Energy efficiency optimization method for hybrid ris communication system with topology structure

Abstract

The application 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. A multi-linear isotropic reinforcement model assisted by a hybrid RIS is constructed by adopting a topological structure; a transmission problem optimization model is constructed, the transmission problem optimization model is used for jointly optimizing a hybrid RIS topological structure, a base station transmission precoding and a hybrid RIS reflection precoding under the condition of satisfying a base station transmission power, a hybrid RIS power consumption and a hybrid RIS element amplitude and phase constraint, so as to maximize energy efficiency; a nonlinear programming problem in the transmission problem optimization model is converted into a non-fractional optimization problem, an alternating optimization algorithm is adopted, the hybrid RIS topological structure, the base station transmission precoding and the hybrid RIS phase shift matrix are alternately optimized, and an optimized hybrid RIS topological structure, an optimized base station transmission precoding and an optimized hybrid RIS phase shift matrix are obtained. The application aims to solve the problem of how to perform energy efficiency optimization on a hybrid RIS structure communication system.

Description

Technical Field

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

[0002] To address the ever-increasing demand for high-speed mobile communication traffic and high-quality mobile communication services, reconfigurable intelligent surfaces (RIS) offer an effective solution. Utilizing numerous low-cost reflective elements, RIS dynamically adjusts the amplitude and phase of incident electromagnetic waves to reshape the electromagnetic propagation environment, significantly improving the system's spectral and energy efficiency, and further expanding the coverage of communication systems.

[0003] However, traditional passive RIS cannot amplify the incident signal and usually requires a large number of components to compensate for severe cascade path loss. In order to overcome the inherent limitations of passive RIS, active RIS structures have been proposed. Each active component of an active RIS is connected to an additional amplifier 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 proposed. The hybrid RIS contains both passive and active components. On the one hand, active reflection helps to amplify power and effectively reduce multiplicative fading. On the other hand, it can reduce cost and energy consumption. This application aims to provide an optimization method for the energy efficiency of communication systems based on the hybrid RIS structure.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main objective of this application is to provide an energy efficiency optimization method for a hybrid RIS communication system with a topology structure, aiming to solve the problem of how to optimize the energy efficiency of a hybrid RIS communication system.

[0007] To achieve the above objectives, this application provides an energy efficiency optimization method for a hybrid RIS communication system employing a topology structure, the method comprising:

[0008] S1 employs a topological structure to construct a hybrid RIS-assisted multilinear isotropic reinforcement model;

[0009] S2, Construct a transmission problem optimization model. 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 conditions of satisfying the base station transmit power, hybrid RIS power consumption, and hybrid RIS element amplitude and phase constraints, so as to maximize energy efficiency.

[0010] S3, the nonlinear programming problem in the transmission problem optimization model is transformed into a non-fractional optimization problem. An alternating optimization algorithm is used to alternately optimize the hybrid RIS topology, base station transmit precoding, and hybrid RIS phase shift matrix to obtain the optimized hybrid RIS topology, optimized base station transmit precoding, and optimized hybrid RIS phase shift matrix. The optimized hybrid RIS topology is optimized by the relationship between the number of active elements and the amplitude coefficients. The optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix are optimized by Lagrange dual transformation and quadratic transformation.

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

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

[0013]

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

[0015] In the formula, It is a hybrid RIS mode scheduling variable, and the location of the active element is... China and Israel The locations of active and passive components are predefined as follows:

[0016]

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

[0018]

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

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

[0021]

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

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

[0024]

[0025] in:

[0026]

[0027] In the formula, R sum Let P be the reachable rate for all users, η be the system energy efficiency, and P be the achievable rate for all users. tot γ represents the total power consumption of the system. k The signal-to-dryness ratio of user k is represented by α, and the efficiency of the power amplifier is represented by α.

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

[0029] C2 indicates a hybrid RIS power constraint. σ represents the total power of the active RIS matrix. v This indicates the noise power of the active RIS;

[0030] C3 and C4 both represent topological constraints, 1 T z represents the sum of the elements of vector z;

[0031] C5, C6, and C7 all represent the amplitude and phase shift constraints of the hybrid RIS element, θ mρ represents the phase shift of the m-th element in a hybrid RIS. max This represents the maximum power amplification gain provided by the active element.

