A multi-RIS-assisted MIMO system beamforming method and device

Abstract

The present invention discloses a beamforming method for a multi-RIS-assisted MIMO system, comprising the following steps: S1, establishing a multi-RIS-assisted MIMO communication system model under the influence of non-ideal CSI; S2, obtaining the actual CSI error, maximizing the energy efficiency of the multi-RIS-assisted MIMO communication system model, establishing an optimization problem, and jointly optimizing the specified parameters; S3, solving the optimization problem, determining the optimal value of the specified parameter, so as to optimize the energy efficiency of the system under non-ideal CSI conditions. The present invention can simulate the situation where there are errors in channel state information in actual applications, and can maximize the system performance while considering energy consumption, so as to facilitate determining the optimal value of the specified parameter under non-ideal CSI conditions, realize effective optimization of the system energy efficiency under non-ideal CSI conditions, and significantly improve the energy efficiency of the wireless communication system.

Description

Technical Field

[0001] The present invention relates to the technical field of communication system beamforming, and in particular to a multi-RIS-assisted MIMO system beamforming method, device and storage medium based on non-ideal CSI. Background Art

[0002] Reconfigurable smart surface (RIS) is an emerging wireless communication technology that aims to achieve more efficient signal transmission and improve network performance through intelligent control of radio waves. RIS is usually composed of a large number of low-power controllable reflection units. These reflection units can dynamically manipulate the incident signal by adjusting the phase and amplitude, thereby changing the direction, intensity and coverage of signal propagation. Unlike traditional repeaters or amplifiers, RIS does not rely on active signal processing, but uses passive control technology, which introduces almost no additional power consumption in signal transmission. This passive characteristic significantly reduces network energy consumption, making it an important part of future green communication technology. In addition, RIS has the advantages of being light, low-cost, and easy to deploy, and can be flexibly applied to various scenarios.

[0003] The potential application of RIS technology in wireless communications is very extensive: in indoor communications, RIS can overcome the signal attenuation problem caused by obstacles such as walls by optimizing the signal reflection path, thereby improving indoor coverage. In outdoor cellular networks, RIS can enhance the penetration of signals in areas with densely populated high-rise buildings, effectively improving signal coverage blind spots. In addition, RIS can also be used to enhance the stability of millimeter-wave communications and solve the problem of high-frequency signals being easily blocked.

[0004] In RIS-assisted communication, beamforming is the core technology for achieving signal control. Through beamforming, the system can accurately focus signal energy on the target user, thereby improving signal strength and coverage.

[0005] However, in actual scenarios, channel state information (CSI) is often difficult to obtain perfectly, and beamforming design is often not easy. Non-ideal CSI may cause the system parameter design to deviate from the theoretical optimal value, thereby reducing the performance of the communication system. In addition, with the increase in system scale and the complexity of multi-user scenarios, beamforming also needs to solve the problem of multi-user interference and deal with the computational difficulty brought by a large number of variables in the optimization process.

[0006] To this end, the present application proposes a multi-RIS-assisted MIMO system beamforming method based on non-ideal CSI to solve the above technical problems. Summary of the invention

[0007] The main purpose of the present invention is to provide a multi-RIS assisted MIMO system beamforming method, which is used to optimize the system energy efficiency under the condition that the acquired CSI has errors, so as to solve the technical problems raised in the background technology.

[0008] The present invention adopts the following technical solutions to solve the above technical problems:

[0009] A multi-RIS assisted MIMO system beamforming method comprises the following steps:

[0010] S1. Establish a multi-RIS-assisted MIMO communication system model under the influence of non-ideal CSI;

[0011] S2. Obtain the actual CSI error, establish an optimization problem based on the energy efficiency maximization of the multi-RIS-assisted MIMO communication system model, and jointly optimize the base station transmit beamforming parameters, RIS 1 reflection coefficient, RIS 2 reflection coefficient, and user receive beamforming parameters;

[0012] S3. Solve the optimization problem and determine the optimal base station transmit beamforming parameters, the optimal RIS 1 reflection coefficient, the optimal RIS 2 reflection coefficient, and the optimal user receive beamforming parameters to optimize the system energy efficiency under non-ideal CSI conditions.

[0013] Preferably, the multi-RIS assisted MIMO communication system in step S1 includes:

[0014] Proximal user cluster, a user cluster consisting of multiple proximal users;

[0015] Remote user cluster, a user cluster consisting of multiple remote users;

[0016] A base station, wherein the base station and the user are both equipped with multiple antennas;

[0017] RIS 1, deployed near the base station to assist in communication;

[0018] RIS 2, deployed near the remote user cluster, for auxiliary communication;

[0019] The base station transmits signals to serve the near-end user cluster via the direct link and the RIS 1 reflection link, and to serve the far-end user cluster via the inter-RIS reflection link.

