Communication method and communication device

Abstract

The invention provides a communication method and communication equipment. The method is applied to a cellular-free network, the cellular-free network comprises an access point and a reconfigurable intelligent metasurface, and the method comprises the following steps: aiming at the cellular-free network, constructing a first optimization model, the first optimization model is used for indicating the safety energy efficiency of the cellular-free network, optimization variables of the first optimization model comprise configuration parameters of the reconfigurable intelligent metasurface and beam forming parameters of the access point; and configuring the RIS and the access point according to the solution of the first optimization model.

Description

Technical Field

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

[0002] Reconfigurable intelligence surface (RIS) can be introduced into the cellular-free network to improve the security of communication in the cellular-free network. After introducing RIS into the cellular-free network, how to optimize energy efficiency while ensuring communication security, that is, how to optimize the safety and energy efficiency of the cellular-free network, is a technical problem that needs to be solved. Summary of the invention

[0003] The present application provides a communication method and a communication device. The following introduces various aspects involved in the present application.

[0004] In a first aspect, a communication method is provided, which is applied to a non-cellular network, wherein the non-cellular network includes an access point and a RIS, and the method includes: constructing a first optimization model for the non-cellular network, the first optimization model being used to indicate the safety 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 a solution of the first optimization model.

[0005] In a second aspect, a communication device is provided, which is applied to a non-cellular network, wherein the non-cellular network includes an access point and a RIS, and the device includes: a construction module, used to construct a first optimization model for the non-cellular network, the first optimization model is used to indicate the safety energy efficiency of the non-cellular network, and the optimization variables of the first optimization model include configuration parameters of the RIS and beamforming parameters of the access point; a configuration module, used to configure the RIS and the access point according to the solution of the first optimization model.

[0006] According to a third aspect, a communication device is provided, comprising a memory and a processor, wherein the memory is used to store a program, and the processor is used to call the program in the memory so that the communication device executes the method according to the first aspect.

[0007] In a fourth aspect, a device is provided, comprising a processor, configured to call a program from a memory so that the device executes the method as described in the first aspect.

[0008] In a fifth aspect, a chip is provided, comprising a processor for calling a program from a memory so that a device equipped with the chip executes the method described in the first aspect.

[0009] In a sixth aspect, a computer-readable storage medium is provided, on which a program is stored, wherein the program enables a computer to execute 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, and the program enables a computer to execute the method described in the first aspect.

[0011] According to an eighth aspect, a computer program is provided, wherein the computer program enables a computer to execute the method as described in the first aspect.

[0012] In an embodiment of the present application, for a non-cellular network that has introduced RIS, a first optimization model indicating the security energy efficiency of the non-cellular network can be constructed, and according to the solution of the first optimization model, the RIS and access points in the non-cellular network can be configured, thereby optimizing the security energy efficiency of the non-cellular network while ensuring communication security. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art description are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application, and other drawings obtained by those skilled in the art based on these drawings fall within the protection scope of the present application.

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

[0015] Figure 2 This is an example diagram of an architecture without a cellular network in the related technology provided in an embodiment of the present application.

[0016] Figure 3 This is a structural example diagram of RIS in the related technology provided in the embodiment of the present application.

[0017] Figure 4 This 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 the present application.

[0018] Figure 5 This 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 the present application.

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

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

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

[0022] Fig. 9 It is another schematic flowchart of the method for wireless communication provided in an embodiment of the present application.

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

[0024] Fig.11 It is a structural diagram of a communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments in the present application fall within the protection scope of the present application.

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

[0027] The embodiments of the present application involve a non-cellular network and a RIS. For ease of understanding, the non-cellular network and the RIS involved in the embodiments of the present application are first introduced in conjunction with the accompanying drawings.

[0028] No cellular network

[0029] Traditional mobile communications use cellular network technology. Figure 11 is an example diagram of the architecture of a cellular network in the related art provided by an embodiment of the present application. In a cellular network, the service area can be divided into multiple cells 110. The cell 110 can be understood as a specific geographical area. Each cell 110 may include a network device 120 and a user device 130. The network device 120 can provide network coverage for the cell 110 where the network device 120 is located, and can communicate with the user device 130 located in the cell 110. The user device 120 can access a network (such as a wireless network) through the network device 110. The cell 110 is usually a regular hexagon, so the entire network can be referred to as a cellular network. Cellular networks usually improve spectrum utilization by frequency reuse technology, that is, using the same frequency resources in different cells. However, this approach can cause signals between adjacent cells to interfere with each other.

[0030] In order to eliminate the interference between adjacent cells in the cellular network and thus improve the network capacity, a cellular-free network technology is proposed. Figure 2 1 is an example diagram of an architecture without a cellular network in the related technology provided in the embodiment of the present application. Figure 2 As shown, the cellular-free network breaks the concept of cell division and boundaries of the traditional cellular network. Unlike the traditional cellular-centric network, the cellular-free network adopts a transmission design centered on the user device. In the cellular-free network, each user device can be served by multiple access points in cooperation. The user device can connect to the network through the access point. In order to provide seamless coverage for the user device, a large number of access points can be deployed in the cellular-free network. In some cases, the access point can also be referred to as a base station. The cellular-free network can also include a central processing unit (CPU). The CPU can be connected to all antennas on the access point via a cable. The CPU can be used to perform one or more of the following operations: baseband signal processing, calculation of beamforming signals, signal detection and precoding, and processing of signals transmitted from multiple access points.

[0031] User equipment in a non-cellular network may 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 device, user agent or user device. The user equipment in the embodiment of the present application may include a device that provides voice and / or data connectivity to the user, which can be used to connect people, objects and machines, such as handheld devices with wireless connection functions, vehicle-mounted devices, etc. The user equipment in the embodiment of the present application may be a mobile phone, a tablet computer, a laptop computer, a PDA, a mobile Internet device, a wearable device, a virtual reality device, an augmented reality device, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a wireless terminal in remote surgery, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc.

