Energy efficiency solving method, device and equipment for converged communication system

Abstract

The invention discloses an energy efficiency solving method, device and equipment of a converged communication system, relates to the technical field of smart power grid and wireless communication convergence, and can realize optimal safety energy efficiency of the converged communication system on the premise of ensuring basic safety and energy efficiency requirements of all users. The method comprises the following steps: on the basis of a model architecture of a converged communication system, defining an optimal problem of system safety energy efficiency by jointly designing base station active beam forming and IRS passive beam forming; by introducing an auxiliary variable, the optimal problem of the system safety energy efficiency is converted into a parameterization problem of iterative solution; decomposing the iteratively solved parameterization problem into a design sub-problem of base station active beam forming and a design sub-problem of IRS passive beam forming by using an alternating optimization algorithm; and solving a design sub-problem of base station active beam forming and a design sub-problem of IRS passive beam forming by using semi-definite relaxation and one-section Taylor approximation iteration to obtain an approximate optimal solution of system safety energy efficiency.

Description

Technical Field

[0001] This application relates to the field of smart grid and wireless communication integration technology, and in particular to a method, apparatus and equipment for solving the energy efficiency of an integrated communication system. Background Technology

[0002] With the development of sixth-generation (6G) wireless networks, communication systems face the challenge of a rapid increase in the number of users and an explosive increase in network traffic. It is estimated that by 2040, the number of Internet of Things (IoT) terminals will grow to hundreds of billions, which will lead to a shortage of spectrum resources and a deterioration of the wireless transmission environment.

[0003] To address the aforementioned issues, Intelligent Reflecting Surface (IRS) technology has been widely researched as a key technology for 6G. Due to its low power consumption and ease of deployment, IRS can dynamically alter the propagation characteristics of electromagnetic waves, creating a programmable wireless transmission environment and effectively improving the transmission rate, coverage, and energy efficiency of communication systems. Although IRS can efficiently reconstruct signals and improve signal transmission conditions, a single IRS-assisted system still faces the bottleneck of insufficient spectrum resource utilization, making it difficult to meet the explosive growth in network traffic caused by massive concurrent access of devices in 6G scenarios. Non-Orthogonal Multiple Access (NOMA) technology, by allowing multiple users to share the same resource block, sacrifices receiver complexity to improve system spectrum efficiency, throughput, and user fairness. By combining NOMA with IRS, a synergistic converged communication system can be obtained. This system can provide a better channel foundation for NOMA multiplexing through IRS channel reconstruction, and fully release the regulatory value of IRS through NOMA resource multiplexing, forming a technologically complementary converged system.

[0004] However, the actual deployment and scenario adaptation of converged communication systems present two main challenges. First, the broadcast nature of wireless communication remains unchanged. The multipath reflection signal transmission mode in converged systems may actually expand the signal exposure range, making it more difficult to control the risks of illegal eavesdropping and information leakage. Second, although the passive nature of IRS reduces some energy consumption, base stations still require additional energy to ensure multi-user reuse, signal anti-interference, and security protection. Simply pursuing performance improvements while neglecting energy consumption control will significantly increase operating costs. Therefore, security and energy efficiency, as a performance indicator that considers both communication security and energy consumption, provides a more comprehensive performance measurement standard for converged communication systems.

[0005] Currently, the application of security and energy efficiency in converged communication systems mainly focuses on single-user communication scenarios. However, in multi-user converged communication scenarios, different users may have different security and energy efficiency requirements. How to achieve optimal security and energy efficiency in converged communication systems while ensuring the basic security and energy efficiency needs of all users is a complex problem. Summary of the Invention

[0006] In view of this, this application provides a method, apparatus and equipment for solving the energy efficiency of a converged communication system. The main purpose is to solve the problem of how to achieve the optimal safety and energy efficiency of the converged communication system while ensuring the basic safety and energy efficiency needs of all users.

[0007] According to the first aspect of this application, a method for solving the energy efficiency of a converged communication system is provided, comprising: Based on the model architecture of the converged communication system, the optimal problem of system security and energy efficiency is defined by jointly designing base station active beamforming and IRS passive beamforming. The system security and energy efficiency is the ratio of the secure rate of the converged communication system to the total power consumption of the system. The secure rate is the sum of the secure rates of all users. The total power of the system includes at least the base station transmit power, base station circuit consumption and IRS circuit consumption. The converged communication system is an IRS-assisted NOMA converged communication system. By introducing auxiliary variables, the problem of optimizing the system's safety and energy efficiency is transformed into a parameterized problem that can be solved iteratively. The parameterized problem solved iteratively is decomposed into a design subproblem of active beamforming for base stations and a design subproblem of passive beamforming for IRS using an alternating optimization algorithm. The design subproblems of active beamforming for the base station and passive beamforming for the IRS are solved iteratively using semidefinite relaxation and a first-order Taylor approximation until the system safety and energy efficiency meet the iteration stopping condition, thus obtaining an approximate optimal solution for the system safety and energy efficiency.

[0008] Furthermore, before defining the optimal system security and energy efficiency by jointly designing active beamforming at the base station and passive beamforming at the IRS based on the model architecture of the converged communication system, the method further includes: constructing a model architecture of the converged communication system, wherein the model architecture includes at least a base station equipped with multiple antennas and an IRS with multiple reflective elements, wherein the base station communicates with multiple user groups through the IRS, each user group is multiplexed in the same resource block, the multiple user groups do not interfere with each other, and there is an eavesdropping user attempting to eavesdrop on user information in the user group, wherein the user in the user group and the eavesdropping user have a single antenna.

