Communication rate maximization solving method and system based on MM algorithm and medium

Abstract

The invention discloses a communication rate maximization solving method and system based on an MM algorithm and a medium, and belongs to the technical field of wireless communication. The method comprises the following steps: S1, a multi-user system construction stage: constructing a multi-input single-output URLLC system under the assistance of an intelligent reflecting surface; s2, a system summation rate maximization problem construction stage: according to a multi-input single-output URLLC system, jointly optimizing a data packet decoding error probability of each single-antenna user, a precoding vector at a BS and distributed passive beam forming with discrete phase shift to obtain a system summation rate maximization problem; and S3, in a system summing rate maximization problem solving stage, an MM algorithm is used to deform an objective function of the system summing rate maximization problem, an optimization variable is solved through an AO algorithm, and finally an optimal solution of system parameters is obtained through iteration. A complex optimization problem is simplified through an iterative approximation method, and a closed solution expression for optimizing a variable quantum problem is effectively obtained.

Description

Technical Field

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

[0002] The performance optimization of wireless communication systems is one of the core issues in the field of modern communications. 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 hot topic of research. In existing wireless communication systems, beamforming technology and precoding technology are widely used in signal transmission and reception to improve the quality and rate of communication. 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 methods for solving such optimization problems include convex optimization methods and heuristic algorithms. Although convex optimization methods can ensure the finding of the global optimal solution, their computational complexity is high and they are not suitable for communication systems with high real-time requirements. Heuristic algorithms such as genetic algorithms and particle swarm optimization, although they have fast computational speeds, can often only obtain approximate solutions, and the quality of the solution is greatly affected by parameter settings and initial conditions.

[0004] How to design efficient iterative algorithms and how to deal with variable coupling and algorithm convergence are still technical problems that need to be solved. In addition, some existing algorithms, such as SCA technology, although they reduce the computational complexity to a certain extent, still have a large amount of computation 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 to provide a method, system and medium for solving the communication rate maximization problem based on the MM algorithm.

[0006] The object of the present invention is achieved through the following technical solutions: The first aspect of the present invention provides: a communication rate maximization solution method based on the MM algorithm, characterized in that: it includes the following steps: S1: Multi-user system construction phase, a multi-input single-output URLLC system is constructed with the assistance of a smart reflector; 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; 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.

[0007] Preferably, 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; S1: multi-user system construction phase, including the following steps: definition represents the set of single-antenna users, Represents a set of distributed intelligent reflection surfaces RIS, Represents a set of reflection units; a data transmission link from an antenna base station to a single antenna user Base station-user direct link , 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; 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; Definition Representation vector No. s elements and satisfy , At this time, the single-antenna user signal-to-noise ratio is defined as: ,in Indicates that the received Gaussian white noise is mild, 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 .

[0008] Preferably, the system sum rate maximization problem is formulated as the optimization problem P 1: Among them, the constraints C 1 indicates the maximum transmit power of the system; P max Indicates the actual maximum transmit power threshold; constraint C 2 represents the discrete phase shift of the distributed intelligent reflector RIS; constraint C 3 represents the reliability of a single-antenna user; is the set of discrete phase shift values; is the probability of packet decoding error.

[0009] Preferably, the S3: system sum rate maximization problem solving stage further 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: ; 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, is the channel interference coefficient; is the interference power; f 0 is a constant representing 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; Fixed Distributed Intelligent Reflector RIS reflector unit , 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: ; Precoding vector for fixed single-antenna user , and according to formula (26) the reflection unit of the distributed intelligent reflection surface RIS Optimize The expected signal power of single-antenna user k is formulated as: Reflection unit of distributed intelligent reflector RIS The optimization subproblem P 6 are as follows: ; Use SRA algorithm to find the optimization subproblem P 6, while maintaining the reflection units of other distributed intelligent reflection surfaces 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.

[0010] The second aspect of the present invention provides: a communication rate maximization solution system based on the MM algorithm, which is used to implement any of the above communication rate maximization solution methods based on the MM algorithm, 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. The third aspect of the present invention provides: a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by a processor, any of the above-mentioned communication rate maximization solution methods based on the MM algorithm is implemented.

[0011] The beneficial effects of the present invention are: 1) The Majorization-Minimization (MM) technique is used to solve the total rate maximization problem in wireless communication systems. The complex optimization problem is simplified through an iterative approximation method, and the closed-form solution expression of the optimization variable subproblem is effectively obtained.

[0012] 2) The original objective function was 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 the foundation for subsequent optimization steps.