[0032] User devices in a non-cellular network can include legitimate user devices and illegal user devices. Legitimate user devices here can be understood as devices that are authenticated and comply with the regulations and standards of the communication network. Legitimate user devices communicate in accordance with established rules and protocols and generally do not pose a threat to the communication network. Illegal user devices can be understood as unauthorized or maliciously modified devices. Illegal user devices may obtain or assist in obtaining telecommunications services without the permission of the telecommunications service provider. Illegal user devices may also hide their true identity or location, thereby bypassing normal network access control, which may pose a threat to the communication network. In a non-cellular network, due to the inherent broadcast characteristics of the wireless channel, any user device within the coverage of the access point can receive the signal, which may cause an illegal user device to receive the signal sent by the access point to the legitimate user device. In other words, illegal users may eavesdrop, thereby increasing the risk of information leakage in the non-cellular network. Therefore, communication security is a challenge for non-cellular networks.

[0033] Reconfigurable smart metasurface

[0034] Figure 3 1 is a structural example diagram of RIS in the related technology provided by the embodiment of the present application. Figure 3As shown, RIS can be regarded as a two-dimensional array composed of a large number of low-cost reconfigurable units 310. The reconfigurable unit 310 may include an adjustable element. The adjustable element here may be, for example, a phase shifter. By adjusting the adjustable element, the phase and / or amplitude of the reconfigurable unit 310 can be adjusted, so that the electromagnetic characteristics of the entire RIS can be adjusted to achieve regulation of the wireless channel. In other words, RIS can reconstruct the wireless environment on demand. If RIS is introduced into a communication network, with the assistance of RIS, signals from different links can be superimposed at certain locations to enhance the signal strength, while at other locations they can be offset or weakened to reduce the signal strength. Using the above characteristics of RIS, RIS can be introduced into a non-cellular network to enhance the signal strength at the legitimate user device and reduce the signal strength at the illegal user device, thereby enhancing the secure reception of the legitimate user device and suppressing the information leakage of the illegal user device. In other words, RIS can ensure the communication security of the non-cellular network in an economical way.

[0035] According to different functions, 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 SF-RIS, the direction of the reflected wave can be changed, thereby achieving regulation of the propagation path of the wireless signal. In addition to being used to reflect signals, MF-RIS can also be used to refract and / or amplify signals. For example, the reconfigurable unit of MF-RIS may include an amplifier, thereby achieving signal amplification. By adjusting the phase of the reconfigurable unit of MF-RIS, in addition to changing the direction of the reflected wave, the direction of the transmitted wave can also be changed, thereby achieving regulation of the propagation path of the wireless signal. By adjusting the amplitude coefficient of the reconfigurable unit of MF-RIS, the amplification factor of the wireless signal can be regulated.

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

[0037] In view of the above problems, an embodiment of the present application provides a communication method. Using this method, the values ​​of the configuration parameters of the RIS and the values ​​of the beamforming parameters of the access point when the security energy efficiency is maximized can be solved, and the RIS and the access point can be configured according to the values ​​of the configuration parameters and the values ​​of the beamforming parameters, so that the security energy efficiency of the non-cellular network can be maximized.

[0038] The communication method provided in the embodiment of the present application can be applied to a RIS-assisted non-cellular network. Figure 4 and Figure 5 The RIS-assisted cellular-free network is introduced in detail.

[0039] like Figure 4 and Figure 5 As shown, a RIS-assisted non-cellular network may include B access points, U legal user devices, E illegal user devices, a RIS, and one or more CPUs. For detailed descriptions of access points, legal user devices, and illegal user devices, please refer to the introduction of the relevant parts in the previous text, which 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 legal user devices can be represented as B = {1, 2,…, B}, and each legal user device can be equipped with a single antenna. The set of illegal users can be represented as E = {1, 2,…, E}, and each illegal user device can be equipped with a single antenna. RIS can be used to assist transmission from B access points to U legal user devices. RIS can include M reconfigurable units. In the description below, these units may 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 RIS via optical fiber or wireless backhaul. The channel from access point a to legal user device b can be represented as The channel from access point a to illegal user device e is expressed as The channel from access point a to RIS is represented as The channel from RIS to the legitimate user device b is expressed as The channel from RIS to the illegal user device e is expressed as By using the existing channel estimation method, the channel state information of all channels can be perfectly obtained. Here, the process of mathematically representing the elements and parameters involved in the RIS-assisted cellular-free network can also be called the modeling process of the RIS-assisted cellular-free network.

[0040] The RIS here can be the SF-RIS mentioned above, or the MF-RIS mentioned above. Figure 4 , when RIS is SF-RIS, since SF-RIS can reflect the signal in the non-cellular network, SF-RIS can ensure the communication security in the half space, that is, the reflection space. Since the user devices and access points in the non-cellular network are spread all over the space, SF-RIS may find it difficult to cope with ubiquitous security threats. In addition, since there are two cascaded sub-channels from the access point to SF-RIS and from SF-RIS to the user device, double attenuation may occur, which may limit the channel gain brought by SF-RIS to the non-cellular network.

[0041] See also Figure 5, when RIS is MF-RIS, since MF-RIS can not only reflect signals in the non-cellular network, but also refract signals in the non-cellular network, MF-RIS can ensure the communication security of the whole space. The whole space can be defined as K = {r, t}. When MF-RIS is used for reflection, k = r. When MF-RIS is used for refraction, k = t. In addition, since MF-RIS can also amplify signals in the non-cellular network, MF-RIS can bring additional channel gain to the non-cellular network.

[0042] The above introduces the RIS-assisted non-cellular network. Figure 6 The wireless communication method provided by the embodiment of the present application is introduced. Figure 6 As shown, the wireless communication method provided in the embodiment of the present application may include the following steps S610 and S620.

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

[0044] The first optimization model can be used to indicate the safety energy efficiency of the non-cellular network. Therefore, "constructing the first optimization model" can also be understood as formulating the safety energy efficiency of the non-cellular network. The safety energy efficiency of the non-cellular network can be determined based on the safe achievable rate of the legal user equipment in the non-cellular network and the power consumption of the non-cellular network. The optimization variables of the first optimization model may include the configuration parameters of the RIS and the beamforming parameters of the access point.

