: A solution method for maximizing the communication rate based on the MM algorithm, which is characterized in that it includes the following steps:

[0068] S1: In the multi-user system construction stage, a multiple-input single-output URLLC system is constructed with the assistance of an intelligent reflecting surface;

[0069] S2: In the construction stage of the system sum rate maximization problem, according to the multiple-input single-output (MISO) URLLC system, jointly optimize the packet decoding error probability of each single-antenna user, the precoding vector at the BS, and the distributed passive beamforming with discrete phase shifts to obtain the system sum rate maximization problem;

[0070] S3: In the solution stage of the system sum rate maximization problem, use the MM algorithm to transform the objective function of the system sum rate maximization problem, and solve the optimization variables through the AO algorithm. Finally, obtain the optimal solution of the system parameters through iteration.

[0071] In some embodiments, the multiple-input single-output (MISO) URLLC system includes: K Single-antenna users, N An antenna base station, R Distributed intelligent reflecting surfaces (RISs), and each distributed intelligent reflecting surface (RIS) is composed of M Reflection units; S1: In the multi-user system construction stage, it includes the following steps:

[0072] Define To represent the set of single-antenna users, To represent the set of distributed intelligent reflecting surfaces (RISs), To represent the set of reflection units; The data transmission link from the antenna base station to the single-antenna user Consists of the base station-user direct link , the RIS-base station reflection link And the RIS-user reflection link Where, Represents a complex vector with dimensions of N Rows and 1 column; Represents a complex vector with dimensions of M Rows and 1 column; Represents a complex matrix with dimensions of M Rows N Columns;

[0073] Assume that the antenna base station uses linear precoding, and define the precoding vector of the antenna base station for the single-antenna user as , and the passive beamforming vector of the reflection unit is defined as:

[0074] Where, Represents the phase shift of the first reflection unit converted into a complex function, that is, the Euler transformation formula; Represents the phase shift of the Mth reflection unit converted into a complex function; Represents the imaginary unit; Define To represent the Th s Element of the vector and satisfies ,

[0075] At this time, the signal-to-noise ratio of a single-antenna user is defined as:

[0076] , where represents the intensity of received Gaussian white noise, and the symbol H represents conjugate transpose; represents the set of single-antenna users except for the single-antenna user among all other single-antenna users; represents the precoding vector of the single-antenna user i .

[0077] In some embodiments, after formulating the system sum rate maximization problem, the optimization problem P 1 is obtained:

[0078]

[0079] where the constraint C 1 represents the maximum transmit power of the system; P max represents the actual maximum transmit power threshold; the constraint C 2 represents the discrete phase shift of the distributed intelligent reflecting surface (RIS); the constraint C 3 represents the reliability of a single-antenna user; is the value set of the discrete phase shift; is the packet decoding error probability.

[0080] In this embodiment, the optimization problem P 1 in formula (7) is a mixed integer non-convex optimization problem with multi-parameter coupling, which makes it difficult to find the global optimal solution for the formulated resource optimization scheme. Therefore, it needs to be transformed.

[0081] In some embodiments, in the S3: system sum rate maximization problem solving stage, the following steps are further included:

[0082] Set the optimal reliability threshold of the finite block length transmission system , and the achievable transmission rate of the single-antenna user k is rewritten as:

[0083] In the formula, , is the maximum reliability threshold of the finite block length transmission system, is a constant related to , is the vector 's maximum value, is the packet transmission block length;

[0084] According to the finite block length coding theory, the achievable transmission rate of single-antenna user k is expressed as:

[0085] , where represents the channel dispersion, respectively represent the block length and the packet transmission error probability of the single-antenna user, represents the Gaussian inverse of the function, defined as where, t represents the variable of the integral function; when , the achievable transmission rate of single-antenna user k is further expressed as:

[0086] ;

[0087] Reconstruction optimization problem P The objective function of 1 obtains its lower bound, The convex lower bound of is as follows:

[0088]

[0089]

[0090] In the formula, is the channel interference coefficient; is the interference power; f 0 is a constant, representing a fixed frequency offset or noise floor; is the real part of the complex product, used for signal enhancement or phase adjustment; represents the combination of the channel response and the weight, used to optimize signal transmission; ; is the weight coefficient in channel estimation; (n) represents the nth iteration;

[0091] Fix the of the reflection unit of the fixed distributed intelligent reflecting surface RIS, and according to the convex lower bound of, optimize the precoding vector of the single-antenna user to obtain the optimization sub-problem P 5:

[0092]

