Communication method, and communication device

Abstract

Provided are a communication method, and a communication device. The method is applied to a cell-free network, the cell-free network comprises an access point and a reconfigurable intelligent surface (RIS), and the method comprises: for the cell-free network, constructing a first optimization model, wherein the first optimization model is used for indicating the security and energy efficiency of the cell-free network, and optimization variables of the first optimization model include configuration parameters of the RIS and beamforming parameters of the access point; and on the basis of the solution of the first optimization model, configuring the RIS and the access point.

Description

Communication methods and communication equipment Technical Field

[0001] This application relates to the field of communication technology, and more specifically, to a communication method and a communication device. Background Technology

[0002] In non-cellular networks, reconfigurable intelligence surfaces (RIS) can be introduced to improve communication security. After introducing RIS into non-cellular networks, how to optimize energy efficiency while ensuring communication security—in other words, how to optimize the security and energy efficiency of non-cellular networks—is a technical problem that needs to be solved. Summary of the Invention

[0003] This application provides a communication method and a communication device. The various aspects covered by this application are described below.

[0004] In a first aspect, a communication method is provided, the method being applied to a non-cellular network, the non-cellular network including an access point and a RIS (Radio Router Array). The method includes: constructing a first optimization model for the non-cellular network, the first optimization model being used to indicate the security and energy efficiency of the non-cellular network, the optimization variables of the first optimization model including configuration parameters of the RIS and beamforming parameters of the access point; and configuring the RIS and the access point according to the solution of the first optimization model.

[0005] In a second aspect, a communication device is provided, the device being applied to a non-cellular network, the non-cellular network including an access point and a RIS (Radio Router Array). The device includes: a construction module, configured to construct a first optimization model for the non-cellular network, the first optimization model being used to indicate the security and energy efficiency of the non-cellular network, the optimization variables of the first optimization model including configuration parameters of the RIS and beamforming parameters of the access point; and a configuration module, configured to configure the RIS and the access point according to the solution of the first optimization model.

[0006] Thirdly, a communication device is provided, including a memory and a processor, the memory being used to store a program, and the processor being used to invoke the program in the memory to cause the communication device to perform the method as described in the first aspect.

[0007] Fourthly, an apparatus is provided, including a processor for calling a program from memory to cause the apparatus to perform the method as described in the first aspect.

[0008] Fifthly, a chip is provided, including a processor for calling a program from memory, causing a device on which the chip is mounted to perform the method as described in the first aspect.

[0009] In a sixth aspect, a computer-readable storage medium is provided having a program stored thereon that causes a computer to perform the method as described in the first aspect.

[0010] In a seventh aspect, a computer program product is provided, characterized in that it includes a program that causes a computer to perform the method as described in the first aspect.

[0011] Eighthly, a computer program is provided that causes a computer to perform the method as described in the first aspect.

[0012] In this embodiment of the application, for a non-cellular network that has introduced RIS, a first optimization model that indicates the security and energy efficiency of the non-cellular network can be constructed, and the RIS and access points in the non-cellular network can be configured according to the solution of the first optimization model, thereby optimizing the security and energy efficiency of the non-cellular network while ensuring communication security. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application, and other drawings obtained by those skilled in the art based on these drawings all fall within the protection scope of this application.

[0014] Figure 1 is an example diagram of the architecture of a cellular network in the related technology provided in the embodiments of this application.

[0015] Figure 2 is an example diagram of a non-cellular network architecture in the related technologies provided in the embodiments of this application.

[0016] Figure 3 is a structural example diagram of RIS in the related technology provided in the embodiments of this application.

[0017] Figure 4 is an example diagram of the architecture of a cellular-free network assisted by a single functional reconfigurable intelligence surface (SF-RIS) provided in an embodiment of this application.

[0018] Figure 5 is an example diagram of the architecture of a cellular-free network assisted by a multi-functional reconfigurable intelligence surface (MF-RIS) provided in an embodiment of this application.

[0019] Figure 6 is a schematic flowchart of a method for wireless communication provided in an embodiment of this application.

[0020] Figure 7 is another schematic flowchart of a method for wireless communication provided in an embodiment of this application.

[0021] Figure 8 is another schematic flowchart of a method for wireless communication provided in an embodiment of this application.

[0022] Figure 9 is another schematic flowchart of a method for wireless communication provided in an embodiment of this application.

[0023] Figure 10 is a schematic diagram of the structure of a device for wireless communication provided in an embodiment of this application.

[0024] Figure 11 is a schematic diagram of the structure of the communication device provided in the embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application fall within the protection scope of this application.

[0026] It should be understood that the terminology used in this application specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] This application relates to non-cellular networks and RIS. For ease of understanding, the non-cellular networks and RIS involved in this application will be described first with reference to the accompanying drawings.

[0028] Cellular network

[0029] Traditional mobile communication uses cellular network technology. Figure 1 is an example diagram of the architecture of a cellular network provided in the embodiments of this application. In a cellular network, the service area can be divided into multiple cells 110. Cell 110 can be understood as a specific geographical area. Each cell 110 may include a network device 120 and a user equipment 130. The network device 120 can provide network coverage for the cell 110 where it is located and can communicate with the user equipment 130 located in that cell 110. The user equipment 120 can access the network (such as a wireless network) through the network device 110. Cells 110 are typically hexagonal, so the entire network can be called a cellular network. Cellular networks typically improve spectrum utilization through frequency reuse technology, that is, using the same frequency resources in different cells. However, this practice can lead to signal interference between adjacent cells.

[0030] To eliminate interference between adjacent cells in cellular networks and thus improve network capacity, cellular network technology has been proposed. Figure 2 is an example architecture diagram of a cellular network provided in the embodiments of this application. As shown in Figure 2, cellular networks break away from the cell division and boundary concepts of traditional cellular networks. Unlike traditional cellular-centric networks, cellular networks adopt a user equipment-centric transmission design. In a cellular network, each user equipment can be served collaboratively by multiple access points. User equipment can connect to the network through access points. To provide seamless coverage for user equipment, a large number of access points can be deployed in a cellular network. In some cases, access points can also be referred to as base stations. A cellular network may also include a central processing unit (CPU). The CPU can be connected to all antennas on the access points via cables. The CPU can be used to perform one or more of the following operations: baseband signal processing, beamforming signal calculation, signal detection and precoding, and processing signals from multiple access points.

[0031] In non-cellular networks, user equipment can also be referred to as terminal equipment, access terminal, user unit, user station, mobile station, mobile station (MS), mobile terminal, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent, or user device. The user equipment in the embodiments of this application may include devices that provide voice and / or data connectivity to users, and can be used to connect people, objects, and machines, such as handheld devices with wireless connectivity, in-vehicle devices, etc. The user equipment in the embodiments of this application may be a mobile phone, tablet computer, laptop computer, PDA, mobile internet device, wearable device, virtual reality device, augmented reality device, wireless terminal in industrial control, wireless terminal in autonomous driving, wireless terminal in remote surgery, wireless terminal in smart grid, wireless terminal in transportation safety, wireless terminal in smart city, wireless terminal in smart home, etc.