[0045] The configuration parameters of the RIS may include the phase shift and amplitude coefficients of the units within the RIS. For example, when the RIS is a MF-RIS, the configuration parameters of the RIS may be expressed as Among them, Θ k represents the coefficient matrix of MF-RIS for reflection (k=r) or refraction (k=t), represents the phase shift of the mth unit in MF-RIS, Indicates the amplitude coefficient of the mth unit in MF-RIS. When the RIS is MF-RIS, the configuration parameters of the RIS can also be called MF-RIS coefficients.

[0046] The beamforming parameters of the access point may include the precoding vector w of the access point to the user equipment in the non-cellular network. b . For an access point equipped with N antennas, the beamforming parameters of the access point may include a precoding vector of each antenna for a user device in a non-cellular network. The user devices here may include legal user devices and illegal user devices. That is, the beamforming parameters of the access point may include a precoding vector of each access point a for each legal user device b in a non-cellular network and a precoding vector of each access point a for each illegal user device e in a non-cellular network.

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

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

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

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

[0051] After the first optimization model is constructed in step S610, the first optimization model can be solved with the maximum safety energy efficiency as the optimization goal to determine the values ​​of the configuration parameters and the values ​​of the beamforming parameters. The first optimization model here can also be called an objective function. When solving the first optimization model, constraints can be set for the first optimization model.

[0052] Optionally, in some embodiments, the constraint condition 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, that is, Among them, w ab represents the precoding vector of access point a to the legitimate user equipment b, 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 a MF-RIS, the constraint condition may be expressed as: in, G as represents the channel from access point a to MF-RIS, Θ k represents the coefficient matrix of MF-RIS for reflection (k = r) or refraction (k = t), w abrepresents the precoding vector of access point a to the legitimate user equipment b, represents the power of the thermal noise introduced at the MF-RIS.

[0054] Optionally, in some embodiments, the constraint condition of the first optimization model may include: the phase shift of the unit in the RIS is greater than or equal to zero and less than 2π, that is,

[0055] Optionally, in some embodiments, the constraint condition of the first optimization model may include: the amplitude coefficient of the unit in the RIS is less than or equal to the maximum amplitude coefficient, that is, where β max ≥1 indicates the maximum amplification factor. Here, the maximum amplification factor can be used to indicate the amplification ability of RIS to the signal. max The larger the value, the stronger the signal amplification ability of RIS. The signal amplification ability of RIS can be changed by changing the structure of RIS, thereby changing β max The value of .

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

[0057] Optionally, in some embodiments, the constraint condition of the first optimization model may include: the rate of the legal user equipment without a cellular network meets the preset minimum rate requirement. The rate of the legal user equipment meets the preset minimum rate requirement, which can be understood as the rate of the legal user equipment is greater than or equal to the preset minimum rate of the user equipment. Where R b represents the rate of the legal user equipment b, R min Indicates the preset minimum rate of the legal user equipment. The preset minimum rate of the legal user equipment can be understood as the minimum data transmission rate that the legal user equipment needs to ensure when using network services.

[0058] After determining the values ​​of the configuration parameters and the values ​​of the beamforming parameters, the RIS and the access point can be configured according to the values ​​of the configuration parameters and the values ​​of the beamforming parameters. "Configuring the RIS and the access point according to the values ​​of the configuration parameters and the values ​​of the beamforming parameters" may include: configuring the units in the RIS and the antennas of the access point according to the values ​​of the configuration parameters and the values ​​of the beamforming parameters. The configuration here can be implemented by a CPU in a non-cellular network, for example. As mentioned above, when the RIS is an MF-RIS, the configuration parameters may include the phase shift of each unit in the MF-RIS and the amplitude coefficient of each unit, and the beamforming parameters may include the precoding vector of each antenna of the access point to each user device (including legal user devices and illegal user devices) in the non-cellular network. In this case, the phase shift and amplitude coefficient of each unit in the MF-RIS can be adjusted to the phase shift and amplitude coefficient obtained by the solution, and the precoding vector of each antenna of each access point to each user device can be adjusted to be consistent with the precoding vector obtained by the solution, so as to maximize the security energy efficiency of the non-cellular network.

[0059] As mentioned in step S610, the safety energy efficiency of the non-cellular network can be determined based on the safe achievable rate of the legitimate user equipment in the non-cellular network and the power consumption of the non-cellular network. Therefore, the first optimization model can be constructed based on the safe achievable rate of the legitimate user equipment in the non-cellular network and the power consumption of the non-cellular network. In this case, see Figure 7 , step S610 is for a non-cellular network, and constructing a first optimization model may include the following steps S710 to S730.

[0060] In step S710, a safe achievable rate model of a legitimate user equipment in a non-cellular network is constructed according to the configuration parameters of the RIS and the beamforming parameters of the access point.

[0061] The safe achievable rate model here can be used to indicate the sum of the safe achievable rates of B legal user devices in a non-cellular network. The sum of the safe achievable rates of B legal user devices can be expressed as in, Indicates the safe achievable rate of the legitimate user equipment b.

[0062] In step S720, a power consumption model of a non-cellular network is constructed according to 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 the non-cellular network. The total power consumption of the non-cellular network can include the output power of A access points, the output power of RIS, the power consumption of A access points, the power consumption of B legitimate user devices, and the power consumption of RIS. When RIS is MF-RIS, the total power consumption of the non-cellular network can be expressed as Where δ1 and δ2 represent the inverse of the energy conversion coefficient at MF-RIS and each access point, respectively, P C =AP A +BP B +2MP S +MP Am , P A represents the power dissipated by each access point, P B represents the power dissipated by each legitimate user equipment, P S represents the power dissipation of each phase shifter, P Am Represents the power dissipated by each power amplifier.

[0064] In step S730, a first optimization model is constructed according to the safe achievable rate model and the power consumption model.