[0013] 3) Through the alternating optimization (AO) method, the reflection unit parameters of the distributed intelligent reflection surface RIS and the BS precoding vector are fixed respectively, achieving effective collaborative optimization of the two. This strategy significantly improves the performance of the wireless communication system.

[0014] 4) In the RIS passive beamforming optimization, the SRA algorithm is used to find the suboptimal solution with low complexity and high efficiency, which is suitable for actual system deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The flowchart of the method for maximizing the communication rate based on the MM algorithm; Figure 2 This is a performance comparison chart of the four algorithms in the experimental comparison; Figure 3 This is the performance comparison chart of the three algorithms in the experimental comparison at 10dB and 30dB; Figure 4 This is the performance comparison chart of the four algorithms under different RIS numbers in the experimental comparison; Figure 5 This is a performance comparison chart of the three algorithms under the influence of BL in the experimental comparison. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only 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 work are within the scope of protection of the present invention.

[0017] See also Figure 1-Figure 5 The first aspect of the present invention provides: a method for maximizing communication rate based on MM algorithm, characterized in that it includes the following steps: S1: Multi-user system construction phase, a multi-input single-output URLLC system is constructed with the assistance of a smart reflector; 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; 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.

[0018] In some embodiments, the multiple-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; S1: multi-user system construction phase, including the following steps: definition represents the set of single-antenna users, Represents a set of distributed intelligent reflection surfaces RIS, Represents a set of reflection units; a data transmission link from an antenna base station to a single antenna user Base station-user direct link , 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; 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; Definition Representation vector No. s elements and satisfy , At this time, the single-antenna user signal-to-noise ratio is defined as: ,in Indicates that the received Gaussian white noise is mild, 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 .

[0019] In some embodiments, the system sum rate maximization problem is formulated as the optimization problem P 1: Among them, the constraints C 1 indicates the maximum transmit power of the system; P max Indicates the actual maximum transmit power threshold; constraint C 2 represents the discrete phase shift of the distributed intelligent reflector RIS; constraint C 3 represents the reliability of a single-antenna user; is the set of discrete phase shift values; is the probability of packet decoding error.

[0020] In this embodiment, the optimization problem in formula (7) is P 1 is a mixed integer non-convex optimization problem with multiple parameters coupling, which makes it difficult to find the global optimal solution for the resource optimization scheme developed, so it needs to be transformed.

[0021] In some embodiments, the S3: system sum rate maximization problem solving stage further 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: ; 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, is the channel interference coefficient; is the interference power; f 0 is a constant representing 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; Fixed Distributed Intelligent Reflector RIS reflector unit , 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: ; Precoding vector for fixed single-antenna user , and according to formula (26) the reflection unit of the distributed intelligent reflection surface RIS Optimize The expected signal power of single-antenna user k is formulated as: Reflection unit of distributed intelligent reflector RIS The optimization subproblem P 6 are as follows: ; Use SRA algorithm to find the optimization subproblem P 6, while maintaining the reflection units of other distributed intelligent reflection surfaces 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.

[0022] In this embodiment, the rewritten formula (26) is easier to handle than the original expression, but It still exists in a coupled form, and the present invention solves it through the AO algorithm. Formula (31) is still non-convex in nature, and the present invention uses a low-complexity successive refinement algorithm (SRA) to obtain a suboptimal solution. The convergence of the objective function of the system sum rate maximization problem is proved as follows: The above formula proves that ( a )、( b )、( c ) holds true. For the BS precoding vector optimization problem, since the optimal It exists in the form of a closed-form solution. Therefore, the computational complexity of this design can be ignored.

[0023] The following is an experimental comparison. For the convenience of description, the algorithm of the present invention is named DL_MM, and the system performance is compared with the following three benchmark schemes, namely DL_SCA, DL_AO_ZF and DL_MM_RPS. DL_SCA: uses the successive convex approximation (SCA) method and semidefinite relaxation (SDR) technology to deal with non-convex problems; DL_AO_ZF: The ZF algorithm is used to convert the precoding vector at the BS into power optimization; DL_MM_RPS: A scheme similar to DL_MM is used, but the phase shifts of the distributed RIS units are randomly generated.