[0032] User equipment (UEs) in a non-cellular network can include legitimate UEs and illegitimate UEs. Legitimate UEs are understood as certified devices that comply with communication network regulations and standards. Legitimate UEs communicate according to established rules and protocols and generally do not pose a threat to the communication network. Illegitimate UEs are understood as unauthorized or maliciously modified devices. Illegitimate UEs may obtain or assist in obtaining telecommunications services without the permission of the telecommunications service provider. Illegitimate UEs may also conceal their true identity or location, thereby bypassing normal network access controls and potentially threatening the communication network. In a non-cellular network, due to the inherent broadcast characteristics of wireless channels, any UE within the access point's coverage area can receive the signal. This could lead to illegitimate UEs receiving signals sent from the access point to legitimate UEs. In other words, illegitimate users may eavesdrop, increasing the risk of information leakage in non-cellular networks. Therefore, communication security is a challenge for non-cellular networks.

[0033] Reconfigurable smart metasurface

[0034] Figure 3 is a structural example diagram of the RIS (Reconfigurable Array) in the related technology provided in the embodiments of this application. As shown in Figure 3, the RIS can be regarded as a two-dimensional array composed of a large number of low-cost reconfigurable units 310. The reconfigurable units 310 may include adjustable elements. The adjustable elements here may be, for example, phase shifters. By adjusting the adjustable elements, the phase and / or amplitude of the reconfigurable units 310 can be adjusted, thereby adjusting the electromagnetic characteristics of the entire RIS and realizing the control of the wireless channel. That is to say, the RIS can reconfigure the wireless environment as needed. If the RIS is introduced into a communication network, with the assistance of the RIS, signals from different links can be superimposed at some locations to enhance signal strength, while canceling or weakening each other at other locations to reduce signal strength. Utilizing the above characteristics of the RIS, the RIS can be introduced into a non-cellular network to enhance the signal strength at legitimate user equipment and reduce the signal strength at illegitimate user equipment, thereby enhancing the secure reception of legitimate user equipment and suppressing the information leakage of illegitimate user equipment. In other words, the RIS can ensure the communication security of a non-cellular network in an economical way.

[0035] Based on their functions, Reconstruction Signals (RIS) can be divided into SF-RIS and MF-RIS. SF-RIS can be used to reflect signals. By adjusting the phase of the reconfigurable unit of the SF-RIS, the direction of the reflected wave can be changed, thereby controlling the propagation path of the wireless signal. MF-RIS, in addition to reflecting signals, can also be used to refract and / or amplify signals. For example, the reconfigurable unit of the MF-RIS can include an amplifier, thereby amplifying the signal. By adjusting the phase of the reconfigurable unit of the MF-RIS, not only the direction of the reflected wave but also the direction of the transmitted wave can be changed, thereby controlling the propagation path of the wireless signal. By adjusting the amplitude coefficient of the reconfigurable unit of the MF-RIS, the amplification factor of the wireless signal can be controlled.

[0036] As mentioned earlier, a large number of access points are deployed in non-cellular networks to provide seamless coverage for user devices, which may lead to significant network energy consumption. For non-cellular networks that have introduced RIS (Reliability and Safety System), how to maximize energy efficiency while ensuring communication security, that is, how to achieve maximum security and energy efficiency, is a technical problem that needs to be solved.

[0037] To address the aforementioned issues, this application provides a communication method. Using this method, the values ​​of the RIS configuration parameters and the beamforming parameters of the access point can be determined to maximize security and energy efficiency. Based on these values, the RIS and access point are configured, thereby maximizing the security and energy efficiency of the non-cellular network.

[0038] The communication method provided in this application embodiment can be applied to RIS-assisted cellular networks. For ease of understanding, the RIS-assisted cellular network will be described in detail below with reference to Figures 4 and 5.

[0039] As shown in Figures 4 and 5, a RIS-assisted cellular network can include B access points, U legitimate user equipments (User Equipment), E illegitimate user equipments (User Equipment), one RIS, and one or more CPUs. Detailed descriptions of access points, legitimate User Equipments, and illegitimate User Equipments (User Equipment) can be found in the relevant sections above and will not be repeated here. The set of access points can be represented as A = {1, 2, ..., A}, and each access point can be equipped with N antennas. The set of legitimate User Equipments can be represented as B = {1, 2, ..., B}, and each legitimate User Equipment (User Equipment) can be equipped with a single antenna. The set of illegitimate User Equipments (User Equipment) can be represented as E = {1, 2, ..., E}, and each illegitimate User Equipment (User Equipment) can be equipped with a single antenna. The RIS can be used to assist transmission from the B access points to the U legitimate User Equipments. The RIS can include M reconfigurable units. In the following description, these units can be referred to as RIS units. The set of RIS units can be represented as M = {1, 2, ..., M}. The CPU can be used to control all access points and the RIS via fiber optic or wireless backhaul. The channel from access point a to legitimate User Equipment b can be represented as... The channel from access point a to unauthorized user equipment e is represented as: The channel from access point a to RIS is represented as follows: The channel from RIS to legitimate user equipment b is represented as follows: And represent the channel from RIS to illegal user equipment e as Using existing channel estimation methods, the channel state information of all channels can be perfectly obtained. The process of mathematically representing the elements and parameters involved in the RIS-assisted cellular network can also be called the modeling process of the RIS-assisted cellular network.

[0040] The RIS here can be either the SF-RIS or the MF-RIS mentioned earlier. Referring to Figure 4, when the RIS is an SF-RIS, it can ensure communication security in the half-space, i.e., the reflection space, because it can reflect signals from the non-cellular network. However, since user equipment and access points in the non-cellular network are distributed throughout the entire space, SF-RIS may struggle to cope with ubiquitous security threats. Furthermore, the existence of two cascaded sub-channels—one from the access point to the SF-RIS and the other from the SF-RIS to the user equipment—may lead to double attenuation, potentially limiting the channel gain that SF-RIS can provide to the non-cellular network.

[0041] Referring to Figure 5, when the RIS is an MF-RIS, since the MF-RIS can not only reflect but also refract signals from the cellular network, it can ensure communication security throughout the entire space. The entire space can be defined as K = {r, t}. When the MF-RIS is used for reflection, k = r. When the MF-RIS is used for refraction, k = t. Furthermore, since the MF-RIS can also amplify signals from the cellular network, it can provide additional channel gain to the cellular network.

[0042] The RIS-assisted cellular network has been described above. The wireless communication method provided in this application embodiment is described below with reference to Figure 6. As shown in Figure 6, the wireless communication method provided in this application embodiment may include the following steps S610 and S620.

[0043] In step S610, a first optimization model is constructed for non-cellular networks.

[0044] The first optimization model can be used to indicate the security and energy efficiency of non-cellular networks. Therefore, "constructing the first optimization model" can also be understood as formulating the security and energy efficiency of non-cellular networks. The security and energy efficiency of non-cellular networks can be determined based on the secure achievable rates of legitimate user equipment within the non-cellular network and the power consumption of the non-cellular network. The optimization variables of the first optimization model can include the configuration parameters of the RIS (Real-Integrated System) and the beamforming parameters of the access points.

