User equipment and intelligent metasurface matching method and apparatus

Abstract

The present application relates to a kind of user equipment and intelligent super surface matching method and device, belong to communication technical field, solve if present user equipment if randomly selecting intelligent super surface carries out auxiliary communication, it will inevitably cause resource waste, make the performance of entire communication system cannot reach optimal problem.The method includes: constructing intelligent super surface assisted system model, wherein, system model includes a base station, N intelligent super surfaces and N user equipments;According to system model, channel model is constructed, wherein, channel model includes direct channel from base station to single user equipment, channel from base station to single intelligent super surface and channel from single intelligent super surface to single user equipment;Using fixed point iteration method carries out the active passive joint beamforming of base station and intelligent super surface;And using hungarian algorithm carries out the optimization matching of intelligent super surface and user equipment. System utility maximization is realized.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a method and apparatus for matching user equipment with a smart metasurface. Background Technology

[0002] With the advent of the Internet of Things (IoT) and the 5G era, the throughput of user devices in a single cell has increased dramatically, and the data rate requirements of user devices have also gradually increased. However, deploying more access points and radio frequency links inevitably leads to higher power consumption. Therefore, future wireless communication requires a new paradigm with higher spectral efficiency and higher energy efficiency, and the emergence of smart metasurfaces has made this vision possible.

[0003] As a low-cost passive device, smart metasurfaces allow each unit to independently control the phase or amplitude of the incident signal, enabling the modification of the wireless channel environment with extremely low power consumption. Furthermore, due to their planar structure, smart metasurfaces can be easily integrated into building facades, ceilings, laptop surfaces, and even clothing. Therefore, the use of smart metasurfaces is bound to become a new trend in the development of next-generation wireless communication systems.

[0004] In research on smart metasurface-assisted wireless communication, improvements to the quality of service (QoS) of user equipment (UE) are typically considered from the perspectives of beamforming and channel estimation methods. However, in scenarios where multiple UEs and multiple smart metasurfaces are distributed within the same cell, the optimal matching problem between UEs and smart metasurfaces has not been taken into account. If UEs randomly select smart metasurfaces for assisted communication, it will inevitably lead to resource waste, preventing the overall communication system from achieving optimal performance. Summary of the Invention

[0005] Based on the above analysis, the present invention aims to provide a method and apparatus for matching user equipment with smart metasurfaces, in order to solve the problem that if existing user equipment randomly selects smart metasurfaces for auxiliary communication, it will inevitably lead to a waste of resources and prevent the performance of the entire communication system from reaching its optimal level.

[0006] On one hand, embodiments of the present invention provide a method for matching user equipment with a smart metasurface, comprising: constructing a smart metasurface-assisted system model, wherein the system model includes a base station, N smart metasurfaces, and N user equipments; constructing a channel model based on the system model, wherein the channel model includes a direct channel from the base station to a single user equipment, a channel from the base station to a single smart metasurface, and a channel from the single smart metasurface to the single user equipment; performing active and passive joint beamforming of the base station and the smart metasurface using a fixed-point iterative method; and using a Hungarian algorithm to optimize the matching between the smart metasurface and the user equipment.

[0007] The beneficial effects of the above technical solution are as follows: Modeling is performed for scenarios with multiple user devices and multiple smart metasurfaces, and the positions of the user devices and smart metasurfaces are randomized, making the model more consistent with actual characteristics. The Hungarian algorithm is used for optimal matching of user devices and smart metasurfaces in a single cell, maximizing system utility.

[0008] Based on further improvements to the above method, the user equipments are independent of each other and do not interfere with each other. Each user equipment communicates directly with the base station and indirectly with the base station with the assistance of the single smart metasurface.

[0009] Further improvements to the above method, constructing a channel model based on the system model, include: for the large-scale fading model, calculating the distance-related path loss model using the following formula:

[0010]

[0011] Where, d Bu d BR d Ru Here, d0 represents the distance between the base station and the user equipment, the distance between the base station and the smart metasurface, and the distance between the smart metasurface and the user equipment, respectively; C0 is the path loss at a reference distance d0 = 1m; k is the path loss exponent; and h represents the direct channel h from the base station to the user equipment. d The channel G from the base station to the single smart metasurface and the channel h from the single smart metasurface to the single user equipment. r The path loss exponent is set to k. Bu =3.5, k BR =2.2, k Ru =2.8.

[0012] Further improvements to the above method, based on the system model, include constructing a channel model: for small-scale fading, considering the Ricean fading channel model, the direct channel h from the base station to the user equipment is expressed by the following formula. d :

[0013]

[0014] Among them, w Bu H represents the Rice factor. LoS and H NLoS Representing the line-of-sight channel component and the Rayleigh fading component respectively, when w Bu As we approach infinity, H becomes equivalent to a line-of-sight channel, and when w Bu When H = 0, H is equivalent to a Rayleigh fading channel;

[0015] The channel h is expressed by the following formula. d :

[0016] When w Bu and w Ru When representing the Rice factor, respectively, the channel G from the base station to the single smart metasurface and the channel h from the single smart metasurface to the single user equipment are generated. r .