[0032] Optionally, S3 specifically includes:

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

[0034]

[0035] stC1-C7

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

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

[0038]

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

[0040]

[0041] For near-field channels Will Arranged in ascending order as follows:

[0042]

[0043] Suppose that:

[0044]

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

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

[0047] S3.3, for N active elements s A hybrid RIS with given upper bounds on N, i.e., N ≤ [N...]. act ] ub Substituting different values ​​of N back into the hybrid RIS power constraint formula, the corresponding p is calculated. n The value of is obtained, that is, the relationship between the number of active elements and the amplitude coefficient in the hybrid RIS;

[0048] S3.4, Based on the relationship between the number of active elements and the amplitude coefficients in the hybrid RIS, a tabu search is used to perform a topology search to find the optimal hybrid RIS topology, which is then used as the optimized hybrid RIS topology.

[0049] S3.5 employs a joint alternating optimization algorithm to alternately optimize the base station transmit precoding and the hybrid RIS phase shift matrix, thereby obtaining the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix.

[0050] Optionally, S3.5 specifically includes:

[0051] In S3.5.1, using Lagrange dual transformation and quadratic transformation, two auxiliary variables μ and ν are introduced to transform the non-convex optimization problem P1 into a convex problem P2:

[0052]

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

[0054]

[0055] In the formula, μ k and v k This represents the k-th value of the auxiliary variables μ and ν. Indicates to v k Perform conjugate operation:

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

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

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

[0059] S3.5.5: Fixed (w,μ,ν) optimization Φ to obtain local optimum Φ opt ;

[0060] S3.5.6: The iteration ends when η′ converges. The alternating optimized base station transmit precoding and hybrid RIS phase shift matrix obtained at the end of the iteration are 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 system's energy efficiency is improved, and its performance is significantly better than that of traditional passive RIS systems. By deploying appropriate active components, thermal noise interference and system power consumption are reduced. Attached Figure Description

[0063] Figure 1 This is a transmission model diagram of a multi-user MISO system using hybrid RIS assistance, as described in an embodiment of this application.

[0064] Figure 2 This is a flowchart illustrating the energy efficiency optimization method for a hybrid RIS communication system employing a topology, as described in an embodiment of this application.

[0065] Figure 3 This is a diagram showing the relationship between user distance and user energy efficiency in the embodiments of this application;

[0066] Figure 4 This is a graph showing the relationship between the number of iterations and user energy efficiency in the embodiments of this application;

[0067] Figure 5 This is a schematic diagram of the architecture of the hybrid RIS communication system involved in the embodiments of this application.

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

[0069] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this 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. The hybrid intelligent RIS contains both passive and active components. Each passive component is connected to only one phase shift circuit, and each active component is connected to both a phase shift circuit and an amplifier circuit. Therefore, 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 for the hybrid RIS communication system using the aforementioned topology include:

[0073] S1 employs a topological structure to construct a hybrid RIS-assisted multilinear isotropic reinforcement model;

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

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

[0076]

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

[0078] In the formula, It is a hybrid RIS mode scheduling variable, and the location of the active element is... China and Israel The locations of active and passive components are predefined as follows:

[0079]

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

[0081]

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

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

[0084]

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

[0086] S2, Construct a transmission problem optimization model. 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 conditions of satisfying the base station transmit power, hybrid RIS power consumption, and hybrid RIS element amplitude and phase constraints, so as to maximize energy efficiency.

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

[0088]

[0089] in:

[0090]

[0091] In the formula, R sum Let P be the reachable rate for all users, η be the system energy efficiency, and P be the achievable rate for all users. tot γ represents the total power consumption of the system. k The signal-to-dryness ratio of user k is represented by α, and the efficiency of the power amplifier is represented by α.

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

[0093] C2 indicates a hybrid RIS power constraint. σ represents the total power of the active RIS matrix. v This indicates the noise power of the active RIS;

[0094] C3 and C4 both represent topological constraints, 1 T z represents the sum of the elements of vector z;

[0095] C5, C6, and C7 all represent the amplitude and phase shift constraints of the hybrid RIS element, θ m ρ represents the phase shift of the m-th element in a hybrid RIS. max This represents the maximum power amplification gain provided by the active element.