[0020] Preferably, the process of establishing the optimization problem in step S2 includes:

[0021] S21, respectively calculate the equivalent channel between the base station and RIS 1, and the equivalent channel between RIS 1 and the near-end user Equivalent channel between base station and near-end user Equivalent channels between RISs, and between RIS 2 and remote users Equivalent channel between

[0022] S22, based on the estimated equivalent channel known to the base station and the unknown channel estimation error, calculate the base station to the near-end user and remote users Composite channel of

[0023] S23, after calculating the user decoding, the near-end user and remote users The received signal;

[0024] S24, based on the near-end user and remote users The received signal is used to calculate the near-end user and remote users The received signal-to-interference-noise ratio;

[0025] S25. Obtain the static total power consumption of the multi-RIS assisted MIMO system , and based on the static total power consumption of multi-RIS-assisted MIMO systems , calculate the energy efficiency of multi-RIS-assisted MIMO system ;

[0026] S26, Energy efficiency of MIMO system based on multi-RIS assistance Formulate an energy efficiency optimization problem.

[0027] Preferably, the energy efficiency optimization problem includes:

[0028]

[0029] There are constraints:

[0030] ；

[0031] ；

[0032] ；

[0033] ；

[0034] Among them, the constraints Indicates that the base station transmit power does not exceed the maximum allowed transmit power ,constraint Indicates that the near-end user's received signal-to-interference-and-noise ratio is greater than or equal to the user's minimum acceptable signal-to-interference-and-noise ratio ,constraint Indicates that the received signal-to-interference-and-noise ratio of the remote user is greater than or equal to the minimum signal-to-interference-and-noise ratio acceptable to the user. ,constraint Indicates that the RIS reflection phase is taken from the set Discrete values ​​within are the collection of reflection units on RIS 1 and RIS 2 respectively, The set is a discrete phase set, , is the quantization order, For near-end users The received signal-to-interference-noise ratio, For remote users The received signal-to-interference-noise ratio, and are the sets of near-end users and far-end users respectively, For RIS The equivalent reflection coefficient is For RIS Previous The reflection phase of the reflection unit, is a complex value with reflection amplitude and phase information, j is an imaginary unit, and Represents RIS Previous The reflection coefficient of a reflection unit, and Base station to near-end user and remote users The transmit beamforming vector of and Near-end users and remote users The receive beamforming vector at , It means to find the maximum value that the objective function can reach. Represents the bi-norm of a vector.

[0035] Preferably, the process of solving the optimization problem in step S3 includes:

[0036] S31, converting the energy efficiency optimization problem by setting auxiliary variables, concentrating the channel estimation error into constraints, so as to remove the unknown channel estimation error existing in the energy efficiency optimization problem function;

[0037] S32, using an alternating optimization method, decompose the energy efficiency optimization problem after the conversion process in S31 into four sub-problems and solve them independently;

[0038] S33. Iterate the solutions of the sub-problems until convergence to obtain the optimal base station transmit beamforming parameters, the optimal RIS 1 reflection coefficient, the optimal RIS 2 reflection coefficient, and the optimal user receive beamforming parameters to optimize the system energy efficiency under non-ideal CSI conditions.

[0039] Preferably, the sub-problems in step S32 include sub-problems of optimizing base station transmit beamforming parameters and RIS 1 reflection coefficients, which are expressed as:

[0040]

[0041] There are constraints:

[0042] ；

[0043] ；

[0044] ；

[0045] ；

[0046] in, and are auxiliary variables, is the total static power consumption of the multi-RIS-assisted MIMO system, is the efficiency of the transmit power amplifier, It means to find the minimum value that the objective function can reach. Represents the bi-norm of a vector.

[0047] Preferably, the subproblem in step S32 includes the subproblem of optimizing auxiliary variables, which is expressed as:

[0048]

[0049] There are constraints:

[0050] ；

[0051] ；

[0052] in, and are auxiliary variables, For bandwidth.

[0053] Preferably, the sub-problem in step S32 includes the sub-problem of optimizing the user receiving beamforming parameters, which is expressed as:

[0054]

[0055] There are constraints:

[0056] ；

[0057] ；

[0058] in, and are auxiliary variables, It means finding feasible solutions of optimization variables that satisfy all constraints.

[0059] Preferably, the subproblem in step S32 includes the subproblem of optimizing the RIS 2 reflection coefficient, which is expressed as:

[0060]

[0061] There are constraints:

[0062] ；

[0063] ；

[0064] in, and are auxiliary variables, It means finding feasible solutions of optimization variables that satisfy all constraints.

