A Massive MIMO channel capacity optimization method and system based on intelligent reflective surface

Abstract

A Massive MIMO channel capacity optimization method and system based on smart reflective surfaces, relates to the field of wireless communication technology, and is used to solve the technical problem that traditional channel capacity optimization methods cannot maximize the channel capacity. The technical points of the present invention include: firstly, establishing a Massive MIMO channel capacity model based on smart reflective surfaces, and then taking maximizing the channel capacity as the objective function, optimizing and solving the objective function using the quantum water strider algorithm to obtain the optimal quantum position. The present invention uses the quantum water strider algorithm, a new design algorithm, with the significant advantages of higher search accuracy and search speed, to achieve intelligent joint configuration of channel capacity, greatly improving the Massive MIMO channel capacity based on smart reflective surfaces.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and system for optimizing the capacity of a Massive MIMO channel based on an intelligent reflecting surface. Background Art

[0002] In recent years, Massive MIMO has become an important way to realize 6G Internet of Everything by virtue of its ability to serve data transmission scenarios with large traffic, high reliability and low latency. Intelligent reflective surface technology has also attracted more and more attention due to its advantages such as strong scalability, low energy consumption, low cost and flexible deployment. However, with the continuous development of wireless communication technology, the access of a large number of antennas has brought problems such as high operating costs, excessive energy consumption and uneven network coverage to the communication system, which in turn affects the energy efficiency of traditional collaborative communication methods. Therefore, the ability to improve channel capacity has become a key issue that needs to be solved urgently.

[0003] Through the search of existing literature, it is found that for Massive MIMO communication scenarios based on smart reflective surfaces, Al-Wahhamy et al. published "Efficient evaluation of massive MIMO channel capacity" in "IEEE Systems Journal" (2019, vol. 14, no. 1, pp. 614-620), which proposed a channel capacity optimization method for Massive MIMO systems. However, this method does not take into account the advantages of smart reflective surfaces such as strong scalability, low energy consumption, low cost, and flexible deployment to improve the channel capacity of Massive MIMO systems. Although the existing methods have a certain effect on channel capacity optimization, the algorithms used are still traditional methods. The search accuracy of traditional algorithms is low and it cannot guarantee the maximization of channel capacity. Summary of the invention

[0004] In view of this, the present invention proposes a Massive MIMO channel capacity optimization method and system based on a smart reflective surface to solve the technical problem that traditional channel capacity optimization methods cannot maximize the improvement of channel capacity.

[0005] According to one aspect of the present invention, a method for optimizing the capacity of a Massive MIMO channel based on a smart reflective surface is proposed, comprising the following steps:

[0006] Step 1: Establish a Massive MIMO channel capacity model based on smart reflection surface;

[0007] Step 2: Taking maximizing the channel capacity as the objective function, the objective function is optimized and solved using the quantum water strider algorithm; the solution is iterated according to the following steps:

[0008] Step 21: Initialize the parameters of the quantum water strider algorithm;

[0009] Step 22: Division of the territory of quantum water strider groups;

[0010] Step 2 and 3: The male sends a courtship signal to the female through a corrugated signal in order to wait for an opportunity to mate;

[0011] Step 24: Evaluate the target fitness function and find the optimal quantum position of the quantum water strider;

[0012] Step 25: Determine whether the maximum number of iterations has been reached. If reached, terminate the iteration loop, output the global optimal quantum position of the quantum water strider, obtain the position according to the mapping rule and return the optimal fitness value.

[0013] Furthermore, the Massive MIMO channel capacity model in step 1 is established based on a Massive MIMO communication system, wherein the Massive MIMO communication system includes a base station, a receiving end, a central controller, and an intelligent reflection surface including a plurality of reflection units; wherein the central controller is used to adjust the phase shift of each reflection unit in the intelligent reflection surface in real time; and the Massive MIMO channel capacity model is expressed as:

[0014]

[0015] Where Q represents the covariance matrix of the transmitted signal, tr(Q) represents the trace of Q; P represents the power upper limit; det(.) represents the determinant of the matrix; I N represents the identity matrix, σ 2 represents the average noise power; represents the channel matrix from the transmitter to the receiver, where H represents the channel state information of the direct link from the transmitter to the receiver, R represents the channel state information from the smart reflective surface to the receiver, Φ represents the diagonal reflection matrix of the smart reflective surface, and T represents the channel state information from the transmitter to the smart reflective surface; It means to take the conjugate transpose of each element in H.

