Energy efficiency optimization method of hybrid RIS communication system adopting topological structure

Abstract

The invention relates to the technical field of communication, in particular to an energy efficiency optimization method of a hybrid RIS communication system adopting a topological structure. Constructing a hybrid RIS-assisted multi-linear isotropic strengthening model by adopting a topological structure; a transmission problem optimization model is constructed, and the transmission problem optimization model is used for jointly optimizing a hybrid RIS topological structure, transmission precoding at a base station and reflection precoding of the hybrid RIS under the condition that base station transmitting power, hybrid RIS power consumption and hybrid RIS element amplitude phase constraints are met, so that the energy efficiency is improved to the maximum extent; and converting a nonlinear programming problem in the transmission problem optimization model into a non-fractional optimization problem, and alternately optimizing the hybrid RIS topological structure, the base station transmission precoding and the hybrid RIS phase shift matrix by adopting an alternate optimization algorithm to obtain an optimized hybrid RIS topological structure, an optimized base station transmission precoding and an optimized hybrid RIS phase shift matrix. The objective of the invention is to solve the problem of how to perform energy efficiency optimization on a communication system of a hybrid RIS structure.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to an energy efficiency optimization method for a hybrid RIS communication system using a topology structure. Background Art

[0002] In the face of the growing demand for high-speed mobile communication traffic and high-quality mobile communication services, reconfigurable intelligent surface (RIS) is an effective solution. It uses a large number of low-cost reflective elements to dynamically adjust the reflection amplitude and phase of the incident electromagnetic wave to reshape the electromagnetic propagation environment, which can significantly improve the system's spectral efficiency and energy efficiency, and can further expand the coverage of the communication system.

[0003] However, traditional passive RIS cannot amplify the incident signal and usually requires a large number of components to compensate for the severe cascade path loss. In order to overcome the inherent limitations of passive RIS, an active RIS structure is proposed. Each active component of active RIS is connected to an additional amplification circuit, which can amplify and forward the incident signal to achieve higher spectral efficiency. However, active components will cause additional power consumption limitations, amplified noise / interference and hardware overhead.

[0004] In view of this, a hybrid RIS structure is currently proposed. The hybrid RIS includes both passive components and active components. On the one hand, active reflection helps power amplification and effectively reduces multiplicative fading. On the other hand, it can reduce costs and energy consumption. This application aims to provide a method for optimizing the energy efficiency of a communication system for the hybrid RIS structure.

[0005] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of the present application is to provide a method for optimizing the energy efficiency of a hybrid RIS communication system using a topological structure, aiming to solve the problem of how to optimize the energy efficiency of a communication system with a hybrid RIS structure.

[0007] To achieve the above object, the present application provides a method for optimizing energy efficiency of a hybrid RIS communication system using a topological structure, the method comprising:

[0008] S1, a hybrid RIS-assisted multilinear isotropic strengthening model is constructed using topological structure;

[0009] S2, constructing a transmission problem optimization model, wherein the transmission problem optimization model is used to jointly optimize the hybrid RIS topology, the transmission precoding at the base station, and the reflection precoding of the hybrid RIS under the condition of satisfying the base station transmission power, the hybrid RIS power consumption, and the amplitude and phase constraints of the hybrid RIS components, so as to maximize the energy efficiency;

[0010] S3, converting the nonlinear programming problem in the transmission problem optimization model into a non-fractional optimization problem, using an alternating optimization algorithm, alternately optimizing the hybrid RIS topology structure, the base station transmit precoding and the hybrid RIS phase shift matrix, and obtaining an optimized hybrid RIS topology structure, an optimized base station transmit precoding and an optimized hybrid RIS phase shift matrix, wherein the optimized hybrid RIS topology structure is optimized by the number of active elements and the relationship between the amplitude coefficients, and the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix are optimized by Lagrangian dual transformation and quadratic transformation.