[0009] Furthermore, based on the model architecture of the converged communication system, the optimal problem of system security and energy efficiency is defined by jointly designing base station active beamforming and IRS passive beamforming. This includes: based on the model architecture of the converged communication system, weighting the transmitted signal using multi-antenna precoding technology to obtain base station active beamforming, which is a vector-represented active modulation of the transmitted signal; based on the model architecture of the converged communication system, dynamically adjusting the signal strength of each passive reflector to achieve in-phase superposition of reflected signals at legitimate users and out-of-phase cancellation at eavesdropping users, resulting in IRS passive beamforming, which is a diagonal matrix-represented passive reconstruction channel; and by jointly optimizing the vector-represented active modulation of the transmitted signal and the diagonal matrix-represented passive reconstruction channel, the optimal problem of system security and energy efficiency is defined while satisfying the target constraints.

[0010] Furthermore, before defining the optimal problem of system security and energy efficiency by jointly optimizing the actively modulated transmit signal represented by the vector and the passively reconstructed channel represented by the diagonal matrix, based on satisfying the set conditions, the method further includes: pre-setting target constraints for solving the optimal problem of system security and energy efficiency; wherein the target constraints include at least one or more of the following: minimum user security rate requirement, maximum base station transmit power constraint, constraint for user to successfully perform serial interference cancellation, constraint to ensure user performance fairness, and IRS reflection phase shift constraint.

[0011] Furthermore, the step of transforming the optimal problem of system security and energy efficiency into an iteratively solved parameterized problem by introducing auxiliary variables includes: introducing basic auxiliary variables for legitimate users and eavesdropping users using defined relaxation vectors respectively, so as to handle the coupling between channel and phase through the basic auxiliary variables; transforming the nonlinear relationship in the reachable rate of eavesdropping users caused by the calculation of signal-to-noise ratio by adding new auxiliary variables to transform the nonlinear relationship into a linear relationship, so that the nonlinear influence of the reachable rate of eavesdropping users is transferred to the linear constraints of the added auxiliary variables; and combining the basic auxiliary variables and the added auxiliary variables to transform the optimal problem of system security and energy efficiency into an iteratively solved parameterized problem.

[0012] Furthermore, the step of using an alternating optimization algorithm to decompose the iteratively solved parameterized problem into a design sub-problem of active beamforming for the base station and a design sub-problem of passive beamforming for the IRS includes: using an alternating optimization algorithm to fix the passive beamforming variables of the IRS so that the portion of the total safe rate related to the fixed passive beamforming variables of the IRS becomes a known quantity, and only the portion related to the active beamforming variables of the base station is retained, thus decomposing the iteratively solved parameterized problem into a design sub-problem of active beamforming variables for the base station; and based on the design sub-problem of active beamforming variables for the base station, using an alternating optimization algorithm to fix the active beamforming variables of the base station so that the portion of the total safe rate and the total energy consumption of the base station related to the base station becomes a known quantity, and only the portion related to the fixed passive beamforming variables of the IRS is retained, thus decomposing the iteratively solved parameterized problem into a design sub-problem of passive beamforming variables for the IRS.

[0013] Furthermore, the step of using semidefinite relaxation and first-order Taylor approximation to iteratively solve the design subproblems of active beamforming at the base station and passive beamforming at the IRS until the system safety and energy efficiency meet the iteration stopping condition, thus obtaining an approximate optimal solution for system safety and energy efficiency, includes: fixing the IRS phase and using semidefinite relaxation to solve the design subproblem of active beamforming at the base station to maximize system safety and energy efficiency by optimizing the beamforming vector of the base station; fixing the base station beamforming and using first-order Taylor approximation to iteratively solve the design subproblem of passive beamforming at the IRS to maximize system safety and energy efficiency by optimizing the phase offset of the IRS; repeating the above alternating optimization process until the difference in system safety and energy efficiency between two adjacent complete iterations meets a preset threshold, thus obtaining an approximate optimal solution for system safety and energy efficiency.

[0014] According to a second aspect of this application, an energy efficiency calculation device for a converged communication system is provided, comprising: The definition unit is used to define the optimal problem of system security and energy efficiency based on the model architecture of the converged communication system by jointly designing base station active beamforming and IRS passive beamforming. The system security and energy efficiency is the ratio of the secure rate of the converged communication system to the total power consumption of the system. The secure rate is the sum of the secure rates of all users. The total power of the system includes at least the base station transmit power, base station circuit consumption and IRS circuit consumption. The converged communication system is an IRS-assisted NOMA converged communication system. The transformation unit is used to transform the optimal problem of system safety and energy efficiency into a parameterized problem that can be solved iteratively by introducing auxiliary variables; The decomposition unit is used to decompose the iteratively solved parameterized problem into a design subproblem of active beamforming for the base station and a design subproblem of passive beamforming for the IRS using an alternating optimization algorithm. The solution unit is used to iteratively solve the design subproblems of active beamforming of the base station and passive beamforming of the IRS using semidefinite relaxation and a section Taylor approximation until the system safety and energy efficiency meet the iteration stopping condition, thus obtaining an approximate optimal solution for the system safety and energy efficiency.

[0015] Furthermore, the device also includes: a construction unit, used to construct a model architecture of the converged communication system based on the model architecture of the converged communication system, before defining the optimal problem of system security and energy efficiency by jointly designing active beamforming of the base station and passive beamforming of the IRS. The model architecture includes at least a base station equipped with multiple antennas and an IRS with multiple reflective elements. The base station communicates with multiple user groups through the IRS. Each user group is multiplexed in the same resource block. The multiple user groups do not interfere with each other. There is an eavesdropping user attempting to eavesdrop on user information in the user group. The users in the user group and the eavesdropping user have a single antenna.

[0016] Furthermore, the defining unit is specifically used for: based on the model architecture of the converged communication system, weighting the transmitted signal through multi-antenna precoding technology to obtain base station active beamforming, wherein the base station active beamforming is a vector-represented active modulation transmitted signal; based on the model architecture of the converged communication system, dynamically adjusting the scent of each passive reflection unit to make the reflected signals in-phase superposition at legitimate users and out-of-phase cancellation at eavesdropping users to obtain IRS passive beamforming, wherein the IRS passive beamforming is a diagonal matrix-represented passive reconstruction channel; and by jointly optimizing the vector-represented active modulation transmitted signal and the diagonal matrix-represented passive reconstruction channel, defining the optimal problem of system security and energy efficiency while satisfying the target constraints.