[0017] Based on a further improvement of the above method, the channel model constructed according to the system model includes: when the i-th smart metasurface assists the j-th user equipment in communication, the received signal of the j-th user equipment is represented by the following formula:

[0018]

[0019] Where i,j=1,…,N,h r,j It is the channel from the i-th smart metasurface to the j-th user equipment, Φ i =diag(e jθ1 ,e jθ2 ,…,e jθM G is the phase shift matrix of the i-th smart metasurface. i h is the channel from the base station to the i-th smart metasurface. d,j w is the channel from the base station to the j-th user equipment. j It is the beamforming vector corresponding to the j-th user equipment base station, x j It is the signal transmitted by the base station to the j-th user equipment, n j It is the noise of the j-th user equipment;

[0020] The signal-to-noise ratio of the j-th user equipment is expressed by the following formula:

[0021]

[0022] Let the noise variance σ of each user device be... j All are 10 -11 The data rate of the j-th user equipment can be expressed by the following formula:

[0023] R j =Blog2(1+γ) j ).

[0024] Based on a further improvement of the above method, the active-passive joint beamforming of the base station and the smart metasurface using the fixed-point iteration method includes: when the transmission rate of the base station is P, maximizing the transmission rate of the user equipment is expressed by the following formula:

[0025]

[0026] Wherein, Φ is the phase shift matrix of the smart metasurface and w is the beamforming vector of the base station.

[0027] Based on further improvements to the above method, the active and passive joint beamforming of the base station and the smart metasurface using the fixed-point iteration method includes:

[0028] Initialize t and ε to t = 0 and ε = 10. -6 ;

[0029] calculate

[0030] Construct the intelligent metasurface allocation matrix

[0031] Initialization vector

[0032] When ||Av (t+1) ||1-||Av (t) When ||1≥ε, based on the initialization vector v (0) Calculate v (t+1) =unt(Av (t) );

[0033] t = t + 1, until v (t+1) convergence;

[0034] The beamforming vector is calculated using the following formula.

[0035] Among them, (v (t+1) ) * The first M elements are the main diagonal elements of the phase shift matrix Φ.

[0036] Further improvements to the above method, using the Hungarian algorithm to optimize the matching between the smart metasurface and the user equipment, include:

[0037] System utility is expressed by the following formula, where system utility is defined as the total rate of user equipment in the system:

[0038]

[0039] The elements of the intelligent metasurface allocation matrix A are represented as a ij , i,j=1,…,N,a ij This indicates whether the i-th smart metasurface assists the j-th user device;

[0040]

[0041] The sum of each row and each column of the intelligent metasurface allocation matrix A is equal to 1;

[0042] The objective function of the optimization problem is:

[0043]

[0044] The Hungarian algorithm is used to solve the smart metasurface allocation problem to find the optimal pairing of smart metasurfaces and user equipment. The objective of the Hungarian algorithm is to minimize the objective function, and the objective function is transformed into the solution format of the Hungarian algorithm:

[0045] in, Let R be an element of an N-dimensional nonnegative matrix.

[0046] On the other hand, embodiments of the present invention provide a user equipment and smart metasurface matching device, comprising: a system model for constructing a smart metasurface-assisted system model, wherein the system model includes a base station, N smart metasurfaces and N user equipments; a channel model for constructing a channel model based on the system model, wherein the channel model includes a direct channel from the base station to a single user equipment, a channel from the base station to a single smart metasurface and a channel from the single smart metasurface to the single user equipment; a beamforming module for performing active and passive joint beamforming of the base station and the smart metasurface using a fixed-point iterative method; and a matching module for optimizing the matching of the smart metasurface and the user equipment using a Hungarian algorithm.

[0047] Based on further improvements to the above-mentioned device, the channel model includes: for the large-scale fading model, the distance-related path loss model is calculated using the following formula:

[0048]

[0049] Where, d Bu d BR d Ru Here, d0 represents the distance between the base station and the user equipment, the distance between the base station and the smart metasurface, and the distance between the smart metasurface and the user equipment, respectively; C0 is the path loss at a reference distance d0 = 1m; k is the path loss exponent; and h represents the direct channel h from the base station to the user equipment. d The channel G from the base station to the single smart metasurface and the channel h from the single smart metasurface to the single user equipment. r The path loss exponent is set to k. Bu =3.5, k BR =2.2, k Ru =2.8.

[0050] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0051] 1. Modeling is performed for scenarios with multiple user devices and multiple smart metasurfaces. The positions of the user devices and smart metasurfaces are randomized to make the model more consistent with actual characteristics. The Hungarian algorithm is used to achieve optimal matching between user devices and smart metasurfaces in a single cell, maximizing system utility.

