Communication resource allocation method and device based on intelligent reflector-user association

Abstract

According to the communication resource allocation method and device based on intelligent reflecting surface-user association, in a multi-intelligent reflecting surface and multi-user communication scene, an intelligent reflecting surface-user association allocation matrix is introduced, the matching relation between intelligent reflecting surfaces and users is accurately controlled, the resource utilization rate is maximized, and the user experience is improved. Meanwhile, the influence of multi-user interference in the system is greatly reduced, and reliable support is provided for improving the overall communication performance; according to the intelligent reflecting surface-user joint parameter design method based on the maximum sum rate criterion and the minimum user signal to interference plus noise ratio constraint, a brand new intelligent reflecting surface-user incidence matrix is optimized, and meanwhile, the phase shift of a reflecting unit of the intelligent reflecting surface is adjusted to improve the signal propagation direction; and the transmitting beam forming vector is optimized to enhance the signal gain of the target user, so that the performance of the communication system is comprehensively improved, and the method is particularly suitable for complex scenes of multiple users and large-scale intelligent reflecting surfaces.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent reflecting surface assisted wireless communication, and in particular relates to a communication resource allocation method and device based on intelligent reflecting surface-user association. Background Art

[0002] With the development of wireless communication technology and 6G technology, Reconfigurable Intelligent Surface (RIS) technology has been identified as an important frontier key technology for achieving efficient communication and high data rates in 6G networks because of its ability to enhance signal coverage, improve spectrum efficiency and reduce energy consumption.

[0003] At present, regarding the relationship between RIS and users in multi-RIS multi-user systems, traditional technologies have limitations to a certain extent, which are mainly reflected in the constraints on the functional status and service capabilities of RIS. Only the on / off state of RIS is considered, and its potential intermediate state adjustment capabilities are ignored. In addition, some studies limit the number of users that a single RIS can serve, making it difficult for the system to achieve efficient resource utilization in high-density user scenarios. This limitation simplifies the complexity of the problem to a certain extent, but also leads to resource waste in practical applications, especially in multi-user communication systems, where some RIS may be in a state of resource redundancy, while other RIS may reduce system performance due to overload. At the same time, some studies have performed distributed optimization of the RIS-user association problem, the RIS reflector element phase shift, and the transmit beamforming vector, but have failed to fully consider the coupling relationship and synergistic gain between the three. Summary of the Invention

[0004] In view of this, an object of the present invention is to propose a communication resource allocation method and apparatus based on intelligent reflecting surface-user association, so as to solve or partially solve the problems mentioned in the background technology.

[0005] Based on the above objectives, in a first aspect, the present invention provides a communication resource allocation method based on intelligent reflecting surface-user association, comprising:

[0006] Constructing a multi-intelligent reflector and multi-user communication system signal model, wherein the communication system signal model includes a multi-antenna base station, J intelligent reflectors with N reflective units, and K single-antenna user devices;

[0007] Constructing a smart reflecting surface-user association matrix, wherein the smart reflecting surface-user association matrix uses binary variables to represent associations between the smart reflecting surface and the users, and calculating a received signal-to-interference-and-noise ratio at user k based on the smart reflecting surface-user association matrix, a transmitted signal at a multi-antenna base station, and a received signal at user k;

[0008] A joint parameter design problem P1 based on the maximum system total rate criterion is constructed. The joint parameter design problem P1 aims to maximize the system total rate and takes the user's minimum signal-to-interference-and-noise ratio as a constraint. The smart reflector-user correlation matrix is ​​optimized, the phase shift of the reflector unit is adjusted to improve the signal propagation direction, and the transmit beamforming vector is optimized to enhance the signal gain of the target user.

[0009] As a preferred solution for the communication resource allocation method based on smart reflective surface-user association, the channels between designated facilities are modeled as independent random channels through the communication system signal model and are assumed to obey the Rice distribution to reflect the characteristics of the interaction between direct paths and scattered paths in the actual communication environment. The antenna base station and the smart reflective surface obtain channel state information of all links; the channel of each link is modeled as a path loss model, and the path loss PL is expressed as:

[0010]

[0011] Where d is the average distance between the base station and RIS, d0 is the reference distance, PL0 is the reference path loss, and α is the path loss exponent.

[0012] As a preferred solution of the communication resource allocation method based on intelligent reflection surface-user association, the intelligent reflection surface-user association matrix uses the binary variable L j,k ∈{0,1} represents the association relationship between the intelligent reflective surface and the user. When L j,k =1, it means that the jth smart reflective surface is associated with the kth user equipment. j,k =0, it indicates that the j-th smart reflective surface is not associated with the k-th user equipment.

[0013] As a preferred solution of the communication resource allocation method based on intelligent reflective surface-user association, the transmission signal at the multi-antenna base station is:

[0014]

[0015] The received signal at user k is:

[0016]

[0017] The received signal-to-interference-and-noise ratio γ at user k k for:

[0018]

[0019] Where, L j,k is the relationship between the smart reflective surface and the user, They represent the channel coefficients between the jth smart reflecting surface and the base station, between the jth smart reflecting surface and the kth user, and between the base station and the kth user, respectively;

[0020] Diagonal matrix is the reflection coefficient matrix of the jth smart reflector, and represents the phase shift of the reflective element n, a j,n =1 indicates maximum reflection efficiency; represents the additive white Gaussian noise at the kth user; w k is the beamforming vector of user k; s k is the unit power information symbol of user k; is the index set of the user set; σ k n k The variance of b k The degree of fluctuation in the complex domain.

[0021] As a preferred solution for the communication resource allocation method based on intelligent reflector-user association, the expression of the joint parameter design problem P1 based on the maximum system sum rate criterion is constructed as follows:

[0022]

[0023] Where R k =log2(1+γ k ) is the achievable data rate of the kth user; P max is the maximum transmission power of the base station; w,Θ,L is defined as: w=[w1,…,w k ,…,w K ],Θ=blkdiag(Θ1,…,Θ j ,…,Θ J ) and L=[L j,k ] J×K , that is, L is composed of L j,k The matrix composed of The index set of the RIS collection.