[0017] Furthermore, the defining unit is specifically used to: before defining the optimal problem of system security and energy efficiency by jointly optimizing the actively controlled transmission signal represented by the vector and the passively reconstructed channel represented by the diagonal matrix, based on satisfying the set conditions, pre-set target constraints for solving the optimal problem of system security and energy efficiency; wherein the target constraints include at least one or more of the following: minimum user security rate requirement, maximum base station transmit power constraint, constraint for user to successfully perform serial interference cancellation, constraint to ensure user performance fairness, and IRS reflection phase shift constraint.

[0018] Furthermore, the transformation unit is specifically used to: introduce basic auxiliary variables for legitimate users and eavesdropping users respectively using defined relaxation vectors, so as to handle the coupling between channel and phase through the basic auxiliary variables; for the nonlinear relationship in the reachable rate of eavesdropping users caused by the calculation of signal-to-noise ratio, the nonlinear relationship is transformed into a linear relationship by adding auxiliary variables, so that the nonlinear influence of the reachable rate of eavesdropping users is transferred to the linear constraint of the added auxiliary variables; and combine the basic auxiliary variables and the added auxiliary variables to transform the optimal problem of system security efficiency into a parameterized problem to be solved iteratively.

[0019] Further, the decomposition unit is specifically used for: fixing the IRS passive beamforming variables using an alternating optimization algorithm, so that the portion of the total safe rate related to the fixed IRS passive beamforming variables becomes a known quantity, and only the portion related to the base station active beamforming variables is retained, thus decomposing the iteratively solved parameterized problem into a design sub-problem of the base station active beamforming variables; and, based on the design sub-problem of the base station active beamforming variables, fixing the base station active beamforming variables using an alternating optimization algorithm, so that the portion of the total safe rate and the total base station power consumption related to the base station becomes a known quantity, and only the portion related to the fixed IRS passive beamforming variables is retained, thus decomposing the iteratively solved parameterized problem into a design sub-problem of the IRS passive beamforming variables.

[0020] Furthermore, the solution unit is specifically used for: fixing the IRS phase, using semidefinite relaxation to solve the design sub-problem of the active beamforming of the base station, so as to maximize the system safety and energy efficiency by optimizing the beamforming vector of the base station; fixing the base station beamforming, using first-order Taylor approximation to iteratively solve the design sub-problem of the passive beamforming of the IRS, so as to maximize the system safety and energy efficiency by optimizing the phase offset of the IRS; repeating the above alternating optimization process, when the difference in system safety and energy efficiency between two adjacent complete iterations meets a preset threshold, an approximate optimal solution for system safety and energy efficiency is obtained.

[0021] According to a third aspect of this application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect above.

[0022] According to a fourth aspect of this application, a readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0023] By employing the above technical solutions, this application provides a method, apparatus, and device for solving the energy efficiency problem of a converged communication system. Compared with the current approach of using safety energy efficiency only for single-user scenarios in converged communication systems, this application, based on the model architecture of the converged communication system, defines the optimal problem of system safety energy efficiency by jointly designing base station active beamforming and IRS passive beamforming. The system safety energy efficiency is the ratio of the safe rate of the converged communication system to the total power consumption of the system. The safe rate is the sum of the safe rates of all users. The total power of the system includes at least the base station transmit power, base station circuit consumption, and IRS circuit consumption. The converged communication system is an IRS-assisted NOMA converged communication system. By introducing auxiliary variables, the optimal problem of system safety energy efficiency is transformed into an iteratively solved parameterized problem. The iteratively solved parameterized problem is decomposed into a design subproblem of base station active beamforming and a design subproblem of IRS passive beamforming using an alternating optimization algorithm. The design subproblems of base station active beamforming and IRS passive beamforming are iteratively solved using semidefinite relaxation and a one-section Taylor approximation until the system safety energy efficiency satisfies the iteration stopping condition, thus obtaining an approximate optimal solution for system safety energy efficiency. The entire process maximizes the safe energy consumption of the IRS-assisted NOMA system by jointly optimizing the active beamforming of the base station and the passive beamforming of the IRS. Under the premise of ensuring the basic safety and energy efficiency requirements of all users, it can achieve the optimal safety and energy efficiency of the converged communication system, providing an effective solution for building a safe and energy-saving converged communication system.

[0024] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating the energy efficiency solution method for a converged communication system in one embodiment of this application; Figure 2 This is a flowchart illustrating the energy efficiency solution method for a converged communication system in another embodiment of this application; Figure 3 This is a model architecture diagram of an IRS-assisted NOMA system in one embodiment of this application; Figure 4 yes Figure 1 A flowchart illustrating a specific implementation method of step 101; Figure 5 yes Figure 1 A flowchart illustrating a specific implementation method for step 102; Figure 6 yes Figure 1 A flowchart illustrating a specific implementation method for step 103; Figure 7 yes Figure 1 A flowchart illustrating a specific implementation method for step 104; Figure 8 This is a schematic diagram illustrating the impact of different numbers of IRS reflective units on system safety and energy efficiency in one embodiment of this application; Figure 9 This is a schematic diagram illustrating the impact of changes in the user's minimum security rate requirement on system security and energy efficiency in one embodiment of this application; Figure 10a Different schemes in one embodiment of this application are as follows A schematic diagram showing the comparison results of the impact of changes on the safety rate; Figure 10b Different schemes in one embodiment of this application are as follows A schematic diagram showing the comparison results of the impact of changes on transmission power; Figure 11 This is a schematic diagram of the structure of the energy efficiency solving device for a converged communication system in one embodiment of this application; Figure 12 This is a schematic diagram of the device structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0026] The invention will now be discussed with reference to several exemplary embodiments. It should be understood that these embodiments are described merely to enable those skilled in the art to better understand and thus implement the invention, and are not intended to imply any limitation on the scope of the invention.