[0052] 2. To address the problem that user equipment in a communication system cannot select a suitable intelligent metasurface to assist its communication, this paper constructs a problem to maximize system utility by modeling the system utility as the total rate of multiple user equipment and introducing an allocation matrix.

[0053] 3. Building upon the beneficial effects 1 and 2, the Hungarian algorithm is used to optimize the allocation matrix, improve the overall rate of user equipment, maximize the system utility function, and provide guidance for resource allocation of multiple user equipment and multiple intelligent metasurface systems.

[0054] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0055] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0056] Figure 1 This is a flowchart of a user equipment and smart metasurface matching method according to an embodiment of the present invention;

[0057] Figure 2 This is a system model diagram according to an embodiment of the present invention;

[0058] Figure 3 This is a comparison chart showing the system utility as a function of base station transmit power under different methods according to embodiments of the present invention;

[0059] Figure 4 A comparison graph showing the variation of system utility with the number of smart metasurface reflective units under different methods according to embodiments of the present invention; and

[0060] Figure 5 This is a block diagram of a user equipment and a smart metasurface matching device according to an embodiment of the present invention. Detailed Implementation

[0061] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0062] refer to Figure 1 A specific embodiment of the present invention discloses a method for matching user equipment with a smart metasurface, comprising: in step S102, constructing a system model assisted by a smart metasurface, wherein the system model includes a base station, N smart metasurfaces, and N user equipments; and in step S104, constructing a channel model based on the system model, wherein the channel model includes a direct channel h from the base station to a single user equipment. d Channel G from the base station to a single smart metasurface and channel h from a single smart metasurface to a single user equipment r In step S106, the fixed-point iteration method is used to perform active and passive joint beamforming of the base station and the smart metasurface; and in step S108, the Hungarian algorithm is used to optimize the matching between the smart metasurface and the user equipment.

[0063] Compared with existing technologies, the user equipment and smart metasurface matching method provided in this embodiment models a scenario with multiple user equipment and multiple smart metasurfaces, and randomizes the positions of user equipment and smart metasurfaces, making the model more consistent with actual characteristics. The Hungarian algorithm is used for optimal matching of user equipment and smart metasurfaces in a single cell, maximizing system utility.

[0064] The following text will refer to Figure 1 The steps of the user equipment and smart metasurface matching method according to embodiments of the present invention will be described in detail.

[0065] In step S102, a smart metasurface-assisted system model is constructed, which includes a base station, N smart metasurfaces, and N user equipments. The user equipments are independent of each other and do not interfere with each other. Each user equipment communicates directly with the base station and indirectly with the assistance of a single smart metasurface.

[0066] In step S104, a channel model is constructed based on the system model, wherein the channel model includes the direct channel h from the base station to a single user equipment. d Channel G from the base station to a single smart metasurface and channel h from a single smart metasurface to a single user equipment r .

[0067] Constructing a channel model based on the system model includes: For large-scale fading models, calculating the distance-related path loss model using the following formula:

[0068]

[0069] Where, d Bu d BR d Ru Here, d0 represents the distance between the base station and user equipment, the base station and the smart metasurface, and the smart metasurface and user equipment, respectively. C0 is the path loss at the reference distance d0 = 1m, k is the path loss exponent, and h is the direct channel h from the base station to the user equipment. d Channel G from the base station to a single smart metasurface and channel h from a single smart metasurface to a single user equipment r Set the path loss exponent to k respectively. Bu =3.5, k BR =2.2, k Ru =2.8.

[0070] Constructing a channel model based on the system model includes: For small-scale fading, considering the Ricean fading channel model, the direct channel h from the base station to the user equipment is expressed by the following formula. d :

[0071]

[0072] Among them, w Bu H represents the Rice factor. LoS and H NLoS Representing the line-of-sight channel component and the Rayleigh fading component respectively, when w Bu As we approach infinity, H becomes equivalent to a line-of-sight channel, and when w Bu When H = 0, H is equivalent to a Rayleigh fading channel; then, the channel h d This is expressed as H multiplied by the distance-related path loss L(d) Bu The square root of ), that is, H; when w Bu and w Ru When representing the Rice factor, respectively, the channel G from the base station to a single smart metasurface and the channel h from the single smart metasurface to a single user equipment are generated. r .

[0073] The channel model constructed based on the system model includes: when the i-th smart metasurface assists the j-th user equipment in communication, the received signal of the j-th user equipment is represented by the following formula:

[0074]

[0075] Where i,j=1,…,N,h r,j It is the channel from the i-th smart metasurface to the j-th user equipment, Φ i =diag(e jθ1 ,e jθ2 ,…,ejθM G is the phase shift matrix of the i-th smart metasurface. i h is the channel from the base station to the i-th smart metasurface. d,j It is the channel from the base station to the j-th user equipment, w j It is the beamforming vector corresponding to the j-th user equipment base station, x j It is the signal transmitted by the base station to the j-th user equipment, n j Let be the noise level of the j-th user equipment; the signal-to-noise ratio of the j-th user equipment is expressed by the following formula:

[0076]

[0077] Let the noise variance σ of each user device be... j All are 10 -11 The data rate of the j-th user equipment can be expressed by the following formula:

[0078] R j =Blog2(1+γ) j ).