[0016] Furthermore, the specific steps of step 21 include: setting the size of the quantum water strider group to n, searching in the D-dimensional space, and mapping the quantum position of the i-th quantum water strider in the t-th iteration to the position: in, i=1,2,...,n,d=1,2,...,D,the specific mapping rules are: Among them, U b and L b They represent the upper and lower bounds of the quantum water strider search interval respectively.

[0017] Furthermore, the territory division of the quantum water strider group in step 22 includes: establishing n t territories, sort the quantum water striders according to their fitness values ​​and group them in order, and the jth member of each group is assigned to the jth territory, where j = 1, 2, ..., n t ; Each territory has at least one mature male quantum water strider: The remaining individuals in the territory are female quantum water striders.

[0018] Furthermore, the specific steps of steps 2 and 3 include: the male in each territory sends a courtship signal to the female through a ripple signal, and the female then responds to the mating request with an attraction or repulsion signal, and the female to be mated is selected through a roulette mechanism; wherein, the quantum position update equation of the male is:

[0019]

[0020]

[0021] in, represents the quantum rotation angle of the t+1th iteration of the i-th quantum water strider, d=1,2,…,D; is the quantum position of the dth quantum bit of the ith quantum water strider in the tth iteration, ) represents the quantum position of the male water strider of generation t, is a random number between [0,1]; σ id is a random number that follows a Gaussian distribution with a mean of 0 and a variance of 1; represents the average value of the quantum positions of all quantum water striders of generation t.

[0022] According to another aspect of the present invention, a Massive MIMO channel capacity optimization system based on a smart reflective surface is proposed, the system comprising:

[0023] A model building module configured to build a Massive MIMO channel capacity model based on a smart reflection surface;

[0024] The channel capacity maximization solution module is configured to take maximizing the channel capacity as the objective function, and optimize and solve the objective function using the quantum water strider algorithm; specifically, iteratively solve according to the following steps: initialize the parameters of the quantum water strider algorithm; divide the territory of the quantum water strider group; the male sends a courtship signal to the female through a ripple signal to wait for an opportunity to mate; evaluate the target fitness function and find the optimal quantum position of the quantum water strider; determine whether the maximum number of iterations has been reached, and if so, terminate the iterative cycle, output the global optimal quantum position of the quantum water strider, obtain the position according to the mapping rule and return the optimal fitness value.

[0025] Further, the Massive MIMO channel capacity model in the model building module is established based on a Massive MIMO communication system, wherein the Massive MIMO communication system includes a base station, a receiving end, a central controller, and an intelligent reflection surface including a plurality of reflection units; wherein the central controller is used to adjust the phase shift of each reflection unit in the intelligent reflection surface in real time; and the Massive MIMO channel capacity model is expressed as:

[0026]

[0027] Where Q represents the transmit signal covariance matrix, tr(Q) represents the trace of Q; P represents the power limit; I N represents the identity matrix, σ 2 represents the average noise power; represents the channel matrix from the transmitter to the receiver, where H represents the channel state information of the direct link from the transmitter to the receiver, R represents the channel state information from the smart reflector to the receiver, Φ represents the diagonal reflection matrix of the smart reflector, and T represents the channel state information from the transmitter to the smart reflector; det(.) represents the determinant of the matrix; It means to take the conjugate transpose of each element in H.

[0028] Furthermore, the process of initializing the parameters of the quantum water strider algorithm in the channel capacity maximization solution module includes: setting the size of the quantum water strider group to n, searching in the D-dimensional space, and mapping the quantum position of the i-th quantum water strider in the t-th iteration to the position: in, i=1,2,...,n,d=1,2,...,D,the specific mapping rules are: Among them, U b and L b They represent the upper and lower bounds of the search interval of the quantum water strider respectively;

[0029] The process of dividing the territory of the quantum water strider group includes: establishing n t territories, sort the quantum water striders according to their fitness values ​​and group them in order, and the jth member of each group is assigned to the jth territory, where j = 1, 2, ..., n t ; Each territory has at least one mature male quantum water strider: The remaining individuals in the territory are female quantum water striders.