[0024] As a preferred solution of the communication resource allocation method based on intelligent reflective surface-user association, the joint parameter design problem P1 is split into sub-problems P2 and P3;

[0025] By fixing the phase shift of the reflective elements of the smart reflector, optimizing the smart reflector-user correlation matrix L and the transmit beamforming vector w, we can obtain the sub-problem P2:

[0026]

[0027]

[0028] By fixing the smart reflector-user correlation matrix L and the transmit beamforming vector w, and optimizing the phase shift Θ of the reflector element of the smart reflector, we can obtain the sub-problem P3:

[0029]

[0030] The minimum mean square error-user association method is used to solve the subproblem P2; the semidefinite relaxation combined with the fractional programming method is used to solve the subproblem P3, and the solutions of the subproblems P2 and P3 are iteratively updated through an alternating optimization algorithm to gradually approach the optimal solution of the joint parameter design problem P1.

[0031] In a second aspect, the present invention provides a communication resource allocation device based on intelligent reflecting surface-user association, comprising:

[0032] A communication system signal model construction module is used to construct a communication system signal model for multiple intelligent reflectors and multiple users, wherein the communication system signal model includes a multi-antenna base station, J intelligent reflectors with N reflective units, and K single-antenna user devices;

[0033] A smart reflecting surface-user association matrix construction module is configured to construct a smart reflecting surface-user association matrix, wherein the smart reflecting surface-user association matrix uses binary variables to represent the association between the smart reflecting surface and the user, and calculate the received signal-to-interference-and-noise ratio at user k based on the smart reflecting surface-user association matrix, the transmitted signal at the multi-antenna base station, and the received signal at user k;

[0034] The communication resource allocation optimization module is used to construct a joint parameter design problem P1 based on the maximum system total rate criterion. The joint parameter design problem P1 aims to maximize the system total rate and uses the user's minimum signal-to-interference-and-noise ratio as a constraint. The module optimizes the smart reflector-user association matrix, adjusts the phase shift of the reflector unit to improve the signal propagation direction, and optimizes the transmit beamforming vector to enhance the signal gain of the target user.

[0035] As a preferred solution for the communication resource allocation device based on smart reflective surface-user association, in the communication system signal model construction module, the channels between designated facilities are modeled as independent random channels through the communication system signal model and are assumed to obey the Rice distribution to reflect the characteristics of the direct path and the scattered path in the actual communication environment. The antenna base station and the smart reflective surface obtain the channel state information of all links; the channel model of each link is a path loss model, and the path loss PL is expressed as:

[0036]

[0037] Where d is the average distance between the base station and RIS, d0 is the reference distance, PL0 is the reference path loss, and α is the path loss exponent.

[0038] As a preferred solution of the communication resource allocation device based on intelligent reflecting surface-user association, in the intelligent reflecting surface-user association matrix construction module:

[0039] The intelligent reflection surface-user association matrix uses binary variables L j,k ∈{0,1} represents the association relationship between the intelligent reflective surface and the user. When L j,k =1, it means that the jth smart reflective surface is associated with the kth user equipment. j,k =0, indicating that the j-th smart reflective surface is not associated with the k-th user equipment;

[0040] In the smart reflective surface-user association matrix construction module, the transmission signal at the multi-antenna base station is:

[0041]

[0042] In the smart reflective surface-user association matrix building module, the received signal at user k is:

[0043]

[0044] In the intelligent reflection surface-user correlation matrix building module, the received signal-to-interference-noise ratio γ at user k k for:

[0045]

[0046] Where, L j,k is the relationship between the smart reflective surface and the user, They represent the channel coefficients between the jth smart reflecting surface and the base station, between the jth smart reflecting surface and the kth user, and between the base station and the kth user, respectively;

[0047] Diagonal matrix is the reflection coefficient matrix of the jth smart reflector, and represents the phase shift of the reflective element n, a j,n =1 indicates maximum reflection efficiency; represents the additive white Gaussian noise at the kth user; w k is the beamforming vector of user k; s k is the unit power information symbol of user k; is the index set of the user set; σ k n k The variance of n kThe degree of fluctuation in the complex domain.

[0048] As a preferred solution of the communication resource allocation device based on intelligent reflecting surface-user association, in the communication resource allocation optimization module, the expression of the joint parameter design problem P1 based on the maximum system sum rate criterion is:

[0049]

[0050] Where R k =log2(1+γ k ) is the achievable data rate of the kth user; P max is the maximum transmission power of the base station; w,Θ,L is defined as: w=[w1,…,w k ,…,w K ],Θ=blkdiag(Θ1,…,Θ j ,…,Θ J ) and L=[L j,k ] J×K , that is, L is composed of L j,k The matrix composed of The index set of the RIS collection.

[0051] As a preferred solution of the communication resource allocation device based on intelligent reflective surface-user association, in the communication resource allocation optimization module, the joint parameter design problem P1 is split into sub-problems P2 and P3;

[0052] By fixing the phase shift of the reflective elements of the smart reflector, optimizing the smart reflector-user correlation matrix L and the transmit beamforming vector w, we can obtain the sub-problem P2:

[0053]

[0054] By fixing the smart reflector-user correlation matrix L and the transmit beamforming vector w, and optimizing the phase shift Θ of the reflector element of the smart reflector, we can obtain the sub-problem P3:

[0055]

[0056] In the communication resource allocation optimization module, the minimum mean square error-user association method is used to solve the subproblem P2; the semidefinite relaxation combined with the fractional programming method is used to solve the subproblem P3, and the solutions of the subproblems P2 and P3 are iteratively updated through the alternating optimization algorithm to gradually approach the optimal solution of the joint parameter design problem P1.

[0057] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the method for allocating communication resources based on intelligent reflecting surface-user association according to the first aspect or any possible implementation thereof is implemented.

[0058] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the steps of a communication resource allocation method based on intelligent reflecting surface-user association according to the first aspect or any possible implementation thereof.

[0059] As can be seen from the above description, the technical solution provided by the present invention introduces an intelligent reflector-user association allocation matrix in a multi-intelligent reflector multi-user communication scenario, accurately controls the matching relationship between the intelligent reflector and the user, maximizes resource utilization, and significantly reduces the impact of multi-user interference in the system, providing reliable support for improving overall communication performance. The present invention optimizes a new intelligent reflector-user association matrix based on the maximum sum rate criterion and the minimum user signal-to-interference-and-noise ratio constraint, adjusts the phase shift of the intelligent reflector reflection unit to improve the signal propagation direction, and optimizes the transmit beamforming vector to enhance the signal gain of the target user, thereby comprehensively improving the performance of the communication system. The present invention decomposes the complex joint parameter problem into two sub-problems for solution. For sub-problem one, an improved minimum mean square error-user association method (MMSE-UA) is used to solve it, which can effectively reduce interference in the system and optimize the signal reception quality, thereby improving the overall performance of the system. Sub-problem two is solved by combining semidefinite relaxation (SDR) and fractional programming (FP) methods. By continuously optimizing the solutions to these two sub-problems and gradually approaching the local optimal solution to the problem, the error propagation problem is avoided, the global performance and resource utilization efficiency of the system are improved, and intelligent resource allocation and efficient communication optimization suitable for multi-intelligent reflector multi-user systems are realized. It is particularly suitable for complex scenarios with multiple users and large-scale intelligent reflectors. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 Flowchart of a communication resource allocation method based on intelligent reflective surface-user association provided by an embodiment of the present invention;

