User multi-association method in user-centric dense high frequency wireless communication network

Abstract

The present application relates to a kind of user multi-association methods in user-centered dense high-frequency wireless communication network, belong to wireless communication field, comprising the following steps: S1: construct a user-centered dense high-frequency wireless communication network model;S2: with the maximum user achievable downlink rate as target, user multi-association problem in dense high-frequency wireless communication network is modeled as an optimization problem based on multi-parameter condition constraint;S3: the optimization problem is split into three sub-problems, respectively, user-base station multi-association problem, intelligent relay selection problem and multi-link power allocation problem;S4: the parameter of optimization problem is initialized;S5: the three sub-problems in S3 are jointly iterated optimization, until convergence, obtain the close approximation solution even optimal solution of original optimization problem, including user-base station association scheme, intelligent relay selection scheme and multi-link power allocation scheme.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wireless communication, and relates to a user multi-association method in a user-centric dense high-frequency wireless communication network. BACKGROUND

[0002] High-frequency wireless communication (such as millimeter wave communication and terahertz communication) has huge spectrum resources, which can greatly improve system capacity and effectively alleviate the problem of spectrum resource shortage. However, compared with microwave communication, high-frequency communication signals have serious path loss in the transmission process and are easily blocked by obstacles. Ultra-dense networking technology can solve the problem of limited transmission distance of high-frequency base stations by increasing the number of high-frequency base stations in a unit area, thereby improving network coverage. In a dense high-frequency wireless communication scenario, a user can receive signals transmitted from multiple high-frequency base stations around. However, in a wireless communication environment with many obstacles, there may be no line-of-sight link between the user and the high-frequency base station, and the multi-connection technology cannot play a corresponding role. In this case, some intelligent relay devices for assisting signal transmission can be deployed within the coverage range of the base station, such as a reconfigurable intelligent surface (RIS), which can reconfigure the wireless communication environment and establish a two-hop communication link to improve the reliability of communication.

[0003] Due to the advantages of green energy saving, easy deployment, and low cost of RIS compared to traditional relay devices, RIS is widely used in wireless communication systems and integrated with existing technologies to achieve higher performance wireless communication. In a user-centric dense network scenario, RIS is introduced to realize the complementary advantages of multiple technologies, which can improve the performance of the communication system compared with using a single technology. On the other hand, the performance of a wireless communication system assisted by multiple RISs is better than that of a system assisted by a single RIS to some extent, because multiple RISs can provide more flexible reflection link selection. Due to the existence of multiple base stations and multiple RISs, the reflection link quality of RISs distributed in different positions is quite different, and the association of users and base stations and the selection of RISs become problems to be solved. Related problems of RIS selection include the method of RIS selection and the result of RIS selection. In existing literature and patents, the common RIS selection method is to take distance as the judgment criterion, which includes taking the minimum distance from the base station or user to the RIS as the standard and taking the distance product of the RIS and the base station and the user as the judgment standard, and also taking the received power and signal-to-noise ratio as the judgment standard. They usually consider the application scenario of multiple users and assign an RIS to assist communication for each user through various RIS selection methods, solving the problem of how to improve the transmission quality degradation caused by channel environment difference between the terminal and the transmitting end. SUMMARY

[0004] In view of the above, the purpose of the present application is to provide a user multi-association method in a user-centric dense high-frequency wireless communication network, in a user-centric network architecture, considering a hyper-dense high-frequency wireless communication system supporting multi-connection technology and deployed with multiple intelligent relay devices, taking RIS as an example of intelligent relay device. In the selection of RIS for auxiliary communication, it is jointly implemented based on multiplicative path loss and user quality of service. To compensate for the high-frequency link interruption problem caused by obstacles, the patent establishes multiple communication paths between the user and the base station, including line-of-sight links and RIS-assisted links, which can improve communication reliability and system throughput performance by using spatial division multiplexing.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] A user multi-association method in a user-centric dense high-frequency wireless communication network, comprising the following steps:

[0007] S1: Construct a user-centric dense high-frequency wireless communication network model;

[0008] S2: Take maximizing the achievable downlink rate of the user as the goal, and model the user multi-association problem in the dense high-frequency wireless communication network as a multi-parameter condition-constrained optimization problem;

[0009] S3: Split the optimization problem into three sub-problems, namely the user-base station multi-association problem, the intelligent relay selection problem, and the multi-link power allocation problem;

[0010] S4: Initialize the parameters of the optimization problem;

[0011] S5: Jointly iterate and optimize the three sub-problems in step S3 until convergence, to obtain a close approximation solution or even an optimal solution of the original optimization problem.