[0030] Furthermore, the process in which the male sends a courtship signal to the female through a ripple signal in the channel capacity maximization solution module to wait for an opportunity to mate includes: the male in each territory sends a courtship signal to the female through a ripple signal, and the female then responds to the mating request with an attraction or repulsion signal, and the female to be mated is selected through a roulette mechanism; wherein the quantum position update equation of the male is:

[0031]

[0032]

[0033] in, represents the quantum rotation angle of the t+1th iteration of the i-th quantum water strider, d=1,2,…,D; is the quantum position of the dth quantum bit of the ith quantum water strider in the tth iteration, ) represents the quantum position of the male water strider of generation t, is a random number between [0,1]; σ id is a random number that follows a Gaussian distribution with a mean of 0 and a variance of 1; represents the average value of the quantum positions of all quantum water striders of generation t.

[0034] The beneficial technical effects of the present invention are:

[0035] The present invention proposes a Massive MIMO channel capacity optimization method and system based on smart reflective surfaces. First, a Massive MIMO channel capacity model based on smart reflective surfaces is established. The model is established based on a Massive MIMO communication system, and its smart reflective surface technology significantly improves the channel capacity of the Massive MIMO communication system. Then, maximizing the channel capacity is used as the objective function, and a quantum water strider algorithm is proposed to solve the objective function based on the water strider algorithm, quantum coding, and simulated quantum rotating gates, wherein the quantum position is updated by a cleverly designed quantum rotation angle and quantum rotating gate. The present invention significantly improves the search accuracy and search speed through the new design algorithm of the quantum water strider algorithm.

[0036] This paper applies the quantum water strider algorithm to the Massive MIMO channel capacity optimization method based on smart reflective surfaces, providing a preliminary attempt to solve problems in engineering practice. Experimental data show that the quantum water strider algorithm has significant advantages in search speed and search accuracy. In future work, the quantum water strider algorithm can be more widely used in a wide range of engineering application environments due to its advantages in solving complex optimization and high-dimensional optimization problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention can be better understood by referring to the description given below in conjunction with the accompanying drawings, which together with the following detailed description are included in this specification and form a part of this specification, and are used to further illustrate the preferred embodiments of the present invention and explain the principles and advantages of the present invention.

[0038] Figure 1 It is a flow chart of a method for optimizing the capacity of a Massive MIMO channel based on an intelligent reflecting surface according to an embodiment of the present invention;

[0039] Figure 2 is a curve diagram showing the variation of the channel capacity of the channel capacity optimization method of the present invention and the traditional water strider algorithm with the number of iterations when M=3, N=2, and L=5 in an embodiment of the present invention;

[0040] Figure 3 is a curve diagram showing the variation of the channel capacity of the present invention and the traditional water strider algorithm with the number of iterations when M=30, N=20, and L=50 in an embodiment of the present invention;

[0041] Figure 4 is a curve diagram showing the variation of channel capacity with the number of base station antennas in an embodiment of the present invention;

[0042] Figure 5 is a curve diagram showing the variation of the channel capacity with the number of antennas at the receiving end in an embodiment of the present invention;

[0043] Figure 6 It is a curve diagram showing the variation of the channel capacity with the number of reflection units of the intelligent reflection surface in the embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the scheme of the present invention, exemplary implementations or embodiments of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described implementations or embodiments are only implementations or embodiments of a part of the present invention, not all of them. Based on the implementations or embodiments of the present invention, all other implementations or embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0045] In the first embodiment of the present invention, a method for optimizing the capacity of a Massive MIMO channel based on a smart reflective surface is proposed. Figure 1 As shown, the method comprises the following steps:

[0046] Step 1: Establish a Massive MIMO channel capacity model based on smart reflection surface;

[0047] Step 2: Taking maximizing the channel capacity as the objective function, the objective function is optimized and solved using the quantum water strider algorithm; the solution is iterated according to the following steps:

[0048] Step 21: Initialize the parameters of the quantum water strider algorithm;

[0049] Step 22: Division of the territory of quantum water strider groups;

[0050] Step 2 and 3: The male sends a courtship signal to the female through a corrugated signal in order to wait for an opportunity to mate;

[0051] Step 24: Evaluate the target fitness function and find the optimal quantum position of the quantum water strider;

[0052] Step 25: Determine whether the maximum number of iterations has been reached. If reached, terminate the iteration loop, output the global optimal quantum position of the quantum water strider, obtain the position according to the mapping rule and return the optimal fitness value.