[0011] Optionally, in S1, the multilinear isotropic enhancement model includes an active reflection matrix, a passive reflection matrix and a user received signal expression, wherein:

[0012] The expression of the active reflection matrix Ψ is:

[0013]

[0014] In the formula, θ n ∈(0,2π) and p n They represent the phase shift and amplification factor of the nth component respectively. For passive components, |p n |=1, for active components, 1<|p n |≤ρ max , ρ max >1 is the maximum power amplification gain provided by the active load, N S is the number of reflective elements;

[0015] In the formula, is the mode scheduling variable of hybrid RIS, the location of active components is China-Israel Pre-defined, active and passive component locations are defined as follows:

[0016]

[0017] The expression of the passive reflection matrix Θ is:

[0018]

[0019] In the formula, For a size of N S ×N SThe identity matrix of A set of indices representing active components;

[0020] The user receives the signal y k The expression is:

[0021]

[0022] In the formula, represents the channel vector between the base station and RIS, represents the channel vector between the base station and user k, represents the channel vector between RIS and user k, M represents the number of antennas of the base station, represents the thermal noise and self-interference of active components, represents the additive white Gaussian noise at user k, w j represents the beamforming vector sent by the base station to user j, s j Represents the data symbol sent by the base station to user j.

[0023] Optionally, in S2, the transmission optimization model includes:

[0024]

[0025] in:

[0026]

[0027] In the formula, R sum is the achievable rate of all users, η is the system energy efficiency, P tot represents the total system power consumption, γ k represents the signal-to-noise ratio of user k, and a represents the efficiency of the power amplifier;

[0028] C1 represents the base station transmission power constraint, w k represents the beamforming vector sent by the base station to user k, is the maximum power value of the base station, is the maximum power value of the hybrid RIS;

[0029] C2 represents the hybrid RIS power constraint, represents the total power of the active RIS matrix, σ v represents the noise power of active RIS;

[0030] C3 and C4 both represent topological constraints. T z represents the sum of the elements of vector z;

[0031] C5, C6 and C7 represent the amplitude and phase shift constraints of the hybrid RIS components, θ mrepresents the phase shift of the mth element of the hybrid RIS, ρ max Indicates the maximum power amplification gain value provided for active components.

[0032] Optionally, the S3 specifically includes:

[0033] S3.1, the original objective function P0 in the transmission optimization model is reformulated in fractional form as P1:

[0034]

[0035] stC1-C7

[0036] In the formula, η' represents the optimization target value after fractional programming,

[0037] S3.2, the constraint condition C2 in the transmission optimization model is further written as:

[0038]

[0039] Assume that for any active component, when |p n When |=1, it can be written as:

[0040]

[0041] For near-field channels Will In ascending order:

[0042]

[0043] Assume that:

[0044]

[0045] Combining the above two equations, we get the upper bound of N [N act ] ub :

[0046] [N act ] ub =min{L,N S}

[0047] S3.3, for N active elements s Component hybrid RIS, given an upper bound of N, that is, N ≤ [N act ] ub , substitute the values ​​of different numbers N into the hybrid RIS power constraint formula again to calculate the corresponding p n The value of is obtained, that is, the relationship between the number of active components and the amplitude coefficient in the hybrid RIS;

[0048] S3.4, based on the relationship between the number of active elements and amplitude coefficients in the hybrid RIS, a taboo search is used to perform a topology search to search for an optimal hybrid RIS topology structure as the optimized hybrid RIS topology structure;

[0049] S3.5, adopting a joint alternating optimization algorithm, alternately optimizes the base station transmit precoding and the hybrid RIS phase shift matrix to obtain an optimized base station transmit precoding and an optimized hybrid RIS phase shift matrix.

[0050] Optionally, the S3.5 specifically includes:

[0051] S3.5.1, using Lagrange dual transformation and quadratic transformation, introducing two auxiliary variables μ and ν, the non-convex optimization problem P1 is transformed into a convex problem P2:

[0052]

[0053] stC1,C2,Z=Z0,C5-C7

[0054]

[0055] In the formula, μ k and v k represents the kth value of the auxiliary variables μ and ν, Indicates v k Perform the conjugate operation:

[0056] S3.5.2: Fix (ν,w,Φ) and optimize the auxiliary variable μ to obtain the local optimal solution μ opt ;

[0057] S3.5.3: Fix (μ, w, ​​Φ) and optimize the auxiliary variable ν to obtain the local optimal solution ν opt ;

[0058] S3.5.4: Fix (Φ, μ, ν) and optimize w to obtain the local optimal solution w opt ;

[0059] S3.5.5: Fix (w, μ, ν) and optimize Φ to obtain the local optimal solution Φ opt ;

[0060] S3.5.6: until η′ converges and the iteration ends, the alternating optimized base station transmit precoding and hybrid RIS phase shift matrix obtained when the iteration is completed is used as the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix.