[0079] In step S106, a fixed-point iterative method is used to perform active and passive joint beamforming of the base station and the smart metasurface. The active and passive joint beamforming of the base station and the smart metasurface using the fixed-point iterative method includes: when the base station's transmission rate is P, maximizing the user equipment's transmission rate is expressed by the following formula:

[0080]

[0081] Where Φ is the phase shift matrix of the smart metasurface and w is the beamforming vector of the base station.

[0082] The active and passive joint beamforming of base stations and smart metasurfaces using the fixed-point iterative method includes:

[0083] Initialize t and ε to t = 0 and ε = 10. -6 ;

[0084] calculate

[0085] Constructing a smart metasurface distribution matrix

[0086] Initialization vector

[0087] When ||Av (t+1) ||1-||Av (t) When ||1≥ε, based on the initialization vector v (0) Calculate v (t+1) =unt(Av (t) );

[0088] t = t + 1, until v (t+1) convergence;

[0089] The beamforming vector is calculated using the following formula.

[0090] Among them, (v (t+1 )) * The first M elements are the main diagonal elements of the phase shift matrix Φ.

[0091] In step S108, the Hungarian algorithm is used to optimize the matching between the smart metasurface and the user equipment. Optimizing the matching between the smart metasurface and the user equipment using the Hungarian algorithm includes: expressing the system utility as the total rate of the user equipment in the system using the following formula:

[0092]

[0093] The elements of the intelligent metasurface allocation matrix A are represented as a ij , i,j=1,…,N,a ij This indicates whether the i-th smart metasurface is the j-th auxiliary user device;

[0094]

[0095] The sum of each row and each column of the intelligent metasurface allocation matrix A is equal to 1;

[0096] The objective function of the optimization problem is:

[0097]

[0098] The Hungarian algorithm is used to solve the smart metasurface allocation problem to find the optimal pairing of smart metasurfaces and user equipment. The goal of the Hungarian algorithm is to minimize the objective function, and the objective function is transformed into the solution format of the Hungarian algorithm:

[0099] in, Let R be an element of an N-dimensional nonnegative matrix.

[0100] refer to Figure 5 A specific embodiment of the present invention discloses a user equipment and intelligent metasurface matching device, comprising: a system model 502 for constructing an intelligent metasurface-assisted system model, wherein the system model includes a base station, N intelligent metasurfaces, and N user equipments; and a channel model 504 for constructing a channel model based on the system model, wherein the channel model includes a direct channel h from the base station to a single user equipment. dChannel G from the base station to a single smart metasurface and channel h from a single smart metasurface to a single user equipment r Beamforming module 506 is used to perform active and passive joint beamforming of the base station and the smart metasurface using a fixed-point iterative method; and matching module 508 is used to optimize the matching of the smart metasurface with the user equipment using the Hungarian algorithm.

[0101] The channel model includes: For large-scale fading models, the distance-related path loss model is calculated using the following formula:

[0102]

[0103] Where, d Bu d BR d Ru Here, d0 represents the distance between the base station and user equipment, the base station and the smart metasurface, and the smart metasurface and user equipment, respectively. C0 is the path loss at the reference distance d0 = 1m, k is the path loss exponent, and h is the direct channel h from the base station to the user equipment. d Channel G from the base station to a single smart metasurface and channel h from a single smart metasurface to a single user equipment r Set the path loss exponent to k respectively. Bu =3.5, k BR =2.2, k Ru =2.8.

[0104] In the following text, refer to Figures 2 to 4 The user equipment and intelligent metasurface matching device according to embodiments of the present invention will be described in detail by way of specific examples.

[0105] To address the problem of optimal matching between user equipment and smart metasurfaces in a multi-user wireless communication system assisted by multiple smart metasurfaces, this invention discloses a matching method for user equipment and smart metasurfaces based on the Hungarian algorithm. The Hungarian algorithm is used to perform optimal matching between user equipment and smart metasurfaces in a single cell, thereby maximizing system utility.

[0106] This invention discloses a method for matching user equipment with a smart metasurface based on the Hungarian algorithm, comprising the following steps:

[0107] Step 1: Construct a smart metasurface-assisted system model, which includes one base station, N smart metasurfaces, and N user equipments. The user equipments are independent of each other and do not interfere with each other. Each user equipment can communicate directly with the base station or indirectly with the assistance of a single smart metasurface. First, determine the location of the base station. Second, without loss of generality, the N smart metasurfaces and N user equipments are randomly distributed near the base station, and each smart metasurface has M reflective elements.

[0108] Step 2: Based on the system model constructed in Step 1, construct the channel model. According to the system model, three channels need to be modeled, namely the direct channel h from the base station to the user equipment. d Channel G from the base station to a single smart metasurface and channel h from a single smart metasurface to a single user equipment r .