[0053] The method starts from step 1. In step 1, a Massive MIMO channel capacity model based on a smart reflection surface is established.

[0054] According to an embodiment of the present invention, the Massive MIMO channel capacity model is established based on a Massive MIMO communication system, which mainly includes a base station configured with M antennas, a receiving end with N antennas, a single-antenna eavesdropper, a central controller, and an intelligent reflection surface including L reflection units, wherein the intelligent reflection surface is composed of reflection elements, and the central controller can adjust the phase shift of each reflection unit in the intelligent reflection surface in real time, θ l is the reflection coefficient of the lth reflection unit, where φ l is the phase shift control angle of the lth reflection unit, assuming that the phase of each reflection unit can be flexibly adjusted in [0,2π). Represented as the channel state information from the transmitter to the smart reflector, Represented as channel state information from the smart reflector to the receiver. represents the diagonal reflection matrix of the smart reflective surface, where Φ = diag{θ1,θ2,...,θ L}, where diag{λ} represents the diagonal matrix with all elements on the main diagonal being λ in sequence. represents the transmitted signal vector, Ε[xx H ] means to ask for xx H The mean value of x H represents the transpose of the transmitted signal vector x; the transmitted signal covariance matrix is ​​defined as The channel matrix from transmitter to receiver is: in, is the channel state information of the direct link from the transmitter to the receiver. The received signal vector is expressed as represents the complex Gaussian noise vector at the receiver, σ 2 represents the average noise power, I N Represents the unit matrix. The data transmission rate of the Massive MIMO system based on the smart reflector is expressed as where det(γ) represents the determinant of the matrix γ, In summary, the ultimate goal is to maximize the channel capacity by jointly optimizing the reflection matrix Φ and the transmit covariance matrix Q of the Massive MIMO channel based on the smart reflection surface. The channel capacity model is

[0055] Through the above analysis, for the Massive MIMO communication system including the smart reflector, the optimization goal is set to maximize the channel capacity, which is specifically expressed as The constraint condition is: Φ = diag{θ1,θ2,...,θ L}, θ l |=1(l=1,2,...,L), tr(Q)≤P, P represents the upper power limit. The parameters to be solved are the diagonal reflection matrix Φ of the smart reflector and the covariance matrix Q of the transmitted signal.

[0056] Then, step 2 is executed. In step 2, maximizing the channel capacity is used as the objective function, and the objective function is optimized and solved using the quantum water strider algorithm to obtain the optimal quantum position.

[0057] According to an embodiment of the present invention, first, in step 21, the parameters of the quantum water strider algorithm are initialized.

[0058] Specifically, the quantum water strider group is assumed to consist of n quantum water striders, and the search space dimension of each quantum water strider is D, which represents the dimension of the problem to be solved. The quantum position of the ith quantum water strider of the tth generation can be expressed as in i=1,2,...,n,d=1,2,...,D。According to the mapping rule, the position of the i-th quantum water strider is obtained The specific mapping rules are: Among them U b and L bRepresents the upper and lower bounds of the quantum water strider search interval. The quantum position of each quantum water strider corresponds to a channel capacity optimization solution for the Massive MIMO system based on smart reflective surfaces. The fitness of the position of the ith quantum water strider of the tth generation is calculated by the fitness function of the channel capacity optimization problem of the Massive MIMO system based on smart reflective surfaces. After the initial quantum water strider position is generated, the channel capacity optimization problem of the Massive MIMO system based on smart reflective surfaces is used as the objective function to evaluate it. The fitness function Calculate the fitness of the position of the i-th quantum water strider of the t-th generation. Its value also represents the fitness of the corresponding position of the i-th quantum water strider of the t-th generation.

[0059] Then in step 22, the territory of the quantum water strider group is divided.

[0060] Specifically, use the following steps to establish n t First, the quantum water striders are sorted according to their fitness values ​​and grouped in order. The jth member of each group is assigned to the jth territory, where j = 1, 2, ..., n t Each territory has at least one mature male quantum water strider. The remaining individuals in the territory are female quantum water striders.