[0065] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0066] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0067] It can be seen from the above technical solution that the present invention provides a multi-RIS assisted MIMO system beamforming method. Compared with the prior art, the present invention has the following advantages:

[0068] 1. The present invention constructs a system model under the influence of non-ideal CSI in a multi-RIS-assisted MIMO communication system, which can simulate the situation where errors exist in channel state information in actual applications, thereby achieving a system performance evaluation effect that is closer to the actual situation and providing a reliable basis for system optimization.

[0069] 2. Under the condition that the acquired CSI has errors, the present invention further calculates the system energy efficiency based on the calculated received signal-to-interference-and-noise ratio and static total power consumption of the near-end and far-end users, and establishes a corresponding energy efficiency optimization problem with the goal of maximizing the system energy efficiency and solves the corresponding problem. It can maximize the system performance while considering energy consumption, thereby facilitating the determination of the optimal values ​​of the specified parameters under non-ideal CSI conditions, and significantly improving the energy utilization efficiency of the wireless communication system.

[0070] 3. The present invention decomposes the energy efficiency optimization problem by adopting an alternating optimization method and solves it iteratively, which can simplify complex optimization problems into manageable sub-problems, thereby reducing the difficulty of solving the problem, facilitating fast and efficient acquisition of the optimal values ​​of the specified parameters, and achieving effective optimization of the system energy efficiency under non-ideal CSI conditions.

[0071] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become easy to understand through the following description. Of course, it is not necessary to achieve all of the advantages described above simultaneously for any product implementing the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0073] Figure 1 It is a schematic diagram of the overall process of the method of the present invention;

[0074] Figure 2 A schematic diagram of a use scenario of a multi-RIS-assisted MIMO communication system of the present invention;

[0075] Figure 3 It is a schematic diagram comparing the system energy efficiency of the method of the present invention, an ideal channel state scenario, and two existing methods. DETAILED DESCRIPTION

[0076] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. In the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0077] In the embodiment, see Figures 1 to 3 .

[0078] like Figure 1 As shown, a multi-RIS assisted MIMO system beamforming method proposed in an embodiment of the present invention is used to optimize the system energy efficiency under the condition that the acquired CSI has errors, including the following steps:

[0079] S1. Establish a multi-RIS-assisted MIMO communication system model under the influence of non-ideal CSI.

[0080] The multi-RIS assisted MIMO communication system includes:

[0081] Proximal user cluster, a user cluster consisting of multiple proximal users;

[0082] Remote user cluster, a user cluster consisting of multiple remote users;

[0083] The base station, base station and users are equipped with multiple antennas, and RIS is configured near the base station and remote user clusters for auxiliary communication;

[0084] RIS 1, deployed near the base station;

[0085] RIS 2, deployed near the remote user cluster;

[0086] The base station transmits signals to serve the near-end user cluster via the direct link and the RIS 1 reflection link, and to serve the far-end user cluster via the inter-RIS reflection link;

[0087] In addition, affected by external factors, the system has errors in channel estimation of some communication links, the obtained CSI is not completely accurate, and the base station serves the near-end user group and the far-end user group through RIS 1 and RIS 2.

[0088] It should be noted at this point that by constructing a system model under the influence of non-ideal CSI in a multi-RIS-assisted MIMO communication system, it is possible to simulate the situation in which errors exist in the channel state information in actual applications, thereby achieving a system performance evaluation effect that is closer to the actual situation and providing a reliable basis for system optimization.

[0089] S2. Based on the established system model, under the condition that the acquired CSI has errors, an optimization problem is designed with the goal of maximizing the system energy efficiency.

[0090] The optimization problem establishment process includes:

[0091] S21, set They represent the equivalent channel between the base station and RIS 1, and the equivalent channel between RIS 1 and the near-end user. Equivalent channel between base station and near-end user Equivalent channels between RIS, equivalent channels between RIS 2 and remote users Equivalent channel between

[0092] set up , represents the estimated equivalent channel known to the base station, , , represents the unknown channel estimation error;

[0093] Equivalent Channel , , Respectively expressed as:

[0094]

[0095]

[0096]

[0097] in, represents the Frobenius-norm of the matrix, , , They are , , The upper bound of the Frobenius-norm of ; and They are respectively the collection of near-end users and far-end users.

[0098] S22, from base station to near-end user and remote users The composite channels are expressed as:

[0099]

[0100]

[0101] in:

[0102] Represents RIS The reflection coefficient matrix of

[0103] For RIS The equivalent reflection coefficient of

[0104] is a complex value with reflection amplitude and phase information, j is an imaginary unit, and Represents RIS Previous The reflection coefficient of each reflection unit;

[0105] Represents RIS Previous The reflection coefficient phase of each reflection unit;

[0106] Represents the Hermitian transpose of a matrix.