[0062] Figure 2 An architecture diagram of a multi-intelligent reflecting surface multi-user communication system to which the communication resource allocation method based on intelligent reflecting surface-user association provided by an embodiment of the present invention is applied;

[0063] Figure 3 A simulation diagram of the intelligent reflecting surface-user association matrix in the communication resource allocation method based on intelligent reflecting surface-user association provided in an embodiment of the present invention;

[0064] Figure 4 Comparison of time complexity in the communication resource allocation method based on intelligent reflective surface-user association provided in an embodiment of the present invention;

[0065] Figure 5 This is a diagram illustrating the architecture of a communication resource allocation device based on intelligent reflective surface-user association provided by an embodiment of the present invention;

[0066] Figure 6 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0068] It should be noted that, unless otherwise defined, technical or scientific terms used in the embodiments of the present invention should have the same general meaning as those understood by persons of ordinary skill in the art to which the present invention pertains. The words "include" or "comprise" and similar expressions used in the embodiments of the present invention mean that the elements or objects preceding the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0069] With the development of wireless communication technology and 6G technology, Reconfigurable Intelligent Surface (RIS) technology has been identified as an important frontier key technology for achieving efficient communication and high data rates in 6G networks because of its ability to enhance signal coverage, improve spectrum efficiency, and reduce energy consumption. This technology consists of a large number of passive components that can independently control the reflected phase and amplitude of the incident wave, thereby dynamically optimizing the wireless channel environment. By adjusting the coherent superposition of the reflected signal and the direct signal from the base station, RIS can significantly increase the data rate and bypass obstacles by creating a virtual line-of-sight link, thereby enhancing network coverage. Since no RF link or digital signal processor is required, this technology has the advantages of low cost, low energy consumption, and high energy efficiency.

[0070] Multi-RIS-assisted wireless communication systems offer significant advantages. The distributed deployment of multiple RISs not only improves network coverage but also enhances data transmission robustness and system robustness. Multiple RISs make it easier to establish virtual line-of-sight links and boost signal strength. However, the deployment of multiple RISs inevitably introduces more reflection paths. While these reflection paths provide additional signal boost to the intended user, they also interfere with the communication links of other users. This interference effect can be exacerbated as the number of RISs increases, especially in high-density user scenarios, negatively impacting overall system performance. Therefore, in multi-RIS systems, more efficient resource management algorithms are needed to coordinate signal control between multiple RISs and multiple users, minimizing the impact of multi-user interference while maintaining system performance advantages.

[0071] In the related art, existing research on the relationship between RIS and users in multi-RIS multi-user systems has limitations to a certain extent, mainly reflected in the constraints on the functional state and service capabilities of the RIS. Some studies only consider the on / off state of the RIS, ignoring its potential ability to adjust intermediate states. In addition, some studies limit the number of users that a single RIS can serve, making it difficult for the system to achieve efficient resource utilization in high-density user scenarios. This limitation simplifies the complexity of the problem to a certain extent, but also leads to resource waste in practical applications. In particular, in multi-user communication systems, some RIS may be in a state of resource redundancy, while others may be overloaded, reducing system performance. At the same time, some studies have performed distributed optimization of the RIS-user association problem, the RIS reflector element phase shift, and the transmit beamforming vector, but have failed to fully consider the coupling relationship and synergistic gain among the three.

[0072] In view of this, to avoid error propagation and further improve the system's global performance and resource utilization efficiency, an embodiment of the present invention provides a communication resource allocation method and apparatus based on intelligent reflecting surface-user association, which minimizes multi-user interference while maximizing system performance. This method also introduces a binary variable to represent the association weight between the RIS and the user to achieve resource allocation, adjusts the phase shift of the RIS reflector element to improve the signal propagation direction, optimizes the transmit beamforming vector to enhance the signal gain of the target user, and performs joint parameter design based on maximizing the system's total rate, achieving intelligent resource allocation and efficient communication optimization for multi-RIS multi-user systems. The following is the specific content of this embodiment of the present invention.

[0073] See also Figure 1 The embodiment of the present invention provides a communication resource allocation method based on intelligent reflective surface-user association, comprising the following steps:

[0074] S1. Construct a multi-intelligent reflecting surface multi-user communication system signal model, wherein the communication system signal model includes a multi-antenna base station, J intelligent reflecting surfaces with N reflecting units, and K single-antenna user equipments;

[0075] S2. Constructing a smart reflecting surface-user association matrix, wherein the smart reflecting surface-user association matrix uses binary variables to represent the association relationship between the smart reflecting surface and the user, and calculating the received signal-to-interference-and-noise ratio at user k based on the smart reflecting surface-user association matrix, the transmitted signal at the multi-antenna base station, and the received signal at user k;

[0076] S3. Construct a joint parameter design problem P1 based on the maximum system total rate criterion. The joint parameter design problem P1 aims to maximize the system total rate and uses the user's minimum signal-to-interference-and-noise ratio as a constraint. It optimizes the smart reflector-user association matrix, adjusts the phase shift of the reflector unit to improve the signal propagation direction, and optimizes the transmit beamforming vector to enhance the signal gain of the target user.

[0077] See also Figure 2 In this embodiment, the architecture of the communication system signal model mainly includes the following three facilities: base station; smart reflective surface; user equipment.

[0078] Base stations are core nodes in wireless communication systems, such as cellular base stations, broadcast base stations, or small base stations. They transmit high-power signals to provide communication services to user devices and are responsible for receiving and forwarding signals to maintain network operation. Base stations not only support traditional wireless communication tasks but also work in conjunction with smart reflectors to enhance network performance by optimizing signal transmission paths. They improve communication coverage and signal quality through reflection and modulation, thereby improving overall network efficiency.