[0061] Then in steps two and three, the male sends courtship signals to the female through corrugated signals, waiting for an opportunity to mate.

[0062] Specifically, quantum water striders spend most of their lives mating or attempting to mate. During this process, males in each territory send courtship signals to females through ripple signals, and females then respond to mating requests with attraction or repulsion signals. Females to be mated can be selected through a roulette mechanism. The quantum position update equation of males is as follows: and Where d=1,2,…,D, is the quantum position of the dth qubit of the ith quantum water strider in the tth generation, is a random number between 0 and 1, σ id is a random number with a mean of 0 and a variance of 1 that satisfies a Gaussian distribution. t is the average of all quantum water strider positions in the territory and is expressed as

[0063] Then in step 24, the target fitness function is evaluated and the quantum water strider searches for a better quantum position.

[0064] Specifically, the fitness is calculated first, and the new quantum position of the quantum water strider is evaluated by the fitness (i.e., objective function) of the channel capacity optimization problem of the Massive MIMO system based on the smart reflective surface. If the new fitness is better than the previous state, it means that it has found a better habitat. The optimal quantum water strider position on the lake is recorded as Update the quantum position according to the optimal quantum water strider position on the lake surface: and Where d = 1, 2,…, D.

[0065] Then in step 25, if the number of iterations is less than the preset maximum number of iterations, set t=t+1 and return to step 23; otherwise, the iteration is terminated, the global optimal quantum position of the quantum water strider is output, the position is obtained according to the mapping rule, and the optimal fitness value is returned.

[0066] The technical effect of the present invention is further verified through experiments.

[0067] The experimental parameters are set as follows: the population size of quantum water striders is n = 50, and the number of territories is set to n t =5, the upper and lower limits are set to U b =1 and L b =-1, randomly generating the initial quantum position of the quantum water strider within the quantum position definition domain.

[0068] Comparison of the performance of the quantum water strider mechanism: The traditional water strider algorithm is applied to solve the model proposed in this invention for comparison. The population size of the two algorithms is assumed to be the same, the maximum number of iterations is 1000, and all comparison results are the average of 100 simulation experiments. Other parameter settings of the water strider algorithm refer to [1].

[0069] Figure 2 A curve diagram showing the variation of the channel capacity of the present invention and the channel capacity optimization method based on the traditional water strider algorithm with the number of iterations when M=3, N=2, and L=5 is given.

[0070] Figure 3 The curves of the channel capacity of the present invention and the traditional water strider algorithm as a function of the number of iterations are given when M=30, N=20, and L=50. Figure 3 It can be seen that the channel capacity of the quantum water strider algorithm designed by the present invention increases with the increase of the number of base station antennas, the number of receiving end antennas and the number of reflective elements of the smart reflector surface, and the quantum water strider algorithm is superior to the water strider algorithm in terms of convergence performance.

[0071] Figure 4 A curve diagram showing the change of the channel capacity designed by the present invention with the number of base station antennas when the number of base station antennas increases from 3 to 30 is given. Figure 4It can be seen that the channel capacity designed by the present invention maintains an increasing trend as the number of base station antennas increases, and the quantum water strider algorithm always maintains the best performance. When the number of base station antennas is fixed, the channel capacity is increased by increasing the number of antennas of the receiving terminal and the number of reflection units of the smart reflection surface respectively.

[0072] Figure 5 A curve diagram showing the change of the channel capacity designed by the present invention with the number of receiving antennas when the number of receiving antennas increases from 2 to 20 is given. Figure 5 It can be seen from the figure that as the number of antennas at the receiving end increases, the channel capacity of the quantum water strider algorithm designed by the present invention increases. When the number of antennas at the receiving end is fixed, the channel capacity is increased by increasing the number of base station antennas and the number of reflection unit elements of the smart reflection surface.

[0073] Figure 6 A curve diagram showing the change in the channel capacity of the present invention as the number of reflective units increases from 5 to 50 is given. Figure 6 It can be seen that as the number of reflection units of the smart reflection surface increases, the channel capacity of the quantum water strider algorithm designed by the present invention shows an increasing trend. When the number of reflection units of the smart reflection surface is fixed, the channel capacity is increased by increasing the number of base station antennas and the number of receiving terminal antennas respectively.