[0096] S3, the nonlinear programming problem in the transmission problem optimization model is transformed into a non-fractional optimization problem. An alternating optimization algorithm is used to alternately optimize the hybrid RIS topology, base station transmit precoding, and hybrid RIS phase shift matrix to obtain the optimized hybrid RIS topology, optimized base station transmit precoding, and optimized hybrid RIS phase shift matrix. The optimized hybrid RIS topology is optimized by the relationship between the number of active elements and the amplitude coefficients. The optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix are optimized by Lagrange dual transformation and quadratic transformation.

[0097] In this embodiment, S3 specifically includes:

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

[0099]

[0100] stC 1- C7

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

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

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

[0104]

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

[0106]

[0107] For near-field channels Will Arranged in ascending order as follows:

[0108]

[0109] Suppose that:

[0110]

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

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

[0113] S3.3, for N active elements s A hybrid RIS with given upper bounds on N, i.e., N ≤ [N...]. act ] ub Substituting different values ​​of N back into the hybrid RIS power constraint formula, the corresponding p is calculated. n The value of is obtained, that is, the relationship between the number of active elements and the amplitude coefficient in the hybrid RIS;

[0114] S3.4, Based on the relationship between the number of active elements and the amplitude coefficients in the hybrid RIS, a tabu search is used to perform a topology search to find the optimal hybrid RIS topology, which is then used as the optimized hybrid RIS topology.

[0115] S3.5 employs a joint alternating optimization algorithm to alternately optimize the base station transmit precoding and the hybrid RIS phase shift matrix, thereby obtaining the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix.

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

[0117] In S3.5.1, using Lagrange dual transformation and quadratic transformation, two auxiliary variables μ and ν are introduced to transform the non-convex optimization problem P1 into a convex problem P2:

[0118]

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

[0120]

[0121] In the formula, μ k and v k This represents the k-th value of the auxiliary variables μ and ν. Indicates to v k Perform conjugate operation:

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

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

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

[0125] S3.5.5: Fixed (w,μ,ν) optimization Φ to obtain local optimum Φopt ;

[0126] S3.5.6: The iteration ends when η′ converges. The alternating optimized base station transmit precoding and hybrid RIS phase shift matrix obtained at the end of the iteration are 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 four users are randomly distributed in a large circle with a radius of 5m centered at 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=4, N=4, N=10m ... S =512 and N=80. User noise power and mixed RIS noise are set to σ. 2 = -100dBm and The total power consumption is limited to 40dBm, with the fully active RIS case as follows: and The hybrid RIS case is as follows: and Other benchmark systems are set as follows

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

[0130] Reference Figure 4 , Figure 4 The convergence of the algorithm was verified, and it can be noted that the proposed algorithm converges very quickly in about 5 iterations.

[0131] Furthermore, as an implementation scheme, Figure 5 This is a schematic diagram of the hardware operating environment of the hybrid RIS communication system involved in the embodiments of this 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. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also 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 does not constitute a limitation on the hybrid RIS communication system, which may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0134] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and an energy efficiency optimization program for the hybrid RIS communication system using a topology structure. The operating system is a program that manages and controls the hardware and software resources of the hybrid RIS communication system, and the energy efficiency optimization program for the hybrid RIS communication system using a topology structure, along with the operation of other software or programs, are also included.

[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 with the terminal; the network interface 1004 is mainly used to communicate with the backend server; the processor 1001 can be used to call the energy efficiency optimization of the hybrid RIS communication system using the topology stored in the memory 1005.

[0136] In this embodiment, the hybrid RIS communication system includes: a memory 1005, a processor 1001, and an energy-efficient topology optimized for the hybrid RIS communication system, stored in the memory and capable of running on the processor, wherein:

[0137] When processor 1001 calls the energy efficiency optimization of the hybrid RIS communication system using topology stored in memory 1005, it performs the following operations:

[0138] S1 employs a topological structure to construct a hybrid RIS-assisted multilinear isotropic reinforcement model;

[0139] S2, Construct a transmission problem optimization model. 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 conditions of satisfying the base station transmit power, hybrid RIS power consumption, and hybrid RIS element amplitude and phase constraints, so as to maximize energy efficiency.