[0107] S23, set the base station to the near-end user and remote users The transmit beamforming vectors are and , Near-end user and remote users The receive beamforming vectors at are and ,set up and Respectively indicate sending to the near-end user and remote users signal, satisfying and , the base station's transmission signal is expressed as .

[0108] After decoding by the user, the near-end user and remote users The received signals are expressed as:

[0109]

[0110]

[0111]

[0112]

[0113] in, For near-end users The received signal, For remote users The received signal; and Near-end users and remote users The additive white Gaussian noise vector at , and is the equivalent noise power; is the identity matrix.

[0114] S24. According to the definition of signal-to-interference-noise ratio, the near-end user and remote users The received signal-to-interference-noise ratio is expressed as:

[0115]

[0116]

[0117] in For near-end users The received signal-to-interference-noise ratio, For remote users The received signal-to-interference-noise ratio.

[0118] S25. Obtain the static total power consumption of the multi-RIS assisted MIMO system , and based on the static total power consumption of multi-RIS-assisted MIMO systems , calculate the energy efficiency of multi-RIS-assisted MIMO system ,have:

[0119] Static total power consumption of multi-RIS assisted MIMO system It is expressed as follows:

[0120]

[0121] in, , , , Near-end users , Remote User , the hardware static power consumed by the base station and each RIS reflection unit, is the total number of reflector units on RIS 1 and RIS 2

[0122] Energy efficiency of multi-RIS-assisted MIMO systems Defined as:

[0123]

[0124] in, is the bandwidth, is the efficiency of the transmit power amplifier, Represents the bi-norm of a vector.

[0125] S26. The energy efficiency optimization problem (P1) is formulated as follows:

[0126] (P1):

[0127]

[0128] There are constraints :

[0129] ;

[0130] ;

[0131] ;

[0132] ;

[0133] Among them, the constraints Indicates that the base station transmit power does not exceed the maximum allowed transmit power ,constraint Indicates that the near-end user's received signal-to-interference-and-noise ratio is greater than or equal to the user's minimum acceptable signal-to-interference-and-noise ratio ,constraint Indicates that the received signal-to-interference-and-noise ratio of the remote user is greater than or equal to the minimum signal-to-interference-and-noise ratio acceptable to the user. ,constraint Indicates that the RIS reflection phase is taken from the set Discrete values ​​within is the set of reflection units on RIS 1 and RIS 2, The set is a discrete phase set, , is the quantization order, For near-end users The received signal-to-interference-noise ratio, For remote users The received signal-to-interference-noise ratio, It means to find the maximum value that the objective function can reach. Represents the bi-norm of a vector.

[0134] In summary, the base station transmit beamforming parameters, RIS 1 reflection coefficient, RIS 2 reflection coefficient and user receive beamforming parameters are jointly optimized.

[0135] S3. Solve the optimization problem and determine the optimal base station transmit beamforming parameters, the optimal RIS 1 reflection coefficient, the optimal RIS 2 reflection coefficient, and the optimal user receive beamforming parameters to optimize the system energy efficiency under non-ideal CSI conditions.

[0136] Solving the optimization problem includes the following steps:

[0137] S31. Affected by non-ideal CSI, there is an unknown channel estimation error in the objective function of problem (P1), which needs to be removed.

[0138] To this end, we introduce an auxiliary variable and , and meet and , the channel estimation error is concentrated into the constraint for easy solution. The energy efficiency optimization problem (P1) is transformed into:

[0139] (P1.1):

[0140]

[0141] There are constraints :

[0142] ;

[0143] ;

[0144] ;

[0145] ;

[0146] in, It means to find the maximum value that the objective function can reach. Represents the bi-norm of a vector.

[0147] S32, after the conversion step in S31, the channel estimation errors in problem (P1.1) have all been concentrated into constraints C5 and C6;

[0148] Affected by non-convex constraints and coupling of optimization variables, problem (P1.1) is still difficult to solve;

[0149] To this end, an alternating optimization method is used to decompose the problem (P1.1) into four sub-problems and solve them independently. The first sub-problem optimizes the base station transmit beamforming parameters and RIS 1 reflection coefficient, the second sub-problem optimizes the auxiliary variables, the third sub-problem optimizes the user receive beamforming parameters, and the fourth sub-problem optimizes the RIS 2 reflection coefficient.

[0150] (1) The sub-problem (P2) of optimizing the base station transmit beamforming parameters and RIS 1 reflection coefficient is expressed as follows:

[0151] (P2):

[0152]

[0153] There are constraints :

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] in It means to find the minimum value that the objective function can reach. Represents the bi-norm of a vector.

