Optimization method for maximizing summation rate of RIS-assisted uplink URLLC system

Abstract

The invention discloses an optimization method for maximizing the summing rate of an RIS-assisted uplink URLLC system, and relates to the technical field of wireless communication, and the optimization method comprises the following steps: S1, constructing a reconfigurable intelligent surface RIS-assisted uplink multi-user URLLC system model which is a single-input-multiple-output SIMO architecture, and establishing a signal transmission mathematical expression according to the system model; s2, constructing a system summation rate maximization problem model based on a communication theory and an optimization theory, and improving the system summation rate maximization problem model through problem conversion; s3, the constraint is converted through an SCA technology, and a single optimal solution is obtained through a BCD algorithm; and S4, based on the single optimal solution obtained in the step S3, solving a suboptimal solution of the original problem through an iterative convergence mode. According to the method, the problem of beam forming optimization of the RIS-assisted uplink URLLC system is solved, particularly, a closed-form solution for receiving beam forming at the BS is provided for the uplink URLLC, and meanwhile, the superiority of the algorithm is proved through parameter simulation.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically to an optimization method for maximizing the summation rate of a RIS-assisted uplink URLLC system. Background Technology

[0002] The upcoming 6G wireless network is expected to evolve towards intelligence and software reconfigurability, enabling seamless and ubiquitous communication between humans and devices. Simultaneously, by sensing, controlling, and optimizing the wireless environment, it aims to achieve the vision of low-power, high-throughput, high-energy-efficiency, massive connectivity, high reliability, and low-latency communication. Currently, Ultra-Reliable Low-Latency Communication (URLLC) is one of the key standards supporting 6G, meeting its stringent requirements for reliability and latency through finite block length transmission. In recent years, Reconfigurable Intelligent Surfaces (RIS), composed of a large number of electromagnetic units, have become one of the most promising technologies for 6G URLLC networks due to their ability to intelligently control electromagnetic waves. However, most current research focuses on RIS-assisted downlink URLLC systems, with relatively little research on resource optimization in the uplink. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide an optimization method for maximizing the summation rate of a RIS-assisted uplink URLLC system.

[0004] The objective of this invention is achieved through the following technical solution: This invention discloses an optimization method for maximizing the summation rate of a RIS-assisted uplink URLLC system, comprising: S1. Construct a reconfigurable smart surface RIS-assisted uplink multi-user URLLC system model. The URLLC system model is a single-input multiple-output SIMO architecture. Establish signal transmission mathematical expressions based on the system model. S2. Construct a system summation rate maximization problem model based on communication theory and optimization theory, and improve it through problem transformation; S3. The constraints are transformed using the SCA technique, and the single optimal solution is obtained using the BCD algorithm. S4. Based on the single optimal solution obtained in step S3, the suboptimal solution of the original problem is obtained through iterative convergence.

[0005] Furthermore, in step S1, the URLLC system consists of a base station (BS) and a reconfigurable smart surface (RIS), which coordinate to provide services. There are single-antenna uplink users, and the user set is defined as follows: Meanwhile, the base station (BS) is equipped with One antenna, specifically including: S11, Assume RIS includes It consists of several reflective units, and the unit set is as follows: The RIS phase shift matrix is ,in, The dimension is M OK M A complex matrix of columns, This represents a diagonal matrix, where the phase shift vector corresponds to the m-th reflecting unit. The angle of the phase shift generation range of RIS , , The imaginary unit representing Euler's formula; S12. Assume that the system channel gain satisfies quasi-static flat fading within the coherent block, and define RIS up to the th... The channel gain for each user is The channel gain from BS to RIS is BS to the The channel gain for each user is ,in, 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; S13, Define User The signal sent to the BS is The signal received at BS for: ;in, This represents the sum of signals from all users after passing through the channel link. This represents the additive white Gaussian noise received at BS, where H represents the conjugate transpose. (User) Signal-to-interference-to-noise ratio at the location for: , ;in, Represents a set of users In addition to users All other users, This indicates that the user has been restored at the BS. The receiving beamforming vector of the transmitted signal, Indicates user The power of the transmitted signal. Indicates a single-antenna user The receiving beamforming vector, Indicates a single-antenna user The power of the transmitted signal. Describing the 2-norm, Indicates the power of Gaussian white noise; Represents the set of B / S to users Excluding users Other channel gains; Represents RIS to user set Excluding users Other channel gains; S14. Based on the finite block length coding theory, the user... The achievable transmission rate is expressed as ,in, Indicates channel dispersion; Indicates user The block length, Indicates user The probability of decoding errors, Gaussian function The reverse, if Then the user The achievable transmission rate is .