[0061] This application has at least the following beneficial effects:

[0062] By alternately optimizing the hybrid RIS topology, base station transmit precoding and hybrid RIS phase shift matrix, the energy efficiency of the system is improved, and the system performance is significantly better than the traditional passive RIS system. Appropriate active components are deployed to reduce thermal noise interference and system power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a transmission model diagram of a multi-user MISO system using hybrid RIS assistance involved in an embodiment of the present application;

[0064] Figure 2 A flowchart of a method for optimizing energy efficiency of a hybrid RIS communication system using a topology structure according to an embodiment of the present application;

[0065] Figure 3 A relationship diagram between user distance and user energy efficiency involved in an embodiment of the present application;

[0066] Figure 4 A relationship diagram between the number of iterations and user energy efficiency involved in an embodiment of the present application;

[0067] Figure 5 Schematic diagram of the architecture of the hybrid RIS communication system involved in the embodiment of the present application.

[0068] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0069] In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0070] First embodiment

[0071] Reference Figure 1 In this embodiment, the hybrid RIS communication system is specifically described as follows: a base station (BS) equipped with an active antenna communicates with a single-antenna user through a hybrid RIS, wherein the hybrid intelligent RIS includes both passive components and active components, each passive component is connected to only one phase shift circuit, and each active component is connected to a phase shift circuit and an amplification circuit at the same time, so the active component has the function of amplifying and forwarding the incident signal.

[0072] Reference Figure 2 The steps of the energy efficiency optimization method of the hybrid RIS communication system using the topology structure include:

[0073] S1, a hybrid RIS-assisted multilinear isotropic strengthening model is constructed using topological structure;

[0074] In this embodiment, the multilinear isotropic enhancement model includes an active reflection matrix, a passive reflection matrix and a user received signal expression, wherein:

[0075] The expression of the active reflection matrix Ψ is:

[0076]

[0077] In the formula, θ n ∈(0,2π) and p n They represent the phase shift and amplification factor of the nth component respectively. For passive components, |p n |=1, for active components, 1<|p n |≤ρ max , ρ max >1 is the maximum power amplification gain provided by the active load, N S is the number of reflective elements;

[0078] In the formula, is the mode scheduling variable of hybrid RIS, the location of active components is China-Israel Pre-defined, active and passive component locations are defined as follows:

[0079]

[0080] The expression of the passive reflection matrix Θ is:

[0081]

[0082] In the formula, For a size of N S ×N S The identity matrix

[0083] The user receives the signal y k The expression is:

[0084]

[0085] In the formula, represents the channel vector between the base station and RIS, represents the channel vector between the base station and user k, represents the channel vector between RIS and user k, M represents the number of antennas of the base station, represents the thermal noise and self-interference of active components, represents the additive white Gaussian noise at user k, wj represents the beamforming vector sent by the base station to user j, s j Represents the data symbol sent by the base station to user j.

[0086] S2, constructing a transmission problem optimization model, wherein the transmission problem optimization model is used to jointly optimize the hybrid RIS topology, the transmission precoding at the base station, and the reflection precoding of the hybrid RIS under the condition of satisfying the base station transmission power, the hybrid RIS power consumption, and the amplitude and phase constraints of the hybrid RIS components, so as to maximize the energy efficiency;

[0087] In this embodiment, under the conditions of satisfying the base station transmit power, hybrid RIS power consumption and hybrid RIS component amplitude and phase constraints, the hybrid RIS topology, the base station transmit precoding and the hybrid RIS reflection precoding are jointly optimized, and the energy efficiency maximization problem is modeled as follows:

[0088]

[0089] in:

[0090]

[0091] In the formula, R sum is the achievable rate of all users, η is the system energy efficiency, P tot represents the total system power consumption, γ k represents the signal-to-noise ratio of user k, and a represents the efficiency of the power amplifier;

[0092] C1 represents the base station transmission power constraint, w k represents the beamforming vector sent by the base station to user k, is the maximum power value of the base station, is the maximum power value of the hybrid RIS;

[0093] C2 represents the hybrid RIS power constraint, represents the total power of the active RIS matrix, σ v represents the noise power of active RIS;

[0094] C3 and C4 both represent topological constraints. T z represents the sum of the elements of vector z;

[0095] C5, C6 and C7 represent the amplitude and phase shift constraints of the hybrid RIS components, θ m represents the phase shift of the mth element of the hybrid RIS, ρ max Indicates the maximum power amplification gain value provided for active components.