[0027] As used herein, the term "comprising" and its variations are to be interpreted as open-ended terms meaning "including but not limited to". The term "based on" is to be interpreted as "at least partially based on". The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment". The term "another embodiment" is to be interpreted as "at least one other embodiment".

[0028] In related technologies, the scenarios for using security and energy efficiency in converged communication systems are mainly aimed at single-user communication scenarios. However, in multi-user converged communication scenarios, the security needs and energy efficiency requirements of different users may differ. How to achieve the optimal security and energy efficiency of converged communication systems while ensuring the basic security and energy efficiency needs of all users is a complex problem.

[0029] To address this issue, this embodiment provides a method for solving the energy efficiency problem of a converged communication system, such as... Figure 1 As shown, it includes the following steps: 101. Based on the model architecture of the converged communication system, the optimal problem of system security and energy efficiency is defined by jointly designing active beamforming at the base station and passive beamforming at the IRS.

[0030] The system security efficiency is defined as the ratio of the secure rate of the converged communication system to the total power consumption of the system. The secure rate is the sum of the secure rates of all users. The total system power includes at least the base station transmit power, base station circuit power consumption, and IRS circuit power consumption. The converged communication system is an IRS-assisted NOMA converged communication system. The specific converged communication system architecture mainly consists of a base station, an IRS, and multiple user terminals.

[0031] Correspondingly, in the communication process, the base station, as the core hub, is responsible for exchanging information with user terminals and controlling signal transmission. The IRS (Incoming Signal Controller), deployed in appropriate locations, enhances signal coverage and transmission quality through signal reflection and phase adjustment, improving the user's communication experience. This is especially true for users in areas with weak signals, where the IRS can significantly improve the strength and stability of the received signal. Multiple user terminals are the final nodes in the communication process, transmitting and receiving data by receiving signals directly transmitted by the base station and signals reflected by the IRS.

[0032] As a key technology in IRS-assisted NOMA converged communication systems, active beamforming at base stations utilizes multiple antennas to precisely control the phase and amplitude of the signals transmitted by each antenna. This allows the signals to be superimposed in phase in a specific direction, forming a beam with strong directionality and gain, thus enhancing signal transmission. In this way, the base station can concentrate signal energy in the direction of the target user, improving the signal strength received by the target user while reducing interference in other directions. For example, in a cell, the base station can dynamically adjust the direction and shape of the beam according to the location of each user and the channel conditions, precisely providing high-quality communication services to each user. When users move, the base station can also track their location in real time and adjust the beam accordingly to ensure that users always receive a stable signal.

[0033] As another key technology in IRS-assisted NOMA converged communication systems, the IRS consists of a large number of passive reflective elements, which can independently adjust the phase of the incident signal. When the signal transmitted by the base station reaches the IRS, the IRS optimizes the phase configuration of each reflective element to achieve constructive interference of the reflected signal in the direction of the target user, thereby enhancing the signal strength received by the user. Unlike active beamforming by the base station, the IRS does not actively transmit signals. Instead, it cleverly adjusts the phase of the reflected signal to change the signal propagation path and distribution, enabling the signal to be reflected to the desired location and achieving effective signal control. For example, when a user is in an area at the edge of the base station's signal coverage where the signal is weak, the IRS can reflect the received base station signal and, through precise phase adjustment, superimpose the reflected signal with the signal directly transmitted by the base station at the user's location, improving the signal quality received by the user.

[0034] In the embodiments of the present invention, the joint design of active beamforming at the base station and passive beamforming at the IRS can effectively utilize signal energy, enhance the signal strength in the direction of the target user, reduce signal interference, and improve the overall performance of the system, so that the signal transmitted by the base station and the signal reflected by the IRS can achieve the best superposition effect at the user end, significantly improving the user's communication quality.

[0035] 102. By introducing auxiliary variables, the optimal problem of system safety and energy efficiency is transformed into a parameterized problem that can be solved iteratively.

[0036] In this embodiment, auxiliary variables are intermediate variables introduced to simplify the complexity of the optimization problem of system security and energy efficiency. They can transform complex coupling or nonlinear relationships into a tractable form by associating with variables such as base station beamforming and IRS phase. In the context of IRS-assisted NOMA system security and energy efficiency optimization, auxiliary variables are mainly divided into two categories: The first category is basic decoupling auxiliary variables, whose core function is to decouple the strong coupling between the channel and phase. For example, defining relaxation variables to replace the IRS phase matrix, as well as the structured matrix of the legitimate user channel and the structured matrix of the eavesdropping user channel. The second category is nonlinear relaxation auxiliary variables, whose core function is to linearize the nonlinear terms in rate calculation. For example, defining logarithmic terms to replace the eavesdropping user rate and quadratic terms to replace the eavesdropping signal-to-noise ratio.

[0037] Specifically, in the process of transforming the optimal problem of system security and energy efficiency into a parameterized problem of iterative solution, the received signal-to-noise ratio of legitimate users and eavesdropping users can be transformed into a linear expression containing only auxiliary variables and beamforming vectors through basic decoupling variables. By nonlinearly relaxing the auxiliary variables, the logarithmic nonlinear term of the eavesdropping rate can be transformed into a linear constraint. Then, a parameterized transformation is introduced to transform the fractional objective into a parameterized linear objective, so as to obtain the linear objective optimization problem containing auxiliary variables and original variables, that is, the parameterized problem of iterative solution.

[0038] 103. The parameterized problem solved iteratively is decomposed into a design subproblem of active beamforming for the base station and a design subproblem of passive beamforming for the IRS using an alternating optimization algorithm.