[0074] Another embodiment of the present invention provides a Massive MIMO channel capacity optimization system based on a smart reflective surface, the system comprising:

[0075] A model building module configured to build a Massive MIMO channel capacity model based on a smart reflection surface;

[0076] The channel capacity maximization solution module is configured to take maximizing the channel capacity as the objective function, and optimize and solve the objective function using the quantum water strider algorithm; specifically, iteratively solve according to the following steps: initialize the parameters of the quantum water strider algorithm; divide the territory of the quantum water strider group; the male sends a courtship signal to the female through a ripple signal to wait for an opportunity to mate; evaluate the target fitness function and find the optimal quantum position of the quantum water strider; determine whether the maximum number of iterations has been reached, and if so, terminate the iterative cycle, output the global optimal quantum position of the quantum water strider, obtain the position according to the mapping rule and return the optimal fitness value.

[0077] In this embodiment, preferably, the Massive MIMO channel capacity model in the model building module is established based on a Massive MIMO communication system, wherein the Massive MIMO communication system includes a base station, a receiving end, a central controller, and an intelligent reflection surface including a plurality of reflection units; wherein the central controller is used to adjust the phase shift of each reflection unit in the intelligent reflection surface in real time; and the Massive MIMO channel capacity model is expressed as:

[0078]

[0079] Where Q represents the transmit signal covariance matrix, tr(Q) represents the trace of Q; P represents the power limit; I N represents the identity matrix, σ 2 represents the average noise power; represents the channel matrix from the transmitter to the receiver, where H represents the channel state information of the direct link from the transmitter to the receiver, R represents the channel state information from the smart reflector to the receiver, Φ represents the diagonal reflection matrix of the smart reflector, and T represents the channel state information from the transmitter to the smart reflector; det(.) represents the determinant of the matrix; It means to take the conjugate transpose of each element in H.

[0080] In this embodiment, preferably, the process of initializing the parameters of the quantum water strider algorithm in the channel capacity maximization solution module includes: setting the size of the quantum water strider group to n, searching in the D-dimensional space, and mapping the quantum position of the i-th quantum water strider in the t-th iteration to the position: in, i=1,2,...,n,d=1,2,...,D,the specific mapping rules are: Among them, U b and L b They represent the upper and lower bounds of the search interval of the quantum water strider respectively;

[0081] The process of dividing the territory of the quantum water strider group includes: establishing n t territories, sort the quantum water striders according to their fitness values ​​and group them in order, and the jth member of each group is assigned to the jth territory, where j = 1, 2, ..., n t ; Each territory has at least one mature male quantum water strider: The remaining individuals in the territory are female quantum water striders.

[0082] In this embodiment, preferably, the process in which the male sends a courtship signal to the female through a ripple signal in the channel capacity maximization solution module to wait for an opportunity to mate includes: the male in each territory sends a courtship signal to the female through a ripple signal, and the female then responds to the mating request with an attraction or repulsion signal, and the female to be mated is selected through a roulette mechanism; wherein the quantum position update equation of the male is:

[0083]

[0084]

[0085] in, represents the quantum rotation angle of the t+1th iteration of the i-th quantum water strider, d=1,2,…,D; is the quantum position of the dth quantum bit of the ith quantum water strider in the tth iteration, represents the quantum position of the male water strider of generation t, is a random number between [0,1]; σ id is a random number that follows a Gaussian distribution with a mean of 0 and a variance of 1; represents the average value of the quantum positions of all quantum water striders of generation t.

[0086] The functions of a Massive MIMO channel capacity optimization system based on a smart reflecting surface in an embodiment of the present invention can be described by the aforementioned Massive MIMO channel capacity optimization method based on a smart reflecting surface. Therefore, for the parts not described in detail in the system embodiment, please refer to the above method embodiment and will not be repeated here.

[0087] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments are contemplated within the scope of the invention thus described. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

[0088] The documents cited in the present invention are as follows:

[0089] [1]Kaveh, A. Water strider algorithm: Anew metaheuristic and applications. "Structures", 2021, vol.25, pp.520-541.