[0045] The configuration parameters of a RIS can include the phase shift and amplitude coefficients of the cells within the RIS. For example, when the RIS is an MF-RIS, the configuration parameters of the RIS can be expressed as follows: Where, Θ k This represents the coefficient matrix of MF-RIS used for reflection (k = r) or refraction (k = t). This represents the phase shift of the m-th unit in MF-RIS. This represents the amplitude coefficient of the m-th unit in the MF-RIS. When the RIS is an MF-RIS, the configuration parameters of the RIS can also be referred to as MF-RIS coefficients.

[0046] The beamforming parameters of the access point can include the precoded vector w of the access point for user equipment in the non-cellular network. b For an access point equipped with N antennas, the beamforming parameters of the access point can include the precoding vectors of each antenna for user equipment in the non-cellular network. Here, user equipment can include both legitimate and illegitimate user equipment. That is, the beamforming parameters of the access point can include the precoding vectors of each access point a for each legitimate user equipment b in the non-cellular network and the precoding vectors of each access point a for each illegitimate user equipment e in the non-cellular network.

[0047] In step S620, the RIS and access point are configured according to the solution of the first optimization model.

[0048] After constructing the first optimization model in step S610, the first optimization model can be solved. The solution to the first optimization model may include the values ​​of the RIS configuration parameters and the values ​​of the beamforming parameters of the access points. The RIS and access points can be configured according to the solution of the first optimization model.

[0049] As can be seen from the above description of steps S610 and S620, in this embodiment of the application, for a non-cellular network that has introduced RIS, a first optimization model indicating the security and energy efficiency of the non-cellular network can be constructed, and the RIS and access points in the non-cellular network can be configured according to the solution of the first optimization model, thereby optimizing the security and energy efficiency of the non-cellular network while ensuring communication security.

[0050] In some implementations, step S620, which configures the RIS and access point based on the solution of the first optimization model, may include: solving the first optimization model with the optimization objective of maximizing safety and energy efficiency to determine the values ​​of the configuration parameters and beamforming parameters; and configuring the RIS and access point based on the values ​​of the configuration parameters and beamforming parameters.

[0051] After constructing the first optimization model in step S610, the first optimization model can be solved with the optimization objective of maximizing safety and energy efficiency to determine the values ​​of the configuration parameters and beamforming parameters. This first optimization model can also be called the objective function. Constraints can be set for the first optimization model when solving it.

[0052] Optionally, in some embodiments, the constraints of the first optimization model may include: the output power of the access point is less than or equal to the maximum transmit power of the access point, i.e. Among them, w ab This represents the precoding vector of access point a for legitimate user equipment b. This indicates the maximum transmit power of each access point.

[0053] Optionally, in some embodiments, the constraint condition of the first optimization model may include: the output power of the RIS is less than or equal to the maximum output power of the RIS. When the RIS is an MF-RIS, this constraint condition can be expressed as: in, G as Θ represents the channel from access point a to MF-RIS. k w represents the coefficient matrix of MF-RIS used for reflection (k = r) or refraction (k = t). ab This represents the precoding vector of access point a for legitimate user equipment b. This represents the power of the thermal noise introduced at MF-RIS.

[0054] Optionally, in some embodiments, the constraints of the first optimization model may include: the phase shift of the RIS internal cells is greater than or equal to zero and less than 2π, i.e.

[0055] Optionally, in some embodiments, the constraints of the first optimization model may include: the amplitude coefficient of the RIS internal cell is less than or equal to the maximum amplitude coefficient, i.e. Where β max ≥1 indicates the maximum amplification factor. Here, the maximum amplification factor can be used to indicate the RIS's ability to amplify signals. β max A larger β value indicates a stronger signal amplification capability of the RIS. The amplification capability of the RIS can be altered by changing its structure, thereby changing β. max The value of .

[0056] Optionally, in some embodiments, the constraints of the first optimization model may include: the sum of the amplitude coefficients of the cells within the RIS is less than or equal to the maximum amplitude coefficient, i.e. In other words, the amplitude coefficients of the internal cells of the RIS need to satisfy the energy conservation constraint.

[0057] Optionally, in some embodiments, the constraints of the first optimization model may include: the rate of legitimate user equipment without a cellular network meets a preset minimum rate requirement. Meeting the preset minimum rate requirement can be understood as the rate of the legitimate user equipment being greater than or equal to the preset minimum rate of the user equipment. That is, R b ≥R min , Where R b R represents the rate of legitimate user equipment b. min This indicates the preset minimum rate for legitimate user equipment. The preset minimum rate for legitimate user equipment can be understood as the minimum data transmission rate that legitimate user equipment must guarantee when using network services.

[0058] After determining the values ​​of the configuration parameters and beamforming parameters, the RIS and access points can be configured according to these values. "Configuring the RIS and access points according to the values ​​of the configuration parameters and beamforming parameters" can include configuring the antennas of the units within the RIS and the access points according to these values. This configuration can, for example, be implemented by the CPU in the cellular network. As mentioned earlier, when the RIS is an MF-RIS, the configuration parameters can include the phase shift and amplitude coefficient of each unit in the MF-RIS, and the beamforming parameters can include the precoding vector of each antenna of the access point for each user equipment (including legitimate and illegitimate user equipment) in the cellular network. In this case, the phase shift and amplitude coefficient of each unit in the MF-RIS can be adjusted to the solved phase shift and amplitude coefficients, and the precoding vector of each antenna of each access point for each user equipment can be adjusted to match the solved precoding vector, thereby maximizing the security and energy efficiency of the cellular network.

[0059] As mentioned in step S610, the security efficiency of a cellular network can be determined based on the secure reachability rate of legitimate user equipment in the cellular network and the power consumption of the cellular network. Therefore, a first optimization model can be constructed based on the secure reachability rate of legitimate user equipment in the cellular network and the power consumption of the cellular network. In this case, referring to Figure 7, step S610, for the cellular network, constructing the first optimization model may include the following steps S710 to S730.

[0060] In step S710, a secure reach rate model for legitimate user equipment in a non-cellular network is constructed based on the configuration parameters of the RIS and the beamforming parameters of the access point.

[0061] The secure reachability rate model here can be used to indicate the sum of the secure reachability rates of B legitimate user devices in a non-cellular network. The sum of the secure reachability rates of B legitimate user devices can be expressed as: in, This indicates the secure reachability rate of legitimate user equipment b.

[0062] In step S720, a power consumption model for the non-cellular network is constructed based on the configuration parameters of the RIS and the beamforming parameters of the access point.