[0039] In this embodiment, the alternating optimization algorithm is an efficient solution strategy for multivariate coupled optimization problems. Its core logic is to decompose the complex original problem with multiple strongly coupled variables into multiple simpler subproblems containing only a subset of the variables. By fixing other variables, solving individual subproblems, updating variables, and iteratively solving, the algorithm gradually approaches the optimal solution. The purpose of the alternating optimization algorithm is to reduce the solution complexity. That is, the process does not require optimizing all coupled variables at once, but rather iteratively updates the solution through the local optima of the subproblems, eventually converging to a globally approximate optimal solution. This is particularly suitable for strongly coupled bivariate optimization scenarios such as base station beamforming and IRS phase, balancing solution efficiency and accuracy.

[0040] Specifically, the core variables of the parameterized problem solved iteratively are the base station active beamforming vector and the IRS passive beamforming phase matrix. The goal is to maximize the ratio of the secure rate of the converged communication system to the total power consumption of the system. The decomposition logic can be performed in two iterative steps: On the one hand, the IRS phase is fixed, which is decomposed into a base station active beamforming design subproblem. Its optimization variable is only the base station active beamforming vector. The objective function is simplified to a term containing only the base station active beamforming vector. The constraints include constraints related to the base station active beamforming vector.

[0041] On the other hand, there is fixed base station beamforming, which is decomposed into the IRS passive beamforming design subproblem. Its optimization variable is only the IRS passive beamforming phase matrix, and the objective function is simplified to a term containing only the IRS passive beamforming phase matrix. The constraint condition is the IRS element mode constraint.

[0042] 104. Use semidefinite relaxation and a Taylor approximation to iteratively solve the design subproblem of active beamforming of the base station and the design subproblem of passive beamforming of the IRS until the system safety and energy efficiency meet the iteration stopping condition, and obtain the approximate optimal solution of the system safety and energy efficiency.

[0043] In this embodiment, semidefinite relaxation is a mathematical transformation technique that converts non-convex quadratic constraints into convex constraints. Its core purpose is to solve the nonlinear challenges of the active beamforming subproblem in base stations. Since the optimization variables in base station beamforming are vectors, their power constraints and signal-to-noise ratio calculations involve quadratic operations. These quadratic constraints cause the subproblem to exhibit non-convexity, making it difficult to solve directly. By defining a semidefinite matrix, the quadratic operations on vectors can be transformed into linear operations on matrices. The original non-convex quadratic constraints are relaxed into convex constraints, transforming the subproblem into a standard convex optimization problem, which can be solved efficiently using convex optimization tools.

[0044] In this embodiment, the first-order Taylor approximation is a local linearization method, primarily used to address the phase nonlinearity problem in the IRS passive beamforming subproblem. Since the optimization variable in IRS is the phase matrix, the exponential form of the phase term leads to strong nonlinearity in the channel gain term, and the element mode constraint increases the difficulty of solving the problem. Therefore, at the phase point of the current iteration, a first-order Taylor expansion can be performed on the nonlinear term, approximating the exponential phase variable as a linear expression. This transforms the nonlinear channel term into a linear function of the phase. Combined with the relaxation of the phase constraint, the IRS subproblem is transformed into a solvable convex optimization problem.

[0045] In the specific iterative solution process, the IRS phase of the current iteration can be fixed, and the active beamforming vector of the base station can be transformed based on the semidefinite relaxation technique to construct the first convex optimization subproblem. Solving the first convex optimization subproblem yields the optimal matrix of the active beamforming vector of the base station. Fixing the optimal matrix of the active beamforming vector of the base station obtained by the solution, at the current phase, a first-order Taylor expansion is performed on the nonlinear channel term containing the phase to linearize it, constructing the second convex optimization subproblem. Solving the second convex optimization subproblem yields a new IRS passive beamforming phase matrix. Using the obtained optimal matrix of the active beamforming vector of the base station and the IRS passive beamforming phase matrix, the above two solution processes can be regarded as a loop iteration. After each iteration, the system safety energy consumption is calculated. When the energy efficiency difference between two adjacent iterations is less than a set threshold, or when the maximum number of iterations is reached, the loop stops. Finally, the convergent solutions of the design subproblem of active beamforming of the base station and the design subproblem of passive beamforming of the IRS are obtained, which is the approximate optimal solution of the system safety energy efficiency.

[0046] The energy efficiency solution method for converged communication systems provided in this application differs from current approaches that only address single-user scenarios in converged communication systems. This application, based on the converged communication system's model architecture, defines the optimal problem for system energy efficiency by jointly designing base station active beamforming and IRS passive beamforming. System energy efficiency is defined as the ratio of the system's safe rate to its total power consumption. The safe rate is the sum of the safe rates of all users. The total system power includes at least the base station transmit power, base station circuit consumption, and IRS circuit consumption. The converged communication system is an IRS-assisted NOMA converged communication system. By introducing auxiliary variables, the optimal problem for system energy efficiency is transformed into an iteratively solved parameterized problem. An alternating optimization algorithm is used to decompose the iteratively solved parameterized problem into design subproblems for base station active beamforming and IRS passive beamforming. Semidefinite relaxation and a one-section Taylor approximation are used to iteratively solve these subproblems until the system energy efficiency satisfies the iteration stopping condition, thus obtaining an approximate optimal solution for system energy efficiency. The entire process maximizes the safe energy consumption of the IRS-assisted NOMA system by jointly optimizing the active beamforming of the base station and the passive beamforming of the IRS. Under the premise of ensuring the basic safety and energy efficiency requirements of all users, it can achieve the optimal safety and energy efficiency of the converged communication system, providing an effective solution for building a safe and energy-saving converged communication system.

[0047] In practical applications, the core of security and energy efficiency optimization lies in the joint control of variables such as base station beamforming and IRS phase. However, the effects of these variables depend on the system composition. The model architecture of a converged communication system clearly defines core components such as base stations, IRS, legitimate users, and eavesdroppers, as well as the signal transmission links between components, and clarifies the coupling logic between variables. Correspondingly, such as Figure 2 As shown, prior to step 101, the method further includes the following steps: 201. Construct a model architecture for a converged communication system.