[0006] Preferably, step S2 specifically includes the following steps: S21. Construct a system summation rate maximization problem, i.e. ,in, This represents the first optimization problem. Indicates user Receive beamforming unit energy constraint Indicates user Maximum transmit power constraint, This represents the phase shift vector constraint of RIS. Indicates will When used as an optimization variable, it maximizes the first optimization problem. The objective function; S22. Obtain the uplink data from BS to RIS to user. Cascaded channels ,in This indicates the process from BS to RIS to the user. The cascaded channel link is calculated using the following formula: , Represents the phase shift vector of RIS. Then define the first covariance channel matrix. , ,in The expectation operator is represented here; it is assumed that the channel gain is constant within each coherent block, and the expectation operator is ignored. , will users The signal-to-noise ratio is rewritten as , Let the second channel covariance matrix be represented, and then the first optimization problem will be addressed. Equivalently transformed into a second optimization problem ,Right now: ;in, This represents the m-th element in the phase shift vector of RIS. Indicates user Maximum power threshold for transmission.

[0007] Preferably, step S3 specifically includes the following steps: S31, will As a parameter, for users recovering at BS Receive beamforming vector of transmitted signal Optimize the second optimization problem. Transform into a third optimization problem ,Right now ; in, Indicates will Maximizing the third optimization problem when used as an optimization variable. The objective function; S32. Based on the relevant lemmas in the paper, find... Closed-form solution , Representation matrix The reverse; This represents the composite channel gain; its calculation formula is: ; S33, will As a parameter, for the user Transmitted signal power Optimize the second optimization problem. Transform into the fourth optimization problem ,Right now ;in, Indicates will When used as an optimization variable, it maximizes the fourth optimization problem. The objective function; S34, will As parameters, respectively and Optimize the second optimization problem. Transform into the sixth optimization problem ,Right now ;in, Indicates will Maximizing the sixth optimization problem when used as an optimization variable. The objective function.

[0008] Preferably, the fourth optimization problem in step S33 The nonconvexity comes from the first objective function, and the fourth optimization problem is obtained using SCA iteration. The optimal solution includes the following steps: S331. Rewrite the first objective function as follows: , where the user set The first ln logarithm sum of the expected power and the noise power The calculation formula is , Let the first function be represented, and its calculation formula be: Receive beamforming matrix at BS The calculation formula is , Describes the rank of a matrix; S332. Using SCA iteration, the first feasible point in the nth iteration is... The first function is derived by using a first-order Taylor series. The lower bound unfolds: Among them, Represents the first function The convex lower bound inequality express The derivative, With the first function The product is ;in, Represents a set of users Excluding users and users The remaining users; Represents the third channel covariance matrix; Represents the covariance matrix of the fourth channel; Represents a set of users In addition to users All other users besides; Represents a set of users In addition to users All other users besides; S333, Using the BCD algorithm to solve the fourth optimization problem Transform into the fifth optimization problem : Among them, the fifth optimization problem It is convex, and it is solved using CVX.

[0009] Preferably, step S34 includes the following steps: S341, User The expected signal power is written as: ; in, This indicates taking the real part of the complex variable. This indicates the process from BS to RIS to the user. The cascaded channel link is calculated using the following formula: ; Indicates the integration of cascaded channel links and BS to the first The RIS beamforming vector after channel gain for each user is calculated using the following formula: ; Indicates the integration of cascaded channel links and BS to the first The system channel gain characterization after the channel gain of each user is expressed by the following formula: Hermitian matrix The calculation formula is ; S342, will the user Rewrite the signal-to-interference-plus-noise ratio: At this point, the second objective function is ;in, Represents a set of users The sum of the second ln logarithms of the expected power and the noise power is calculated using the following formula: , Indicates the integration of cascaded channel links and BS to the first The system channel gain representation after excluding user k, using the channel gain of each user, is represented by the second function. The calculation formula is ; S343. Using SCA iteration, define the second feasible point in the nth iteration as... The second function is derived using a first-order Taylor series. The lower bound expansion is: ; where, Indicates the second function The convex lower bound inequality The derivative is represented by . With the second function The product is The sixth optimization problem Transform into the seventh optimization problem : ;in, Describes the rank of a matrix; Indicates will Maximizing the seventh optimization problem when used as an optimization variable. The objective function; S344, Constraints The seventh optimization problem Non-convex rank 1 constraints, using equations By replacing it and transforming it into the form of the difference of convex functions, we obtain the non-convex constraint. ;in, Representing Hermitian matrices The sum of eigenvalues; Representing Hermitian matrices The largest eigenvalue; S345. Using SCA iteration and expanding the convex lower bound inequality on the left side of the inequality in the non-convex constraint using a first-order Taylor series: ;in, express The eigenvector corresponding to the largest eigenvalue. Representing Hermitian matrices The convex lower bound of the maximum eigenvalue is finally addressed using the BCD algorithm to solve the seventh optimization problem. Transformed into the eighth optimization problem : The eighth optimization problem It is convex, and it is solved using CVX.