[0093] In the formula, ; represents the precoding vector of the ith antenna base station; Optimization sub-problemP Optimal solution of 5 As follows:

[0094]

[0095] In the formula, is the Lagrangian multiplier; represents an N - order identity matrix; Optimal value of is defined as:

[0096] ;

[0097] Precoding vector for fixed single - antenna users , and optimize the of the reflection elements of the distributed intelligent reflecting surface (RIS) according to formula (26);

[0098] Formulate the desired signal power formula for single - antenna user k as:

[0099]

[0100] For the of the reflection elements of the distributed intelligent reflecting surface (RIS), the optimization sub - problem P 6 is as follows:

[0101]

[0102] ;

[0103] Use the SRA algorithm to find the sub - optimal solution of the optimization sub - problem P 6, and keep the of the reflection elements of other distributed intelligent reflecting surfaces (RIS) fixed during the solution, , where, , , , is the first specific angle when solving the optimization problem, is the second specific angle when solving the optimization problem;

[0104] Re - formulate the optimization problem into a linear form with respect to , and write the objective function in formula (31) as:

[0105]

[0106]

[0107] In the formula, is in the form of complex exponential, represents the phase angle,j represents the imaginary unit; is an element of a matrix or transformation matrix, and the subscript represents the indices of rows and columns; is the angle related to the index; is the element on the diagonal of a matrix, and the subscript indicates that the row and column are the same;

[0108] Based on formulas (33) and (34), the optimal value of is formulated as:

[0109]

[0110] wherein, is a parameter related to ; define the objective function of as formulated as follows:

[0111] ;

[0112] Initialize to a feasible value, set the maximum number of iterations , and obtain ; continuously calculate to obtain , and let ; continuously update , and let ; until the objective function of the system sum rate maximization problem converges or , obtain the optimal solution of the system parameters.

[0113] In this embodiment, the rewritten in formula (26) is easier to handle than the original expression, but still exists in a coupled form. The present invention solves it through the AO algorithm. Formula (31) is still non-convex in nature. The present invention uses a low-complexity successive refinement algorithm (SRA) to obtain a sub-optimal solution. The convergence proof of the objective function of the system sum rate maximization problem is as follows:

[0114]

[0115] The foregoing formula proves that ( a ), ( b ), ( c ) hold. For the BS precoding vector optimization problem, since the optimal exists in a closed-form solution. Therefore, the computational complexity of this design can be ignored.

[0116] The following is an experimental comparison. For the sake of description, the algorithm of the present invention is named DL_MM, and its system performance is compared with the following three benchmark schemes, namely DL_SCA, DL_AO_ZF, and DL_MM_RPS. DL_SCA: The successive convex approximation (SCA) method and the semidefinite relaxation (SDR) technique are used to handle non-convex problems; DL_AO_ZF: The zero-forcing (ZF) algorithm is used to transform the precoding vector at the BS into power optimization; DL_MM_RPS: A scheme similar to DL_MM is adopted, but the phase shifts of the distributed RIS units are all randomly generated.

[0117] Figure 2 The system sum-rate of the DL-SCA, DL-MM, DL-AO-ZF, and DL-MM-RPS algorithms under the influence of the BS transmission power is given. By Figure 2 It can be seen that the sum-rates of the above four algorithms increase with the increase of the BS transmission power, but their growth rates slow down accordingly. In particular, the DL-MM algorithm performs the best, followed by the DL-SCA algorithm, and the performance difference between the two is about 4.02%. This is because the system performance of the SCA iterative algorithm is affected by the selection of the initial value. The DL-MM-RPS algorithm performs the worst, proving that the advantage of deploying RIS in the MISO system can only be highlighted by optimizing the RIS phase shift. Finally, observing the performance difference between the DL-MM algorithm and the DL-AO-ZF algorithm, it can be seen that the performance gap is only 4.4% when the BS transmission power is 10 dB, and reaches the maximum of 19.6% at 20 dB. However, with the increase of the BS transmission power, the performance gap between the two decreases again. The reason for this is that when the BS transmission power is small, the system's handling of multi-user interference dominates in the optimization processes of the DL-MM algorithm and the DL-AO-ZF algorithm. Since the ZF algorithm cannot optimally handle multi-user interference, but due to the small BS transmission power, the difference in the handling of multi-user interference between the two is small. Therefore, when the BS transmission power is 10 dB, the performance gap between the DL-MM algorithm and the DL-AO-ZF algorithm is small. With the increase of the BS transmission power, the BS transmission power dominates in the optimization processes of the algorithms, and the impact of multi-user interference on the system increases accordingly. Therefore, the performance gap between the two increases with the increase of the BS transmission power and reaches the maximum at 20 dB, and then stabilizes.