[0065] After the safe achievable rate model is constructed in step S710 and the power consumption model is constructed in step S720, a first optimization model can be constructed based on the safe achievable rate model and the power consumption model. For example, when the safe achievable rate model is expressed as The power consumption model is represented by P T When , the first optimization model can be expressed as

[0066] As mentioned above, in step S710, a safe achievable rate model of a legitimate user equipment in a non-cellular network can be constructed according to 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 according to the configuration parameters of the RIS; determining a second equivalent channel model according to the configuration parameters of the RIS; and determining a safe achievable rate model according to 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 an equivalent channel model from the access point to the legal user device. The first equivalent channel model may include a channel model from the access point to the legal user device and a channel model from the access point to the legal user device via the RIS. The channel model from the access point to the legal user device and the channel model from the access point to the legal user device via the RIS can be added to obtain the first equivalent channel model. The channel model from the access point to the legal user device via the RIS can be expressed as in, represents the channel from RIS to the legitimate user device b, Θ k represents the coefficient matrix used by RIS for reflection (k=r) or refraction (k=t). If access point a and legitimate user device b are located on the same side of RIS, then k=r. If access point a and legitimate user device b are located on opposite sides of RIS, then k=t. G asrepresents the channel from access point a to RIS. Then the first equivalent channel model can be expressed as in, represents the equivalent channel from access point a to legitimate user equipment b, Indicates the channel from access point a to legitimate user equipment b.

[0068] The second equivalent channel model here can be understood as an equivalent channel model from the access point to the illegal user device in the non-cellular network. The second equivalent channel model may include a channel model from the access point to the illegal user device and a channel model from the access point to the illegal user device via the RIS. The channel model from the access point to the illegal user device and the channel model from the access point to the illegal user device via the RIS can be added to obtain the second equivalent channel model. The channel model from the access point to the illegal user device via the RIS can be expressed as in, represents the channel from RIS to illegal user device e, Θ k G represents the coefficient matrix used by RIS for reflection (k = r) or refraction (k = t). If access point a and illegal user device e are located on the same side of RIS, then k = r. If access point a and illegal user device e are located on both sides of RIS, then k = t. as represents the channel from access point a to RIS. Then the second equivalent channel model can be expressed as in, represents the equivalent channel from access point a to illegal user equipment e, Represents the channel from access point a to illegal user equipment e.

[0069] After the first equivalent channel model and the second equivalent channel model are determined, a safe achievable rate model may be determined according to the beamforming parameters, the first equivalent channel model, and the second equivalent channel model.

[0070] In some implementations, determining the safe achievable rate model according to the beamforming parameters, and the first equivalent channel model and the second equivalent channel model may include: determining a first signal to interference plus noise ratio according to the beamforming parameters and the first equivalent channel model; determining a second signal to interference plus noise ratio according to the second equivalent channel model; and determining the safe achievable rate model according to the first signal to interference plus noise ratio and the second signal to interference plus noise ratio.

[0071] The first signal to interference and noise ratio can be understood as the signal to interference and noise ratio when the legitimate user equipment demodulates the signal transmitted by the access point to the legitimate user equipment. It can be defined as ∑ b∈B w ab s b is the outgoing signal of access point a, where w ab represents the precoding vector of access point a to the legitimate user equipment b, s bRepresents the transmission symbol of the legal user equipment b, satisfying Therefore, the signal received by the legitimate user equipment b can be expressed as:

[0072]

[0073] in, represents the thermal noise introduced at MF-RIS, and its power is represents the additive Gaussian white noise at the legitimate user equipment, and the power is

[0074] Therefore, the first signal to interference noise ratio can be expressed as Among them, γ b It represents the signal to interference and noise ratio when the legitimate user equipment b demodulates the signal transmitted by the access point to the legitimate user equipment b.

[0075] The second signal to interference and noise ratio here can be understood as the signal to interference and noise ratio when the illegal user equipment demodulates the signal transmitted by the access point to the legal user equipment. Similar to the process of determining the first signal to interference and noise ratio, the signal of the illegal user equipment e eavesdropping on the information of the legal user equipment b can be expressed as:

[0076]

[0077] in, Indicates that the power at the illegal user equipment is Additive white Gaussian noise.

[0078] Therefore, the second signal to interference noise ratio can be expressed as Among them, γ eb It represents the signal to interference and noise ratio when the illegal user equipment e demodulates the signal transmitted by the access point to the legal user equipment b.

[0079] After determining the first signal to interference plus noise ratio and the second signal to interference plus noise ratio, the safe achievable rate model can be determined according to the first signal to interference plus noise ratio and the second signal to interference plus noise ratio. b And the signal-to-interference-to-noise ratio γ when the illegal user device e demodulates the signal transmitted by the access point to the legitimate user device b eb Determine the safe achievable rate of legitimate user equipment b Then, according to the safe achievable rate of the legitimate user device b Determine the safe achievable rate model for B legitimate user devices in the cellular network. The safe achievable rate of legitimate user device b It can be expressed as:

[0080]

[0081] Among them, R b =log2(1+γ b ), R eb =log2(1+γ eb ), operator[x] + =max{x,0}. Due to the non-negative property of the optimal safety rate, this operator can be omitted later.

[0082] After the above operations, the first optimization model can be expressed as:

[0083]

[0084] The above describes in detail how to construct the first optimization model in conjunction with the accompanying drawings. The following describes how to solve the first optimization model.

[0085] According to the foregoing description, it can be seen that the first optimization model and the constraints of the first optimization model (hereinafter referred to as the first constraints) may be relatively complex. For example, the first optimization model may be in fractional form, the first constraints may include multiple constraints, and some constraints may include complex logarithmic forms. Therefore, in the process of solving the first optimization model, the optimization target of the first optimization model (hereinafter referred to as the first optimization target), the first optimization model and the first constraints can be converted into the second optimization target, the second optimization model and the second constraints according to the first processing method. The first processing method may include one or more of the following: introducing slack variables, introducing auxiliary variables, continuous convex approximation of non-convex constraints, ignoring rank-one constraints, and sequential rank-one constraint relaxation.