[0010] Preferably, step S4 specifically includes: Will As a parameter and initialized, the iteration count n is set to 0, and the maximum iteration count is set to 0. Set to 100; Will As a parameter, through the formula right Update; This indicates the (n+1)th time the user is restored at the BS. The receiving beamforming vector of the transmitted signal; Will As a parameter, for the fifth optimization problem Solve the problem and update the third feasible point in the (n+1)th iteration. , This indicates the nth time the user is restored at the BS. The receiving beamforming vector of the transmitted signal; Will As a parameter, for the eighth optimization problem Solve the problem and update the fourth feasible point in the (n+1)th iteration. ; Until the second optimization problem The objective function converges.

[0011] The beneficial effects of this invention are: 1) This application solves the beamforming optimization problem of RIS-assisted uplink URLLC system, and in particular provides a closed-form solution for receive beamforming at the BS for uplink URLLC. Attached Figure Description

[0012] Figure 1 This is a schematic diagram illustrating the steps of an optimization method for maximizing the summation rate of a RIS-assisted uplink URLLC system according to an embodiment of the present invention. Figure 2 This is a flowchart of the BCD algorithm according to an embodiment of the present invention; Figure 3 This is a schematic diagram showing the performance comparison between an embodiment of the present invention and a traditional single-input single-output URLLC system. Detailed Implementation

[0013] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] This invention discloses an optimization method for maximizing the summation rate in a RIS-assisted uplink URLLC system. This method is applied to RIS-assisted uplink multi-user single-input multi-output (SIMO) URLLC systems. By jointly optimizing the transmission power at the user end, the receive beamforming at the base station (BS), and the passive beamforming at the RIS, a system summation rate maximization optimization problem is formulated. Since this problem is highly non-convex, this application uses Successive Convex Approximation (SCA) to transform the non-convex constraints and proposes a Block Coordinate Descent (BCD) iterative algorithm. Finally, multi-parameter experimental simulations demonstrate the superiority of the algorithm. A schematic diagram of the method steps is shown below. Figure 1 As shown, the specific steps include: S1. Construct a reconfigurable smart surface RIS-assisted uplink multi-user URLLC system model. The URLLC system model is a single-input multiple-output SIMO architecture. Establish signal transmission mathematical expressions based on the system model. S2. Construct a system summation rate maximization problem model based on communication theory and optimization theory, and improve it through problem transformation; S3. The constraints are transformed using the SCA technique, and the single optimal solution is obtained using the BCD algorithm. S4. Based on the single optimal solution obtained in step S3, the suboptimal solution of the original problem is obtained through iterative convergence.