[0109] For a single user equipment and a single smart metasurface, the base station's transmitted signal is represented by x, and The received signal at the user equipment can be represented as follows:

[0110]

[0111] Where Φ is the phase shift matrix of the smart metasurface, which can be represented as Φ i =diag(e jθ1 ,e jθ2 ,…,e jθM ),θ i The phase shift of the i-th element of the smart metasurface is represented by ; w represents the beamforming vector of the base station; n is a vector with variance σ. 2 Additive white Gaussian noise.

[0112] Therefore, the signal-to-noise ratio of a single user equipment can be expressed as

[0113]

[0114] The data rate of a single user device can be expressed as

[0115] R = Blog2(1+γ) (3)

[0116] Where B represents the bandwidth available to the user equipment.

[0117] To calculate the achievable data rate when each user equipment is matched with each smart metasurface, it is necessary to obtain the phase shift matrix Φ of the smart metasurface and the beamforming vector w of the base station at the time of matching.

[0118] Step 3: Perform active and passive joint beamforming of the base station and smart metasurface using the classic fixed-point iteration method.

[0119] When the base station's transmission rate is P, maximizing the transmission rate achievable by the user equipment can be expressed as the following optimization problem:

[0120]

[0121] According to Algorithm 1, the phase shift matrix Φ of the smart metasurface and the beamforming vector w of the base station can be obtained with low computational complexity. unt(a)=[a1 / |a1|,…,a n / |a n |],λ max (A) refers to the eigenvector corresponding to the largest eigenvalue of matrix A.

[0122] Algorithm 1: Active and passive joint beamforming of base station and smart metasurface using fixed-point iteration method

[0123]

[0124] Step 4: Since a smart metasurface at a specific location may provide good assistance to multiple user devices, these user devices all hope to cooperate with it. Therefore, to meet the overall system performance requirements, a global perspective is needed, and the system's utility is defined as the total rate of user devices within the system:

[0125]

[0126] Therefore, the problem of maximizing system utility can be reduced to an allocation problem. Let A be the allocation matrix of the intelligent metasurface, and let a be the element of matrix A. ij , i,j=1,…,N. a ij This is a 0-1 binary indicator parameter representing whether the smart metasurface i assists user equipment j. When the smart metasurface i assists user equipment j, a ij =1, otherwise, a ij =0, that is:

[0127]

[0128] Clearly, the sum of each row and each column of matrix A is equal to 1. This assignment problem can be summarized as follows:

[0129]

[0130] The Hungarian algorithm can be used to solve the above allocation problem, and it can always find the optimal pairing of smart metasurfaces and user devices. It is worth noting that the goal of the Hungarian algorithm is to minimize the objective function, therefore the objective function needs to be transformed into:

[0131]

[0132] Define the elements of an N-dimensional nonnegative matrix R as follows: By processing matrix R using the Hungarian algorithm with specific steps as shown in Algorithm 2, the optimal smart metasurface allocation matrix A can be obtained.

[0133] Algorithm 2: Hungarian Algorithm for Matching Smart Metasurfaces with User Equipment

[0134]

[0135] Beneficial effects:

[0136] 1. The present invention discloses a user equipment and smart metasurface matching method based on the Hungarian algorithm, which models a scenario with multiple user equipment and multiple smart metasurfaces, and randomizes the positions of user equipment and smart metasurfaces to make the model more consistent with actual characteristics.

[0137] 2. The present invention discloses a user equipment and smart metasurface matching method based on the Hungarian algorithm. It addresses the problem that user equipment in a communication system cannot select a suitable smart metasurface to assist its communication. The method constructs a system utility maximization problem by modeling the system utility as the total rate of multiple user equipment and introducing an allocation matrix.

[0138] 3. The present invention discloses a user equipment and intelligent metasurface matching method based on the Hungarian algorithm. On the basis of achieving the beneficial effects 1 and 2, the Hungarian algorithm is used to optimize the allocation matrix, improve the total rate of user equipment, maximize the system utility function, and provide guidance for resource allocation of multiple user equipment and multiple intelligent metasurface systems.

[0139] This example discloses a user equipment and smart metasurface matching method based on the Hungarian algorithm. The specific implementation steps are as follows:

[0140] 1. For example Figure 2 As shown, a multi-user wireless communication system assisted by multiple smart metasurfaces is constructed, including one base station, N user equipments, and N smart metasurfaces. Each smart metasurface has M reflective elements, the base station is equipped with 6 antennas, and each user equipment is a single-antenna device. The system bandwidth is 750MHz, and the user equipments are independent of each other and do not interfere with each other; any user equipment can communicate with the base station in real time.

[0141] Define a 100m x 100m square area with base station coordinates (100m, 50m) and a height of 3m. Within this area (0~50m, 0~100m), randomly distribute N user devices and N smart metasurfaces. The height of the smart metasurfaces is 2m, and the height of the user devices is 1.5m.