[0096] S3, converting the nonlinear programming problem in the transmission problem optimization model into a non-fractional optimization problem, using an alternating optimization algorithm, alternately optimizing the hybrid RIS topology structure, the base station transmit precoding and the hybrid RIS phase shift matrix, and obtaining an optimized hybrid RIS topology structure, an optimized base station transmit precoding and an optimized hybrid RIS phase shift matrix, wherein the optimized hybrid RIS topology structure is optimized by the number of active elements and the relationship between the amplitude coefficients, and the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix are optimized by Lagrangian dual transformation and quadratic transformation.

[0097] In this embodiment, S3 specifically includes:

[0098] S3.1, the original objective function P0 in the transmission optimization model is reformulated in fractional form as P1:

[0099]

[0100] sC 1- C7

[0101] In the formula, η' represents the optimization target value after fractional programming,

[0102] Alternatively, the Dinkelbach method can be used for reformulation.

[0103] S3.2, the constraint condition C2 in the transmission optimization model is further written as:

[0104]

[0105] Assume that for any active component, when |p n When |=1, it can be written as:

[0106]

[0107] For near-field channels Will In ascending order:

[0108]

[0109] Assume that:

[0110]

[0111] Combining the above two equations, we get the upper bound of N [N act ] ub :

[0112] [N act ] ub =min{L,N S}

[0113] S3.3, for N active elements s Component hybrid RIS, given an upper bound of N, that is, N ≤ [N act ] ub , and then the values ​​of different numbers N are again substituted into the hybrid RIS power constraint formula to calculate the corresponding p n The value of is obtained, that is, the relationship between the number of active components and the amplitude coefficient in the hybrid RIS;

[0114] S3.4, based on the relationship between the number of active elements and amplitude coefficients in the hybrid RIS, a taboo search is used to perform a topology search to search for an optimal hybrid RIS topology structure as the optimized hybrid RIS topology structure;

[0115] S3.5, adopting a joint alternating optimization algorithm, alternately optimizes the base station transmit precoding and the hybrid RIS phase shift matrix to obtain an optimized base station transmit precoding and an optimized hybrid RIS phase shift matrix.

[0116] Further and optionally, in this embodiment, step S3.5 specifically includes:

[0117] S3.5.1, using Lagrange dual transformation and quadratic transformation, introducing two auxiliary variables μ and ν, the non-convex optimization problem P1 is transformed into a convex problem P2:

[0118]

[0119] stC1,C2,Z=Z0,C5-C7

[0120]

[0121] In the formula, μ k and v k represents the kth value of the auxiliary variables μ and ν, Indicates v k Perform the conjugate operation:

[0122] S3.5.2: Fix (ν,w,Φ) and optimize the auxiliary variable μ to obtain the local optimal solution μ opt ;

[0123] S3.5.3: Fix (μ, w, ​​Φ) and optimize the auxiliary variable ν to obtain the local optimal solution ν opt ;

[0124] S3.5.4: Fix (Φ, μ, ν) and optimize w to obtain the local optimal solution w opt ;

[0125] S3.5.5: Fix (w, μ, ν) and optimize Φ to obtain the local optimal solution Φopt ;

[0126] S3.5.6: until η′ converges and the iteration ends, the alternating optimized base station transmit precoding and hybrid RIS phase shift matrix obtained when the iteration is completed is used as the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix.