[0159] (2) The subproblem (P3) about optimizing auxiliary variables is expressed as follows:

[0160] (P3):

[0161]

[0162] There are constraints :

[0163] ;

[0164] ;

[0165] in, It means to find the maximum value that the objective function can reach. Represents the bi-norm of a vector.

[0166] (3) The sub-problem (P4) of optimizing the user receive beamforming parameters is expressed as follows:

[0167] (P4):

[0168]

[0169] There are constraints :

[0170] ;

[0171] ;

[0172] in, It means finding feasible solutions of optimization variables that satisfy all constraints.

[0173] (4) The subproblem (P5) of optimizing the RIS 2 reflection coefficient is expressed as follows:

[0174] (P5):

[0175]

[0176] There are constraints :

[0177] ；

[0178] ；

[0179] in, It means finding feasible solutions of optimization variables that satisfy all constraints.

[0180] S33, regarding the sub-problem (P2) of optimizing the base station transmit beamforming parameters and RIS 1 reflection coefficient, it is first necessary to eliminate the channel estimation errors in constraints C5 and C6. After matrix transformation and S-procedure approximation, constraints C5 and C6 are transformed into:

[0181]

[0182]

[0183] Among them, constraint C7 is transformed from C5, and constraint C8 is transformed from C6. , , , , , are matrices and variables used to simplify the derivation. and is the slack variable introduced by the S-procedure, and is the upper limit of the matrix norm obtained in the derivation, is the identity matrix.

[0184] Substituting constraints C7 and C8 into problem (P2) and applying the SDR method, the problem is transformed into:

[0185] (P2.1):

[0186]

[0187] There are constraints :

[0188] ;

[0189] ;

[0190] ;

[0191] ;

[0192] ;

[0193] ;

[0194] ;

[0195] ;

[0196] ;

[0197] ;

[0198] ;

[0199] in , and They are respectively , and The constructed matrix, It means to find the minimum value that the objective function can reach. represents the trace of the matrix, represents the rank of the matrix, Indicates the matrix Line Elements of a column.

[0200] Due to the variable coupling, problem (P2.1) needs further processing. Using the alternating optimization algorithm, problem (P2.1) is decomposed into two sub-problems. The first sub-problem optimizes the base station transmit beamforming parameters and relaxation variables, and the second sub-problem optimizes the RIS 1 reflection coefficient.

[0201] The subproblem (P2.2) of optimizing the base station transmit beamforming parameters and slack variables is expressed as follows:

[0202] (P2.2):

[0203]

[0204] There are constraints :

[0205] ;

[0206] ;

[0207] ;

[0208] ;

[0209] ;

[0210] ;

[0211] ;

[0212] in, represents the trace of the matrix, Represents the rank of the matrix.

[0213] By relaxing the rank-one constraints C10 and C12, problem (P2.2) is transformed into a standard SDP problem, which can be solved efficiently using standard convex optimization tools. and Perform EVD decomposition on each matrix in and recover the optimal base station transmit beamforming parameters;

[0214] The subproblem (P2.3) of optimizing the RIS 1 reflection coefficient is expressed as follows:

[0215] (P2.3):

[0216]

[0217] There are constraints :

[0218] ；

[0219] ;

[0220] ;

[0221] ;

[0222] ;

[0223] ;

[0224] in, It means finding a feasible solution of the optimization variables that satisfies all constraints. represents the rank of the matrix, Indicates the matrix Line Elements of a column.

[0225] By relaxing the discrete phase constraint C4 and the rank-one constraint C15, problem (P2.3) can be effectively solved using standard convex optimization tools. The resulting matrix is ​​decomposed by EVD to obtain the optimal RIS 1 continuous phase coefficient, and then quantized to obtain the optimal RIS1 discrete phase coefficient.

[0226] S34. Regarding the subproblem (P3) of optimizing auxiliary variables, the objective function of the problem is in the form of logarithmic function summation. After Lagrange dual transformation and quadratic transformation, and substituting constraints C5 and C6, the problem is transformed into:

[0227] (P3.1):

[0228]

[0229] There are constraints :

[0230] ;

[0231] ;

[0232] in, and is the auxiliary variable introduced by the Lagrange dual transformation, and is the auxiliary variable introduced by the quadratic transformation. Use standard convex optimization tools to solve problem (P3.1) and obtain the optimal auxiliary variable value. It means to find the maximum value that the objective function can reach.