[0142] 2. Construct a channel model for the direct channel h from the base station to the user equipment. d Channel G from the base station to a single smart metasurface and channel h from a single smart metasurface to a single user equipment rThree channels are modeled. For the large-scale fading model, firstly, d Bu d BR d Ru These are defined as the distances between the base station and the user equipment, the base station and the smart metasurface, and the smart metasurface and the user equipment, respectively. Therefore, the distance-related path loss model can be expressed as:

[0143]

[0144] Where C0 is the path loss at the reference distance d0 = 1m, and k is the path loss exponent. For the direct channel h from the base station to the user equipment... d Channel G from the base station to a single smart metasurface and channel h from a single smart metasurface to a single user equipment r Set the path loss exponent to k respectively. Bu =3.5, k BR =2.2, k Ru =2.8. For small-scale fading, the direct channel h from the base station to the user equipment... d For example, considering the Ricean fading channel model, its channel can be represented as:

[0145]

[0146] Among them, w Bu H represents the Rice factor. LoS and H NLoS These represent the line-of-sight channel component and the Rayleigh fading component, respectively. When w Bu As we approach infinity, H becomes equivalent to a line-of-sight channel, and when w Bu When H = 0, H is equivalent to a Rayleigh fading channel. Then, the channel h... d This can be expressed as H multiplied by the distance-related path loss L(d) in equation (8). Bu The square root of ), that is Similarly, when w BR and w Ru When representing Rice factors, the channel G from the base station to a single smart metasurface and the channel h from the single smart metasurface to a single user equipment are respectively... r It can also be generated through the process described above. To better reflect reality, we assume that all three channels are Rayleigh fading channels, i.e., w Bu =w BR =w Ru =0.

[0147] When the i-th smart metasurface assists the j-th user equipment in communication, the received signal of the j-th user equipment can be expressed as:

[0148]

[0149] Among them, h r,j It is the channel from the i-th smart metasurface to the j-th user equipment, Φ i =diag(e jθ1 ,e jθ2 ,…,e j θM G is the phase shift matrix of the i-th smart metasurface. i h is the channel from the base station to the i-th smart metasurface. d,j It is the channel from the base station to the j-th user equipment, w j It is the beamforming vector corresponding to the j-th user equipment base station, x j It is the signal transmitted by the base station to the j-th user equipment, n j It is the noise of the j-th user equipment.

[0150] Therefore, the signal-to-noise ratio of user equipment j can be expressed as:

[0151]

[0152] Without loss of generality, let the noise variance σ of each user device be... j 2 All are 10 -11 The data rate of the j-th user equipment can be expressed as:

[0153] R j =Blog2(1+γ) j (12)

[0154] 3. The classic fixed-point iterative method is used to perform active and passive joint beamforming of the base station and the smart metasurface.

[0155] Let the base station's transmission power be P. According to the process of Algorithm 1, initialize t = 0 and ε = 10. -6 Using the channel from the i-th smart metasurface to the j-th user equipment Channel G from the base station to the i-th smart metasurface i , is the channel h from the base station to the j-th user equipment. j Construct matrix A and initialize it. Secondly, the vector v is calculated iteratively according to the algorithm flow until convergence, thereby obtaining the phase shift matrix Φ of the i-th smart metasurface. i Finally, the beamforming vector w of the base station corresponding to the j-th user equipment is obtained according to the formula. j Calculate the data rate that can be achieved by the j-th user device and the i-th smart metasurface matching for each user device, where i,j=1,…,N.

[0156] 4. Use the Hungarian algorithm to optimize the matching problem between smart metasurfaces and user equipment.

[0157] After obtaining the data rates of the corresponding user equipment and the smart metasurface, solving optimization problem (7) yields the optimal smart metasurface allocation matrix A, maximizing the system utility function. The objective function of optimization problem (7) is then transformed into a format solvable by the Hungarian algorithm. Then, according to the process of Algorithm 2, the corresponding elements are processed as follows: By obtaining the N-dimensional non-negative matrix R, we can obtain the optimal smart metasurface allocation matrix A, and thus obtain the optimal matching scheme between user equipment and smart metasurface in this scenario.

[0158] The number of user equipment (UE) devices and smart metasurfaces (N) is set to 8, and the number of reflective elements (M) on each smart metasurface is set to 64. Following the above procedure, 1000 scenarios are randomly generated, meaning 1000 random UE and smart metasurface locations are generated, along with 1000 random channel locations. The base station's transmit power (P) is varied from 0 dBm to 10 dBm. The system utility obtained by matching UE and smart metasurfaces using the Hungarian algorithm is compared with that of the random matching method and the method without smart metasurfaces. The results are as follows: Figure 3 As shown, the system utility of all three methods increases with increasing transmit power P. Using the Hungarian algorithm at a transmit power of 0 dBm, the system utility gain reaches 43.18% compared to the random matching method and 91.61% compared to the method without smart metasurfaces. At a transmit power of 10 dBm, the system utility gain using the Hungarian algorithm reaches 23.48% compared to the random matching method and 48.48% compared to the method without smart metasurfaces. This demonstrates that using smart metasurfaces to assist communication can significantly improve system utility without requiring more RF links. Furthermore, using the Hungarian algorithm to match user equipment and smart metasurfaces can further enhance system utility beyond random matching, better meeting the data rate demands of user equipment in the IoT and 5G era.