[0063] The power consumption model here can be used to indicate the total power consumption of a non-cellular network. The total power consumption of a non-cellular network can include the output power of A access points, the output power of the RIS (Resource Identifier), the power consumption of A access points, the power consumption of B legitimate user equipment, and the power consumption of the RIS. When the RIS is an MF-RIS, the total power consumption of the non-cellular network can be expressed as: Where δ1 and δ2 represent the reciprocals of MF-RIS and the energy conversion coefficient at each access point, respectively, P C =AP A +BP B +2MP S +MP Am P A P represents the power dissipation of each access point. B P represents the power dissipation of each legitimate user equipment. S P represents the power dissipation of each phase shifter. Am This represents the power dissipation of each power amplifier.

[0064] In step S730, a first optimization model is constructed based on the secure achievable rate model and the power consumption model.

[0065] After constructing the secure reachable rate model in step S710 and the power consumption model in step S720, a first optimization model can be constructed based on the secure reachable rate model and the power consumption model. For example, when the secure reachable rate model is represented as... The power consumption model is represented by P T When this happens, the first optimization model can be represented as:

[0066] As mentioned above, in step S710, a secure reachability rate model for legitimate user equipment in a non-cellular network can be constructed based on the configuration parameters of the RIS and the beamforming parameters of the access point. In some implementations, step S710 may include: determining a first equivalent channel model based on the configuration parameters of the RIS; determining a second equivalent channel model based on the configuration parameters of the RIS; and determining a secure reachability rate model based on the beamforming parameters, the first equivalent channel model, and the second equivalent channel model.

[0067] The first equivalent channel model here can be understood as the equivalent channel model from the access point to the legitimate user equipment (RFE). The first equivalent channel model can include the channel model from the access point to the REE, and the channel model from the access point via the RIS to the REE. The first equivalent channel model can be obtained by adding the channel model from the access point to the REE to the REE. The channel model from the access point to the REE via the RIS can be expressed as follows: in, Θ represents the channel from RIS to legitimate user equipment b. k Let G be the coefficient matrix used by the RIS for reflection (k = r) or refraction (k = t). If access point a and legitimate user equipment b are on the same side of the RIS, then k = r; if access point a and legitimate user equipment b are on opposite sides of the RIS, then k = t. asLet represent the channel from access point a to RIS. Then the first equivalent channel model can be expressed as: in, This represents the equivalent channel from access point a to legitimate user equipment b. This represents the channel from access point a to legitimate user equipment b.

[0068] The second equivalent channel model here can be understood as the equivalent channel model from the access point to the unauthorized user equipment in the non-cellular network. The second equivalent channel model can include the channel model from the access point to the unauthorized user equipment, and the channel model from the access point via the RIS to the unauthorized user equipment. The second equivalent channel model can be obtained by adding the channel model from the access point to the unauthorized user equipment to the channel model from the access point via the RIS. The channel model from the access point via the RIS to the unauthorized user equipment can be expressed as follows: in, Θ represents the channel from RIS to the illegal user equipment e. k Let G be the coefficient matrix used by the RIS for reflection (k = r) or refraction (k = t). If access point a and illegal user equipment e are on the same side of the RIS, then k = r; if access point a and illegal user equipment e are on opposite sides of the RIS, then k = t. as Let represent the channel from access point a to RIS. Then the second equivalent channel model can be expressed as: in, This represents the equivalent channel from access point a to the illegal user equipment e. This represents the channel from access point a to the illegal user equipment e.

[0069] After determining the first equivalent channel model and the second equivalent channel model, the secure achievable rate model can be determined based on the beamforming parameters, the first equivalent channel model, and the second equivalent channel model.

[0070] In some implementations, determining the secure reachable rate model based on beamforming parameters, a first equivalent channel model, and a second equivalent channel model may include: determining a first signal-to-interference-plus-noise ratio (SIR) based on beamforming parameters and the first equivalent channel model; determining a second SIR based on the second equivalent channel model; and determining the secure reachable rate model based on the first SIR and the second SIR.

[0071] The first signal-to-interference-plus-noise ratio (SIR) can be understood as the SIR of the signal transmitted from the access point to the legitimate user equipment (UE) when the UE demodulates the signal. This can be defined as ∑ b∈B w ab s b Let w be the emitted signal from access point a, where w is the emitted signal from access point a. ab s represents the precoding vector of access point a to legitimate user equipment b. bThe transmitted symbol of legitimate user equipment b satisfies Therefore, the signal received by legitimate user equipment b can be represented as:

[0072] in, This represents the thermal noise introduced at the MF-RIS, with a power of This represents additive white Gaussian noise at the legitimate user equipment location, with a power of

[0073] Therefore, the first signal-to-interference-plus-noise ratio can be expressed as: Where, γ b This represents the signal-to-interference-plus-noise ratio (SIR) when the access point demodulates the signal transmitted from the legitimate user equipment (B) to the legitimate user equipment (B).

[0074] The second signal-to-interference-plus-noise ratio (SIR) here can be understood as the SIR of the signal transmitted from the demodulated access point of the unauthorized user equipment (UE) to the legitimate UE. Similar to the process of determining the first SIR, the signal from which the unauthorized UE e eavesdrops on the information of the legitimate UE b can be represented as:

[0075] in, This indicates that the power at the illegal user equipment is Additive white Gaussian noise.

[0076] Therefore, the second signal-to-interference-plus-noise ratio can be expressed as: Where, γ eb This represents the signal-to-interference-plus-noise ratio (SIR) when the illegal user equipment e demodulates the signal transmitted from the access point to the legitimate user equipment b.

[0077] After determining the first signal-to-interference-plus-noise ratio (SIR) and the second SIR, a secure achievable rate model can be determined based on the first and second SIRs. The SIR γ of the signal transmitted from the access point to legitimate user equipment (B) can be used as a reference. b And the signal-to-interference-plus-noise ratio γ when the unauthorized user equipment e demodulates the access point to the legitimate user equipment b. eb Determine the secure reach rate of legitimate user equipment b. Then, based on the secure reachability rate of legitimate user equipment b. Determine the secure reachability rate model for B legitimate user equipments in a non-cellular network. The secure reachability rate of legitimate user equipment b. It can be represented as:

[0078] Among them, R b =log2(1+γ) b ), R eb =log2(1+γ)eb ), operator [x] + =max{x,0}, due to the non-negativity of the optimal safe rate, this operator can be omitted subsequently.

[0079] After the above operations, the first optimization model can be represented as:

[0080] The above, with reference to the accompanying diagram, details how to construct the first optimization model. The following section describes how to solve the first optimization model.

[0081] As described above, the first optimization model and its constraints (hereinafter referred to as the first constraints) can be quite complex. For example, the first optimization model may be in fractional form, and the first constraints may include multiple constraints, some of which may involve complex logarithmic forms. Therefore, in solving the first optimization model, the optimization objective (hereinafter referred to as the first optimization objective), the first optimization model, and the first constraints can be transformed into a second optimization objective, a second optimization model, and second constraints using a first processing method. The first processing method may include one or more of the following: introducing relaxation variables, introducing auxiliary variables, continuous convex approximation of non-convex constraints, ignoring rank-one constraints, and relaxation of sequential rank-one constraints.