[0140] S3, the nonlinear programming problem in the transmission problem optimization model is transformed into a non-fractional optimization problem. An alternating optimization algorithm is used to alternately optimize the hybrid RIS topology, base station transmit precoding, and hybrid RIS phase shift matrix to obtain the optimized hybrid RIS topology, optimized base station transmit precoding, and optimized hybrid RIS phase shift matrix. The optimized hybrid RIS topology is optimized by the relationship between the number of active elements and the amplitude coefficients. The optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix are optimized by Lagrange dual transformation and quadratic transformation.

[0141] When processor 1001 calls the energy efficiency optimization of the hybrid RIS communication system using topology stored in memory 1005, it performs the following operations:

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

[0143]

[0144] stC 1- C7

[0145] In the formula, η' represents the optimization objective value after fractional programming.

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

[0147]

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

[0149]

[0150] For near-field channels Will Arranged in ascending order as follows:

[0151]

[0152] Suppose that:

[0153]

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

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

[0156] S3.3, for N active elements s A hybrid RIS with given upper bounds on N, i.e., N ≤ [N...]. act ] ub Substituting different values ​​of N back into the hybrid RIS power constraint formula, the corresponding p is calculated. n The value of is obtained, that is, the relationship between the number of active elements and the amplitude coefficient in the hybrid RIS;

[0157] S3.4, Based on the relationship between the number of active elements and the amplitude coefficients in the hybrid RIS, a tabu search is used to perform a topology search to find the optimal hybrid RIS topology, which is then used as the optimized hybrid RIS topology.

[0158] S3.5 employs a joint alternating optimization algorithm to alternately optimize the base station transmit precoding and the hybrid RIS phase shift matrix, thereby obtaining the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix.

[0159] When processor 1001 calls the energy efficiency optimization of the hybrid RIS communication system using topology stored in memory 1005, it performs the following operations:

[0160] In S3.5.1, using Lagrange dual transformation and quadratic transformation, two auxiliary variables μ and ν are introduced to transform the non-convex optimization problem P1 into a convex problem P2:

[0161]

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

[0163]

[0164] In the formula, μ k and v k This represents the k-th value of the auxiliary variables μ and ν. Indicates to v k Perform conjugate operation:

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

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

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

[0168] S3.5.5: Fixed (w,μ,ν) optimization Φ to obtain local optimum Φ opt ;

[0169] S3.5.6: The iteration ends when η′ converges. The alternating optimized base station transmit precoding and hybrid RIS phase shift matrix obtained at the end of the iteration are used as the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix.

[0170] Furthermore, those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and 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 embodiments of the above methods.

[0171] Therefore, this application also provides a computer-readable storage medium storing energy efficiency optimization of a hybrid RIS communication system using a topology, wherein when the energy efficiency optimization of a hybrid RIS communication system using a topology is executed by a processor, it implements the various steps of the energy efficiency optimization method for a hybrid RIS communication system using a topology as described in the above embodiments.

[0172] The computer-readable storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0173] It should be noted that, since the storage medium provided in the embodiments of this application is the storage medium used to implement the methods of the embodiments of this application, those skilled in the art can understand the specific structure and variations of the storage medium based on the methods described in the embodiments of this application, and therefore will not be repeated here. All storage media used in the methods of the embodiments of this application fall within the scope of protection of this application.

[0174] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0175] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0176] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0177] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0178] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. This application can 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 can be interpreted as names.