[0233] S35. Regarding the sub-problem (P4) of optimizing the user receiving beamforming parameters, it is first necessary to eliminate the channel estimation errors in constraints C5 and C6. After matrix transformation and S-procedure approximation, constraints C5 and C6 are transformed into:

[0234] ;

[0235] ;

[0236] Among them, constraint C16 is transformed from C5, and constraint C17 is transformed from C6. and is the slack variable introduced by the S-procedure, , , , , , are matrices and variables used to simplify the derivation. and is the upper limit of the matrix norm obtained in the derivation, is the identity matrix. Substituting constraints C16 and C17, problem (P4) is transformed into:

[0237] (P4.1):

[0238]

[0239] There are constraints :

[0240] ;

[0241] ;

[0242] ;

[0243] ;

[0244] ;

[0245] ;

[0246] in, and They are respectively and The constructed matrix, It means finding a feasible solution of the optimization variables that satisfies all constraints. represents the rank of the matrix. By relaxing the rank-one constraints C19 and C21, problem (P4.1) is transformed into a standard SDP problem, which can be solved using standard convex optimization tools. Perform EVD decomposition on each matrix in the obtained matrix set to obtain the optimal user receive beamforming parameters.

[0247] S36. Regarding the subproblem (P5) of optimizing the RIS 2 reflection coefficient, we first need to eliminate the channel estimation error in constraint C5. After matrix transformation and S-procedure approximation, constraint C5 is transformed into:

[0248]

[0249] in, is the slack variable introduced by the S-procedure, , , are matrices and variables used to simplify the derivation. is the upper limit of the matrix norm obtained in the derivation, is the identity matrix. Substituting constraint C22, problem (P5) is transformed into:

[0250] (P5.1):

[0251]

[0252] There are constraints :

[0253] ;

[0254] ;

[0255] ;

[0256] ;

[0257] ;

[0258] in, Is The constructed matrix, It means finding a feasible solution of the optimization variables that satisfies all constraints. represents the rank of the matrix, Indicates the matrix Line The discrete phase constraint C4 and the rank-one constraint C25 are relaxed, and the problem (P5.1) is transformed into a standard SDP problem, which can be solved efficiently using standard convex optimization tools. The optimal RIS 2 continuous phase coefficients are obtained by EVD decomposition of the obtained matrix, and the optimal RIS 2 discrete phase coefficients are obtained by quantization.

[0259] S37. Iterate the solutions to the above sub-problems until convergence to obtain the optimal base station transmit beamforming parameters, the optimal RIS 1 reflection coefficient, the optimal RIS 2 reflection coefficient, and the optimal user receive beamforming parameters, so as to optimize the system energy efficiency under non-ideal CSI conditions.

[0260] Therefore, under the condition that the obtained CSI has errors, the system energy efficiency is further calculated based on the calculated received signal-to-interference-noise ratio and static total power consumption of the near-end and far-end users, and the corresponding energy efficiency optimization problem is established and solved with the goal of maximizing the system energy efficiency. It is possible to maximize the system performance while considering energy consumption, thereby facilitating the determination of the optimal base station transmit beamforming parameters, the optimal RIS 1 reflection coefficient, the optimal RIS 2 reflection coefficient and the optimal user receive beamforming parameters under non-ideal CSI conditions, significantly improving the energy utilization efficiency of the wireless communication system, and by adopting the alternating optimization method to decompose the energy efficiency optimization problem and iteratively solve it, it is possible to simplify the complex optimization problem into manageable sub-problems, thereby reducing the difficulty of solving the problem, facilitating the rapid and efficient acquisition of the optimal base station transmit beamforming parameters, RIS reflection coefficient and user receive beamforming parameters, and achieving effective optimization of the system energy efficiency under non-ideal CSI conditions.

[0261] In addition, in a specific embodiment, the energy efficiency optimization effect achieved by the present invention can be obtained by combining the following table: Figure 3 Further explanation:

[0262] Comparison table of system energy efficiency data of the method of the present invention, the ideal channel state scenario and two existing methods:

[0263]

[0264] Combining this table and Figure 3 It can be seen that under the same simulation parameter settings, the system energy efficiency achievable by the method of the present invention is better than that of the random RIS reflection coefficient method and the random beamforming method, and is closer to the system energy efficiency value under the ideal channel state scenario, that is, the method of the present invention has a better system energy efficiency optimization effect.

[0265] In summary, by jointly optimizing the base station transmit beamforming parameters, RIS 1 reflection coefficient, RIS 2 reflection coefficient and user receive beamforming parameters, the composite channel can be calculated based on the known estimated equivalent channel and the unknown channel estimation error, so as to accurately calculate the near-end and far-end user receiving signals, effectively improving the system's transmission efficiency and service quality.