[0012] Further, the joint iterative optimization of step S5 specifically includes:

[0013] S6: Optimize the user-base station multi-association problem;

[0014] S7: Update the user association parameters;

[0015] S8: Optimize the intelligent relay selection problem between the user and each associated base station;

[0016] S9: Update the selection result of the intelligent relay;

[0017] S10: Optimize the multi-link transmit power allocation problem between the user and each associated base station;

[0018] S11: Update the transmit power of each link;

[0019] S12: Determine whether the original optimization problem converges, if yes, the algorithm ends; otherwise, return to step S6.

[0020] Further, the user-centric dense high-frequency wireless communication network in step S1 includes a plurality of densely deployed high-frequency base stations, a user, and a plurality of smart relay devices discretely distributed around the base stations and the user, the user uses multi-connection technology to communicate with multiple adjacent base stations at the same time, and establishes a virtual line of sight link through the reflection link of the selected smart relay device, and establishes multiple communication paths with the base station;

[0021] Then, a user-centric dense high-frequency wireless communication network model is constructed, in which the smart metasurface RIS is used as a smart relay device, and the specific construction is as follows:

[0022] A specific user u is served by B high-frequency base stations and L RIS, and the set of base stations and RIS is respectively denoted as and

[0023] The user u is associated with multiple base stations, and x u,b represents the association indication variable between the user u and the base station If the user u is associated with the base station b, x u,b = 1, otherwise x u,b = 0;

[0024] y u,l,b is defined as the auxiliary RIS selection variable between the user u and when x u,b = 1, if the user u selects the RIS l to assist communication, y u,l,b = 1, otherwise y u,l,b = 0;

[0025] The power gain between the user u and the base station b is defined as where represents the gain of the transmitting antenna, represents the gain of the receiving antenna, λ represents the signal wavelength, and ρ (ρ ∈ [2, 6]) represents the path loss exponent; the signal-to-interference-plus-noise ratio of the user u receiving from the base station b is represented as where is the transmission power of the base station b, and σ 2 is the received noise power; according to the Shannon formula, the achievable rate of the line-of-sight link of the user u is expressed as R u,b = log2(1+γ u,b );

[0026] Similarly, the power gain of the RIS-assisted link between user u and base station b is denoted as where N l is the total number of reflecting elements, d b,l and d l,u are the distances from the RIS to base station b and user u, respectively, and p1(p2) denotes the corresponding path loss exponent; in this case, the signal-to-interference-plus-noise ratio (SINR) of user u receiving from base station b is where is the transmit power of base station b allocated to the RIS-assisted link; according to the Shannon formula, the achievable rate of the RIS-assisted link of user u is expressed as R u,l,b = log2(1 + g u,l,b ).

[0027] Further, the optimization problem established in step S2 is expressed as:

[0028]

[0029]

[0030] where C1 denotes the value range of the association variable of base station and user, C2 denotes the value range of the selection variable of the assisting RIS, C3 and C4 denote the maximum number constraint of the associated base station of user u and the total number constraint of the communication links after multi-association, respectively, C5 and C6 denote the communication link quality constraint, and C7 is the total transmit power constraint of the base station; wherein the parameter X max denotes the maximum number of associated base stations of the user; let where denotes the probability of the line-of-sight link blockage; M u denotes the maximum number of parallel receiving beams of user u; and η is the signal-to-interference-plus-noise ratio threshold value; is the maximum transmit power allocated by base station b to user u.

[0031] Further, the parameters of the initialization optimization problem in step S4 include: initializing the user-base station association indicator variable, the assisting RIS selection variable, and the transmit power allocated by the base station to each link serving the user; determining the set of candidate base stations according to the distance between the user and each base station or the set signal-to-interference-plus-noise ratio threshold value of the received signal; and determining the set of optimal candidate RIS according to the multiplicative path loss or position between the RIS and the user and each base station associated with the user.

[0032] Further, step S4 specifically includes: assuming that the base station and the user have not been associated, then and assuming that the initial transmit power of each link adopts the average allocation method, then

[0033] Then, the initial candidate base station set is determined according to the distance between the user and each base station, and the determination condition is: where d u,b represents the distance between the user and the base station, represents the candidate base station set.