[0048] The model architecture includes at least a base station equipped with multiple antennas and an IRS with multiple reflective elements. The base station communicates with multiple user groups through the IRS. Each user group is multiplexed in the same resource block. The multiple user groups do not interfere with each other. There is an eavesdropping user attempting to eavesdrop on user information in the user group. The users in the user group and the eavesdropping user have a single antenna.

[0049] In this embodiment, the model architecture based on the IRS-assisted NOMA system can be referred to as follows: Figure 3 As shown, specifically in Figure 3 In this process, the base station communicates with the IRS. Several user groups communicate, and an eavesdropper attempts to intercept user information. In a converged communication system, the base station is equipped with... Single antenna; users and eavesdroppers have a single antenna; IRS contains There are 10 reflective units, and the set of reflective units is represented as . Its reflection coefficient is ,in , Take in this system The user group set is represented as For simplicity, assume all user groups are pre-formed based on user locations. Considering the design complexity of the receiver and the performance improvement brought by user multiplexing, each group contains only two users sharing the same resource block, and groups do not interfere with each other. The [number]th... The first in the group Each user is defined as ,in .

[0050] Assuming all channels are quasi-static flat fading channels, and the base station can perfectly acquire the CSI of both the user and the eavesdropper, the channel gain from the base station to the IRS is defined as follows: ;IRS to and the channel gain of the eavesdropper are respectively and The base station superimposes the information from each user group into a NOMA signal and transmits it to the user. Its transmitted signal can be represented as:

[0051] in, This indicates that the base station sends the message to the user. The signal, and satisfy ; For users The beamforming vector. Therefore, the user Received signal and the eavesdropper's received signal It can be represented as:

[0052]

[0053] in, , and It is the additive complex white Gaussian noise of the channel.

[0054] It is understandable that, considering the use of NOMA technology by each user group to improve spectral efficiency, the impact of SIC technology on system performance must be considered at the receiver. Accordingly, in a downlink NOMA system, the performance of SIC is closely related to the decoding order at the receiver; the optimal decoding order is descending order of equivalent channel gain, meaning that users with the largest equivalent channel gain decode their information last. In IRS-assisted communication systems, since the IRS can change the channel gain, the optimal decoding order may change. The main research content of this application's embodiments is the security and energy efficiency problem of IRS-assisted NOMA systems; therefore, to simplify the complexity of the problem, it is assumed that the... The decoding order for each user group is as follows: ,Right now Therefore, users Directly decode your own information and the user The information is treated as interference, and its signal-to-interference-plus-noise ratio is:

[0055] At this time, the user The achievable data transmission rate is And users First decode the user The signal is processed by first deleting it using SIC technology and then decoding its own signal. The signal-to-noise ratio is:

[0056] user The achievable data transmission rate for decoding one's own information is It is worth noting that, in order to ensure user... The successful implementation of SIC technology requires meeting certain conditions. ,in User Decoding User The reachability rate of information can be expressed as:

[0057] In addition, to ensure performance fairness among users, users with lower channel gain should be allocated more power. Therefore, the following inequality must hold:

[0058] For an eavesdropper, assume they can distinguish information across different resource blocks but lack SIC (Search Engine Components) capabilities. Therefore, the eavesdropper decodes the user's... The achievable rate of the signal is:

[0059] According to Shannon's formula, users The safe rate can be expressed as:

[0060] in, Therefore, the safe rate of the system can be expressed as:

[0061] On the other hand, the total power consumption of the system is modeled as follows:

[0062] in, and The circuit consumption for the base station and the IRS are respectively. This refers to the transmission power consumption of the base station.

[0063] Specifically, such as Figure 4 As shown, step 101 above includes the following steps: 301. Based on the model architecture of the converged communication system, the transmitted signal is weighted through multi-antenna precoding technology to obtain active beamforming of the base station, wherein the active beamforming of the base station is an actively modulated transmitted signal represented by a vector.

[0064] 302. Based on the model architecture of the converged communication system, by dynamically adjusting the scent of each passive reflection unit, the reflected signals are superimposed in phase at the legitimate user and canceled out of phase at the eavesdropping user, thus obtaining IRS passive beamforming, wherein the IRS passive beamforming is a passive reconfigured channel represented by a diagonal matrix.

[0065] 303. By jointly optimizing the actively controlled transmission signal represented by the vector and the passively reconstructed channel represented by the diagonal matrix, the optimal problem of system security and energy efficiency is defined on the basis of satisfying the target constraints.

[0066] In this embodiment, to study the safety and energy efficiency of an IRS-assisted NOMA system, the system's safety and energy efficiency are maximized by jointly designing the active beamforming of the base station and the passive beamforming of the IRS. The specific problem can be expressed as:

[0067] (1) (2) (3) (4) (5) in, The minimum secure rate requirement for each user, constraint (1) is to ensure the secure communication quality of users; constraint (3) indicates that the sum of the power allocated to each user should not exceed the maximum transmit power of the base station; constraint (3) is to ensure that users in each NOMA user group SIC can be successfully performed; constraint (4) ensures user performance fairness in each NOMA user group; constraint (5) is a phase shift constraint for the IRS reflection coefficient.

[0068] Accordingly, before defining the optimal problem of system security and energy efficiency by jointly optimizing the actively controlled transmission signal represented by the vector and the passively reconstructed channel represented by the diagonal matrix, and on the basis of satisfying the set conditions, target constraints for solving the optimal problem of system security and energy efficiency are pre-set; wherein, the target constraints include at least one or more of the following: minimum user security rate requirement, maximum base station transmit power constraint, constraint for user to successfully perform serial interference cancellation, constraint to ensure user performance fairness, and IRS reflection phase shift constraint.