Claims

1. A method for optimizing the capacity of a Massive MIMO channel based on a smart reflective surface, characterized in that: The following steps are involved: Step 1: Establish a Massive MIMO channel capacity model based on smart reflection surface; Step 2: Taking maximizing the channel capacity as the objective function, the quantum water strider algorithm is used to optimize and solve the objective function; wherein the ultimate goal is to maximize the channel capacity by jointly optimizing the reflection matrix Φ and the transmit covariance matrix Q of the Massive MIMO channel based on the smart reflection surface. The optimization objective is specifically expressed as R s is the data transmission rate of the Massive MIMO system with smart reflective surface, det(γ) represents the determinant of the matrix γ, Indicates Find the conjugate transpose of each element in ; σ 2 represents the average noise power, I N represents the identity matrix; represents the channel matrix from transmitter to receiver; Follow the steps below to iterate the solution: Step 21: Initialize the parameters of the quantum water strider algorithm; Step 22: Division of the territory of quantum water strider groups; Step 2 and 3: The male sends a courtship signal to the female through a corrugated signal in order to wait for an opportunity to mate; Step 24: Evaluate the target fitness function, which is the target function about fitness, and find the optimal quantum position of the quantum water strider; Step 25: Determine whether the maximum number of iterations has been reached. If reached, terminate the iteration loop, output the global optimal quantum position of the quantum water strider, obtain the position according to the mapping rule and return the optimal fitness value, that is, calculate the fitness of each quantum water strider position in each generation through the fitness function of the channel capacity optimization problem, and finally return the optimal fitness value to obtain the optimized channel capacity.

2. The method for optimizing the capacity of a Massive MIMO channel based on a smart reflective surface according to claim 1, characterized in that: The Massive MIMO channel capacity model in step 1 is established based on a Massive MIMO communication system, which includes a base station, a receiving end, a central controller, and an intelligent reflection surface including multiple reflection units; wherein the central controller is used to adjust the phase shift of each reflection unit in the intelligent reflection surface in real time; the Massive MIMO channel capacity model is expressed as: Where Q represents the transmit signal covariance matrix, tr(Q) represents the trace of Q; P represents the power upper limit; de t(.) means to find the determinant of the matrix; I N represents the identity matrix, σ 2 represents the average noise power; represents the channel matrix from the transmitter to the receiver, where H represents the channel state information of the direct link from the transmitter to the receiver, R represents the channel state information from the smart reflective surface to the receiver, Φ represents the diagonal reflection matrix of the smart reflective surface, and T represents the channel state information from the transmitter to the smart reflective surface; Indicates Take the conjugate transpose of each element in .

3. The method for optimizing the capacity of a Massive MIMO channel based on a smart reflective surface according to claim 2, characterized in that: The specific steps of step 21 include: setting the size of the quantum water strider group to n, searching in the D-dimensional space, and mapping the quantum position of the i-th quantum water strider in the t-th iteration into the position: in, i=1,2,...,n,d=1,2,...,D,the specific mapping rules are: Among them, U b and L b They represent the upper and lower bounds of the quantum water strider search interval respectively.

4. The method for optimizing Massive MIMO channel capacity based on smart reflective surface according to claim 3, characterized in that: Step 2: The territory division of the quantum water strider group includes: establishing n t territories, sort the quantum water striders according to their fitness values ​​and group them in order, and the jth member of each group is assigned to the jth territory, where j = 1, 2, ..., n t ; Each territory has at least one mature male quantum water strider: The remaining individuals in the territory are female quantum water striders.

5. The method for optimizing the capacity of a Massive MIMO channel based on a smart reflective surface according to claim 4, characterized in that: The specific steps of step 2 and step 3 include: the male in each territory sends a courtship signal to the female through a ripple signal, and the female then responds to the mating request with an attraction or repulsion signal, and the female to be mated is selected through a roulette mechanism; among them, the quantum position update equation of the male is: in, represents the quantum rotation angle of the t+1th iteration of the i-th quantum water strider, d=1,2,…,D; is the quantum position of the dth quantum bit of the ith quantum water strider in the tth iteration, ) represents the quantum position of the male water strider of generation t, is a random number between [0,1]; σ id is a random number that follows a Gaussian distribution with a mean of 0 and a variance of 1; represents the average value of the quantum positions of all quantum water striders of generation t.