[0034] The optimal candidate RIS set is determined according to the multiplicative path loss between the RIS and the user and the base station associated with the user, and the expression is: where d b,l represents the distance between the base station and the RIS, and d l,u represents the distance between the RIS and the user.

[0035] Further, the step S6 specifically includes:

[0036] S61: decoupling the optimization variables of the joint optimization problem to obtain a user-base station multi-association problem by using a decomposition technique; specifically, x u,b and y u,l,b in the problem P1 are relaxed to continuous variables, i.e.

[0037] S62: analyzing the user-base station multi-association problem by using the Lagrange dual theory; specifically including:

[0038] S621: introducing Lagrange multipliers δ, μ, ν = [ν1,..., ν B ] T , constructing a Lagrange function as:

[0039]

[0040] S622: processing the Lagrange function to obtain a Lagrange dual function The expression is: Then, the Lagrange dual problem is expressed as:

[0041]

[0042] S623: optimizing The update expression is wherein, b * represents the optimal associable base station, and

[0043] S624: updating the Lagrange multipliers by using a subgradient algorithm, and the expression is wherein, and are the step size of the i-th iteration. The dual problem reaches global optimum when the Lagrange multipliers converge.

[0044] Furthermore, step S8 specifically includes:

[0045] After updating the user association parameters, the optimization variables of the joint optimization problem are decoupled to obtain the auxiliary RIS selection subproblem, expressed as:

[0046]

[0047] By introducing Lagrange multipliers to construct Lagrange functions, we obtain the Lagrange dual function. Minimizing the Lagrange dual function within the feasible set of dual multipliers yields a more compact upper bound. Iteratively updating the optimization variables and Lagrange multipliers, the dual problem reaches global optimum when the Lagrange multipliers converge.

[0048] Furthermore, in step S10, based on updating the user association parameters and RIS selection results, the joint optimization problem is decoupled to obtain a multi-link power allocation subproblem, expressed as:

[0049]

[0050] Power allocation problem Simplified to The specific expression is:

[0051]

[0052] The signal-to-interference-plus-noise ratio (SIR / NNR) component is processed using multi-score programming techniques, with the following steps:

[0053] First, the original objective function is reconstructed using the Lagrange dual transformation. Then, a quadratic transformation is used to transform the problem into a form suitable for iterative optimization.

[0054]

[0055] in, It is the set of auxiliary variables introduced for each ratio term;

[0056] for We can obtain:

[0057]

[0058] for Optimization, when given At that time, you only need to pay attention to The last item Introduce an auxiliary variable for each ratio Through a second transformation Convert to:

[0059]

[0060] wherein

[0061] The optimal y when other variables are fixed * obtained by obtained by

[0062] The optimal obtained by obtained by

[0063] Under the constraints of quality of service and maximum power, is expressed as:

[0064]

[0065] When the iteration converges, the stationary point of is obtained and the optimal solution of the power allocation problem of multi-link is obtained

[0066] Further, in step S12, it is judged whether the original optimization problem converges, if yes, the algorithm ends, and a close approximation solution or an optimal solution of the original optimization problem is obtained, otherwise, it returns to step S6.

[0067] The method of the application adopts an iterative optimization method, and the closed-form update in the optimization process can reduce the calculation complexity. The association scheme proposed in the application considers the constraints on multi-connection capability, the influence of the deployment position of the intelligent relay device, the requirements of user quality of service and the constraints on transmission power. Simulation verification shows that the performance of the intelligent relay device selection method is better than that of the method based on the shortest distance, and the power allocation method can significantly improve the user sum rate compared with the average power allocation method and the water-filling allocation method. In the scenario of the patent, the user selects the base station that can maximize the user sum rate to associate, and after determining the base station association, the intelligent relay device is selected and the multi-link power allocation is performed, which can reduce the energy consumption and meet the development demand of green communication.

[0068] Other advantages, objects, and features of the application will be set forth in part by the description that follows, and in part will become apparent to those skilled in the art upon examination of the following specification or can be learned by the practice of the application. The objects and other advantages of the application can be realized and attained by the methods and compositions particularly pointed out in the specification. BRIEF DESCRIPTION OF DRAWINGS

[0069] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred embodiments of the present application will be described in detail below with reference to the drawings, in which:

[0070] Figure 1 Flow chart of the user multi-association scheme in the user-centric dense high-frequency wireless communication network according to the present application;

[0071] Figure 2 System model diagram of the RIS-assisted dense millimeter wave network according to the present application;