[0266] And by comparing the simulation results, its superiority over the existing methods is demonstrated. Under the same simulation parameter settings, the advantage of the method of the present invention in system energy efficiency can be proved, thereby achieving a system energy efficiency value close to that in the ideal channel state scenario. Finally, it can verify the effectiveness of the present invention in solving the energy efficiency optimization problem under non-ideal channel information acquisition conditions.

[0267] In addition, based on the above embodiments, the present invention also discloses a multi-RIS assisted MIMO communication system, such as Figure 2 As shown, the above-mentioned embodiment method is used to construct a specified model, and then further construct a system. They represent the equivalent channel between the base station and RIS 1, and the equivalent channel between RIS 1 and the near-end user. Equivalent channel between base station and near-end user Equivalent channels between RIS, equivalent channels between RIS 2 and remote users The base station provides communication services for the near-end user group and the far-end user group respectively through multiple RIS. The method of the present invention can effectively solve the energy efficiency optimization problem of such a system under the condition of non-ideal channel information acquisition.

[0268] Specifically, when there are errors in the channel state information, the goal is to maximize the system energy efficiency, while constraining the base station transmit power and meeting the user service quality requirements, so as to determine the optimal base station transmit beamforming parameters, the optimal RIS1 reflection coefficient, the optimal RIS 2 reflection coefficient and the optimal user receive beamforming parameters to achieve system energy efficiency optimization.

[0269] On the other hand, the present invention also discloses a non-temporary computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the above method to optimize and configure the base station transmit beamforming parameters, RIS 1 reflection coefficient, RIS 2 reflection coefficient and user receive beamforming parameters.

[0270] The present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0271] In another embodiment provided by the present application, a computer program product including instructions is also provided. When the computer program product is executed on a computer, the computer executes any of the multi-RIS-assisted MIMO system beamforming methods in the above embodiments.

[0272] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

[0273] The embodiment of the present application also provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus.

[0274] Memory, used to store computer programs;

[0275] The processor is used to implement the above-mentioned multi-RIS assisted MIMO system beamforming method when executing the program stored in the memory.

[0276] The communication bus mentioned in the above electronic device can be a peripheral component interconnect standard bus or an extended industrial standard architecture bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0277] The communication interface is used for communication between the above electronic device and other devices.

[0278] The memory may include a random access memory, or may include a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0279] The above-mentioned processor can be a general-purpose processor, including a central processing unit, a network processor, etc.; it can also be a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component.

[0280] It should also be noted that electronic devices also include terminal devices, which can also be called terminals, user equipment, mobile stations, mobile terminals, etc. Terminal devices can be mobile phones, smart TVs, wearable devices, tablet computers, computers with wireless transceiver functions, virtual reality terminal devices, augmented reality terminal devices, wireless terminals in industrial control, wireless terminals in unmanned driving, wireless terminals in remote surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, etc. The embodiments of this application do not limit the specific technology and specific device form used by the terminal devices.

[0281] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium, or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0282] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0283] In addition, it should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0284] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or schemes that A and B meet at the same time. In addition, in the embodiments of the present invention, "multiple" refers to more than two. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

Claims

1. A multi-RIS assisted MIMO system beamforming method, characterized in that: The following steps are involved: S1. Establish a multi-RIS-assisted MIMO communication system model under the influence of non-ideal CSI; S2. Obtain the actual CSI error, establish an optimization problem based on the energy efficiency maximization of the multi-RIS-assisted MIMO communication system model, and jointly optimize the base station transmit beamforming parameters, RIS 1 reflection coefficient, RIS 2 reflection coefficient, and user receive beamforming parameters; S3. Solve the optimization problem and determine the optimal base station transmit beamforming parameters, the optimal RIS 1 reflection coefficient, the optimal RIS 2 reflection coefficient, and the optimal user receive beamforming parameters to optimize the system energy efficiency under non-ideal CSI conditions; The solution process of the optimization problem is to transform the optimization problem by setting auxiliary variables, and then use an alternating optimization method to decompose the transformed optimization problem into four sub-problems and solve them independently, and iterate the solutions of the sub-problems until convergence; The subproblem includes the subproblem of optimizing auxiliary variables, which can be expressed as: There are corresponding constraints, and are auxiliary variables, is the bandwidth, It means to find the maximum value that the objective function can reach. and Near-end users and remote users A collection of .

2. The multi-RIS-assisted MIMO system beamforming method according to claim 1, characterized in that: The multi-RIS assisted MIMO communication system in step S1 includes: Proximal user cluster, a user cluster consisting of multiple proximal users; Remote user cluster, a user cluster consisting of multiple remote users; A base station, wherein the base station and the user are both equipped with multiple antennas; RIS 1, deployed near the base station to assist in communication; RIS 2, deployed near the remote user cluster, for auxiliary communication; The base station transmits signals to serve the near-end user cluster via the direct link and the RIS 1 reflection link, and to serve the far-end user cluster via the inter-RIS reflection link.