[0086] As mentioned above, the optimization variables of the first optimization model may include the configuration parameters of the RIS and the beamforming parameters of the access point. In some implementations, the configuration parameters of the RIS and the beamforming parameters of the access point may be solved in an iterative manner using a variable substitution method. When the variable substitution method is used to solve the first optimization model, the configuration parameters and the beamforming parameters may be used alternately as optimization variables during the iterative process. Figure 8 , the first optimization model can be solved by repeatedly executing the iterative process including the following steps A and B, so as to determine the values ​​of the configuration parameters and the values ​​of the beamforming parameters. The process including one step A and one step B can be called an iterative process. It should be understood that the first iterative process may first execute step A and then step B. In this case, each iterative process executes step A first and then step B. Alternatively, the first iterative process may first execute step B and then step A. In this case, each iterative process executes step B first and then step A.

[0087] Step A may include: given the values ​​of the configuration parameters, taking the maximization of safety and energy efficiency as the optimization goal, solving the first optimization model to determine the values ​​of the beamforming parameters.

[0088] That is to say, in step A, the values ​​of the configuration parameters can be fixed, and the first optimization model can be solved with the beamforming parameters as optimization variables. In the case where step A is step A in the first iteration and step A is executed before step B in the first iteration, the values ​​of the configuration parameters given here can be obtained by simulation. In other words, before executing step A in the first iteration, the first optimization model can be simulated to obtain a feasible solution for the configuration parameters as the iteration initial value of the configuration parameters. In other cases, the values ​​of the configuration parameters given here can be the solutions of the configuration parameters obtained in step B before step A. Other cases here may include any of the following cases: step A is step A in the first iteration and step B is executed before step A in the first iteration, and step A is step A in a non-first iteration.

[0089] Step B may include: given the values ​​of the beamforming parameters, taking the maximization of safety and energy efficiency as the optimization goal, solving the first optimization model to determine the values ​​of the configuration parameters.

[0090] That is to say, in step B, the values ​​of the beamforming parameters can be fixed, and the first optimization model can be solved with the configuration parameters as optimization variables. In the case where step B is step B in the first iteration and step B is executed first and then step A in the first iteration, the values ​​of the beamforming parameters given here can be obtained by simulation. In other cases, before executing step B in the first iteration, the first optimization model can be simulated to obtain a feasible solution for the beamforming parameters as the iteration initial value of the beamforming parameters. 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. Other cases here may include any of the following cases: step B is step B in the first iteration and step A is executed first and then step B in the first iteration, and step B is step B in a non-first iteration.

[0091] After each iteration process including step A and step B is executed, it can be determined whether the iteration stop condition is met. If the iteration stop condition is not met, the iteration process including step A and step B can be continued. If the iteration stop condition is met, the values ​​of the configuration parameters and the values ​​of the beamforming parameters in the current iteration process can be output as the optimal solution of the first optimization model.

[0092] The iteration stop condition here may include one or more of the following: iteration convergence condition, iteration timeout condition. For example, a threshold value (hereinafter referred to as the first threshold value) may be set. When the difference between the safety energy efficiency obtained by the current iteration process and the safety energy efficiency obtained by the previous iteration process divided by the safety energy efficiency obtained by the previous iteration process is less than or equal to the first threshold value, the iteration convergence condition may be considered to be met. For another example, an iteration number threshold value (hereinafter referred to as the second threshold value) may be set. When the number of times the iteration process including step A and step B is executed is greater than or equal to the second threshold value, the iteration timeout condition is considered to be met. In practical applications, the iteration convergence condition and the iteration timeout condition may be set at the same time. When either the iteration convergence condition or the iteration timeout condition is met, the iteration stop condition may be considered to be met.

[0093] The following takes the MF-RIS-assisted non-cellular network as an example. Fig. 9 This article introduces how to use the method provided in the embodiment of the present application to solve the MF-RIS coefficients and beamforming parameters. Fig. 9 As shown, the method for wireless communication provided in the embodiment of the present application may include the following steps S910 to S980.

[0094] In step S910, a MF-RIS-assisted non-cellular network is modeled.

[0095] In step S920, a safety energy efficiency maximization problem is formulated.

[0096] In step S930, variable substitution and continuous convex approximation methods are used to process the objective function and constraints of the fractional structure.

[0097] In step S940, the initial value of the MF-RIS coefficient is determined.

[0098] In step S950, the value of the MF-RIS coefficient is fixed, and the beamforming parameter is used as an optimization variable to obtain a solution of the beamforming parameter.

[0099] In step S960, the beamforming parameters are fixed to the solution of the beamforming parameters obtained in the previous step, and the MF-RIS coefficients are used as optimization variables to obtain the solution of the MF-RIS coefficients.

[0100] 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, the process proceeds to step S980.

[0101] In step S980, the values ​​of the beamforming parameters solved in step S950 during this iteration and the values ​​of the MF-RIS coefficients solved in step S960 during this iteration are output as the optimal solution.

[0102] In order to better understand the process of solving the first optimization model, the first optimization model is taken as The first optimization goal is to maximize The optimization variable is w b and θ k , the first constraint includes: and R b ≥R min Taking the example, the process of converting and solving the first optimization model by using the above-mentioned first processing method is described in detail.

[0103] In order to deal with the first optimization model in fractional form, we can first introduce relaxation variables ζ,ρ,r to transform the above first optimization model into:

[0104]

[0105] P T ≤ρ,

[0106]

[0107] in Due to constraints and P T ≤ρ takes effect at the optimal solution, and the transformed problem is equivalent to the original problem.

[0108] Furthermore, in order to simplify the constraints and The complex logarithmic form in the fractional structure and the expression of the signal-to-interference-noise ratio can be introduced into the auxiliary variable set in,

[0109] Thus, the first optimization model can be reconstructed as:

[0110]

[0111]

[0112] P T ≤ρ,

[0113]

[0114] Among them, Δ1={ζ,ρ,r,Δ}, due to the product term ζρ and the logarithmic term constraint and is non-convex. Here we can use the method of continuous convex approximation to approximate them. At the given point {ζ (l) ,ρ(l)}and At , the linearity of these terms can be approximated as:

[0115] (ζρ) LN =ζρ (l) +ζ (l) ρ―ζ (l) ρ (l) ,

[0116]

[0117] Finally, the original optimization problem can be rewritten as:

[0118]

[0119] P T ≤ρ,

[0120]

[0121] The beamforming parameters w can be iteratively optimized using the variable substitution method b and MF-RIS coefficient Θ k , to solve the above non-convex nonlinear optimization problem.