[0072] Figure 3 User and rate simulation diagram under different connections according to the present application;

[0073] Figure 4 User and rate simulation diagram under different RIS selection methods according to the present application;

[0074] Figure 5 User and rate simulation diagram under different power allocation methods according to the present application. DETAILED DESCRIPTION

[0075] The present application will be described in greater detail by way of specific embodiments, from which the skilled person will readily appreciate other advantages and utility of the present application. The present application can also be carried out or applied in other different embodiments, and the details in the present description can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0076] The drawings are only used for illustrative description, and the representation is only a schematic diagram, not a physical diagram, and should not be understood as a limitation of the present application; in order to better illustrate the embodiments of the present application, some components in the drawings may be omitted, enlarged or reduced, and do not represent the actual size of the product; it is understandable for those skilled in the art that some well-known structures and their descriptions in the drawings may be omitted.

[0077] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "front", "back" and the like is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationship in the drawings are only used for illustrative description, and should not be understood as a limitation of the present application, and for those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0078] Please see Figures 1-5 The implementation process of a user-centric, dense high-frequency wireless communication network user multi-association method is as follows: Figure 1 As shown.

[0079] In this embodiment, a RIS-assisted, multi-connectivity, user-centric ultra-dense millimeter-wave network is considered, and the system model is as follows: Figure 2 As shown, the communication system consists of B multi-antenna millimeter-wave base stations, one multi-antenna user, and L RIS (Radio Reflectors). The millimeter-wave base stations are densely deployed, while the RIS are randomly deployed around the millimeter-wave base stations and the user. A few millimeter-wave base stations are blocked by obstacles, preventing direct communication with the user. Communication is achieved by using RIS to reflect signals. The multi-antenna user can utilize multi-connection technology to communicate with multiple nearby millimeter-wave base stations simultaneously. If there are no obstacles between the user and the base station, the user can communicate directly with the millimeter-wave base station. Alternatively, multiple communication paths can be established between the user and the millimeter-wave base station through selected RIS to improve communication quality. If there are obstacles between the user and the base station, the user can establish a virtual line-of-sight link with the millimeter-wave base station through the reflection link of the selected RIS.

[0080] The specific implementation steps of this plan are as follows:

[0081] Step 1: Modeling the RIS-assisted dense millimeter-wave communication network. A user-centric dense high-frequency wireless communication network is modeled. In this invention, high-frequency base stations are densely deployed, allowing users to receive signals from multiple surrounding base stations at specific locations. Simultaneously, multiple discretely distributed auxiliary signal transmission relay devices, such as smart metasurfaces, are configured within the coverage area of ​​the high-frequency base stations to improve network coverage and overcome high-frequency wireless communication link interruption issues. To improve user service quality and enhance link robustness, this invention employs multi-connection technology, enabling users to associate with multiple neighboring base stations.

[0082] In this embodiment, a RIS-assisted dense millimeter-wave network is configured, wherein B millimeter-wave base stations and L RIS serve a specific user u, and the sets of base stations and RIS are denoted as follows: and User u can be associated with multiple millimeter-wave base stations, using x u,b Indicates user u and millimeter-wave base station The correlation indicator variable between them, if user u is associated with millimeter-wave base station b, then x has u,b =1, otherwise x u,b =0; Define y u,l,b For x u,b When = 1, user u and The auxiliary RIS selection variable between the user u and the base station b, if the user u chooses the RIS l to assist the communication, y u,l,b = 1, otherwise y u,l,b = 0; the power gain between the user u and the base station b is defined as where denotes the gain of the transmitting antenna, denotes the gain of the receiving antenna, λ denotes the signal wavelength, and ρ (ρ ∈ [2, 6]) denotes the path loss exponent; the signal-to-interference-plus-noise ratio (SINR) received by the user u from the base station b is denoted as where is the transmitting power of the base station b, and σ 2 is the receiving noise power; according to the Shannon formula, the achievable rate of the line-of-sight (LoS) link of the user u is expressed as R u,b = log2 (1 + γ u,b ); similarly, the power gain between the user u and the base station b with the existence of RIS assistance is denoted as where N l is the total number of reflecting units, d b,l and d l,u are the distances between the RIS and the base station b and the user u, respectively, and ρ1 (ρ2) denotes the corresponding path loss exponent; the signal-to-interference-plus-noise ratio (SINR) received by the user u from the base station b is denoted as where is the transmitting power of the base station b allocated to the RIS-assisted link; according to the Shannon formula, the achievable rate of the RIS-assisted link of the user u is expressed as R u,l,b = log2 (1 + γ u,l,b ).