[0127] Second embodiment

[0128] In this embodiment, MATLAB is used to simulate the hybrid RIS communication system optimized by the method proposed in the first embodiment. In this embodiment, the base station (BS) and the hybrid RIS are located at (0m, -60m, 0m) and (300m, 10m, 0m) respectively, and 4 users are randomly distributed in a large circle with a radius of 5m centered at the point (300m, 0m, 0m). The number of BS antennas, the total number of hybrid RIS components, and the number of active components are set to M = 4, N = 1, and N = 2, respectively. S =512 and N=80. The user noise power and the mixed RIS noise are set to σ 2 =-100dBm and Limiting the total power consumption to 40dBm, the fully active RIS case is: and The mixed RIS situation is: and Other baseline system settings

[0129] Reference Figure 3 , Figure 3 The relationship between energy efficiency and user-base station distance is shown. The energy efficiency of the proposed topology-optimized hybrid RIS is higher than that of the other four schemes, indicating that the proposed scheme has a better performance.

[0130] Reference Figure 4 , Figure 4 The convergence of the algorithm is verified, and it can be noticed that the proposed algorithm converges quickly within about 5 times.

[0131] In addition, as an implementation solution, Figure 5 This is a schematic diagram of the architecture of the hardware operating environment of the hybrid RIS communication system involved in the embodiment of the present application.

[0132] like Figure 1As shown, the hybrid RIS communication system may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the user interface 1003 may optionally include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may optionally be a storage device independent of the aforementioned processor 1001.

[0133] Those skilled in the art will understand that Figure 1 The hybrid RIS communication system architecture shown in the figure does not constitute a limitation on the hybrid RIS communication system, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0134] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a program for optimizing the energy efficiency of a hybrid RIS communication system using a topology structure. The operating system is a program for managing and controlling the hardware and software resources of the hybrid RIS communication system, and the program for optimizing the energy efficiency of a hybrid RIS communication system using a topology structure and other software or programs are operated.

[0135] exist Figure 1 In the hybrid RIS communication system shown, the user interface 1003 is mainly used to connect to the terminal and communicate data with the terminal; the network interface 1004 is mainly used for the background server and communicates data with the background server; the processor 1001 can be used to call the topology structure stored in the memory 1005 to optimize the energy efficiency of the hybrid RIS communication system.

[0136] In this embodiment, the hybrid RIS communication system includes: a memory 1005, a processor 1001, and a topology structure for optimizing the energy efficiency of the hybrid RIS communication system stored in the memory and executable on the processor, wherein:

[0137] When the processor 1001 calls the topology structure stored in the memory 1005 to optimize the energy efficiency of the hybrid RIS communication system, the following operations are performed:

[0138] S1, a hybrid RIS-assisted multilinear isotropic strengthening model is constructed using topological structure;

[0139] S2, constructing a transmission problem optimization model, wherein the transmission problem optimization model is used to jointly optimize the hybrid RIS topology, the transmission precoding at the base station, and the reflection precoding of the hybrid RIS under the condition of satisfying the base station transmission power, the hybrid RIS power consumption, and the amplitude and phase constraints of the hybrid RIS components, so as to maximize the energy efficiency;

[0140] S3, converting the nonlinear programming problem in the transmission problem optimization model into a non-fractional optimization problem, using an alternating optimization algorithm, alternately optimizing the hybrid RIS topology structure, the base station transmit precoding and the hybrid RIS phase shift matrix, and obtaining an optimized hybrid RIS topology structure, an optimized base station transmit precoding and an optimized hybrid RIS phase shift matrix, wherein the optimized hybrid RIS topology structure is optimized by the number of active elements and the relationship between the amplitude coefficients, and the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix are optimized by Lagrangian dual transformation and quadratic transformation.

[0141] When the processor 1001 calls the topology structure stored in the memory 1005 to optimize the energy efficiency of the hybrid RIS communication system, the following operations are performed:

[0142] S3.1, the original objective function P0 in the transmission optimization model is reformulated in fractional form as P1:

[0143]

[0144] sC 1- C7

[0145] Where η' represents the optimization target value after fractional programming.