6. A Massive MIMO channel capacity optimization system based on intelligent reflective surface, characterized in that: include: A model building module configured to build a Massive MIMO channel capacity model based on a smart reflection surface; The channel capacity maximization solution module is configured to take maximizing the channel capacity as the objective function and optimize and solve the objective function using the quantum water strider algorithm; wherein the ultimate goal is to maximize the channel capacity by jointly optimizing the reflection matrix Φ and the transmission covariance matrix Q of the Massive MIMO channel based on the smart reflection surface. The optimization target is specifically expressed as R s is the data transmission rate of the Massive MIMO system with smart reflective surface, det(γ) represents the determinant of the matrix γ, Indicates Find the conjugate transpose of each element in ; σ 2 represents the average noise power, I N represents the identity matrix; represents the channel matrix from transmitter to receiver; Specifically, the iterative solution is performed according to the following steps: initializing the parameters of the quantum water strider algorithm; dividing the territory of the quantum water strider group; the male sends a courtship signal to the female through a ripple signal to wait for an opportunity to mate; evaluating the objective function and finding the optimal quantum position of the quantum water strider; judging whether the maximum number of iterations has been reached, terminating the iteration loop if reached, outputting the global optimal quantum position of the quantum water strider, obtaining the position according to the mapping rule and returning the optimal fitness value, that is, calculating the fitness of each quantum water strider position in each generation through the target fitness function of the channel capacity optimization problem, and finally returning the optimal fitness value, thereby obtaining the optimized channel capacity, and the target fitness function is the target function about fitness.

7. The Massive MIMO channel capacity optimization system based on smart reflective surface according to claim 6, characterized in that: The Massive MIMO channel capacity model in the model building module is established based on a Massive MIMO communication system, wherein the Massive MIMO communication system includes a base station, a receiving end, a central controller, and an intelligent reflection surface including a plurality of reflection units; wherein the central controller is used to adjust the phase shift of each reflection unit in the intelligent reflection surface in real time; and the Massive MIMO channel capacity model is expressed as: Where Q represents the transmit signal covariance matrix, tr(Q) represents the trace of Q; P represents the power upper limit; I N represents the identity matrix, σ 2 represents the average noise power; represents the channel matrix from the transmitter to the receiver, where H represents the channel state information of the direct link from the transmitter to the receiver, R represents the channel state information from the smart reflective surface to the receiver, Φ represents the diagonal reflection matrix of the smart reflective surface, and T represents the channel state information from the transmitter to the smart reflective surface; det(.) means finding the determinant of the matrix; Indicates Take the conjugate transpose of each element in .

8. The Massive MIMO channel capacity optimization system based on smart reflective surface according to claim 7, characterized in that: The process of initializing the parameters of the quantum water strider algorithm in the channel capacity maximization solution module includes: setting the size of the quantum water strider group to n, searching in the D-dimensional space, and mapping the quantum position of the i-th quantum water strider in the t-th iteration to the position: in, The specific mapping rules are: Among them, U b and L b They represent the upper and lower bounds of the search interval of the quantum water strider respectively; The process of dividing the territory of the quantum water strider group includes: establishing n t territories, sort the quantum water striders according to their fitness values ​​and group them in order, and the jth member of each group is assigned to the jth territory, where j = 1, 2, ..., n t ; Each territory has at least one mature male quantum water strider: The remaining individuals in the territory are female quantum water striders.

9. The Massive MIMO channel capacity optimization system based on smart reflective surface according to claim 8, characterized in that: The process in which the male sends a courtship signal to the female through a ripple signal in the channel capacity maximization solution module to wait for an opportunity to mate includes: the male in each territory sends a courtship signal to the female through a ripple signal, and the female then responds to the mating request with an attraction or repulsion signal, and the female to be mated is selected through a roulette mechanism; wherein the quantum position update equation of the male is: in, represents the quantum rotation angle of the t+1th iteration of the i-th quantum water strider, d=1,2,…,D; is the quantum position of the dth quantum bit of the ith quantum water strider in the tth iteration, ) represents the quantum position of the male water strider of generation t, is a random number between [0,1]; σ id is a random number that follows a Gaussian distribution with a mean of 0 and a variance of 1; represents the average value of the quantum positions of all quantum water striders of generation t.

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