[0082] As mentioned earlier, the optimization variables of the first optimization model can include the configuration parameters of the RIS and the beamforming parameters of the access points. In some implementations, the configuration parameters of the RIS and the beamforming parameters of the access points can be solved iteratively using the variable substitution method. When solving the first optimization model using the variable substitution method, the configuration parameters and beamforming parameters can be alternately used as optimization variables during the iteration process. Referring to Figure 8, the first optimization model can be solved by repeatedly executing an iterative process including steps A and B, thereby determining the values ​​of the configuration parameters and beamforming parameters. A process including one step A and one step B can be called an iteration process. It should be understood that the first iteration process can execute step A first and then step B. In this case, each iteration process executes step A first and then step B. Alternatively, the first iteration process can execute step B first and then step A. In this case, each iteration process executes step B first and then step A.

[0083] Step A may include: given the values ​​of the configuration parameters, solving the first optimization model with the optimization objective of maximizing safety and energy efficiency, in order to determine the values ​​of the beamforming parameters.

[0084] In other words, in step A, the values ​​of the configuration parameters can be fixed, and the beamforming parameters can be used as optimization variables to solve the first optimization model. If step A is step A in the first iteration process and step A is executed before step B in the first iteration process, the values ​​of the configuration parameters given here can be obtained through simulation. That is, before executing step A in the first iteration process, the first optimization model can be simulated to obtain a feasible solution for the configuration parameters as the initial values ​​for the configuration parameters during iteration. In other cases, the values ​​of the configuration parameters given here can be the solutions to the configuration parameters obtained in step B before step A. These other cases can include any of the following: step A is step A in the first iteration process and step B is executed before step A in the first iteration process; or step A is step A in a non-first iteration process.

[0085] Step B may include: given the values ​​of the beamforming parameters, solving the first optimization model with the goal of maximizing safety and energy efficiency, in order to determine the values ​​of the configuration parameters.

[0086] In other words, in step B, the values ​​of the beamforming parameters can be fixed, and the configuration parameters can be used as optimization variables to solve the first optimization model. If step B is step B in the first iteration process and step B is executed before step A in the first iteration process, the values ​​of the beamforming parameters given here can be obtained through simulation. That is, before executing step B in the first iteration process, the first optimization model can be simulated to obtain a feasible solution for the beamforming parameters as the initial values ​​for the beamforming parameters in the iteration. In other cases, the values ​​of the beamforming parameters given here can be the solution of the beamforming parameters obtained in step A before step B. These other cases can include any of the following: step B is step B in the first iteration process and step A is executed before step B in the first iteration process; or step B is step B in a non-first iteration process.

[0087] After each iteration of steps A and B, it can be determined whether the iteration stopping condition is met. If the stopping condition is not met, the iteration process containing steps A and B can continue. If the stopping condition is met, the values ​​of the configuration parameters and beamforming parameters in the current iteration can be output as the optimal solution of the first optimization model.

[0088] The iteration stopping conditions here can include one or more of the following: iteration convergence conditions and iteration timeout conditions. For example, a threshold (hereinafter referred to as the first threshold) can be set. When the difference between the safety energy efficiency obtained in the current iteration and the safety energy efficiency obtained in the previous iteration, divided by the quotient of the safety energy efficiency obtained in the previous iteration, is less than or equal to the first threshold, the iteration convergence condition is considered satisfied. As another example, an iteration count threshold (hereinafter referred to as the second threshold) can be set. When the number of times the iteration process including steps A and B is executed is greater than or equal to the second threshold, the iteration timeout condition is considered satisfied. In practical applications, both iteration convergence conditions and iteration timeout conditions can be set simultaneously. When either the iteration convergence condition or the iteration timeout condition is satisfied, the iteration stopping condition is considered satisfied.

[0089] The following uses an MF-RIS-assisted non-cellular network as an example, and describes, with reference to Figure 9, how to use the method provided in the embodiments of this application to solve for MF-RIS coefficients and beamforming parameters. As shown in Figure 9, the method for wireless communication provided in the embodiments of this application may include the following steps S910 to S980.

[0090] In step S910, the MF-RIS-assisted cellular network is modeled.

[0091] In step S920, the problem of maximizing safety and energy efficiency is formalized.

[0092] In step S930, the objective function and constraints of the fractional structure are processed using variable substitution and continuous convex approximation methods.

[0093] In step S940, the initial values ​​of the MF-RIS coefficients are determined.

[0094] In step S950, the values ​​of the MF-RIS coefficients are fixed, and the beamforming parameters are used as optimization variables to obtain the solution of the beamforming parameters.

[0095] In step S960, the beamforming parameters are fixed to the solution of the beamforming parameters obtained in the previous step, and the solution of the MF-RIS coefficients is obtained with the MF-RIS coefficients as the optimization variable.

[0096] In step S970, it is determined whether the iteration has converged or timed out. If the iteration has not converged and has not timed out, the process returns to step S950. If the iteration has converged or timed out, step S980 is executed.

[0097] In step S980, the values ​​of the beamforming parameters obtained in step S950 and the values ​​of the MF-RIS coefficients obtained in step S960 during this iteration are output as the optimal solution.

[0098] To better understand the process of solving the first optimization model, the following uses the first optimization model as an example. The primary optimization objective is to maximize... The optimization variable is w b and Θ k The first constraint includes: and R b ≥R min Taking this as an example, we will describe in detail the process of transforming and solving the first optimization model using the first processing method described above.

[0099] To handle the first optimization model in fractional form, we can first introduce slack variables ζ, ρ, r, transforming the above first optimization model into:

[0100] in Due to constraint ∑ b∈B (r b ―r eb )≥ζρ, and P T ≤ρ takes effect at the optimal solution, and the transformed problem is equivalent to the original problem.

[0101] Furthermore, in order to simplify the constraint R b ≥r b , and R eb ≤r e , The complex logarithmic form of the signal-to-interference-plus-noise ratio expression, simplified to a fractional structure, can be addressed by introducing a set of auxiliary variables. in,

[0102] Therefore, the first optimization model can be reconstructed as:

[0103] Where Δ1={ζ,ρ,r,Δ}, due to the product term ζρ and the logarithmic term constraint and They are non-convex. Here, we can approximate them using the continuous convex approximation method. In the... Given the point of the next iteration and At this point, the linearity of these terms can be approximated as:

[0104] Ultimately, the original optimization problem can be rewritten as:

[0105] The beamforming parameters w can be iteratively optimized using the variable substitution method. b and MF-RIS coefficient Θ k We can use this to solve the above nonconvex nonlinear optimization problem.

[0106] First, the MF-RIS coefficient Θ can be fixed. k Solve for the beamforming parameters w b Before solving, a matrix can be defined. and Satisfy W b ±0 and Rank(W) b ) = 1. Therefore, the optimization problem of the first optimization model can be equivalently transformed into:

[0107] in, Due to nonconvex constraints Rank(W) constraint b ) = 1, The optimization problem described above is a nonconvex optimization problem. Using the continuous convex approximation method, in the... Given the point of the next iteration Place, The lower bound can be represented as Therefore, the constraint can be rewritten in the following convex form:

[0108] Based on the semidefinite relaxation method, the rank-one constraint can be directly ignored, resulting in the following relaxation problem:

[0109] The problem described above is a convex semidefinite programming problem, and therefore can be solved efficiently using existing convex optimization tools, such as the convex optimization toolkits (convex, CVX) in Matlab. We will now prove that the solution obtained from solving this relaxation problem satisfies the rank-one constraint.