[0015] Specifically, in step S1, the URLLC system consists of a base station (BS) and a reconfigurable smart surface (RIS), which work together to provide services. There are single-antenna uplink users, and the user set is defined as follows: Meanwhile, the base station (BS) is equipped with One antenna, including: S11, Assume RIS includes It consists of several reflective units, and the unit set is as follows: The RIS phase shift matrix is ,in, The dimension is M OK M A complex matrix of columns, This represents a diagonal matrix, where the phase shift vector corresponds to the m-th reflecting unit. Through Euler's formula Solve , , The angle representing the phase shift range of RIS, with a value ranging from 0 to... ,Right now , , The imaginary unit representing Euler's formula; S12. Without loss of generality, assume that the system channel gain satisfies quasi-static flat fading within the coherent block, and define RIS up to the th... The channel gain for each user is The channel gain from BS to RIS is BS to the The channel gain for each user is ,in, 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; S13, Define User The signal sent to the BS is ,in, This indicates that the distribution follows a Gaussian distribution with a mean of 0 and a variance of 1. The signal received at BS for: ;in, This represents the sum of signals from all users after passing through the channel link. This represents the additive white Gaussian noise received at BS, i.e. ,in This represents the power of Gaussian white noise. Represents an N x N matrix of zeros. Let H represent an N x N identity matrix, where H denotes the conjugate transpose. Signal-to-interference-to-noise ratio at the location for: , ;in, Represents a set of users In addition to users All other users, This indicates that the user has been restored at the BS. The receiving beamforming vector of the transmitted signal, Indicates user The power of the transmitted signal. Indicates a single-antenna user The receiving beamforming vector, Indicates a single-antenna user The power of the transmitted signal. Represents the 2-norm; Represents the set of B / S to users Excluding users Other channel gains; Represents RIS to user set Excluding users Other channel gains; S14. Based on the finite block length coding theory, the user... The achievable transmission rate is expressed as ,in, Indicates channel dispersion; Indicates user The block length, Indicates user The probability of decoding errors, Gaussian function Inverse of Gaussian function The calculation formula is , where t represents the variable of the integral function; if ,but It can be approximated as 1, then the user The achievable transmission rate can be expressed as .

[0016] Specifically, step S2 includes the following steps: S21. Construct a system summation rate maximization problem, i.e. ,in, This represents the first optimization problem. Indicates user Receive beamforming unit energy constraint Indicates user Maximum transmit power constraint, This represents the phase shift vector constraint of RIS. Indicates will When used as an optimization variable, it maximizes the first optimization problem. The objective function; S22. In order to solve the above non-convex first optimization problem This step introduces a problem transformation; firstly, it obtains the upstream path from BS to RIS to user. Cascaded channels The calculation process is as follows: ;in This indicates the process from BS to RIS to the user. The cascaded channel link is calculated using the following formula: , Represents the phase shift vector of RIS. Then define the first covariance channel matrix. , ,in Let this represent the expectation operator, assuming the channel gain is constant within each coherent block, and ignore the expectation operator. , will users The signal-to-noise ratio is rewritten as , Represents the second channel covariance matrix (i.e. Represents the covariance channel matrix Excluded users The channel covariance matrix (followed by the first optimization problem) Equivalently transformed into a second optimization problem ,Right now: ;in, This represents the m-th element in the RIS phase shift vector. Indicates user Maximum power threshold for transmission.

[0017] Specifically, in order to solve the aforementioned non-convex second optimization problem This step transforms the non-convex constraints using SCA technology and proposes the BCD algorithm to solve the optimal solutions to each subproblem; step S3 includes the following steps: S31, will As a parameter, for users recovering at BS Receive beamforming vector of transmitted signal Optimize the second optimization problem. Transform into a third optimization problem ,Right now ; in, Indicates will Maximizing the third optimization problem when used as an optimization variable. The objective function; S32, Although this is the third optimization problem It is still nonconvex, but it can be explained based on the relevant lemmas in the paper. Here, we will explain the relevant lemmas in the paper: Although the third optimization problem It remains non-convex, but note the difference in... The third optimization problem involves scaling by any positive factor. The objective function remains unchanged; therefore, the third optimization problem can be safely removed. The constraints are then scaled. Make Subsequently, the optimization problem will be transformed into a standard eigenvalue problem, namely, BS reception. The closed-form solution is Find out Closed-form solution , Representation matrix The inverse of the matrix The calculation formula is ; This represents the composite channel gain; its calculation formula is: This indicates the integration of cascaded channel gain and BS to the first Channel gain for each user; S33, will As a parameter, for the user Transmitted signal power Optimize the second optimization problem. Transform into the fourth optimization problem ,Right now ;in, Indicates will When used as an optimization variable, it maximizes the fourth optimization problem. The objective function; S34, will As parameters, respectively and Optimize the second optimization problem. Transform into the sixth optimization problem ,Right now ;in, Indicates will Maximizing the sixth optimization problem when used as an optimization variable. The objective function.