[0083] Step 2: To maximize the achievable downlink rate of the user, the user multi-association problem in the RIS-assisted dense millimeter wave communication network is modeled as a multi-parameter condition-constrained optimization problem P1, for any high-frequency base station, the user transmission rate is composed of two parts, the first part is the rate of the link with the intelligent relay RIS, and the second part is the rate of the direct transmission link between the base station and the user; to maximize the user transmission rate, three key parameters are involved, which are the association of the user and the base station, the RIS selection, and the allocation of the base station transmitting power; the constraint conditions of the key parameters are set, and the optimization problem with the target of maximizing the user rate is established. The optimization problem P1 is expressed as:

[0084]

[0085] wherein C1 represents the value range of the association variable between the mmWave base station and the user, C2 represents the value range of the auxiliary RIS selection variable, C3 and C4 represent the maximum number constraint of the associated mmWave base station of the user u and the total communication link number constraint after multi-association, respectively, C5 and C6 represent the communication link quality constraint, and C7 is the total transmission power constraint of the base station; wherein the parameter X max represents the maximum number of associated base stations of the user; let wherein represents the probability of the line-of-sight link blockage; M u represents the maximum number of parallel receiving beams of the user u; and η is the signal-to-interference noise ratio threshold. is the maximum transmission power allocated by the mmWave base station b for the user u.

[0086] Step 3: To reduce the complexity of calculation and implementation, the optimization problem in step 2 is split into three sub-problems. The original optimization problem is a mixed integer nonlinear programming problem with high coupling of optimization variables, which is not easy to solve. The decomposition technique is used to decouple the three optimization variables in the original optimization problem into three sub-problems, which are the multi-association problem of the user-base station, the selection problem of the auxiliary RIS, and the power allocation problem of the multi-link.

[0087] Step 4: Parameter initialization of the optimization problem. The user-base station association indicator variable, the RIS selection variable, and the transmission power of each link of the base station to the served user are initialized; to accelerate the convergence rate of the optimization problem, the set of user-associable base stations and the set of selectable RIS can be first narrowed down, for example, the set of candidate base stations can be determined according to the distance between the user and each high-frequency base station or the signal-to-interference noise ratio threshold of the received signal, and the set of optimal candidate RIS can be determined according to the multiplicative path loss or position between the RIS and the user and the base stations associated with the user.

[0088] Assuming that the high-frequency base station and the user have not been associated, then and Assuming that the initial transmission power of each link adopts the average allocation method, then Then the initial candidate mmWave base station set is determined according to the distance between the user and each mmWave base station, and the determination condition is wherein d u,b represents the distance between the user and the mmWave base station, represents the candidate mmWave base station set; the optimal candidate RIS set is determined according to the multiplicative path loss between the RIS and the user and the base stations associated with the user, and the expression is wherein d b,l represents the distance between the base station and the RIS, and d l,u represents the distance between the RIS and the user.

[0089] Step 5: Combine the three sub-problems in step 3 and iterate the optimization process, which is detailed in steps 6-11.

[0090] Step 6: Optimize the user-base station multi-association problem. Decouple the optimization variables of the combined optimization problem using decomposition techniques to obtain the user-base station multi-association problem; this problem is a convex optimization problem that can be analyzed using the Lagrangian duality theory; construct a Lagrangian function by introducing Lagrange multipliers to relax the coupling constraints; further processing the Lagrangian function can obtain the Lagrangian dual function; minimize the Lagrangian dual function over the feasible region of the dual multipliers to obtain the dual problem; then iteratively update the optimization variables and Lagrange multipliers, where the subgradient algorithm is used to update the Lagrange multipliers, and when the Lagrange multipliers converge, the dual problem reaches global optimality.

[0091] In this embodiment, first relax x u,b and y u,l,b in problem P1 to continuous variables, i.e. Then decouple the user-base station multi-association problem, which is a convex problem that can be analyzed using the Lagrangian duality theory. Specifically, by introducing Lagrange multipliers δ, μ, ν = [ν1,...,ν B ] T , The Lagrangian function expression is:

[0092]

[0093] Further processing of the Lagrangian function can obtain the Lagrangian dual function The expression is: The Lagrangian dual problem is expressed as:

[0094]

[0095] This Lagrangian dual problem can be solved iteratively. First, optimize χ b , whose update expression is where b * represents the optimal associable base station, and

[0096]

[0097] Subsequently, update the Lagrange multipliers using the subgradient algorithm, whose expression is and where and are the step sizes for the i-th iteration. The dual problem reaches global optimum when the Lagrange multipliers converge.