[0024] Figure 2 The system Sum-Rate of DL-SCA, DL-MM, DL-AO-ZF and DL-MM-RPS algorithms under the influence of BS transmission power is given. Figure 2 It can be seen that the above four algorithms increase with the increase of BS transmission power, but their growth rate slows down. In particular, the DL-MM algorithm performs best, followed by the DL-SCA algorithm, and the performance difference between the two is about 4.02%. This is because the performance of the SCA iterative algorithm system 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 10dB, and it reaches the maximum at 20dB, which is 19.6%. However, as the BS transmission power increases, the performance gap between the two decreases. The reason for this is that when the BS transmission power is small, the system's processing of multi-user interference is dominant during the optimization process 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 between the two in processing multi-user interference is small. Therefore, when the BS transmission power is 10dB, the performance gap between the DL-MM algorithm and the DL-AO-ZF algorithm is small. As the BS transmission power increases, the BS transmission power dominates the algorithm optimization process, and the impact of multi-user interference on the system increases. Therefore, the performance gap between the two increases with the increase of BS transmission power, but reaches the maximum at 20dB and then stabilizes.

[0025] Figure 3The influence of the number of distributed RIS on the system Sum-Rate for DL-SCA, DL-MM, and DL-AO-ZF when the BS transmission power is 10dB and 30dB respectively is given. It can be clearly seen that with the increase in the number of distributed RIS, more hierarchical gain will be brought to the system, so the system sum rate shows an increasing trend. However, the corresponding system growth rate will decrease accordingly because multi-user interference is inevitably enhanced. In particular, when the BS transmission power is 10dB, 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.

[0026] 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 RIS units is given. Figure 3 By comparison, similar conclusions can be drawn. In particular, the performance of the DL-MM-RPS algorithm remains basically the same as the number of RIS units increases. This is because the passive beamforming at the RIS cannot play its advantage in reconstructing the wireless channel without being optimized.

[0027] Figure 5 The system Sum-Rate of DL-SCA, DL-MM and DL-AO-ZF under the influence of BL is given. Due to the limitation of FBL transmission expression, there is a positive correlation between the system sum rate and the transmission block length, that is, the larger the system transmission block length, the higher the system performance. However, there is a theoretical upper limit for FBL transmission, that is, the system sum rate using IFBL transmission. When the number of RIS increases, the system limitation under IFBL can be broken. Specifically, the DL-MM algorithm uses FBL transmission. R =9 when the system performance exceeds R =3 when the system performance of IFBL transmission is adopted.

[0028] The second aspect of the present invention provides: a communication rate maximization solution system based on the MM algorithm, which is used to implement any of the above communication rate maximization solution methods based on the MM algorithm, 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. The third aspect of the present invention provides: a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by a processor, any of the above-mentioned communication rate maximization solution methods based on the MM algorithm is implemented.

[0029] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A method for maximizing communication rate based on MM algorithm. Features: 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; 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; 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.

2. The communication rate maximization solution method based on the MM algorithm according to claim 1, Features: 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; S1: Multi-user system construction phase, including the following steps: definition represents the set of single-antenna users, Represents a set of distributed intelligent reflection surfaces RIS, Represents a set of reflection units; a data transmission link from an antenna base station to a single antenna user Base station-user direct link , 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; 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; Definition Representation vector No. s elements and satisfy , At this time, the single-antenna user signal-to-noise ratio is defined as: ,in Indicates that the received Gaussian white noise is mild, 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.

3. The communication rate maximization solution method based on the MM algorithm according to claim 2, Features: The system sum rate maximization problem is formulated as the optimization problem P 1: Among them, the constraints C 1 indicates the maximum transmit power of the system; P max Indicates the actual maximum transmit power threshold; constraint C 2 represents the discrete phase shift of the distributed intelligent reflector RIS; constraint C 3 represents the reliability of a single-antenna user; is the set of discrete phase shift values; is the probability of packet decoding error.

4. The communication rate maximization solution method based on the MM algorithm according to claim 3, Features: 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, respectively. 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: ； 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, is the channel interference coefficient; is the interference power; f 0 is a constant representing 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; Fixed Distributed Intelligent Reflector RIS reflector unit , 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: ； Precoding vector for fixed single-antenna user , and according to formula (26) the reflection unit of the distributed intelligent reflection surface RIS Optimize The expected signal power of single-antenna user k is formulated as: Reflection unit of distributed intelligent reflector RIS The optimization subproblem P 6 are as follows: ； Use SRA algorithm to find the optimization subproblem P 6, while maintaining the reflection units of other distributed intelligent reflection surfaces RIS fixed, ,in, , , , The first specific angle when solving the optimization problem, The second specific angle when 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.

5. A communication rate maximization solution system based on MM algorithm, Features: A method for maximizing the communication rate based on the MM algorithm according to any one of claims 1 to 4, 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.

6. A computer-readable storage medium, Features: 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 4 is implemented.

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