[0159] like Figure 4As shown, the fixed base station transmit power P is 10 dBm, and the number of user equipment and smart metasurfaces N is 8 each. The number of reflection units M per smart metasurface is changed from 40 to 100. Without smart metasurfaces, the system utility is independent of the number of reflection units M and remains at a low level. However, the system utility obtained by both the Hungarian algorithm and the random matching method increases with the increase of the number of reflection units M. Furthermore, the system utility gain of the Hungarian algorithm compared to the random matching method also increases with the increase of the number of reflection units M. When the number of reflection units M is 40, the system utility gain reaches 15.96% compared to the random matching method and 29.67% compared to the method without smart metasurfaces. When the number of reflection units M is 100, the system utility gain reaches 23.45% compared to the random matching method and 49.43% compared to the method without smart metasurfaces. Therefore, it can be seen that in a smart metasurface-assisted wireless communication system, increasing the number M of smart metasurface reflective units can improve system utility. After using the Hungarian algorithm to perform optimal matching between user equipment and smart metasurface, the gain in system utility can be further expanded.

[0160] In summary, this example primarily employs a user equipment (UE) and smart metasurface matching method based on the Hungarian algorithm. In a multi-user wireless communication system assisted by multiple smart metasurfaces, the optimal matching between UEs and smart metasurfaces is addressed by first modeling the system and channel. Then, joint beamforming of the base station and smart metasurface is performed for each UE and smart metasurface. Finally, the optimal matching between UEs and smart metasurfaces is solved using the Hungarian algorithm. Figure 3 and Figure 4 The simulation results show that the present invention greatly improves the system effectiveness and can serve as a guiding strategy for matching user equipment with smart metasurfaces in a multi-user wireless communication system assisted by multiple smart metasurfaces.

[0161] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0162] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for matching user equipment with a smart metasurface, characterized in that, include: A system assisted by intelligent metasurfaces is constructed, wherein the system includes a base station, N intelligent metasurfaces, and N user equipment; A channel model is constructed based on the system, wherein the channel model includes a direct channel from the base station to a single user equipment, a channel from the base station to a single smart metasurface, and a channel from the single smart metasurface to the single user equipment; The system employs a fixed-point iterative method for active and passive joint beamforming of the base station and the smart metasurface; and uses the Hungarian algorithm to optimize the matching between the smart metasurface and the user equipment, thereby maximizing system utility. The active-passive joint beamforming of the base station and the smart metasurface using the fixed-point iterative method includes: when the transmission rate of the base station is... P At that time, the maximum transmission rate of the user equipment is expressed by the following formula: ； Where Φ is the phase shift matrix of the intelligent metasurface and w Provide the beamforming vector for the base station. , … This represents the phase shift of the 1st, 2nd, ..., Mth elements of the intelligent metasurface. h d This refers to the direct channel from the base station to the user equipment. G For the channel from the base station to the single smart metasurface and h r This refers to the channel from the single smart metasurface to the single user equipment. Optimizing the matching of the smart metasurface with the user equipment using the Hungarian algorithm includes: representing the system utility by the following formula, where the system utility is defined as the total rate of the user equipment in the system: ； The elements of the intelligent metasurface allocation matrix A are represented as follows: a ij , i , j =1,…, N , a ij Representing the i Is the intelligent metasurface the first...? j One auxiliary user device; ； The sum of each row and each column of the intelligent metasurface allocation matrix A is equal to 1; The objective function of the optimization problem is: ； The Hungarian algorithm is used to solve the smart metasurface allocation problem to find the optimal pairing of smart metasurfaces and user equipment. The objective of the Hungarian algorithm is to minimize the objective function, and the objective function is transformed into the solution format of the Hungarian algorithm: ,in, Let R be the elements of an N-dimensional nonnegative matrix. R j Indicates the first j Data rate per user device.

2. The user equipment and intelligent metasurface matching method according to claim 1, characterized in that, The user equipments are independent of each other and do not interfere with each other. Each user equipment communicates directly with the base station and indirectly with the base station with the assistance of the single smart metasurface.

3. The user equipment and intelligent metasurface matching method according to claim 1, characterized in that, The channel model constructed based on the system includes: For large-scale fading models, the distance-related path loss model is calculated using the following formula: ； in, d Bu , d BR , d Ru These are the distances between the base station and the user equipment, the base station and the smart metasurface, and the smart metasurface and the user equipment, respectively. C 0 is the reference distance d Path loss at 0=1m k This is the path loss index. h d This refers to the direct channel from the base station to the user equipment. G For the channel from the base station to the single smart metasurface and h r For the channel from the single smart metasurface to the single user equipment, the path loss exponent is set to... k Bu =3.5、 k BR =2.2、 k Ru =2.