[0098] Step 7: Update user association parameters

[0099] Step 8: Optimize the auxiliary RIS selection problem between users and their associated base stations. After updating the user association parameters, decouple the optimization variables of the joint optimization problem to obtain the auxiliary RIS selection subproblem, expressed as:

[0100]

[0101] This problem is similar to the solution method for the user-base station multi-association problem. A Lagrange function can be constructed by introducing Lagrange multipliers, and further processing yields the Lagrange dual function. Minimizing the Lagrange dual function within the feasible set of the dual multipliers provides a more compact upper bound. Iterative updates of the optimization variables and Lagrange multipliers lead to the global optimum of the dual problem when the Lagrange multipliers converge.

[0102] Step 9: Update RIS selection results

[0103] Step 10: Optimize the transmit power allocation problem across multiple links between the user and its associated base stations. Based on updating the user association parameters and RIS selection results, the decoupled joint optimization problem can be further derived into a multi-link power allocation sub-problem, expressed as:

[0104]

[0105] Considering the characteristics of millimeter-wave signals, inter-cell interference is negligible, therefore power allocation is not a problem. It can be simplified to The specific expression is:

[0106]

[0107] This is a non-convex optimization problem, which can be addressed by using multi-score programming techniques to handle the signal-to-interference-plus-noise ratio (SINR) component. First, the original objective function is reconstructed using a Lagrange dual transformation. Then, a quadratic transformation is used to transform the problem into a form suitable for iterative optimization:

[0108]

[0109] in, It is the set of auxiliary variables introduced for each ratio term. Because It is a concave differentiable function, when given Time-optimal It can be done Please solve this. For The following can be obtained:

[0110]

[0111] For the optimization of when is given, only the last term of needs to be considered An auxiliary variable is introduced for each ratio By a quadratic transformation, is converted into:

[0112]

[0113] where When other variables are fixed, the optimal y * can be obtained by , and Thus, the optimal can be obtained by , and Under the constraints of quality of service and maximum power, can be expressed as:

[0114]

[0115] When the iteration converges, the stationary point of can be obtained And the optimal solution of the power allocation problem of multiple links can be obtained

[0116] Step 11: Update the transmission power of each link

[0117] Step 12: Determine whether the original optimization problem converges. If yes, the algorithm ends; if no, jump to step 6. Through the above description, the iteration updates and until convergence, a close approximation solution or even the optimal solution of the original optimization problem can be obtained.

[0118] The application effect of the present application is described in detail below in combination with simulation.

[0119] The simulation parameters are given in Table 1:

[0120] Table 1

[0121]

[0122] Simulation results:

[0123] In the simulation experiment, firstly, the method proposed in the present application is compared with the rate performance of single connection, and the change of user and rate with the number of millimeter wave base stations under different connection technologies is evaluated. In order to show the performance of the method proposed in the present application, it can be evaluated by the following 6 cases. It is assumed that there are three connections between the user and the base station associated with it in case 1 to case 4, and only 1 connection in case 5 and case 6. Among them, the three connections of case 1 are all user-base station line-of-sight connections; the three connections of case 2 are two user-base station line-of-sight connections and one RIS assisted connection; case 3 is one user-base station line-of-sight connection and two RIS assisted connections; case 4 is all RIS assisted connection; case 5 is one user-base station line-of-sight connection; and case 6 is one RIS assisted connection. As shown in Figure 3 , the sum rate of the user increases with the increase of the number of millimeter wave base stations, because increasing the number of millimeter wave base stations within a certain range can reduce the distance between the user and the base station, in a sense, reducing the path loss. In addition, from the figure, it can be concluded that compared with single connection, multi-connection can greatly improve the sum rate of the user. As shown in Figure 4 , the difference between the shortest distance RIS selection method and the RIS selection method proposed in the present application in the sum rate of the user under different number of user and base station association and transmission power conditions. From the figure, it can be known that with the increase of the base station association number and the transmission power, the sum rate of all cases increases, and the method proposed in the present application is obviously superior to the shortest distance RIS selection method under the premise that the variables remain the same. Finally, the power allocation method proposed in the present application is compared with the water-filling power allocation method and the average power allocation method under different user and base station association numbers and transmission powers, and the simulation performance results are shown in Figure 5 . From the figure, it can be known that with the increase of the base station association number, the rate performance of each method increases, because the increase of the association number brings more links to the user, further improving the system performance. In addition, with the increase of the maximum transmission power, the gap between the method proposed in the present application and the water-filling power allocation algorithm and the average power allocation algorithm becomes larger and larger. The above can conclude that the method proposed in the present application can effectively improve the service quality of the user in the transmission rate direction.