[0146] S3.2, the constraint condition C2 in the transmission optimization model is further written as:

[0147]

[0148] Assume that for any active component, when |p n When |=1, it can be written as:

[0149]

[0150] For near-field channels Will In ascending order:

[0151]

[0152] Assume that:

[0153]

[0154] Combining the above two equations, we get the upper bound of N [N act ] ub :

[0155] [N act ] ub =min{L,N S}

[0156] S3.3, for N active elements s Component hybrid RIS, given an upper bound of N, that is, N ≤ [N act ] ub , and then the values ​​of different numbers N are again substituted into the hybrid RIS power constraint formula to calculate the corresponding p n The value of is obtained, that is, the relationship between the number of active components and the amplitude coefficient in the hybrid RIS;

[0157] S3.4, based on the relationship between the number of active elements and amplitude coefficients in the hybrid RIS, a taboo search is used to perform a topology search to search for an optimal hybrid RIS topology structure as the optimized hybrid RIS topology structure;

[0158] S3.5, adopting a joint alternating optimization algorithm, alternately optimizes the base station transmit precoding and the hybrid RIS phase shift matrix to obtain an optimized base station transmit precoding and an optimized hybrid RIS phase shift matrix.

[0159] When the processor 1001 calls the topology structure stored in the memory 1005 to optimize the energy efficiency of the hybrid RIS communication system, the following operations are performed:

[0160] S3.5.1, using Lagrange dual transformation and quadratic transformation, introducing two auxiliary variables μ and ν, the non-convex optimization problem P1 is transformed into a convex problem P2:

[0161]

[0162] stC1,C2,Z=Z0,C5-C7

[0163]

[0164] In the formula, μ k and v k represents the kth value of the auxiliary variables μ and ν, Indicates v k Perform the conjugate operation:

[0165] S3.5.2: Fix (ν,w,Φ) and optimize the auxiliary variable μ to obtain the local optimal solution μ opt ;

[0166] S3.5.3: Fix (μ, w, ​​Φ) and optimize the auxiliary variable ν to obtain the local optimal solution ν opt ;

[0167] S3.5.4: Fix (Φ, μ, ν) and optimize w to obtain the local optimal solution w opt ;

[0168] S3.5.5: Fix (w, μ, ν) and optimize Φ to obtain the local optimal solution Φ opt ;

[0169] S3.5.6: until η′ converges and the iteration ends, the alternating optimized base station transmit precoding and hybrid RIS phase shift matrix obtained when the iteration is completed is used as the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix.

[0170] In addition, it can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the hybrid RIS communication system to implement the process steps of the embodiment of the above method.

[0171] Therefore, the present application also provides a computer-readable storage medium, which stores the energy efficiency optimization of a hybrid RIS communication system using a topology structure. When the energy efficiency optimization of a hybrid RIS communication system using a topology structure is executed by a processor, the various steps of the method for optimizing the energy efficiency of a hybrid RIS communication system using a topology structure as described in the above embodiment are implemented.

[0172] The computer-readable storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which may store program codes.

[0173] It should be noted that since the storage medium provided in the embodiment of the present application is the storage medium used to implement the method of the embodiment of the present application, based on the method introduced in the embodiment of the present application, the person skilled in the art can understand the specific structure and deformation of the storage medium, so it is not repeated here. All storage media used in the method of the embodiment of the present application belong to the scope of protection of this application.

[0174] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0175] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0176] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0177] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0178] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present application may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0179] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0180] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for optimizing energy efficiency of a hybrid RIS communication system using a topological structure, characterized in that: The method comprises the following steps: S1, a hybrid RIS-assisted multilinear isotropic strengthening model is constructed using topological structure; S2, constructing a transmission problem optimization model, wherein the transmission problem optimization model is used to jointly optimize the hybrid RIS topology, the transmission precoding at the base station, and the reflection precoding of the hybrid RIS under the condition of satisfying the base station transmission power, the hybrid RIS power consumption, and the amplitude and phase constraints of the hybrid RIS components, so as to maximize the energy efficiency; S3, converting the nonlinear programming problem in the transmission problem optimization model into a non-fractional optimization problem, using an alternating optimization algorithm, alternately optimizing the hybrid RIS topology structure, the base station transmit precoding and the hybrid RIS phase shift matrix, and obtaining an optimized hybrid RIS topology structure, an optimized base station transmit precoding and an optimized hybrid RIS phase shift matrix, wherein the optimized hybrid RIS topology structure is optimized by the number of active elements and the relationship between the amplitude coefficients, and the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix are optimized by Lagrangian dual transformation and quadratic transformation.