3. The multi-RIS-assisted MIMO system beamforming method according to claim 2, characterized in that: The process of establishing the optimization problem in step S2 includes: S21, respectively calculate the equivalent channel between the base station and RIS 1, and the equivalent channel between RIS 1 and the near-end user Equivalent channel between base station and near-end user Equivalent channels between RISs, and between RIS 2 and remote users Equivalent channel between S22, based on the estimated equivalent channel known to the base station and the unknown channel estimation error, calculate the base station to the near-end user and remote users Composite channel of S23, after calculating the user decoding, the near-end user and remote users The received signal; S24, based on the near-end user and remote users The received signal is used to calculate the near-end user and remote users The received signal-to-interference-noise ratio; S25. Obtain the static total power consumption of the multi-RIS assisted MIMO system , and based on the static total power consumption of multi-RIS-assisted MIMO systems , calculate the energy efficiency of multi-RIS-assisted MIMO system ; S26, Energy efficiency of MIMO system based on multi-RIS assistance Formulate an energy efficiency optimization problem.

4. The multi-RIS-assisted MIMO system beamforming method according to claim 3, characterized in that: The energy efficiency optimization problem includes: There are constraints: ； ； ； ； Among them, the constraints Indicates that the base station transmit power does not exceed the maximum allowed transmit power ,constraint Indicates that the near-end user's received signal-to-interference-and-noise ratio is greater than or equal to the user's minimum acceptable signal-to-interference-and-noise ratio ,constraint Indicates that the received signal-to-interference-and-noise ratio of the remote user is greater than or equal to the minimum signal-to-interference-and-noise ratio acceptable to the user. ,constraint Indicates that the RIS reflection phase is taken from the set Discrete values ​​within is the set of reflection units on RIS 1 and RIS 2, The set is a discrete phase set, , is the quantization order, For near-end users The received signal-to-interference-noise ratio, For remote users The received signal-to-interference-noise ratio, and are the sets of near-end users and far-end users respectively, For RIS The equivalent reflection coefficient is For RIS Previous The reflection phase of the reflection unit, is a complex value with reflection amplitude and phase information, j is an imaginary unit, and Represents RIS Previous The reflection coefficient of a reflection unit, and Base station to near-end user and remote users The transmit beamforming vector of and Near-end users and remote users The receive beamforming vector at , It means to find the maximum value that the objective function can reach. Represents the bi-norm of a vector.

5. The multi-RIS-assisted MIMO system beamforming method according to claim 4, characterized in that: The process of solving the optimization problem in step S3 includes: S31, converting the energy efficiency optimization problem by setting auxiliary variables, concentrating the channel estimation error into constraints, so as to remove the unknown channel estimation error existing in the energy efficiency optimization problem function; S32, using an alternating optimization method, decompose the energy efficiency optimization problem after the conversion process in S31 into four sub-problems and solve them independently; S33. Iterate the solutions of the sub-problems until convergence to obtain the optimal base station transmit beamforming parameters, the optimal RIS 1 reflection coefficient, the optimal RIS 2 reflection coefficient, and the optimal user receive beamforming parameters to optimize the system energy efficiency under non-ideal CSI conditions.

6. The multi-RIS-assisted MIMO system beamforming method according to claim 5, characterized in that: The sub-problems in step S32 include the sub-problems of optimizing the base station transmit beamforming parameters and the RIS 1 reflection coefficient, which are expressed as: There are constraints: ； ； ； ； in, and are auxiliary variables, is the total static power consumption of the multi-RIS-assisted MIMO system, is the efficiency of the transmit power amplifier, It means to find the minimum value that the objective function can reach. Represents the bi-norm of a vector.

7. The multi-RIS-assisted MIMO system beamforming method according to claim 5, characterized in that: The subproblem of optimizing the auxiliary variables in step S32 has constraints: ； ； in, and are auxiliary variables, For bandwidth.

8. The multi-RIS-assisted MIMO system beamforming method according to claim 5, characterized in that: The sub-problem in step S32 includes the sub-problem of optimizing the user receiving beamforming parameters, which is expressed as: There are constraints: ； ； in, and are auxiliary variables, It means finding feasible solutions of optimization variables that satisfy all constraints.

9. The multi-RIS-assisted MIMO system beamforming method according to claim 5, characterized in that: The subproblem in step S32 includes the subproblem of optimizing the RIS 2 reflection coefficient, which is expressed as: There are constraints: ； ； in, and are auxiliary variables, It means finding feasible solutions of optimization variables that satisfy all constraints.

10. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 8.

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