[0018] Specifically, the fourth optimization problem in step S33 The nonconvexity comes from the first objective function, and the fourth optimization problem is obtained using SCA iteration. The optimal solution includes the following steps: S331. Rewrite the first objective function as follows: , where the user set The first ln logarithm sum of the expected power and the noise power The calculation formula is , Let the first function be represented, and its calculation formula be: Receive beamforming matrix at BS The calculation formula is , Describes the rank of a matrix; S332. At this point, the non-convexity of the first objective function comes from the first function. Therefore, using SCA iteration, the first feasible point in the nth iteration is... The first function is derived by using a first-order Taylor series. The lower bound unfolds: Among them, Represents the first function The convex lower bound inequality express The derivative, With the first function The product is ; Represents a set of users Excluding users and users The remaining users; Represents the third channel covariance matrix (i.e., excluding the user set). users in (the channel covariance matrix after that) This represents the fourth channel covariance matrix (i.e., excluding the user set). Excluding users and users (The remaining channel covariance matrix after that). Represents a set of users In addition to users All other users besides; Represents a set of users In addition to users All other users besides; S333, Using the BCD algorithm to solve the fourth optimization problem Transform into the fifth optimization problem : Among them, the fifth optimization problem The convex shape is solved using CVX, a commonly used convex optimization solver in MATLAB.

[0019] Specifically, note the sixth optimization problem The nonconvexity in the equation comes from the second objective function and the constraints. The sixth optimization problem is obtained by using SCA iteration. The suboptimal solution. Step S34 includes the following steps: S341, User The expected signal power is written as: ; in, This indicates taking the real part of the complex variable; Indicates the integration of cascaded channel links and BS to the first The RIS beamforming vector after channel gain for each user is calculated using the following formula: ; Indicates the integration of cascaded channel links and BS to the first The system channel gain characterization after the channel gain of each user is expressed by the following formula: Hermitian matrix The calculation formula is ; S342, will the user Rewrite the signal-to-interference-plus-noise ratio: At this point, the second objective function is ;in, Represents a set of users The sum of the second ln logarithms of the expected power and the noise power is calculated using the following formula: , Indicates the integration of cascaded channel links and BS to the first The system channel gain representation after excluding user k, using the channel gain of each user, is represented by the second function. The calculation formula is ; S343. At this point, the nonconvexity of the second objective function comes from the second function. Using SCA iteration, the second feasible point in the nth iteration is defined as... The second function is derived using a first-order Taylor series. The lower bound expansion is: ; where, Indicates the second function The convex lower bound inequality The derivative is represented by . With the second function The product is The sixth optimization problem Transform into the seventh optimization problem : ;in, Describes the rank of a matrix; Indicates will Maximizing the seventh optimization problem when used as an optimization variable. The objective function; S344, Constraints The seventh optimization problem Non-convex rank 1 constraints, using equations By replacing it and transforming it into the form of the difference of convex functions, we obtain the non-convex constraint. ;in, Representing Hermitian matrices The sum of eigenvalues; Representing Hermitian matrices The largest eigenvalue; S345. Using SCA iteration and expanding the convex lower bound inequality on the left side of the inequality in the non-convex constraint using a first-order Taylor series: ;in, express The eigenvector corresponding to the largest eigenvalue. Representing Hermitian matrices The convex lower bound of the largest eigenvalue is calculated using the following formula: Finally, the BCD algorithm is used to solve the seventh optimization problem. Transformed into the eighth optimization problem : The eighth optimization problem It is convex, and it is solved using CVX.

[0020] Specifically, the BCD algorithm flowchart is as follows: Figure 2 As shown, step S4 specifically includes: Will As a parameter and initialized, the iteration count n is set to 0, and the maximum iteration count is set to 0. Set to 100; Will As a parameter, through the formula right Update; This indicates the (n+1)th time the user is restored at the BS. The receiving beamforming vector of the transmitted signal; Will As a parameter, for the fifth optimization problem Solve the problem and update the third feasible point in the (n+1)th iteration. , This indicates the nth time the user is restored at the BS. The receiving beamforming vector of the transmitted signal; Will As a parameter, for the eighth optimization problem Solve the problem and update the fourth feasible point in the (n+1)th iteration. ; Until the second optimization problem The objective function converges.

[0021] For example, a schematic diagram showing the performance comparison between this application and a traditional single-input single-output URLLC system is shown below. Figure 3 As shown, this application has a better system summation rate than traditional methods; this application combines RIS with an uplink multi-antenna URLLC system to solve the system summation rate maximization problem, and in particular provides a closed-form solution for the receive beamforming at the BS for uplink URLLC.