2. The method according to claim 1, characterized in that In S1, the multilinear isotropic enhancement model includes an active reflection matrix, a passive reflection matrix and a user received signal expression, wherein: The expression of the active reflection matrix Ψ is: In the formula, θ n ∈(0,2π) and p n They represent the phase shift and amplification factor of the nth component respectively. For passive components, |p n |=1, for active components, 1<|p n |≤ρ max , ρ max >1 is the maximum power amplification gain provided by the active load, N S is the number of reflective elements; In the formula, is the mode scheduling variable of hybrid RIS, the location of active components is China-Israel Pre-defined, active and passive component locations are defined as follows: The expression of the passive reflection matrix Θ is: In the formula, For a size of N S ×N S The identity matrix of A set of indices representing active components; The user receives the signal y k The expression is: In the formula, represents the channel vector between the base station and RIS, represents the channel vector between the base station and user k, represents the channel vector between RIS and user k, M represents the number of antennas of the base station, represents the thermal noise and self-interference of active components, represents the additive white Gaussian noise at user k, w j represents the beamforming vector sent by the base station to user j, s j Represents the data symbol sent by the base station to user j.

3. The method according to claim 2, characterized in that In S2, the transmission optimization model includes: C4:1 T with=N in: In the formula, R sum is the achievable rate of all users, η is the system energy efficiency, P tot represents the total system power consumption, γ k represents the signal-to-noise ratio of user k, and a represents the efficiency of the power amplifier; C1 represents the base station transmission power constraint, w k represents the beamforming vector sent by the base station to user k, is the maximum power value of the base station, is the maximum power value of the hybrid RIS; C2 represents the hybrid RIS power constraint, represents the total power of the active RIS matrix, σ v represents the noise power of active RIS; C3 and C4 both represent topological constraints. T z represents the sum of the elements of vector z; C5, C6 and C7 represent the amplitude and phase shift constraints of the hybrid RIS components, θ m represents the phase shift of the mth element of the hybrid RIS, ρ max Indicates the maximum power amplification gain value provided for active components.

4. The method according to claim 3, characterized in that The S3 specifically includes: S3.1, the original objective function P0 in the transmission optimization model is reformulated in fractional form as P1: stC1-C7 In the formula, η' represents the optimization target value after fractional programming, S3.2, the constraint condition C2 in the transmission optimization model is further written as: Assume that for any active component, when |p n When |=1, it can be written as: For near-field channels Will In ascending order: Assume that: Combining the above two equations, we get the upper bound of N [N act ] ub : [N act ] ub =min{L,N S } S3.3, for N active elements s Component hybrid RIS, given an upper bound of N, that is, N ≤ [N act ] ub , substitute the values ​​of different numbers N into the hybrid RIS power constraint formula again to calculate the corresponding p n The value of is obtained, that is, the relationship between the number of active components and the amplitude coefficient in the hybrid RIS; S3.4, based on the relationship between the number of active elements and amplitude coefficients in the hybrid RIS, a taboo search is used to perform a topology search to search for an optimal hybrid RIS topology structure as the optimized hybrid RIS topology structure; S3.5, adopting a joint alternating optimization algorithm, alternately optimizes the base station transmit precoding and the hybrid RIS phase shift matrix to obtain an optimized base station transmit precoding and an optimized hybrid RIS phase shift matrix.

5. The method according to claim 4, characterized in that The S3.5 specifically includes: S3.5.1, using Lagrange dual transformation and quadratic transformation, introducing two auxiliary variables μ and ν, the non-convex optimization problem P1 is transformed into a convex problem P2: stC1,C2,Z=Z0,C5-C7 In the formula, μ k and v k represents the kth value of the auxiliary variables μ and ν, Indicates v k Perform the conjugate operation: S3.5.2: Fix (ν,w,Φ) and optimize the auxiliary variable μ to obtain the local optimal solution μ opt ; S3.5.3: Fix (μ, w, ​​Φ) and optimize the auxiliary variable ν to obtain the local optimal solution ν opt ; S3.5.4: Fix (Φ, μ, ν) and optimize w to obtain the local optimal solution w opt ; S3.5.5: Fix (w, μ, ν) and optimize Φ to obtain the local optimal solution Φ opt ; S3.5.6: until η′ converges and the iteration ends, the alternating optimized base station transmit precoding and hybrid RIS phase shift matrix obtained when the iteration is completed is used as the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix.

Images