[0110] The aforementioned optimization problem after relaxation concerns beamforming parameters. Since the beamforming parameters are combined, the optimal solution can be characterized using the Carlow-Kuhn-Tucker conditions. The Lagrange function can be expressed as:

[0111] in, and Y represents the Lagrange multiplier. bLet denote the Lagrange multiplier matrix, and Γ denote all quantities independent of the beamforming parameters. Based on the Carlow-Kuhn-Tucker conditions, the optimal solution must satisfy:

[0112] in, This represents the optimal Lagrange multiplier, while This represents the gradient of the Lagrangian function with respect to the beamforming parameters. According to... We can obtain:

[0113] in,

[0114] Due to the matrix It is semi-positive definite, and has The equation holds true. Furthermore, the equation... This indicates that the inequality is satisfied. because The minimum rate requirement R cannot be met. b ≥R min , therefore If true, then the rank-one constraint is satisfied.

[0115] Next, given the beamforming parameters, the MF-RIS coefficients can be solved. For computational convenience, a matrix can be defined. Where vector Satisfy V k ±0, Rank(V) k ) = 1, and [V] k ] M+1,M+1 =1. Therefore, we can obtain the following equation:

[0116] Among them, C c D sc and E ab They respectively satisfy:

[0117] Combining the above equations, the MF-RIS coefficient optimization problem can be expressed as:

[0118] in, The difficulty in solving this problem lies in the rank-one constraint Rank(V) k ) = 1, Since the solutions to the MF-RIS coefficients obtained from solving the relaxation problem do not necessarily satisfy the rank-one constraint, this constraint cannot be directly ignored. Here, a sequential rank-one constraint relaxation method can be used to handle this constraint. The rank-one constraint can be... The rank-one constraint of the next iteration is rewritten as:

[0119] Where, ε max (V k ) represents V k The largest eigenvalue, Indicates the first A relaxation factor for the next iteration. Here, This indicates that the rank-one constraint is completely ignored, while This is equivalent to a rank-one constraint. Therefore, it can be achieved by... Increasing from 0 to 1 approaches a rank-1 solution. Because ε max (V k Since is non-differentiable, the above constraints can be further transformed into the following linear form:

[0120] in Indicates about The eigenvector of the largest eigenvalue. Ultimately, the above optimization problem can be reconstructed as:

[0121] This problem is a convex semidefinite relaxation problem, so it can be solved efficiently using existing convex tools (such as CVX).

[0122] The method embodiments of this application have been described in detail above with reference to Figures 6 to 9. The apparatus embodiments of this application will be described in detail below with reference to Figures 10 and 11. It should be understood that the descriptions of the method embodiments correspond to the descriptions of the apparatus embodiments; therefore, any parts not described in detail can be referred to the preceding method embodiments.

[0123] Figure 10 is a schematic diagram of the structure of a communication device 1000 provided in an embodiment of this application. The communication device 1000 can be applied to a non-cellular network, which may include an access point and a RIS. The communication device 1000 shown in Figure 10 includes:

[0124] The construction module 1010 is used to construct a first optimization model for the non-cellular network. The first optimization model is used to indicate the security and energy efficiency of the non-cellular network. The optimization variables of the first optimization model include the configuration parameters of the RIS and the beamforming parameters of the access point.

[0125] Configuration module 1020 is used to configure the RIS and the access point according to the solution of the first optimization model.

[0126] In some implementations, the configuration module 1020 is further configured to: solve the first optimization model with the goal of maximizing safety and energy efficiency, to determine the values ​​of the configuration parameters and the beamforming parameters; and configure the RIS and the access point according to the values ​​of the configuration parameters and the beamforming parameters.

[0127] In some implementations, the RIS is used to perform one or more of the following operations: reflect signals in the non-cellular network, refract signals in the non-cellular network, and amplify signals in the non-cellular network.

[0128] In some implementations, the construction module is further configured to: construct a secure reachability rate model for legitimate user equipment in the non-cellular network based on the configuration parameters of the RIS and the beamforming parameters of the access point; construct a power consumption model for the non-cellular network based on the configuration parameters of the RIS and the beamforming parameters of the access point; and construct the first optimization model based on the secure reachability rate model and the power consumption model.

[0129] In some implementations, the building module is further configured to: determine a first equivalent channel model based on the configuration parameters of the RIS, wherein the first equivalent channel model is an equivalent channel model from the access point to the legitimate user equipment, and the first equivalent channel model includes a channel model from the access point to the legitimate user equipment and a channel model from the RIS to the legitimate user equipment; determine a second equivalent channel model based on the configuration parameters of the RIS, wherein the second equivalent channel model is an equivalent channel model from the access point to the illegitimate user equipment in the non-cellular network, and the second equivalent channel model includes a channel model from the access point to the illegitimate user equipment and a channel model from the RIS to the illegitimate user equipment; and determine the secure reachable rate model based on the beamforming parameters, the first equivalent channel model, and the second equivalent channel model.

[0130] In some implementations, the building module is further configured to: determine a first signal-to-interference-plus-noise ratio (SINR) based on the beamforming parameters and the first equivalent channel model, wherein the first SINR is the SINR when the legitimate user equipment demodulates the signal transmitted from the access point to the legitimate user equipment; determine a second SINR based on the second equivalent channel model, wherein the second SINR is the SINR when the illegitimate user equipment demodulates the signal transmitted from the access point to the legitimate user equipment; and determine the secure reachable rate model based on the first SINR and the second SINR.

[0131] In some implementations, the optimization objective of the first optimization model is a first optimization objective, and the constraints of the first optimization model are first constraints. The configuration module is further configured to: convert the first optimization objective, the first optimization model, and the first constraints into a second optimization objective, a second optimization model, and second constraints according to a first processing method. The first processing method includes one or more of the following: introducing relaxation variables, introducing auxiliary variables, continuous convex approximation of non-convex constraints, ignoring rank-one constraints, and relaxation of sequential rank-one constraints.

[0132] In some implementations, the configuration module is further configured to: execute step A, which includes: given the values ​​of the configuration parameters, solving the first optimization model with the goal of maximizing safety and energy efficiency to determine the values ​​of the beamforming parameters; execute step B, which includes: given the values ​​of the beamforming parameters, solving the first optimization model with the goal of maximizing safety and energy efficiency to determine the values ​​of the configuration parameters; and repeatedly execute the iterative process including steps A and B until the iterative process meets the iteration stopping condition.