[0122] First, the MF-RIS coefficient Θ can be fixed k , solve the beamforming parameter w b Before solving, we can define the matrix and Satisfy W b ±0 and Rank(W b )=1. Therefore, the optimization problem of the first optimization model can be equivalently transformed into:

[0123]

[0124] P T (W b )≤ρ,

[0125]

[0126] in, Due to the non-convex constraints and rank-one constraint The above optimization problem is a non-convex optimization problem. Using the continuous convex approximation method, at the given point of the lth iteration Department, The lower bound of can be expressed as Thus, the constraint can be rewritten in the following convex form:

[0127]

[0128] Based on the semidefinite relaxation method, the rank-one constraint can be directly ignored, and the following relaxation problem is obtained:

[0129]

[0130] P T (W b )≤ρ,

[0131]

[0132] The above problem is a convex semidefinite programming problem, so it can be effectively solved using existing convex optimization tools, such as the convex optimization toolkit (convex, CVX) in Matlab software. The following proves that the solution obtained by solving this relaxed problem satisfies the rank-one constraint.

[0133] The above relaxed optimization problem is about the beamforming parameters The optimal solution can be characterized by the Carlo-Kuhn-Tucker condition. The Lagrangian function can be expressed as:

[0134]

[0135] Among them, λ ab ,x kab ,μ kab ,v ab , ω bj ,ω bej and represents the Lagrange multiplier. Y b represents the Lagrange multiplier matrix, and Γ represents all quantities that are independent of the beamforming parameters. Based on the Carlo-Kuhn-Tucker condition, the optimal solution needs to satisfy:

[0136]

[0137] in, represents the optimal Lagrange multiplier, and represents the gradient of the Lagrangian function with respect to the beamforming parameters. You can get:

[0138]

[0139] in,

[0140]

[0141] Since the matrix is semi-positive definite, we have In addition, Eq. Show that the inequality is satisfied because Failed to meet minimum rate requirement therefore holds, that is, it satisfies the rank-one constraint.

[0142] Next, we can give the beamforming parameters and solve the MF-RIS coefficients. For the convenience of calculation, we can define the matrix where the vector Meet V k ±0,Rank(V k )=1, and [V k ] M+1,M+1 =1. Thus, the following equation can be obtained:

[0143]

[0144] Among them, C c , D sc and E ab Respectively meet:

[0145]

[0146] Combining the above equations, the MF-RIS coefficient optimization problem can be expressed as:

[0147]

[0148] in, The difficulty in solving this problem lies in the rank-one constraint Since the solution of the MF-RIS coefficient obtained by solving the relaxation problem does not necessarily satisfy the rank-one constraint, the constraint cannot be ignored directly. Here, the sequential rank-one constraint relaxation method can be used to deal with the constraint. The rank-one constraint of the lth iteration can be rewritten as:

[0149]

[0150] Among them, ε max (V k ) indicates V k The maximum eigenvalue of represents a relaxation factor for the lth iteration. Here, means that the rank-one constraint is completely ignored, and This is equivalent to a rank-one constraint. Therefore, From 0 to 1, it approaches a rank-one solution. max (V k ) is not differentiable, the above constraints can be further transformed into the following linear form:

[0151]

[0152] in Indicates about Finally, the above optimization problem can be reconstructed as:

[0153]

[0154] This problem is a convex semidefinite relaxation problem, so it can be effectively solved using existing convex tools (such as CVX, etc.).

[0155] Combination of the above Figures 6 to 9 , describes the method embodiment of the present application in detail, and the following is combined with Fig.10 and Fig.11 , describes the device embodiment of the present application in detail. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment, so the parts not described in detail can refer to the previous method embodiment.

[0156] Fig.10 The structure diagram of the communication device 1000 provided in the embodiment of the present application is as follows. The communication device 1000 may be applied to a non-cellular network, and the non-cellular network may include an access point and a RIS. Fig.10 The communication device 1000 shown comprises:

[0157] A construction module 1010 is used to construct a first optimization model for the non-cellular network, where the first optimization model is used to indicate the safety energy efficiency of the non-cellular network, and the optimization variables of the first optimization model include the configuration parameters of the RIS and the beamforming parameters of the access point;

[0158] The configuration module 1020 is used to configure the RIS and the access point according to the solution of the first optimization model.

[0159] In some implementations, the configuration module 1020 is further used to: solve the first optimization model with the maximization of the safety energy efficiency as the optimization goal to determine the values ​​of the configuration parameters and the values ​​of the beamforming parameters; and configure the RIS and the access point according to the values ​​of the configuration parameters and the values ​​of the beamforming parameters.

[0160] In some implementations, the RIS is configured to perform one or more of the following operations: reflect the signal in the non-cellular network, refract the signal in the non-cellular network, and amplify the signal in the non-cellular network.

[0161] In some implementations, the construction module is further used to: construct a safe achievable 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 of 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 safe achievable rate model and the power consumption model.

[0162] In some implementations, the construction module is further used to: determine a first equivalent channel model according to the configuration parameters of the RIS, the first equivalent channel model being an equivalent channel model from the access point to the legal user equipment, the first equivalent channel model including a channel model from the access point to the legal user equipment, and a channel model from the RIS to the legal user equipment; determine a second equivalent channel model according to the configuration parameters of the RIS, the second equivalent channel model being an equivalent channel model from the access point to an illegal user equipment in the non-cellular network, the second equivalent channel model including a channel model from the access point to the illegal user equipment, and a channel model from the RIS to the illegal user equipment; determine the safe achievable rate model according to the beamforming parameters, as well as the first equivalent channel model and the second equivalent channel model.

[0163] In some implementations, the construction module is further used to: determine a first signal to interference and noise ratio according to the beamforming parameter and the first equivalent channel model, where the first signal to interference and noise ratio is a signal to interference and noise ratio when the legitimate user equipment demodulates a signal transmitted by the access point to the legitimate user equipment; determine a second signal to interference and noise ratio according to the second equivalent channel model, where the second signal to interference and noise ratio is a signal to interference and noise ratio when the illegal user equipment demodulates a signal transmitted by the access point to the legitimate user equipment; and determine the safe achievable rate model according to the first signal to interference and noise ratio and the second signal to interference and noise ratio.