[0179] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0180] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An energy efficiency optimization method for a hybrid RIS communication system with a topology structure, characterized in that, The method comprises the following steps: S1, a multi-linear isotropic reinforcement model assisted by a hybrid RIS is constructed using a topological structure; S2, a transmission problem optimization model is constructed, which is used to jointly optimize the hybrid RIS topological structure, the transmission precoding at the base station, and the reflection precoding of the hybrid RIS to maximize the energy efficiency under the conditions of satisfying the base station transmission power, the hybrid RIS power consumption, and the hybrid RIS element amplitude and phase constraints; S3, the nonlinear programming problem in the transmission problem optimization model is converted into a non-fractional optimization problem, and an alternating optimization algorithm is used to alternately optimize the hybrid RIS topological structure, the base station transmission precoding, and the hybrid RIS phase shift matrix to obtain an optimized hybrid RIS topological structure, an optimized base station transmission precoding, and an optimized hybrid RIS phase shift matrix, wherein the optimized hybrid RIS topological structure is optimized through the number of active elements and the amplitude coefficient relationship, and the optimized base station transmission precoding and the optimized hybrid RIS phase shift matrix are optimized through Lagrange dual transformation and quadratic transformation; In the S1, the multi-linear isotropic reinforcement model comprises an active reflection matrix, a passive reflection matrix, and a user received signal expression, wherein: The active reflection matrix The expression is: ； ； ； wherein and denote the phase shift and amplification factor of the element, respectively, with for passive elements and , the maximum power amplification gain provided by the active load, the number of reflecting elements; In the formula, is the mode scheduling variable of the hybrid RIS, the positions of the active elements are in , and the positions of the active elements and the passive elements are defined as follows: ​ ； The passive reflection matrix The expression is: ； wherein is a unit matrix of size is a unit matrix of size denotes the index set of active elements; The user receives a signal The expression is: ； wherein HBSdenotes the channel vector between the base station and the RIS, HBSdenotes the channel vector between the base station and the user HBSdenotes the channel vector between the base station and the user HBSdenotes the channel vector between the base station and the user HBSdenotes the channel vector between the base station and the user denotes the thermal noise and self-interference of the active elements, denotes the additive white Gaussian noise at the user denotes the additive white Gaussian noise at the user denotes the beamforming vector of the base station transmitting to the user denotes the beamforming vector of the base station transmitting to the user denotes the data symbol of the base station transmitting to the user denotes the data symbol of the base station transmitting to the user 2. The method of claim 1, wherein, In the S2, the transmission problem optimization model comprises: ； Wherein: ； ； ； ； wherein is the achievable rate for all users, is the system energy efficiency, denotes the total power consumption of the system, denotes the user signal-to-interference ratio, denotes the efficiency of the power amplifier; denotes a base station transmit power constraint, denotes a beamforming vector of the base station transmitting to the user , is a maximum power value of the base station, is a maximum power value of the hybrid RIS; denotes a hybrid RIS power constraint, denotes the total power of the active RIS matrix, denotes the noise power of the active RIS; and both represent topological constraints, represents the sum of the elements of the vector of the elements of the vector , and both represent amplitude and phase shift constraints of hybrid RIS elements, represents the phase shift of the hybrid RIS element, represents the maximum power amplification gain value provided for the active element.​ 3. The method of claim 2, wherein, The S3 specifically comprises: S3.

1. re-formulate the original objective function in the transmission problem optimization model in fractional form as :​ ； In the formula, The optimization objective value after being expressed as a fractional programming is S3.

2. optimizing constraints in the transmission problem optimization model Further written as: ； Assuming for any active element that when it can be written as: ； For near field pass The In ascending order: ； Provided that there exists: ； Combining the above two equations, we get the upper bound : ； S3.3, for the hybrid RIS with active elements , given upper bound, i.e. , the values of different number are substituted into the hybrid RIS power constraint formula again to calculate the corresponding values of , i.e. the number and amplitude coefficient relationship of the active elements in the hybrid RIS; S3.4, based on the number of active elements and the amplitude coefficient relationship in the hybrid RIS, a taboo search is used for topological search to search for an optimal hybrid RIS topological structure as the optimized hybrid RIS topological structure; S3.5, a joint alternating optimization algorithm is used to alternately optimize the base station transmission precoding and the hybrid RIS phase shift matrix to obtain the optimized base station transmission precoding and the optimized hybrid RIS phase shift matrix.

4. The method of claim 3, wherein, The S3.5 specifically comprises: S3.5.1, using Lagrange dual transformation and quadratic transformation, two auxiliary variables are introduced and , the non-convex optimization problem of is converted into a convex problem : ； ； wherein denotes a beamforming vector of the base station transmitting to a user, and denotes a k-th value of an auxiliary variable and a k-th value of an auxiliary variable denotes a conjugate operation on ​ S3.5.2: Fixing Optimization auxiliary variables , obtaining a local optimum ; S3.5.3: Fixing Optimization auxiliary variables , obtaining a local optimum ; S3.5.4: Fixing Optimizing , obtaining a local optimum ; S3.5.5: Fixing Optimizing , obtaining a local optimum ; S3.5.6: until converges, the iteration ends, and the alternating optimization base station transmit precoding and hybrid RIS phase shift matrix obtained at the completion of the iteration is taken as the optimized base station transmit precoding and the optimized hybrid RIS phase shift matrix.

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