[0022] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. An optimization method for maximizing the summation rate of a RIS-assisted uplink URLLC system, characterized in that, include: S1. Construct a reconfigurable smart surface RIS-assisted uplink multi-user URLLC system model. The URLLC system model is a single-input multiple-output SIMO architecture. Establish signal transmission mathematical expressions based on the system model. S2. Construct a system summation rate maximization problem model based on communication theory and optimization theory, and improve it through problem transformation; S3. The constraints are transformed using the SCA technique, and the single optimal solution is obtained using the BCD algorithm. S4. Based on the single optimal solution obtained in step S3, the suboptimal solution of the original problem is obtained through iterative convergence.

2. The optimization method for maximizing the summation rate of a RIS-assisted uplink URLLC system according to claim 1, characterized in that, In step S1, the URLLC system consists of a base station (BS) and a reconfigurable smart surface (RIS), which work together to provide services. There are single-antenna uplink users, and the user set is defined as follows: Meanwhile, the base station (BS) is equipped with One antenna, specifically including: S11, Assume RIS includes It consists of several reflective units, and the unit set is as follows: The RIS phase shift matrix is ,in, The dimension is M OK M A complex matrix of columns, This represents a diagonal matrix, where the phase shift vector corresponds to the m-th reflecting unit. The angle of the phase shift generation range of RIS , , The imaginary unit representing Euler's formula; S12. Assume that the system channel gain satisfies quasi-static flat fading within the coherent block, and define RIS up to the th... The channel gain for each user is The channel gain from BS to RIS is BS to the The channel gain for each user is ,in, 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; S13, Define User The signal sent to the BS is The signal received at BS for: ;in, This represents the sum of signals from all users after passing through the channel link. This represents the additive white Gaussian noise received at BS, where H represents the conjugate transpose. (User) Signal-to-interference-to-noise ratio at the location for: , ;in, Represents a set of users In addition to users All other users, This indicates that the user has been restored at the BS. The receiving beamforming vector of the transmitted signal, Indicates user The power of the transmitted signal. Indicates a single-antenna user The receiving beamforming vector, Indicates a single-antenna user The power of the transmitted signal. Describes the 2-norm. Indicates the power of Gaussian white noise; Represents the set of B / S to users Excluding users Other channel gains; Represents RIS to user set Excluding users Other channel gains; S14. Based on the finite block length coding theory, the user... The achievable transmission rate is expressed as ,in, Indicates channel dispersion; Indicates user The block length, Indicates user The probability of decoding errors, Gaussian function The reverse, if Then the user The achievable transmission rate is .

3. The optimization method for maximizing the summation rate of a RIS-assisted uplink URLLC system according to claim 2, characterized in that, Step S2 specifically includes the following steps: S21. Construct a system summation rate maximization problem, i.e. ,in, This represents the first optimization problem. Indicates user Receive beamforming unit energy constraint Indicates user Maximum transmit power constraint This represents the phase shift vector constraint of RIS. Indicates will When used as an optimization variable, it maximizes the first optimization problem. The objective function; S22. Obtain the uplink data from BS to RIS to user. Cascaded channels ,in This indicates the process from BS to RIS to the user. The cascaded channel link is calculated using the following formula: , Represents the phase shift vector of RIS. Then define the first covariance channel matrix. , ,in The expectation operator is represented here; it is assumed that the channel gain is constant within each coherent block, and the expectation operator is ignored. , will users The signal-to-noise ratio is rewritten as , Let the second channel covariance matrix be represented, and then the first optimization problem will be addressed. Equivalently transformed into a second optimization problem ,Right now: ;in, This represents the m-th element in the phase shift vector of RIS. Indicates user Maximum power threshold for transmission.

4. The optimization method for maximizing the summation rate of a RIS-assisted uplink URLLC system according to claim 3, characterized in that, Step S3 specifically includes the following steps: S31, will As a parameter, for users recovering at BS Receive beamforming vector of transmitted signal Optimize the second optimization problem. Transform into a third optimization problem ,Right now ; in, Indicates will Maximizing the third optimization problem when used as an optimization variable. The objective function; S32. Based on the relevant lemmas of the paper, the relevant lemmas of the paper are for... The third optimization problem involves scaling by any positive factor. The objective function remains unchanged, and the third optimization problem is safely removed. Constraints, then scale Make The third optimization problem This becomes a standard eigenvalue problem, namely, BS reception. Find the closed-form solution. Closed-form solution ,in, Representation matrix The reverse; This represents the composite channel gain; its calculation formula is: ; S33, will As a parameter, for the user Transmitted signal power Optimize the second optimization problem. Transform into the fourth optimization problem ,Right now ;in, Indicates will When used as an optimization variable, it maximizes the fourth optimization problem. The objective function; S34, will As parameters, respectively and Optimize the second optimization problem. Transform into the sixth optimization problem ,Right now ;in, Indicates will Maximizing the sixth optimization problem when used as an optimization variable. The objective function.