[0164] In some implementations, the optimization target of the first optimization model is a first optimization target, the constraint condition of the first optimization model is a first constraint condition, and the configuration module is also used to: convert the first optimization target, the first optimization model and the first constraint condition into a second optimization target, a second optimization model and a second constraint condition according to a first processing method, wherein the first processing method includes one or more of the following: introducing slack variables, introducing auxiliary variables, continuous convex approximation of non-convex constraints, ignoring rank-one constraints, and sequential rank-one constraint relaxation.

[0165] In some implementations, the configuration module is also used to: execute step A, wherein step A includes: given the value of the configuration parameter, taking the maximization of the safety energy efficiency as the optimization goal, solving the first optimization model to determine the value of the beamforming parameter; executing step B, wherein step B includes: given the value of the beamforming parameter, taking the maximization of the safety energy efficiency as the optimization goal, solving the first optimization model to determine the value of the configuration parameter; and repeatedly executing the iterative process including steps A and B until the iterative process meets the iteration stop condition.

[0166] 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 unit within the RIS is greater than or equal to zero and less than 2π, the amplitude coefficient of the unit within the RIS is less than or equal to the maximum amplitude coefficient, the sum of the amplitude coefficients of the units within the RIS is less than or equal to the maximum amplitude coefficient, and the rate of the legal user equipment without a cellular network meets a preset minimum rate requirement.

[0167] In some implementations, the configuration parameters of the RIS include phase shift and amplitude coefficients of cells within the RIS.

[0168] In some implementations, the beamforming parameter is a precoding vector of the access point to the user equipment without a cellular network.

[0169] Fig.11 It is a schematic diagram of the structure of a communication device to which an embodiment of the present application can be applied. Fig.11 The dotted line in the figure indicates that the unit or module is optional. The device 1100 can be used to implement the method described in the above method embodiment. The device 1100 can be a chip.

[0170] The device 1100 may include one or more processors 1110. The processor 1110 may support the device 1100 to implement the method described in the foregoing method embodiment. 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 also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0171] The apparatus 1100 may further include one or more memories 1120. The memory 1120 stores a program, which can be executed by the processor 1110, so that the processor 1110 executes the method described in the above method embodiment. The memory 1120 may be independent of the processor 1110 or integrated in the processor 1110.

[0172] The apparatus 1100 may further include a transceiver 1130. The processor 1110 may communicate with other devices or chips through the transceiver 1130. For example, the processor 1110 may transmit and receive data with other devices or chips through the transceiver 1130.

[0173] The embodiment of the present application also provides a computer-readable storage medium for storing a program. The computer-readable storage medium can be applied to the device for wireless communication provided in the embodiment of the present application, and the program enables the computer to execute the method performed by the device for wireless communication in each embodiment of the present application.

[0174] The embodiment of the present application also provides a computer program product. The computer program product includes a program. The computer program product can be applied to the device for wireless communication provided in the embodiment of the present application, and the program enables the computer to execute the method performed by the device for wireless communication in each embodiment of the present application.

[0175] The embodiment of the present application also provides a computer program. The computer program can be applied to the device for wireless communication provided in the embodiment of the present application, and the computer program enables a computer to execute the method executed by the device for wireless communication in each embodiment of the present application.

[0176] It should be understood that the terms "system" and "network" in this application can be used interchangeably. In addition, the terms used in this application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application. The terms "first", "second", "third" and "fourth" in the specification and claims of this application and the accompanying drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions.

[0177] In the embodiments of the present application, the "indication" mentioned can be a direct indication, an indirect indication, or an indication of an association relationship. For example, A indicates B, which can mean that A directly indicates B, for example, B can be obtained through A; it can also mean that A indirectly indicates B, for example, A indicates C, and B can be obtained through C; it can also mean that there is an association relationship between A and B.

[0178] In the embodiment of the present application, "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0179] In the embodiments of the present application, the term "corresponding" may indicate that there is a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship of indication and being indicated, configuration and being configured, etc.

[0180] In the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0181] In various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0182] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0183] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0185] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part 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, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that can be read by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0186] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection 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 includes an access point and a reconfigurable intelligent super surface RIS, and the method includes: For the non-cellular network, construct a first optimization model, the first optimization model is used to indicate the safety energy efficiency of the non-cellular network, and the optimization variables of the first optimization model include the configuration parameters of the RIS and the beamforming parameters of the access point; The RIS and the access point are configured according to the solution of the first optimization model.

2. The method according to claim 1, characterized in that The configuring the RIS and the access point according to the solution of the first optimization model comprises: Taking the maximization of the safety energy efficiency as an optimization goal, solving the first optimization model to determine the values ​​of the configuration parameters and the values ​​of the beamforming parameters; The RIS and the access point are configured according to the values ​​of the configuration parameters and the values ​​of 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 the signal in the non-cellular network, refract the signal in the non-cellular network, and amplify the signal in the non-cellular network.

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

5. The method according to claim 4, characterized in that The step of constructing a safe achievable rate model for a legitimate user equipment in the non-cellular network according to the configuration parameters of the RIS and the beamforming parameters of the access point includes: Determine a first equivalent channel model according to the configuration parameters of the RIS, the first equivalent channel model being an equivalent channel model from the access point to the legal user equipment, the first equivalent channel model including a channel model from the access point to the legal user equipment and a channel model from the access point to the legal user equipment via the RIS; Determine a second equivalent channel model according to the configuration parameters of the RIS, the second equivalent channel model being an equivalent channel model from the access point to an illegal user device in the non-cellular network, the second equivalent channel model including a channel model from the access point to the illegal user device and a channel model from the access point to the illegal user device via the RIS; The safe achievable rate model is determined according to 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 determining the safe achievable rate model according to the beamforming parameter, the first equivalent channel model and the second equivalent channel model includes: Determine a first signal to interference plus noise ratio according to the beamforming parameter and the first equivalent channel model, where the first signal to interference plus noise ratio is a signal to interference plus noise ratio when the legitimate user equipment demodulates a signal transmitted by the access point to the legitimate user equipment; Determine a second signal to interference plus noise ratio according to the second equivalent channel model, where the second signal to interference plus noise ratio is a signal to interference plus noise ratio when the illegal user equipment demodulates a signal transmitted by the access point to the legal user equipment; The safe achievable rate model is determined according to the first signal to interference plus noise ratio and the second signal to interference plus noise ratio.