[0124] Finally, it is pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the purpose and scope of the present application, which should be covered in the claim range of the present application.

Claims

1. A method for user multi-association in a user-centric dense high frequency wireless communication network, characterized in that: The method comprises the following steps: S1: constructing a user-centered dense high-frequency wireless communication network model; S2: modeling a user multi-association problem in the dense high-frequency wireless communication network as a multi-parameter condition-constrained optimization problem with the goal of maximizing the achievable downlink rate of the user; the optimization problem established in step S2 is expressed as: (1) where B a high-frequency base station and L an RIS to serve a specific user u , where the set of base stations and RISs are denoted as and respectively; let u associate with multiple base stations, denoted as , the association indicator between user u and base station b , where if user u is associated with base station b , then , otherwise ; let be the auxiliary RIS selection variable between user and RIS u when l , where if user u selects RIS l for auxiliary communication, then , otherwise ; denote the achievable rate of the line-of-sight link between user u and base station b ; denote the achievable rate of the RIS-aided link for user u ; denote the signal-to-interference-plus-noise ratio (SINR) of user u receiving from base station b ; denote the SINR of user u receiving from base station b with RIS-aided transmission; be the transmit power of base station b ; let be the transmit power of base station b allocated to the RIS-aided link; C1 denotes the range of the association variable between base station and user, C2 denotes the range of the auxiliary RIS selection variable, C3 and C4 denote the maximum number of associated base stations and the total number of communication links after multi-association for user u , respectively, C5 and C6 denote the communication link quality constraints, and C7 is the total transmit power constraint of base station; where parameter denotes the maximum number of associated base stations for user , where denotes the probability of line-of-sight link blockage; denotes the maximum number of parallel receive beams for user u ; is the SINR threshold. is a base station b for a user u allocated maximum transmit power; S3: splitting the optimization problem into three sub-problems, namely, a user-base station multi-association problem, an intelligent relay selection problem and a multi-link power allocation problem; S4: initializing parameters of the optimization problem; S5: jointly iteratively optimizing the three sub-problems in step S3 until convergence, to obtain a close approximation solution or an optimal solution of the original optimization problem; the jointly iterative optimization in step S5 specifically comprises: S6: optimizing the user-base station multi-association problem; S7: updating the user association parameters; S8: optimizing the intelligent relay selection problem between the user and each associated base station; S9: updating the intelligent relay selection result; S10: optimizing the multi-link transmission power allocation problem between the user and each associated base station; S11: updating the transmission power of each link; S12: determining whether the original optimization problem converges, and if yes, ending the algorithm; otherwise, returning to step S6.

2. The method of claim 1, wherein: The user-centered dense high-frequency wireless communication network in step S1 comprises a plurality of densely deployed high-frequency base stations, a user and a plurality of intelligent relay devices discretely distributed around the base stations and the user, the user uses multi-connection technology to communicate with multiple adjacent base stations at the same time, and establishes a virtual line of sight link through the reflection link of the selected intelligent relay device, thereby establishing multiple communication paths between the base station and the user. A user-centered dense high-frequency wireless communication network model is constructed, wherein an intelligent metasurface RIS is used as an intelligent relay device, and the construction is as follows: including B a high frequency base station and L a RIS to serve one particular user u where the set of base station and RIS are denoted as and respectively; Setting a user u Associating a plurality of base stations, an indication variable between a user u and a base station b , if the user u is associated with the base station b , then , otherwise ; Definitions To assist in the selection of a RIS by a user u when the user l is in proximity to a RIS, if the user u has selected a RIS l to assist in communication, then , otherwise ; Defining a user u The power gain between the base station b is where denotes the gain of the transmitting antenna, denotes the gain of the receiving antenna, denotes the signal wavelength, denotes the path loss exponent, ; user u Receive from base station b The signal-to-interference-plus-noise ratio is expressed as ,in It is a base station b The transmission power, It is the received noise power; According to the Shannon formula, the user u achievable rate over a line-of-sight link with a base station b is expressed as ; Similarly, a user u receives a signal from a base station b assisted by a RIS with a signal-to-interference-and-noise ratio (SINR) of where is the total number of reflecting elements, and are the distances from the RIS to the base station b and to the user u respectively, and denotes the corresponding path loss exponent; in this case, the user u receives the signal from the base station b assisted by the RIS with a signal-to-interference-and-noise ratio (SINR) of where is the transmit power assigned by the base station b to the RIS-assisted link; According to the Shannon formula, the achievable rate of a user u with RIS-assisted link is expressed as .