[0079] The intelligent reflecting surface (RIS) consists of a large number of adjustable reflective units, typically constructed using micro-electromechanical systems (MEMS) or metamaterials technology, capable of dynamically adjusting the reflected phase and amplitude of the incident signal. Each reflective unit precisely adjusts the phase by manipulating capacitance or tuning circuits, thereby changing the propagation path of the reflected signal. Through the synergistic effect of these units, the RIS as a whole reconstructs the beam of the incident signal, enhancing the signal gain in the target direction or reducing the signal power in the interfering direction. The core of this reflection control lies in adjusting the phase response, and the accuracy of this adjustment determines the RIS's signal optimization capabilities. A more complex unit structure enables finer phase control, thereby improving the directional performance of the reflected signal. Furthermore, the RIS dynamically adjusts the parameters of the reflective units based on channel state information, either through a preset reflection matrix or incorporating a real-time optimization algorithm, to achieve optimal communication performance.

[0080] User equipment (UE) is a communication terminal with a single or multiple antennas that communicates by receiving signals from base stations and RIS. Based on the characteristics of the received signal, the UE estimates channel state information, analyzes the achievable rate, and transmits feedback information to the base station or RIS control unit to assist in optimizing system parameters. The UE can also adjust the parameters of the demodulation algorithm based on channel conditions, thereby improving the quality of the received signal. Furthermore, in a multi-intelligent reflector multi-user system, the UE achieves higher signal gain and anti-interference capabilities by collaboratively processing signals from multiple intelligent reflector paths.

[0081] In this embodiment, step S1 is implemented for a typical multi-intelligent reflecting surface multi-user communication system. The communication system signal model consists of a multi-antenna base station, J intelligent reflecting surfaces with N reflective units, and K single-antenna user devices. The multi-antenna base station not only collaborates with the intelligent reflecting surfaces but also efficiently communicates with the user devices, thereby achieving the integration of multiple signal transmission paths. Specifically, the user devices can receive signals from the base station via a direct link (a direct connection between the base station and the user) or via an indirect link formed by reflection from the intelligent reflecting surfaces.

[0082] Specifically, considering a single-cell communication scenario in an urban environment, base stations, smart reflective surfaces, and user devices are deployed in a quasi-static manner, meaning the location of each device remains relatively fixed over time, simplifying the model and improving the feasibility of system optimization. In this scenario, the channels between facilities are modeled as independent random channels and assumed to obey the Rice distribution to reflect the combined effects of direct paths (LOS) and scattering paths (NLOS) in actual communication environments. At the same time, base stations and smart reflective surfaces can obtain channel state information for all links. The channel model for each link is a path loss model, where the path loss is expressed as:

[0083]

[0084] Where d is the average distance between the base station and the RIS, d0 = 1m is the reference distance, PL0 is the reference path loss, and α is the path loss exponent. Assuming that the propagation environment of the direct link (the direct path between the base station and the user) is subject to numerous obstacles or severe signal attenuation, the signal propagation process is significantly affected by obstructions, reflections, and scattering. Therefore, the path loss exponent α is set to 3.9 to accurately describe the signal attenuation characteristics in this environment. For reflective links (base station-RIS and RIS-user), since the RIS is typically deployed at a higher location or in an unobstructed area, its propagation environment is relatively open with fewer obstacles and lower signal propagation path loss. Therefore, the path loss exponent α is set to 2.2.

[0085] In this embodiment, in order to achieve flexible matching between smart reflective surfaces and users, thereby optimizing efficient resource allocation while meeting system requirements, and by reducing multi-user interference, the overall performance of the system is maximized. In step S2, the smart reflective surface-user association matrix is ​​constructed using the binary variable L j,k ∈{0,1} represents the association relationship between the intelligent reflective surface and the user. When L j,k =1, it means that the jth smart reflector is associated with the kth user equipment, that is, it can provide a reflection link for the user to enhance the signal. j,k = 0, indicating that the jth smart reflector is not associated with the kth user device. This design maximizes resource utilization by precisely controlling the matching relationship between the smart reflector and the user, while significantly reducing the impact of multi-user interference in the system and providing reliable support for improving overall communication performance.

[0086] Among them, the transmission signal at the multi-antenna base station is:

[0087]

[0088] The received signal at user k is:

[0089]

[0090] The received signal-to-interference-and-noise ratio γ at user k k for:

[0091]

[0092] Where, L j,k is the relationship between the smart reflective surface and the user, They represent the channel coefficients between the jth smart reflecting surface and the base station, between the jth smart reflecting surface and the kth user, and between the base station and the kth user, respectively;

[0093] Diagonal matrix is the reflection coefficient matrix of the jth smart reflector, and represents the phase shift of the reflective element n, a j,n =1 indicates maximum reflection efficiency; represents the additive white Gaussian noise at the kth user; w k is the beamforming vector of user k; s k is the unit power information symbol of user k; is the index set of the user set; σ k n k The variance of n k The degree of fluctuation in the complex domain.

[0094] In this embodiment, by optimizing a new smart reflector-user association allocation matrix, adjusting the phase shift of the smart reflector's reflective units to improve the signal propagation direction, and optimizing the transmit beamforming vector to enhance the target user's signal gain, the overall communication system performance is improved. In step S3, the above parameters are combined for collaborative design, with maximizing the system's total rate as the design goal and the user's minimum signal-to-interference-and-noise ratio (SINR) as the constraint. The optimization problem P1 is designed. The expression for optimization problem P1 is:

[0095]

[0096]

[0097] Where R k =log2(1+γ k ) is the achievable data rate of the kth user; P max is the maximum transmission power of the base station; w,Θ,L is defined as: w=[w1,…,w k ,…,w K ],Θ=blkdiag(Θ1,…,Θ j ,…,Θ J ) and L=[L j,k ] J×K , that is, L is composed of L j,k The matrix composed of The index set of the RIS collection.

[0098] Among them, the constraint (5b) on the phase shift angle of the reflection unit of the smart reflection surface requires that the phase shift of each reflection unit of each smart reflection surface is must be between 0 and 2π, reflecting the phase shift limitation of the actual hardware; constraint (5c) is a constraint on the total transmit power of the base station, representing the beamforming vector w for all users k The total power of the base station cannot exceed the maximum available transmission power P max , which ensures that the base station works within the power limit; Constraint (5d) is the constraint on the association between the smart reflector and the user. Each L j,k It can only be 0 or 1; Constraint (5e) is a constraint on the number of users served by each smart reflector surface, requiring that the total number of users associated with a smart reflector surface cannot exceed the number N of reflective units of the smart reflector surface. This condition reflects the capacity limitation of the smart reflector surface hardware; Constraint (5f) is a constraint on the user signal-to-interference-and-noise ratio (SINR), requiring that the SINR of each user k must meet its minimum requirement This condition ensures the basic communication quality of users.