8.

4. The user equipment and intelligent metasurface matching method according to claim 3, characterized in that, The channel model constructed according to the system includes: for small-scale fading, considering the Ricean fading channel model, the direct channel from the base station to the user equipment is expressed by the following formula. h d : ； in, w Bu H represents the Rice factor. LoS and H NLoS Representing the line-of-sight channel component and the Rayleigh fading component respectively, when w Bu As H approaches infinity, H becomes equivalent to a line-of-sight channel. w Bu When H = 0, H is equivalent to a Rayleigh fading channel; The channel is represented by the following formula. h d : H; when w BR and w Ru When representing the Rice factor, respectively, the channel G from the base station to the single smart metasurface and the channel from the single smart metasurface to the single user equipment are generated. h r .

5. The user equipment and intelligent metasurface matching method according to claim 2, characterized in that, The channel model constructed based on the system includes: When the i The first intelligent metasurface-assisted j When a user equipment communicates, the following formula is used to represent the first user equipment communication. j Received signals from individual user equipment: in, i , j =1,…, N , h r,j It is the first i The first intelligent metasurface to the first j Channels of each user equipment, Φ i =diag(e jθ1 ,e j θ2 ,…,e jθM ) is the first i The phase shift matrix of a smart metasurface, G i It is the base station to the first i Channels of a smart metasurface h d,j It is the base station to the first j Channels of each user equipment, w j It corresponds to the first j Beamforming vectors of each user equipment base station x j Is the base station to the first j Signals transmitted by individual user equipment n j It is the first j Noise from individual user equipment; The following formula represents the first j Signal-to-noise ratio of individual user devices: ； Let the noise variance of each user device All are 10 -11 The following formula represents the first... j Data rate per user device: 。 6. The user equipment and intelligent metasurface matching method according to claim 2, characterized in that, The active-passive joint beamforming of the base station and the smart metasurface using the fixed-point iterative method includes: Will t , ε Initialize to ; calculate ; Construct the intelligent metasurface allocation matrix ; Initialization vector ; when At that time, based on the initialization vector v (0) calculate ; t = t + 1, until v (t+1) convergence; The beamforming vector is calculated using the following formula. ; in, The former The elements are phase shift matrices. The main diagonal element.

7. A user equipment and intelligent metasurface matching device, characterized in that, include: The system, assisted by intelligent metasurfaces, includes a base station, N intelligent metasurfaces, and N user equipment. A channel model is used to construct a channel model based on the system, wherein the channel model includes a direct channel from the base station to a single user equipment, a channel from the base station to a single smart metasurface, and a channel from the single smart metasurface to the single user equipment; A beamforming module is used to perform active and passive joint beamforming of the base station and the smart metasurface using a fixed-point iterative method, when the transmission rate of the base station is... P At that time, the maximum transmission rate of the user equipment is expressed by the following formula: ； Where Φ is the phase shift matrix of the intelligent metasurface and w Provide the beamforming vector for the base station. , … This represents the phase shift of the 1st, 2nd, ..., Mth elements of the intelligent metasurface. h d This refers to the direct channel from the base station to the user equipment. G For the channel from the base station to the single smart metasurface and h r For the channel from the single smart metasurface to the single user equipment; and A matching module is used to optimize the matching of the smart metasurface with the user equipment using the Hungarian algorithm to maximize system utility. System utility is expressed by the following formula, where system utility is defined as the total rate of user equipment in the system: ； The elements of the intelligent metasurface allocation matrix A are represented as follows: a ij , i , j =1,…, N , a ij Representing the i Is the intelligent metasurface the first...? j One auxiliary user device; ； The sum of each row and each column of the intelligent metasurface allocation matrix A is equal to 1; The objective function of the optimization problem is: ； The Hungarian algorithm is used to solve the smart metasurface allocation problem to find the optimal pairing of smart metasurfaces and user equipment. The objective of the Hungarian algorithm is to minimize the objective function, and the objective function is transformed into the solution format of the Hungarian algorithm: ,in, Let R be the elements of an N-dimensional nonnegative matrix. R j Indicates the first j Data rate per user device.

8. The user equipment and intelligent metasurface matching device according to claim 7, characterized in that, The constructed channel model includes: For large-scale fading models, the distance-related path loss model is calculated using the following formula: ， in, d Bu , d BR , d Ru These are the distances between the base station and the user equipment, the base station and the smart metasurface, and the smart metasurface and the user equipment, respectively. C 0 is the reference distance d Path loss at 0=1m k This is the path loss index. h d G is the direct channel from the base station to the user equipment, and G is the channel from the base station to the single smart metasurface. h r For the channel from the single smart metasurface to the single user equipment, the path loss exponent is set to... k Bu =3.5、 k BR =2.2、 k Ru =2.8.

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