[0069] Analysis of the above problems shows that the problem of maximizing the safety and energy efficiency of the IRS-assisted NOMA system is a fractional programming problem. Furthermore, in order to ensure the implementation of SIC technology and performance fairness among users, new constraints have been added, making the problem even more difficult to solve.

[0070] Specifically, such as Figure 5 As shown, step 102 above includes the following steps: 401. Using the defined relaxation vector, basic auxiliary variables are introduced for legitimate users and eavesdropping users respectively, so as to process the coupling between channel and phase through the basic auxiliary variables.

[0071] 402. For the nonlinear relationship in the reachable rate of the eavesdropping user caused by the calculation of the signal-to-noise ratio, the nonlinear relationship is transformed into a linear relationship by adding an auxiliary variable, so that the nonlinear influence of the reachable rate of the eavesdropping user is transferred to the linear constraint of the added auxiliary variable.

[0072] 403. Combining the basic auxiliary variables and the newly added auxiliary variables, the optimal problem of system safety and energy efficiency is transformed into a parameterized problem that is solved iteratively.

[0073] In this embodiment, for ease of solution, during the transformation of the optimal system security and energy efficiency problem, firstly, regarding legitimate user commands... Then there is Then, the following basic auxiliary variables are introduced:

[0074]

[0075] At this time, in the In the user group for ,user Decoding information achievable rate It can be represented as:

[0076] Secondly, regarding the eavesdropper order Then there is And will eavesdroppers eavesdrop on users achievable rate Perform the following transformation:

[0077] Then, new auxiliary variables are introduced. and Specifically, it is expressed as:

[0078]

[0079] Further, the newly added auxiliary variables will be incorporated into the eavesdropper's eavesdropping on users. achievable rate Then there is .

[0080] By introducing the aforementioned basic auxiliary variables and the newly added auxiliary variables, the problem of optimizing system safety and energy efficiency can be transformed into the following form:

[0081] (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) Among them, the constraints (6)-(10) mentioned above are introduced to ensure that the optimal problem of system safety and energy efficiency after transformation is equivalent to the optimal problem of system safety and energy efficiency of the original system. In order to solve the non-convex factors and the coupling of optimization variables in the optimal problem of system safety and energy efficiency after transformation, the problem is transformed into two sub-problems, base station active beamforming design and IRS passive beamforming design, by using an alternating optimization algorithm. The optimal solution of the original problem is obtained by solving them alternately until convergence.

[0082] Specifically, such as Figure 6 As shown, step 103 above includes the following steps: 501. Using an alternating optimization algorithm to fix the IRS passive beamforming variables, so that the part of the total safe rate related to the fixed IRS passive beamforming variables becomes a known quantity, and only the part related to the base station active beamforming variables is retained, the parameterization problem solved iteratively is decomposed into the design sub-problem of the base station active beamforming variables.

[0083] 502. Based on the design subproblem of the active beamforming variables of the base station, the active beamforming variables of the base station are fixed using an alternating optimization algorithm so that the base station-related parts of the total safe rate and the total energy consumption of the base station become known quantities, and only the parts related to the fixed IRS passive beamforming variables are retained. The parameterized problem solved iteratively is decomposed into the design subproblem of the passive beamforming variables of the IRS.

[0084] The variables are decoupled by an alternating optimization algorithm, first fixing the passive beamforming of the IRS. Furthermore, variables are introduced through the Dinkelbach transformation. The active beamforming design problem for base stations can be described as follows:

[0085] The corresponding constraints include (6)-(14) above.

[0086] Specifically, such as Figure 7 As shown, step 104 above includes the following steps: 601. With the IRS phase fixed, use semi-definite relaxation to solve the design subproblem of active beamforming of the base station, so as to maximize the system's security and energy efficiency by optimizing the beamforming vector of the base station.

[0087] 602. Fixed base station beamforming: The design subproblem of passive beamforming of the IRS is solved iteratively using the first-order Taylor approximation to maximize system safety and energy efficiency by optimizing the phase offset of the IRS.

[0088] 603. Repeat the above alternating optimization process. When the difference between the system safety and energy efficiency of two consecutive complete iterations meets the preset threshold, the approximate optimal solution of the system safety and energy efficiency is obtained.

[0089] Regarding the above process, it can be made , Then, using SDR technology, define Then there is , At this point, the active beamforming design problem for base stations can be rewritten as:

[0090] The corresponding constraints include (6), (11), and (13) above, as well as the following constraints: (16) (17) (18) (19) (20) (twenty one) (twenty two) (twenty three) To ensure that the redescribed active beamforming design problem for the base station is equivalent to the original problem, constraints (22) and (23) were added. The redescribed active beamforming design problem for the base station is non-convex due to constraints (6) and (16). Therefore, these two constraints are approximated using a first-order Taylor approximation, and can be specifically expressed as:

[0091]

[0092] in, , and This is a feasible solution to the original problem. By using SDR technology and ignoring the rank-1 constraint, the base station active beamforming design problem can be approximated as the following problem:

[0093] The corresponding constraints include (11), (13), (17)-(22) above, and the following constraints: (twenty four) (25) At this point, the approximate problem of active beamforming design for base stations is about... The standard convex optimization problem can be solved quickly using the CVX toolbox. It is worth noting that because the constraints in the redescribed base station active beamforming design problem are replaced with a first-order Taylor approximation, the solution to the approximate problem of base station active beamforming design is an approximate solution to the original problem. Correspondingly, in obtaining... Afterwards, it is not guaranteed. Found. If The optimal base station beamforming vector is then obtained through eigenvalue decomposition. ,if To ensure the rank-one constraint, an approximate solution needs to be obtained through Gaussian randomization.

[0094] Since the IRS is passive, the design of the IRS reflected beam aims to maximize system and security rates. Therefore, active beamforming is necessary for any feasible base station. The IRS passive beamforming design problem can be expressed as:

[0095] The corresponding constraints include (6)-(11) and (13)-(15) above.