[0099] In this embodiment, the original problem is decomposed into two sub-problems, and the local optimal solution is gradually approached through alternating optimization. Specifically, each iteration of the alternating optimization algorithm only optimizes one of the sub-problems, and by continuously alternating and updating the solutions to each sub-problem, the optimal solution to the problem is ultimately achieved.

[0100] Specifically, the joint parameter design problem P1 is split into sub-problems P2 and P3; sub-problem P2 is solved using the improved minimum mean square error-user association method (MMSE-UA). This method can effectively reduce interference in the system and optimize the quality of signal reception, thereby improving the overall performance of the system. Sub-problem P3 is solved using a technique that combines semidefinite relaxation (SDR) with fractional programming (FP) methods. Through the semidefinite relaxation method, the originally non-convex optimization problem is converted into a convex optimization problem, making it easier to find the global optimal solution; and the fractional programming method helps to deal with optimization problems whose objective function is in the form of a ratio, which can further improve the solution efficiency. By combining these two methods, the present invention can effectively solve complex joint parameter design problems, and ensure computational efficiency and solution accuracy during the solution process. Finally, through the iterative update of the alternating optimization algorithm, we can obtain a local optimal solution to ensure the optimization of system performance. The complete steps for solving the joint parameter optimization problem are as follows:

[0101] Step 1: By fixing the phase shift of the reflective elements of the smart reflector, optimize the smart reflector-user correlation matrix L and the transmit beamforming vector w, and obtain the sub-problem P2:

[0102]

[0103] Specifically, the interference is considered as noise, and a linear receive beamforming strategy is considered. Let U k is the receiving beamformer, the estimated signal is A common solution to the sum rate maximization problem is to convert it into a sum mean square error (sum-MSE) minimization problem. By minimizing the mean square error, the signal quality of the system can be indirectly improved, thereby optimizing the sum rate of the system. This method simplifies the problem solving and facilitates the use of standard optimization algorithms. For the optimization problem of the present invention, under the assumption that and n k In the case of independence, the MSE matrix E k It can be expressed as:

[0104]

[0105] in, Define the semi-positive definite matrix W of user k k≥0 is the weight matrix. The optimization objective of the original sum rate maximization problem can be reformulated as the optimization objective of the sum MSE minimization optimization problem. The converted optimization problem P2.1 is as follows:

[0106]

[0107] Among them, problems (8a) and (6a) are equivalent because the global optimal solutions of these two problems are the same. In other words, although the forms are different, they will eventually converge to the same optimal solution. However, the optimization problem P2.1 is still a multivariable and non-convex optimization problem. Its solution space is complex and contains multiple local extreme values, which makes it extremely difficult to solve directly. In order to effectively solve this highly difficult non-convex problem, avoid falling into local optimal solutions and improve the solution efficiency, the present invention once again proposes a method based on alternating optimization (AO), which solves the optimization problem P2.1 by decomposing it into four sub-problems:

[0108] (1) Solve for the auxiliary variable W k

[0109] Fixing the other three variables, we can get W k Solution:

[0110]

[0111] (2) Solve for the auxiliary variable U k

[0112] Fixing the other three variables, we can get U k Solution:

[0113]

[0114] (3) Solve the intelligent reflection surface-user association matrix L

[0115] Fixing the other three variables, we can get the optimization problem P2.2 about L:

[0116]

[0117] This problem is a mixed integer nonlinear programming (MINLP) problem. Due to its complexity, traditional optimization methods are often difficult to directly apply. Therefore, in order to solve this problem, the present invention uses a mixed integer nonlinear programming (MINLP) solver to solve this problem efficiently.

[0118] (4) Solve the transmit beamforming vector w

[0119] Fixing the other three variables, we can get the optimization problem P2.3 about w:

[0120]

[0121] Using the MMSE method to solve the problem Transformed into Now, the problem's objective function and constraints are convex and can be solved using standard convex optimization algorithms.

[0122] Specifically, subproblem P2 can be solved by iteratively running (9)(10)(11a)(11b)(11c)(12a)(12b)(12c) until the objective function converges. Each iteration updates the values ​​of the relevant variables and gradually reduces the value of the objective function until the convergence condition is met, thereby obtaining an approximate optimal solution to the problem.

[0123] In this embodiment, by fixing the smart reflective surface-user correlation matrix L and the transmit beamforming vector w, the phase shift Θ of the reflective element of the smart reflective surface is optimized, and the sub-problem P3 is obtained:

[0124]

[0125] For the convenience of derivation, the present invention makes:

[0126] H=[H1,…,H k ] J , Θ=blkdiag(Θ1,…,Θ j ,…,Θ J ),

[0127] For the sake of notation, define Therefore, the optimization problem can be reformulated as P3.1:

[0128]

[0129] Through Lagrange dual transformation, the optimization objective can be rewritten as:

[0130]

[0131] where β is a set of auxiliary variables, and for a fixed Θ, the optimal β k The solution is

[0132]

[0133] Then, the present invention defines θ = diag(Θ), the optimization problem is further simplified to P3.2:

[0134]

[0135] Problem P3.2 can be solved by using the fractional programming method (FP). The fractional programming method is applicable to optimization problems where the objective function is a ratio of two polynomials. In this method, the objective function is reformulated into a new form through a quadratic transformation, so that the originally complex ratio structure can be converted into a more tractable form. Specifically, the objective function is reformulated as

[0136]

[0137] By order We can get the optimal ∈ k for:

[0138]

[0139] The simplified problem is thus to optimize θ for a given ∈, and define:

[0140]

[0141] Substituting formulas (19) and (20) into (18), we can obtain the rewritten optimization problem P3.3:

[0142]

[0143] Where C is a constant, Now, this problem is a quadratically constrained quadratic programming (QCQP) problem, which means that the objective function and the constraints are both quadratic, but due to the existence of non-convexity, direct solution is usually very complicated. To solve this problem, a semidefinite relaxation (SDR) method can be used. The SDR method transforms the original non-convex problem into a convex problem and uses the semidefinite constraints of the matrix to make the optimization problem easier to solve. Specifically, the present invention makes And removing the rank-one constraint of Q, the optimization problem is finally expressed as P3.4:

[0144]

[0145] in, And L=NJ+1.