[0133] In some implementations, the constraints of the first optimization model include one or more of the following: the output power of the access point is less than or equal to the maximum transmit power of the access point; the output power of the RIS is less than or equal to the maximum output power of the RIS; the phase shift of the internal unit of the RIS is greater than or equal to zero and less than 2π; the amplitude coefficient of the internal unit of the RIS is less than or equal to the maximum amplitude coefficient; the sum of the amplitude coefficients of the internal units of the RIS is less than or equal to the maximum amplitude coefficient; and the rate of the legitimate user equipment without cellular network meets a preset minimum rate requirement.

[0134] In some implementations, the configuration parameters of the RIS include the phase shift and amplitude coefficients of the cells within the RIS.

[0135] In some implementations, the beamforming parameters are precoded vectors of the access point for the user equipment of the non-cellular network.

[0136] Figure 11 is a schematic diagram of the structure of a communication device applicable to embodiments of this application. The dashed lines in Figure 11 indicate that the unit or module is optional. This device 1100 can be used to implement the methods described in the above method embodiments. Device 1100 may be a chip.

[0137] Apparatus 1100 may include one or more processors 1110. The processor 1110 may support apparatus 1100 in implementing the methods described in the preceding method embodiments. The processor 1110 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a CPU. Alternatively, the processor may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0138] The apparatus 1100 may further include one or more memories 1120. The memories 1120 store a program that can be executed by the processor 1110, causing the processor 1110 to perform the methods described in the preceding method embodiments. The memories 1120 may be independent of the processor 1110 or integrated within the processor 1110.

[0139] The device 1100 may also include a transceiver 1130. The processor 1110 can communicate with other devices or chips via the transceiver 1130. For example, the processor 1110 can send and receive data with other devices or chips via the transceiver 1130.

[0140] This application also provides a computer-readable storage medium for storing a program. This computer-readable storage medium can be applied to a wireless communication device provided in this application, and the program causes a computer to perform the methods executed by the wireless communication device in various embodiments of this application.

[0141] This application also provides a computer program product. The computer program product includes a program. The computer program product can be applied to the wireless communication device provided in this application embodiment, and the program causes a computer to perform the methods executed by the wireless communication device in various embodiments of this application.

[0142] This application also provides a computer program. This computer program can be applied to the wireless communication device provided in this application, and the computer program causes a computer to perform the methods executed by the wireless communication device in various embodiments of this application.

[0143] It should be understood that the terms "system" and "network" in this application can be used interchangeably. Furthermore, the terminology used in this application is only for explaining specific embodiments of the application and is not intended to limit the application. The terms "first," "second," "third," and "fourth," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. In addition, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0144] In the embodiments of this application, the term "instruction" can be a direct instruction, an indirect instruction, or an indication of a relationship. For example, A instructing B can mean that A directly instructs B, such as B being able to obtain information through A; it can also mean that A indirectly instructs B, such as A instructing C, so B can obtain information through C; or it can mean that there is a relationship between A and B.

[0145] In the embodiments of this application, "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean that B is determined solely based on A; B can also be determined based on A and / or other information.

[0146] In the embodiments of this application, the term "correspondence" can indicate a direct or indirect correspondence between two things, or an association between two things, or a relationship such as instruction and being instructed, configuration and being configured.

[0147] In the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0148] In the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0152] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can read or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs, DVDs) or semiconductor media (e.g., solid-state disks, SSDs), etc.

[0153] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for wireless communication, characterized in that, The method is applied to a non-cellular network, the non-cellular network including an access point and a reconfigurable smart metasurface (RIS), the method comprising: For the non-cellular network, a first optimization model is constructed. The first optimization model is used to indicate the security and energy efficiency of the non-cellular network. The optimization variables of the first optimization model include the configuration parameters of the RIS and the beamforming parameters of the access point. Configure the RIS and the access point based on the solution of the first optimization model.

2. The method according to claim 1, characterized in that, The step of configuring the RIS and the access point based on the solution of the first optimization model includes: With the goal of maximizing safety and energy efficiency, the first optimization model is solved to determine the values ​​of the configuration parameters and the beamforming parameters. Configure the RIS and the access point according to the values ​​of the configuration parameters and the beamforming parameters.

3. The method according to claim 1 or 2, characterized in that, The RIS is used to perform one or more of the following operations: reflect signals in the non-cellular network, refract signals in the non-cellular network, and amplify signals in the non-cellular network.

4. The method according to any one of claims 1 to 3, characterized in that, The construction of a first optimization model for the non-cellular network includes: Based on the configuration parameters of the RIS and the beamforming parameters of the access point, a secure reachability rate model for legitimate user equipment in the non-cellular network is constructed. Based on the configuration parameters of the RIS and the beamforming parameters of the access point, a power consumption model of the non-cellular network is constructed. The first optimization model is constructed based on the secure achievable rate model and the power consumption model.

5. The method according to claim 4, characterized in that, The step of constructing a secure reachability rate model for legitimate user equipment in the non-cellular network based on the configuration parameters of the RIS and the beamforming parameters of the access point includes: A first equivalent channel model is determined based on the configuration parameters of the RIS. The first equivalent channel model is the equivalent channel model from the access point to the legitimate user equipment. The first equivalent channel model includes the channel model from the access point to the legitimate user equipment and the channel model from the access point to the legitimate user equipment via the RIS. A second equivalent channel model is determined based on the configuration parameters of the RIS. The second equivalent channel model is an equivalent channel model from the access point to the unauthorized user equipment in the non-cellular network. The second equivalent channel model includes the channel model from the access point to the unauthorized user equipment, and the channel model from the access point to the unauthorized user equipment via the RIS. The secure achievable rate model is determined based on the beamforming parameters, the first equivalent channel model, and the second equivalent channel model.

6. The method according to claim 5, characterized in that, The step of determining the secure achievable rate model based on the beamforming parameters, the first equivalent channel model, and the second equivalent channel model includes: Based on the beamforming parameters and the first equivalent channel model, a first signal-to-interference-plus-noise ratio (SINR) is determined. The first SINR is the SINR when the legitimate user equipment demodulates the signal transmitted from the access point to the legitimate user equipment. Based on the second equivalent channel model, a second signal-to-interference-plus-noise ratio (SINR) is determined. The second SINR is the SINR when the illegal user equipment demodulates the signal transmitted from the access point to the legitimate user equipment. The secure achievable rate model is determined based on the first signal-to-interference-plus-noise ratio (SIR) and the second SIR.

7. The method according to any one of claims 1 to 6, characterized in that, The optimization objective of the first optimization model is the first optimization objective, and the constraints of the first optimization model are the first constraints. The step of solving the first optimization model with the goal of maximizing safety and energy efficiency includes: According to the first processing method, the first optimization objective, the first optimization model, and the first constraint are converted into a second optimization objective, a second optimization model, and a second constraint. The first processing method includes one or more of the following: introducing relaxation variables, introducing auxiliary variables, continuous convex approximation of non-convex constraints, ignoring rank-one constraints, and relaxation of sequential rank-one constraints.