5. The optimization method for maximizing the summation rate of a RIS-assisted uplink URLLC system according to claim 4, characterized in that, The fourth optimization problem in step S33 The nonconvexity comes from the first objective function, and the fourth optimization problem is obtained using SCA iteration. The optimal solution includes the following steps: S331. Rewrite the first objective function as follows: , where the user set The first ln logarithm sum of the expected power and the noise power The calculation formula is , Let the first function be represented, and its calculation formula be: Receive beamforming matrix at BS The calculation formula is , Describes the rank of a matrix; S332. Using SCA iteration, the first feasible point in the nth iteration is... The first function is derived by using a first-order Taylor series. The lower bound unfolds: Among them, Represents the first function The convex lower bound inequality express The derivative of With the first function The product is ;in, Represents a set of users Excluding users and users The remaining users; Represents the third channel covariance matrix; Represents the covariance matrix of the fourth channel; Represents a set of users In addition to users All other users besides; Represents a set of users In addition to users All other users besides; S333, Using the BCD algorithm to solve the fourth optimization problem Transform into the fifth optimization problem : Among them, the fifth optimization problem It is convex, and it is solved using CVX.

6. The optimization method for maximizing the summation rate of a RIS-assisted uplink URLLC system according to claim 5, characterized in that, Step S34 includes the following steps: S341, User The expected signal power is written as: ； in, This indicates taking the real part of the complex variable. This indicates the process from BS to RIS to the user. The cascaded channel link is calculated using the following formula: ; Indicates the integration of cascaded channel links and BS to the first The RIS beamforming vector after channel gain for each user is calculated using the following formula: ; Indicates the integration of cascaded channel links and BS to the first The system channel gain characterization after the channel gain of each user is expressed by the following formula: Hermitian matrix The calculation formula is ; S342, will the user Rewrite the signal-to-interference-plus-noise ratio: At this point, the second objective function is ;in, Represents a set of users The sum of the second ln logarithms of the expected power and the noise power is calculated using the following formula: , Indicates the integration of cascaded channel links and BS to the first The system channel gain representation after excluding user k, using the channel gain of each user, is represented by the second function. The calculation formula is ; S343. Using SCA iteration, define the second feasible point in the nth iteration as... The second function is derived using a first-order Taylor series. The lower bound expansion is: ; where, Indicates the second function The convex lower bound inequality The derivative is represented by . With the second function The product is The sixth optimization problem Transform into the seventh optimization problem : ;in, Describes the rank of a matrix; Indicates will Maximizing the seventh optimization problem when used as an optimization variable. The objective function; S344, Constraints The seventh optimization problem Non-convex rank 1 constraints, using equations By replacing it and transforming it into the form of the difference of convex functions, we obtain the non-convex constraint. ;in, Representing Hermitian matrices The sum of eigenvalues; Representing Hermitian matrices The largest eigenvalue; S345. Using SCA iteration and expanding the convex lower bound inequality on the left side of the inequality in the non-convex constraint using a first-order Taylor series: ;in, express The eigenvector corresponding to the largest eigenvalue. Representing Hermitian matrices The convex lower bound of the maximum eigenvalue is finally addressed using the BCD algorithm to solve the seventh optimization problem. Transformed into the eighth optimization problem : The eighth optimization problem It is convex, and it is solved using CVX.

7. The optimization method for maximizing the summation rate of a RIS-assisted uplink URLLC system according to claim 6, characterized in that, Step S4 specifically includes: Will As a parameter and initialized, the iteration count n is set to 0, and the maximum iteration count is set to 0. Set to 100; Will As a parameter, through the formula right Update; This indicates the (n+1)th time the user is restored at the BS. The receiving beamforming vector of the transmitted signal; Will As a parameter, for the fifth optimization problem Solve the problem and update the third feasible point in the (n+1)th iteration. , This indicates the nth time the user is restored at the BS. The receiving beamforming vector of the transmitted signal; Will As a parameter, for the eighth optimization problem Solve the problem and update the fourth feasible point in the (n+1)th iteration. ; Until the second optimization problem The objective function converges.

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