[0146] The present invention solves problem P3.4 using CVX. During the solution process, the problem is first converted into a standard form suitable for CVX processing, and its powerful optimization and solving capabilities are leveraged to obtain preliminary results. However, the obtained results may not meet the specific requirements of the problem, for example, they may have a higher rank. To obtain a feasible solution with rank one, the present invention further applies a standard Gaussian randomization method. Through Gaussian randomization, the present invention randomly perturbs the results and, based on this process, generates a feasible solution that satisfies the rank-one constraint. This process helps simplify the problem structure while ensuring that the final solution meets the agreed requirements. Ultimately, optimization problem P1 can be solved by iteratively solving subproblems P2 and P3. In each iteration, subproblem P2 is solved first, followed by subproblem P3. By continuously optimizing the solutions to these two subproblems, the optimal solution is gradually approached. After each iteration, the value of the objective function improves until convergence conditions are met. Through this alternating optimization approach, the present invention can effectively optimize the proposed joint parameter design problem based on the maximum system sum rate criterion. After several iterations, the final solution obtained is the local optimal solution to the problem.

[0147] See also Figure 3 , showing the improvement of the total rate performance under the change of base station transmission power. The exhaustive search method is used to find the approximate optimal solution to the problem. The method proposed in this invention is highly consistent with the exhaustive search results, indicating that it can achieve an almost optimal solution. At the same time, the time complexity of the present invention is significantly lower, such as Figure 4 As shown in Figure 2, the proposed algorithm was compared with several other schemes: an algorithm that controls the switching of RISs; a distance-based scheme, in which users are connected to the nearest RIS; a random matching scheme, in which RIS-user associations are randomly selected; a centralized deployment scheme, using a single RIS; and a fully connected scheme, in which each RIS is connected to all users. The results show that the overall rate initially increases and then stabilizes with increasing base station transmit power. Compared to the baseline scheme, the proposed algorithm demonstrates superior performance, with improvements ranging from 9% to 49%.

[0148] In summary, the present invention constructs a communication system signal model with multiple intelligent reflecting surfaces and multiple users, wherein the communication system signal model includes a multi-antenna base station, J intelligent reflecting surfaces with N reflecting units, and K single-antenna user devices; constructs an intelligent reflecting surface-user association matrix, wherein the intelligent reflecting surface-user association matrix uses binary variables to represent the association relationship between the intelligent reflecting surface and the user, and calculates the received signal-to-interference-and-noise ratio at user k based on the intelligent reflecting surface-user association matrix, the transmitted signal at the multi-antenna base station, and the received signal at user k; constructs a joint parameter design problem P1 based on the maximum system total rate criterion, wherein the joint parameter design problem P1 optimizes the intelligent reflecting surface-user association matrix with the goal of maximizing the system total rate and the minimum signal-to-interference-and-noise ratio of the user as a constraint, adjusts the phase shift of the reflecting unit to improve the signal propagation direction, and optimizes the transmit beamforming vector to enhance the signal gain of the target user. In a multi-intelligent reflector multi-user communication scenario, the present invention introduces an intelligent reflector-user association allocation matrix to precisely control the matching relationship between the intelligent reflector and the user, thereby maximizing resource utilization and significantly reducing the impact of multi-user interference in the system, providing reliable support for improving overall communication performance. The present invention optimizes a new intelligent reflector-user association matrix based on the maximum sum rate criterion and the minimum user signal-to-interference-noise ratio constraint, adjusts the phase shift of the intelligent reflector reflection unit to improve the signal propagation direction, and optimizes the transmit beamforming vector to enhance the signal gain of the target user, thereby comprehensively improving the performance of the communication system. The present invention decomposes the complex joint parameter problem into two sub-problems for solution. For sub-problem one, an improved minimum mean square error-user association method (MMSE-UA) is used to solve it, which can effectively reduce interference in the system and optimize the signal reception quality, thereby improving the overall performance of the system. Sub-problem two is solved by combining semidefinite relaxation (SDR) with fractional programming (FP) method. By continuously optimizing the solutions to these two sub-problems and gradually approaching the local optimal solution to the problem, the error propagation problem is avoided, the global performance and resource utilization efficiency of the system are improved, and intelligent resource allocation and efficient communication optimization suitable for multi-intelligent reflector multi-user systems are realized. It is particularly suitable for complex scenarios with multiple users and large-scale intelligent reflectors.

[0149] It should be noted that the method of the embodiment of the present invention can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present invention, and the multiple devices will interact with each other to complete the method.

[0150] It should be noted that the above description is of some embodiments of the present invention. In some cases, the actions or steps described can be performed in a different order than those in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0151] See also Figure 5 Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, an embodiment of the present invention further provides a communication resource allocation device based on intelligent reflective surface-user association, comprising:

[0152] A communication system signal model building module 100 is used to build a communication system signal model for multiple smart reflectors and multiple users, wherein the communication system signal model includes a multi-antenna base station, J smart reflectors with N reflective units, and K single-antenna user equipments;

[0153] A smart reflecting surface-user association matrix construction module 200 is configured to construct a smart reflecting surface-user association matrix, wherein the smart reflecting surface-user association matrix uses binary variables to represent associations between smart reflecting surfaces and users, and calculate a received signal-to-interference-and-noise ratio (SINR) at user k based on the smart reflecting surface-user association matrix, a transmitted signal at a multi-antenna base station, and a received signal at user k.

[0154] The communication resource allocation optimization module 300 is used to construct a joint parameter design problem P1 based on the maximum system total rate criterion. The joint parameter design problem P1 optimizes the smart reflector-user association matrix with the goal of maximizing the system total rate and the user's minimum signal-to-interference-and-noise ratio as a constraint, adjusts the phase shift of the reflector unit to improve the signal propagation direction, and optimizes the transmit beamforming vector to enhance the signal gain of the target user.

[0155] In this embodiment, in the communication system signal model construction module 100, the channels between designated facilities are modeled as independent random channels through the communication system signal model and are assumed to obey the Rice distribution to reflect the characteristics of the direct path and the scattered path in the actual communication environment. The antenna base station and the smart reflector obtain the channel state information of all links; the channel model of each link is a path loss model, and the path loss PL is expressed as:

[0156]

[0157] Where d is the average distance between the base station and RIS, d0 is the reference distance, PL0 is the reference path loss, and α is the path loss exponent.