[0118] Figure 3The impact of the number of distributed RISs on the system sum-rate of DL-SCA, DL-MM, and DL-AO-ZF is given under the BS transmission powers of 10 dB and 30 dB respectively. It can be clearly seen that as the number of distributed RISs increases, more hierarchical gains will be brought to the system. Therefore, the system sum-rate shows an increasing trend. However, the corresponding system growth rate will decrease because multi-user interference is inevitably enhanced. In particular, when the BS transmission power is 10 dB, since the ZF algorithm cannot optimally handle multi-user interference and the BS transmission power is small at this time. Therefore, the performance of the DL_AO_ZF algorithm is close to that of the DL_SCA algorithm.

[0119] Figure 4 The system sum-rate of DL-SCA, DL-MM, DL-AO-ZF, and DL-MM-RPS under the influence of the number of each RIS unit is given. By comparing with Figure 3 the comparison, similar conclusions can be obtained. In particular, the performance of the DL-MM-RPS algorithm basically remains as the number of each RIS unit increases. This is because the passive beamforming at the RIS cannot exert its advantage of reconstructing the wireless channel without being optimized.

[0120] Figure 5 The system sum-rate of DL-SCA, DL-MM, and DL-AO-ZF under the influence of the BL is given. Due to the limitation of the FBL transmission expression, there is a positive correlation between the sum-rate of the system and the transmission block length, that is, the larger the transmission block length of the system, the higher the system performance. However, there is a theoretical upper bound for FBL transmission, that is, the sum-rate of the system using IFBL transmission. When the number of RISs increases, the system limit under IFBL can be broken through. Specifically, the DL-MM algorithm uses FBL transmission, and at R =9, the system performance exceeds that of the system using IFBL transmission at R =3.

[0121] The second aspect of the present invention provides: A communication rate maximization solving system based on the MM algorithm for implementing any one of the above communication rate maximization solving methods based on the MM algorithm, including:

[0122] A multi-user system construction module for constructing a multi-input single-output URLLC system with the assistance of an intelligent reflecting surface;

[0123] A system sum-rate maximization problem construction module for jointly optimizing the packet decoding error probability of each single-antenna user, the precoding vector at the BS, and the distributed passive beamforming with discrete phase shifts according to the multi-input single-output URLLC system to obtain a system sum-rate maximization problem;

[0124] The system sum rate maximization problem solving module is used to transform the objective function of the system sum rate maximization problem using the MM algorithm, solve the optimization variables through the AO algorithm, and finally obtain the optimal solution of the system parameters through iteration.

[0125] The third aspect of the present invention provides: a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are loaded and executed by a processor, any of the above-mentioned communication rate maximization solving methods based on the MM algorithm is implemented.