3. The method of claim 1, wherein: The parameter initialization of the optimization problem in step S4 comprises: initializing user-base station association indicator variables, auxiliary RIS selection variables and base station transmission power allocation for each link serving the user; determining a candidate base station set according to the distance between the user and each base station or a set signal-to-interference-and-noise ratio threshold; and determining a set of optimal candidate RIS according to the multiplicative path loss or position between the RIS and the user and each base station associated with the user.

4. The method of claim 3, wherein: Step S4 specifically includes: assuming that the base station and the user have not been associated, then and assuming that the transmission power of each link at the initial time adopts an average distribution method, ; Then, the initial candidate base station set is determined according to the distance between the user and each base station, and the determination condition is: ; wherein, represents the distance between the user and the base station, represents the candidate base station set; The best candidate RIS set is determined according to the multiplicative path loss between the RIS and the user and each base station associated with the user, expressed as: ; wherein, denotes the distance between the base station and the RIS, denotes the distance between the RIS and the user.

5. The method of claim 4, wherein: The step S6 specifically comprises: S61: decoupling the optimization variables of the joint optimization problem by using decomposition techniques to obtain a multi-association problem for users-bases; in particular, relaxing the continuous variables in the problem P1, i.e. and S62: solving the multi-association problem for users-bases by using a distributed algorithm; in particular, using the algorithm described in the section "Distributed algorithm for the multi-association problem for users-bases". , ; S62: analyzing the user-base station multi-association problem by using the Lagrange dual theory; specifically comprising: S621: Introduce Lagrange multiplier , construct the Lagrange function as: (2) S622: process the Lagrange function to obtain a Lagrange dual function , the expression is: The Lagrange dual problem is expressed as: (3) S623: optimize with the update expression wherein, , denotes the optimal associable base station, and ; S624: Update the Lagrange multiplier by the subgradient algorithm, expressed as , and where, and is the step size of the i th iteration; , The dual problem reaches the global optimum when the Lagrange multiplier converges.

6. The method of claim 5, wherein: The step S8 specifically comprises: After updating the user association parameters, the optimization variables of the joint optimization problem are decoupled to obtain an auxiliary RIS selection sub-problem, which is expressed as: (4) By introducing a Lagrange multiplier, a Lagrange function is constructed, and a Lagrange dual function is obtained by processing; the Lagrange dual function is minimized in the dual multiplier feasible set to obtain a more compact upper bound; the optimization variables and the Lagrange multiplier are iteratively updated, and when the Lagrange multiplier converges, the dual problem reaches the global optimum.

7. The method of claim 6, wherein: In step S10, on the basis of updating the user association parameters and the RIS selection result, the joint optimization problem is decoupled to obtain a multi-link power allocation sub-problem, which is expressed as: (5) Power allocation problem is reduced to , which is expressed as: (6) By using the multi-partitioning programming technique to process the signal-to-noise ratio part, the steps are as follows: Firstly, the original objective function is reconstructed by using Lagrange dual transformation, and then the problem is converted into a form suitable for iterative optimization by using quadratic transformation: (7) wherein are auxiliary variable sets introduced for each ratio term; For , we obtain: (8) For the optimization of , when given , one only needs to take care of the last item of ; introduce an auxiliary variable for each ratio By a quadratic transformation, is transformed into: (9) wherein ; When other variables are fixed, the optimal By obtained, there are ; optimal by obtained, there ; Under the quality of service and maximum power constraints, is represented as: (10) When the iteration converges, the stationary point is obtained, and the optimal solution of the multi-link power allocation problem is further obtained. ​ 8. The user-centric dense high frequency wireless communication network user multi-association scheme according to claim 7, characterized in that: In step S12, it is judged whether the original optimization problem converges or not. If yes, the algorithm ends, and a close approximation solution or an optimal solution of the original optimization problem is obtained. If not, the algorithm returns to step S6.

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