[0158] In this embodiment, in the smart reflective surface-user association matrix construction module 200:

[0159] The intelligent reflection surface-user association matrix uses binary variables L j,k ∈{0,1} represents the association relationship between the intelligent reflective surface and the user. When L j,k =1, it means that the jth smart reflective surface is associated with the kth user equipment. j,k =0, indicating that the j-th smart reflective surface is not associated with the k-th user equipment;

[0160] In the smart reflecting surface-user association matrix building module 200, the transmission signal at the multi-antenna base station is:

[0161]

[0162] In the smart reflective surface-user association matrix building module 200, the received signal at user k is:

[0163]

[0164] In the smart reflective surface-user correlation matrix building module 200, the received signal-to-interference-and-noise ratio γ at user k k for:

[0165]

[0166] Where, L j,k is the relationship between the smart reflective surface and the user, They represent the channel coefficients between the jth smart reflecting surface and the base station, between the jth smart reflecting surface and the kth user, and between the base station and the kth user, respectively;

[0167] Diagonal matrix is the reflection coefficient matrix of the jth smart reflector, and represents the phase shift of the reflective element n, a j,n =1 indicates maximum reflection efficiency; represents the additive white Gaussian noise at the kth user; w k is the beamforming vector of user k; s k is the unit power information symbol of user k; is the index set of the user set; σ k n k The variance of n k The degree of fluctuation in the complex domain.

[0168] In this embodiment, in the communication resource allocation optimization module 300, the expression of the joint parameter design problem P1 based on the maximum system sum rate criterion is:

[0169]

[0170] Where R k =log2(1+γ k ) is the achievable data rate of the kth user; P max is the maximum transmission power of the base station; w,Θ,L is defined as: w=[w1,…,w k ,…,w K ],Θ=blkdiag(Θ1,…,Θ j ,…,Θ J ) and L=[L j,k ] J×K , that is, L is composed of L j,k The matrix composed of The index set of the RIS collection.

[0171] In this embodiment, in the communication resource allocation optimization module 300, the joint parameter design problem P1 is split into sub-problems P2 and P3;

[0172] By fixing the phase shift of the reflective elements of the smart reflector, optimizing the smart reflector-user correlation matrix L and the transmit beamforming vector w, we can obtain the sub-problem P2:

[0173]

[0174]

[0175] By fixing the smart reflector-user correlation matrix L and the transmit beamforming vector w, and optimizing the phase shift Θ of the reflector element of the smart reflector, we can obtain the sub-problem P3:

[0176]

[0177] In the communication resource allocation optimization module, the minimum mean square error-user association method is used to solve the subproblem P2; the semidefinite relaxation combined with the fractional programming method is used to solve the subproblem P3, and the solutions of the subproblems P2 and P3 are iteratively updated through the alternating optimization algorithm to gradually approach the optimal solution of the joint parameter design problem P1.

[0178] The apparatus of the above embodiment is used to implement a corresponding communication resource allocation method based on intelligent reflective surface-user association in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0179] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements a communication resource allocation method based on intelligent reflecting surface-user association as described in any of the above embodiments.

[0180] Figure 6 A more specific hardware structure diagram of an electronic device provided in this embodiment is shown. The device may include: a processor 410, a memory 420, an input / output interface 430, a communication interface 440, and a bus 450. The processor 410, the memory 420, the input / output interface 430, and the communication interface 440 are communicatively connected to each other within the device via the bus 450.

[0181] The processor 410 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0182] The memory 420 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 420 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 420 and is called and executed by the processor 410.

[0183] The input / output interface 430 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0184] The communication interface 440 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WiFi, Bluetooth, etc.).

[0185] The bus 450 comprises a pathway for transmitting information between the various components of the device, such as the processor 410 , the memory 420 , the input / output interface 430 , and the communication interface 440 .

[0186] It should be noted that although the above device only shows the processor 410, the memory 420, the input / output interface 430, the communication interface 440, and the bus 450, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0187] The electronic device of the above embodiment is used to implement a corresponding communication resource allocation method based on intelligent reflective surface-user association in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0188] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present invention also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute a communication resource allocation method based on intelligent reflecting surface-user association as described in any of the above embodiments.

[0189] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0190] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute a communication resource allocation method based on intelligent reflective surface-user association as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0191] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Within the scope of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0192] In addition, to simplify the description and discussion, and in order not to obscure the embodiments of the present invention, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, devices may be shown in the form of block diagrams to avoid obscuring the embodiments of the present invention, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present invention will be implemented (i.e., these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present invention, it will be apparent to those skilled in the art that embodiments of the present invention may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0193] Although the present invention has been described in conjunction with specific embodiments of the present invention, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM DRAM) may use the embodiments discussed.

[0194] The embodiments of the present invention are intended to cover all such substitutions, modifications, and variations that fall within the scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the scope of protection of the present invention.

Claims

1. A communication resource allocation method based on intelligent reflective surface-user association, wherein: include: Constructing a multi-intelligent reflector and multi-user communication system signal model, wherein the communication system signal model includes a multi-antenna base station, J intelligent reflectors with N reflective units, and K single-antenna user devices; Constructing a smart reflecting surface-user association matrix, wherein the smart reflecting surface-user association matrix uses binary variables to represent associations between the smart reflecting surface and the users, and calculating a received signal-to-interference-and-noise ratio at user k based on the smart reflecting surface-user association matrix, a transmitted signal at a multi-antenna base station, and a received signal at user k; A joint parameter design problem P1 based on the maximum system total rate criterion is constructed. The joint parameter design problem P1 aims to maximize the system total rate and takes the user's minimum signal-to-interference-and-noise ratio as a constraint. The smart reflector-user correlation matrix is ​​optimized, the phase shift of the reflector unit is adjusted to improve the signal propagation direction, and the transmit beamforming vector is optimized to enhance the signal gain of the target user.

2. The communication resource allocation method based on intelligent reflecting surface-user association according to claim 1, wherein: The communication system signal model is used to model the channels between designated facilities as independent random channels. These channels are assumed to follow a Rice distribution to reflect the combined effects of direct and scattered paths in actual communication environments. Antenna base stations and smart reflective surfaces obtain channel state information for all links. The channel model for each link is a path loss model, where the path loss PL is expressed as: Where d is the average distance between the base station and RIS, d0 is the reference distance, PL0 is the reference path loss, and α is the path loss exponent.

3. The communication resource allocation method based on intelligent reflective surface-user association according to claim 1, wherein: The intelligent reflection surface-user association matrix uses binary variables L j,k ∈{0,1} represents the association relationship between the intelligent reflective surface and the user. When L j,k =1, it means that the jth smart reflective surface is associated with the kth user equipment. j,k =0, it indicates that the j-th smart reflective surface is not associated with the k-th user equipment.