[0126] The above are only the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be within the scope of the concept described herein, through the above teachings or the technology or knowledge in related fields. And the changes and alterations made by those skilled in the art that do not depart from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A method for maximizing communication rate based on MM algorithm, characterized by: The following steps are involved: S1: Multi-user system construction phase, a multi-input single-output URLLC system is constructed with the assistance of a smart reflector; The multi-input single-output URLLC system comprises: K Single antenna user, N Antenna base stations, R A distributed intelligent reflection surface RIS, each distributed intelligent reflection surface RIS consists of M Reflection units; data transmission link from antenna base station to single antenna user Direct link from base station to user , RIS-Base Station Reflection Link and RIS-User Reflection Link Composition, among which, The dimension is N A complex vector with 1 row and 1 column; The dimension is M A complex vector with 1 row and 1 column; The dimension is M OK N A complex matrix of columns; S2: In the stage of constructing the system sum rate maximization problem, based on the multi-input single-output URLLC system, the packet decoding error probability of each single-antenna user, the precoding vector at the BS, and the distributed passive beamforming with discrete phase shift are jointly optimized to obtain the system sum rate maximization problem; after formulation, the optimization problem is obtained P 1: in, C 1 indicates the maximum transmit power of the system; P max Indicates the actual maximum transmit power threshold; C 2 represents the discrete phase shift of the distributed intelligent reflector RIS; C 3 represents the reliability of a single-antenna user; is the precoding vector for a single-antenna user; represents the set of single-antenna users, Represents a RIS set; Representation vector No. s elements and satisfy , ; represents a set of reflection units; is the set of discrete phase shift values; Decoding error probability for data packets; S3: In the stage of solving the system sum rate maximization problem, the MM algorithm is used to transform the objective function of the system sum rate maximization problem, and the AO algorithm is used to solve the optimization variables, and finally the optimal solution of the system parameters is obtained through iteration; Refactoring the optimization problem P The objective function of 1 obtains its lower bound, The convex lower bound of is as follows: In the formula, Indicates the intensity of received Gaussian white noise; is the channel interference coefficient; is the interference power; f 0 is a constant, indicating a fixed frequency offset or noise floor; It is the real part of the complex product and is used for signal enhancement or phase adjustment; Represents the combination of channel response and weights used to optimize signal transmission; ; is the weight coefficient in channel estimation; (n) represents the nth iteration; Fixing the reflector unit of RIS , and according to The convex lower bound of the precoding vector for a single-antenna user is Optimize and get the optimized sub-problem P 5: In the formula, ; represents the precoding vector of the i-th antenna base station; optimization subproblem P The optimal solution for 5 As follows: In the formula, is the Lagrangian multiplier; Represents an N-order identity matrix; The optimal value of Defined as: ； fixed , and according to formula (26) the reflection unit of RIS Optimize The expected signal power of single-antenna user k is formulated as: RIS reflection unit The optimization subproblem P 6 are as follows: 。 2. The method for maximizing the communication rate based on the MM algorithm according to claim 1, characterized in that: The S1: multi-user system construction phase includes the following steps: Assume that the antenna base station uses linear precoding and define the precoding vector of the antenna base station for a single antenna user as , the passive beamforming vector of the reflector unit is defined as: in, It means that the phase shift of the first reflection unit is converted into a complex function, which is the Euler transformation formula; Represents the phase shift of the Mth reflection unit converted into a complex function; represents an imaginary unit; At this time, the single-antenna user signal-to-noise ratio is defined as: ;in Indicates the intensity of received Gaussian white noise, symbol H represents conjugate transpose; represents the single antenna user set Except for single antenna users All other single-antenna users except Indicates a single antenna user i The precoding vector of .

3. The method for maximizing the communication rate based on the MM algorithm according to claim 2, characterized in that: The S3: system sum rate maximization problem solving stage also includes the following steps: Setting the optimal reliability threshold for finite block length transmission systems , the achievable transmission rate of single-antenna user k is rewritten as: In the formula, , is the maximum reliability threshold of the finite block length transmission system, is a constant, and related, For vector The maximum value of The length of the data packet transmission block; According to the finite block length coding theory, the achievable transmission rate of a single-antenna user k is expressed as: ,in represents the channel dispersion, denote the block length and packet transmission error probability of a single-antenna user, Gauss The inverse of the function is defined as in, t represents the variable of the integral function; when hour, The achievable transmission rate for single-antenna user k is further expressed as: ； Use SRA algorithm to find the optimization subproblem P 6, while maintaining the reflection units of other RIS fixed, ,in, , , , The first specific angle when solving the optimization problem, The second specific angle for solving optimization problems; Reformulate the optimization problem as The linear form of , the objective function in formula (31) is written as: In the formula, is the complex exponential form, represents the phase angle, j represents the imaginary unit; is an element of a matrix or transformation matrix, and the subscripts indicate the row and column indices; is the angle associated with the index; is the diagonal element of a matrix, and the subscript indicates that the row and column are the same; Based on formulas (33) and (34), The optimal value of formulation: in, is a Related parameters; definition The objective function is The formula is as follows: ； initialization Set the maximum number of iterations to a feasible value , and obtain ; Constantly calculate to obtain , and order ; Constantly updated , and order ; until the objective function of the system sum rate maximization problem converges or , and obtain the optimal solution of system parameters.

4. A communication rate maximization solution system based on MM algorithm, characterized by: A method for maximizing the communication rate based on the MM algorithm according to any one of claims 1 to 3, comprising: Multi-user system building module, used to build a multi-input single-output URLLC system with the assistance of a smart reflector; A system sum rate maximization problem building module is used to jointly optimize the packet decoding error probability of each single-antenna user, the precoding vector at the BS, and the distributed passive beamforming with discrete phase shift according to the multiple-input single-output URLLC system to obtain the system sum rate maximization problem; The system sum rate maximization problem solving module is used to transform the objective function of the system sum rate maximization problem using the MM algorithm, and solve the optimization variables through the AO algorithm, and finally obtain the optimal solution of the system parameters through iteration.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by the processor, the communication rate maximization solution method based on the MM algorithm as described in any one of claims 1 to 3 is implemented.

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