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

8. The method according to any one of claims 2 to 7, characterized in that The step of solving the first optimization model with maximizing the safety energy efficiency as the optimization goal to determine the values ​​of the configuration parameters and the values ​​of the beamforming parameters includes: Executing step A, the step A comprising: solving the first optimization model with the maximization of the safety energy efficiency as the optimization goal under the condition that the value of the configuration parameter is given, so as to determine the value of the beamforming parameter; Executing step B, the step B comprising: given the value of the beamforming parameter, taking the maximization of the safety energy efficiency as the optimization goal, solving the first optimization model to determine the value of the configuration parameter; The iterative process including step A and step B is repeatedly executed until the iterative process meets an iteration stop 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 transmission 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 unit within the RIS is greater than or equal to zero and less than 2π, the amplitude coefficient of the unit within the RIS is less than or equal to the maximum amplitude coefficient, the sum of the amplitude coefficients of the units within the RIS is less than or equal to the maximum amplitude coefficient, and the rate of the legal user equipment without a cellular network meets the preset minimum rate requirement.

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

11. The method according to any one of claims 1 to 10, characterized in that The beamforming parameter is a precoding vector of the access point to the user equipment without a cellular network.

12. A device for wireless communication, characterized in that: The device is applied to a non-cellular network, the non-cellular network includes an access point and a reconfigurable intelligent super surface RIS, and the device includes: A construction module, configured to construct a first optimization model for the non-cellular network, wherein the first optimization model is used to indicate the safety energy efficiency of the non-cellular network, and 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 according to the solution of the first optimization model.

13. The device according to claim 12, characterized in that The configuration module is also used to: Taking the maximization of the safety energy efficiency as an optimization goal, solving the first optimization model to determine the values ​​of the configuration parameters and the values ​​of the beamforming parameters; The RIS and the access point are configured according to the values ​​of the configuration parameters and the values ​​of 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 the signal in the non-cellular network, refract the signal in the non-cellular network, and amplify the signal in the non-cellular network.

15. The device according to claim 13 or 14, characterized in that The building blocks are also used to: Constructing a safe achievable rate model of a legitimate user equipment in the non-cellular network according to the configuration parameters of the RIS and the beamforming parameters of the access point; Constructing a power consumption model of the non-cellular network according to the configuration parameters of the RIS and the beamforming parameters of the access point; The first optimization model is constructed according to the safe achievable rate model and the power consumption model.

16. The device according to claim 15, characterized in that The building blocks are also used to: Determine a first equivalent channel model according to the configuration parameters of the RIS, the first equivalent channel model being an equivalent channel model from the access point to the legal user equipment, the first equivalent channel model including a channel model from the access point to the legal user equipment and a channel model from the RIS to the legal user equipment; Determine a second equivalent channel model according to the configuration parameters of the RIS, the second equivalent channel model being an equivalent channel model from the access point to an illegal user device in the non-cellular network, the second equivalent channel model including a channel model from the access point to the illegal user device and a channel model from the RIS to the illegal user device; The safe achievable rate model is determined according to 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 blocks are also used to: Determine a first signal to interference plus noise ratio according to the beamforming parameter and the first equivalent channel model, where the first signal to interference plus noise ratio is a signal to interference plus noise ratio when the legitimate user equipment demodulates a signal transmitted by the access point to the legitimate user equipment; Determine a second signal to interference plus noise ratio according to the second equivalent channel model, where the second signal to interference plus noise ratio is a signal to interference plus noise ratio when the illegal user equipment demodulates a signal transmitted by the access point to the legal user equipment; The safe achievable rate model is determined according to the first signal to interference plus noise ratio and the second signal to interference plus noise ratio.

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

19. The device according to any one of claims 13 to 18, characterized in that The configuration module is also used to: Executing step A, the step A comprising: solving the first optimization model with the maximization of the safety energy efficiency as the optimization goal under the condition that the value of the configuration parameter is given, so as to determine the value of the beamforming parameter; Executing step B, the step B comprising: given the value of the beamforming parameter, taking the maximization of the safety energy efficiency as the optimization goal, solving the first optimization model to determine the value of the configuration parameter; The iterative process including step A and step B is repeatedly executed until the iterative process meets an iteration stop 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 transmission 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 unit within the RIS is greater than or equal to zero and less than 2π, the amplitude coefficient of the unit within the RIS is less than or equal to the maximum amplitude coefficient, the sum of the amplitude coefficients of the units within the RIS is less than or equal to the maximum amplitude coefficient, and the rate of the legal user equipment without a cellular network meets the preset minimum rate requirement.

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

22. The device according to any one of claims 13 to 21, characterized in that The beamforming parameter is a precoding vector of the access point to the user equipment without a cellular network.

23. A device for wireless communication, characterized in that: The device comprises a memory and a processor, wherein the memory is used to store a program, and the processor is used to call the program in the memory so that the communication device executes the method according to any one of claims 1 to 11.

24. A device, characterized in that: The device comprises a processor, configured to call a program from a memory so as to enable the device to execute the method according to any one of claims 1 to 11.

25. A chip, characterized in that: The device comprises a processor, which is used to call a program from a memory so that a device equipped with the chip executes the method according to any one of claims 1 to 11.

26. A computer-readable storage medium, characterized in that: A program is stored thereon, the program causing a computer to execute the method according to any one of claims 1 to 11.

27. A computer program product, characterized in that The method comprises a program for causing a computer to execute the method according to any one of claims 1 to 11.

28. A computer program, characterized in that The computer program causes a computer to execute the method according to any one of claims 1 to 11.

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