4. The communication resource allocation method based on intelligent reflecting surface-user association according to claim 3, wherein: The transmitted signal at the multi-antenna base station is: The received signal at user k is: The received signal-to-interference-and-noise ratio γ at user k k for: Where, L j,k is the relationship between the smart reflective surface and the user, They represent the channel coefficients between the jth smart reflecting surface and the base station, between the jth smart reflecting surface and the kth user, and between the base station and the kth user, respectively; Diagonal matrix is the reflection coefficient matrix of the jth smart reflector, and represents the phase shift of the reflective element n, a j,n =1 indicates maximum reflection efficiency; represents the additive white Gaussian noise at the kth user; w k is the beamforming vector of user k; s k is the unit power information symbol of user k; is the index set of the user set; σ k n k The variance of n k The degree of fluctuation in the complex domain.

5. The communication resource allocation method based on intelligent reflecting surface-user association according to claim 4, wherein: The expression of the joint parameter design problem P1 based on the maximum system sum rate criterion is: Where R k =log2(1+γ k ) is the achievable data rate of the kth user; P max is the maximum transmission power of the base station; w,Θ,L is defined as: w=[w1,…,w k ,…,w K ],Θ=blkdiag(Θ1,…,Θ j ,…,Θ J ) and L=[L j,k ] J×K , that is, L is composed of L j,k The matrix composed of The index set of the RIS collection.

6. The communication resource allocation method based on intelligent reflecting surface-user association according to claim 5, wherein: Splitting the joint parameter design problem P1 into sub-problems P2 and P3; By fixing the phase shift of the reflective elements of the smart reflector, optimizing the smart reflector-user correlation matrix L and the transmit beamforming vector w, we can obtain the sub-problem P2: By fixing the smart reflector-user correlation matrix L and the transmit beamforming vector w, and optimizing the phase shift Θ of the reflective element of the smart reflector, we can obtain the sub-problem P3: The minimum mean square error-user association method is used to solve the subproblem P2; the semidefinite relaxation combined with the fractional programming method is used to solve the subproblem P3, and the solutions of the subproblems P2 and P3 are iteratively updated through an alternating optimization algorithm to gradually approach the optimal solution of the joint parameter design problem P1.

7. A communication resource allocation device based on intelligent reflective surface-user association, wherein: include: A communication system signal model construction module is used to construct a communication system signal model for multiple intelligent reflectors and multiple users, wherein the communication system signal model includes a multi-antenna base station, J intelligent reflectors with N reflective units, and K single-antenna user devices; A smart reflecting surface-user association matrix construction module is configured to construct a smart reflecting surface-user association matrix, wherein the smart reflecting surface-user association matrix uses binary variables to represent the association between the smart reflecting surface and the user, and calculate the received signal-to-interference-and-noise ratio at user k based on the smart reflecting surface-user association matrix, the transmitted signal at the multi-antenna base station, and the received signal at user k; The communication resource allocation optimization module is used to construct a joint parameter design problem P1 based on the maximum system total rate criterion. The joint parameter design problem P1 aims to maximize the system total rate and uses the user's minimum signal-to-interference-and-noise ratio as a constraint. The module optimizes the smart reflector-user association matrix, adjusts the phase shift of the reflector unit to improve the signal propagation direction, and optimizes the transmit beamforming vector to enhance the signal gain of the target user.

8. The communication resource allocation device based on intelligent reflecting surface-user association according to claim 7, wherein: In the communication system signal model construction module, the channels between designated facilities are modeled as independent random channels through the communication system signal model and are assumed to obey the Rice distribution to reflect the characteristics of the direct path and scattered path in the actual communication environment. The antenna base station and the smart reflector obtain the channel state information of all links. The channel of each link is modeled as a path loss model, and the path loss PL is expressed as: Where d is the average distance between the base station and RIS, d0 is the reference distance, PL0 is the reference path loss, and α is the path loss exponent; In the intelligent reflection surface-user association matrix construction module: The intelligent reflection surface-user association matrix uses binary variables L j,k ∈{0,1} represents the association relationship between the intelligent reflective surface and the user. When L j,k =1, it means that the jth smart reflective surface is associated with the kth user equipment. j,k =0, indicating that the j-th smart reflective surface is not associated with the k-th user equipment; In the smart reflective surface-user association matrix building module, the transmission signal at the multi-antenna base station is: In the smart reflective surface-user association matrix building module, the received signal at user k is: In the intelligent reflection surface-user correlation matrix building module, the received signal-to-interference-and-noise ratio γ at user k k for: Where, L j,k is the relationship between the smart reflective surface and the user, They represent the channel coefficients between the jth smart reflecting surface and the base station, between the jth smart reflecting surface and the kth user, and between the base station and the kth user, respectively; Diagonal matrix is the reflection coefficient matrix of the jth smart reflector, and represents the phase shift of the reflective element n, a j,n =1 indicates maximum reflection efficiency; represents the additive white Gaussian noise at the kth user; w k is the beamforming vector of user k; s k is the unit power information symbol of user k; is the index set of the user set; σ k n k The variance of b k The degree of fluctuation in the complex domain.

9. The communication resource allocation device based on intelligent reflecting surface-user association according to claim 8, wherein: In the communication resource allocation optimization module, the expression of the joint parameter design problem P1 based on the maximum system sum rate criterion is: Where R k =log2(1+γ k ) is the achievable data rate of the kth user; P max is the maximum transmission power of the base station; w,Θ,L is defined as: w=[w1,…,w k ,…,w K ],Θ=blkdiag(Θ1,…,Θ j ,…,Θ J ) and L=[L j,k ] J×K , that is, L is composed of L j,k The matrix composed of The index set of the RIS collection.

10. The communication resource allocation device based on intelligent reflecting surface-user association according to claim 9, wherein: In the communication resource allocation optimization module, the joint parameter design problem P1 is split into sub-problems P2 and P3; By fixing the phase shift of the reflective elements of the smart reflector, optimizing the smart reflector-user correlation matrix L and the transmit beamforming vector w, we can obtain the sub-problem P2: By fixing the smart reflector-user correlation matrix L and the transmit beamforming vector w, and optimizing the phase shift Θ of the reflective element of the smart reflector, we can obtain the sub-problem P3: In the communication resource allocation optimization module, the minimum mean square error-user association method is used to solve the subproblem P2; the semidefinite relaxation combined with the fractional programming method is used to solve the subproblem P3, and the solutions of the subproblems P2 and P3 are iteratively updated through the alternating optimization algorithm to gradually approach the optimal solution of the joint parameter design problem P1.

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