8. The method according to any one of claims 2 to 7, characterized in that, The step of solving the first optimization model with the goal of maximizing safety and energy efficiency to determine the values ​​of the configuration parameters and the beamforming parameters includes: Step A is executed, which includes: given the values ​​of the configuration parameters, solving the first optimization model with the goal of maximizing safety and energy efficiency, in order to determine the values ​​of the beamforming parameters; Step B is performed, which includes: given the values ​​of the beamforming parameters, solving the first optimization model with the goal of maximizing safety and energy efficiency, in order to determine the values ​​of the configuration parameters; Repeat the iterative process including steps A and B until the iterative process meets the iteration stopping condition.

9. The method according to any one of claims 1 to 8, characterized in that, The constraints of the first optimization model include one or more of the following: the output power of the access point is less than or equal to the maximum transmit power of the access point; the output power of the RIS is less than or equal to the maximum output power of the RIS; the phase shift of the internal unit of the RIS is greater than or equal to zero and less than 2π; the amplitude coefficient of the internal unit of the RIS is less than or equal to the maximum amplitude coefficient; the sum of the amplitude coefficients of the internal units of the RIS is less than or equal to the maximum amplitude coefficient; and the rate of the legitimate user equipment without cellular network meets the preset minimum rate requirement.

10. The method according to any one of claims 1 to 9, characterized in that, The configuration parameters of the RIS include the phase shift and amplitude coefficients of the cells within the RIS.

11. The method according to any one of claims 1 to 10, characterized in that, The beamforming parameters are the pre-coded vectors of the access point for the user equipment in the non-cellular network.

12. A device for wireless communication, characterized in that, The device is used in a non-cellular network, the non-cellular network including an access point and a reconfigurable smart metasurface RIS, and the device includes: A construction module is used to construct a first optimization model for the non-cellular network. The first optimization model is used to indicate the security and energy efficiency of the non-cellular network. The optimization variables of the first optimization model include the configuration parameters of the RIS and the beamforming parameters of the access point. A configuration module is used to configure the RIS and the access point based on the solution of the first optimization model.

13. The device according to claim 12, characterized in that, The configuration module is also used for: With the goal of maximizing safety and energy efficiency, the first optimization model is solved to determine the values ​​of the configuration parameters and the beamforming parameters. Configure the RIS and the access point according to the values ​​of the configuration parameters and the beamforming parameters.

14. The device according to claim 13, characterized in that, The RIS is used to perform one or more of the following operations: reflect signals in the non-cellular network, refract signals in the non-cellular network, and amplify signals in the non-cellular network.

15. The device according to claim 13 or 14, characterized in that, The building module is also used for: Based on the configuration parameters of the RIS and the beamforming parameters of the access point, a secure reachability rate model for legitimate user equipment in the non-cellular network is constructed. Based on the configuration parameters of the RIS and the beamforming parameters of the access point, a power consumption model of the non-cellular network is constructed. The first optimization model is constructed based on the secure achievable rate model and the power consumption model.

16. The device according to claim 15, characterized in that, The building module is also used for: A first equivalent channel model is determined based on the configuration parameters of the RIS. The first equivalent channel model is the equivalent channel model from the access point to the legitimate user equipment. The first equivalent channel model includes the channel model from the access point to the legitimate user equipment and the channel model from the RIS to the legitimate user equipment. The second equivalent channel model is determined based on the configuration parameters of the RIS. The second equivalent channel model is the equivalent channel model from the access point to the illegal user equipment in the non-cellular network. The second equivalent channel model includes the channel model from the access point to the illegal user equipment and the channel model from the RIS to the illegal user equipment. The secure achievable rate model is determined based on the beamforming parameters, the first equivalent channel model, and the second equivalent channel model.

17. The device according to claim 16, characterized in that, The building module is also used for: Based on the beamforming parameters and the first equivalent channel model, a first signal-to-interference-plus-noise ratio (SINR) is determined. The first SINR is the SINR when the legitimate user equipment demodulates the signal transmitted from the access point to the legitimate user equipment. Based on the second equivalent channel model, a second signal-to-interference-plus-noise ratio (SINR) is determined. The second SINR is the SINR when the illegal user equipment demodulates the signal transmitted from the access point to the legitimate user equipment. The secure achievable rate model is determined based on the first signal-to-interference-plus-noise ratio (SIR) and the second SIR.

18. The device according to any one of claims 13 to 17, characterized in that, The optimization objective of the first optimization model is the first optimization objective, and the constraints of the first optimization model are the first constraints. The configuration module is also used for: According to the first processing method, the first optimization objective, the first optimization model, and the first constraint are converted into a second optimization objective, a second optimization model, and a second constraint. The first processing method includes one or more of the following: introducing relaxation variables, introducing auxiliary variables, continuous convex approximation of non-convex constraints, ignoring rank-one constraints, and relaxation of sequential rank-one constraints.

19. The device according to any one of claims 13 to 18, characterized in that, The configuration module is also used for: Step A is executed, which includes: given the values ​​of the configuration parameters, solving the first optimization model with the goal of maximizing safety and energy efficiency, in order to determine the values ​​of the beamforming parameters; Step B is performed, which includes: given the values ​​of the beamforming parameters, solving the first optimization model with the goal of maximizing safety and energy efficiency, in order to determine the values ​​of the configuration parameters; Repeat the iterative process including steps A and B until the iterative process meets the iteration stopping condition.

20. The device according to any one of claims 13 to 19, characterized in that, The constraints of the first optimization model include one or more of the following: the output power of the access point is less than or equal to the maximum transmit power of the access point; the output power of the RIS is less than or equal to the maximum output power of the RIS; the phase shift of the internal unit of the RIS is greater than or equal to zero and less than 2π; the amplitude coefficient of the internal unit of the RIS is less than or equal to the maximum amplitude coefficient; the sum of the amplitude coefficients of the internal units of the RIS is less than or equal to the maximum amplitude coefficient; and the rate of the legitimate user equipment without cellular network meets the preset minimum rate requirement.

21. The device according to any one of claims 13 to 20, characterized in that, The configuration parameters of the RIS include the phase shift and amplitude coefficients of the cells within the RIS.

22. The device according to any one of claims 13 to 21, characterized in that, The beamforming parameters are the pre-coded vectors of the access point for the user equipment in the non-cellular network.

23. A device for wireless communication, characterized in that, It includes a memory and a processor, the memory being used to store a program, and the processor being used to invoke the program in the memory to cause the communication device to perform the method as described in any one of claims 1 to 11.

24. An apparatus, characterized in that, Includes a processor for calling a program from memory to cause the apparatus to perform the method as described in any one of claims 1 to 11.

25. A chip, characterized in that, Includes a processor for calling a program from memory, causing a device on which the chip is mounted to perform the method as described in any one of claims 1 to 11.

26. A computer-readable storage medium, characterized in that, It contains a program that causes a computer to perform the method as described in any one of claims 1 to 11.

27. A computer program product, characterized in that, Includes a program that causes a computer to perform the method as described in any one of claims 1 to 11.

28. A computer program, characterized in that, The computer program causes the computer to perform the method as described in any one of claims 1 to 11.

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