[0096] For IRS reflection vector ,definition ,make , Then there is , To ensure equivalent transformation, Need to meet , as well as The constraints are relaxed by using SDR technology. At this point, the IRS passive beamforming design problem can be rewritten as:

[0097] The corresponding constraints include (6), (11), and (13) above, as well as the following constraints: (25) (26) (27) (28) (29) (30) Similarly, due to constraints (6) and (25), the IRS passive beamforming design problem is a non-convex problem. After using the first-order Taylor approximation, the IRS passive beamforming design problem can be approximated as:

[0098] The corresponding constraints include (11), (13), (26)-(30) above and the following constraints: (31) (32) At this point, the approximate problem of IRS passive beamforming design is a standard convex optimization problem, and a suboptimal solution to the IRS passive beamforming design problem can be obtained by solving it. The CVX toolbox can be used to quickly solve the approximate problem of IRS passive beamforming design and obtain... After that, we also need to consider Whether the constraints are satisfied. If the constraints are not satisfied, Gaussian randomization is needed to obtain an approximate solution.

[0099] The safety and energy efficiency maximization problem of an IRS-assisted NOMA system was studied using the Dinkelbach transform and the alternating optimization algorithm. Each iteration of solving the active beamforming design problem of the base station and the passive beamforming design problem of the IRS constitutes an iteration, and the variables are updated after each iteration. .

[0100] After the Dinkelbach transformation, the result of each solution is non-decreasing. Therefore, when the algorithm converges, the optimal security and energy efficiency of the original problem can be considered obtained. The resulting optimization problem is mainly solved using the interior-point method in the CVX toolbox, and its complexity is related to the size of the problem and the number of constraints. In the embodiments of this application, the time complexity of the IRS-assisted NOMA system security and energy efficiency maximization problem mainly comes from solving the approximate problems of the active beamforming design of the problem base station and the passive beamforming design of the IRS. Their time complexities are respectively... and Therefore, the total complexity of the algorithm is O(n). ,in Let be the number of Dinkelbach transform iterations. To determine the accuracy of the toolbox.

[0101] In practical applications, to demonstrate the superior safety and energy efficiency of the IRS-assisted NOMA system, it is compared with a Time Division Multiple Access (TDMA) system, which will be referred to as the OMA system below. Furthermore, to illustrate the effectiveness of the proposed algorithm, the following three benchmark schemes are compared: the first scheme aims to minimize system transmit power while ensuring user secure communication requirements; the second scheme aims to maximize system and security rates; and the third scheme uses a randomized phase shift for the IRS reflection, aiming to maximize system safety and energy efficiency.

[0102] Correspondingly, Figure 8 The impact of different numbers of IRS reflector units on system security efficiency is shown. The results indicate that when the IRS reflection beam is controllable, system security efficiency gradually increases with the increase in the number of IRS reflector units. This is because a larger number of reflector units allows for better control of the wireless environment, further enhancing user signal gain while suppressing eavesdroppers, thereby improving system and security rates, and consequently leading to increased system security efficiency. It is worth noting that in the random phase scheme, increasing the number of IRS reflector units may lead to a decrease in security performance. This is because the uncontrollable IRS reflection beam may enhance the signal gain at the eavesdropper's location, resulting in decreased system security performance. Therefore, we can speculate that if the location of the eavesdropper or their CSI is uncertain, even with a controllable IRS reflection beam, the assistance of the IRS may still have a negative impact on system security performance.

[0103] Correspondingly, Figure 9 The impact of changes in user minimum safe rate requirements on system safety and energy efficiency is shown. The results indicate that the safety and energy efficiency of this scheme and the second scheme... The third option is not sensitive to changes, but it may not guarantee communication security under high security requirements. The security efficiency of the second option, however, will change with... The increase is followed by a decrease. The reason is that both the first and second schemes can provide users with better secure communication conditions. The former achieves a balance between security rate and energy consumption, while the latter provides the maximum security rate.

[0104] Therefore, in When large enough, for example, This solution will consume more energy to meet the user's minimum safe rate requirement, thus leading to a decrease in safety and energy efficiency. Combined with... Figure 10a and Figure 10b The experimental results shown indicate that the first scheme will... The increased transmission power leads to higher power consumption, while the gain in secure transmission rate decreases with increasing transmission power. This results in the first scheme's security efficiency initially increasing and then decreasing. Furthermore, unlike the first scheme which only guarantees the user's minimum secure communication needs, and unlike the second scheme which ignores energy loss, this scheme simultaneously considers both user secure communication quality and base station power consumption, achieving a balance between system and secure transmission rate and power consumption.

[0105] Furthermore, as Figure 1-1 0. A specific implementation of the method is provided in this application embodiment, which offers an energy efficiency solution device for a converged communication system, such as... Figure 11 As shown, the device includes: a definition unit 71, a conversion unit 72, a decomposition unit 73, and a solution unit 74.

[0106] Definition unit 71 is used to define the optimal problem of system security and energy efficiency based on the model architecture of the converged communication system by jointly designing base station active beamforming and IRS passive beamforming. The system security and energy efficiency is the ratio of the secure rate of the converged communication system to the total power consumption of the system. The secure rate is the sum of the secure rates of all users. The total power of the system includes at least the base station transmit power, base station circuit consumption and IRS circuit consumption. The converged communication system is an IRS-assisted NOMA converged communication system. The transformation unit 72 is used to transform the optimal problem of system safety and energy efficiency into a parameterized problem that can be solved iteratively by introducing auxiliary variables; Decomposition unit 73 is used to decompose the iteratively solved parameterized problem into a design subproblem of active beamforming for base stations and a design subproblem of passive beamforming for IRS using an alternating optimization algorithm. The solution unit 74 is used to iteratively solve the design subproblem of active beamforming of the base station and the design subproblem of passive beamforming of the IRS using semidefinite relaxation and a section Taylor approximation until the system safety and energy efficiency meet the iteration stopping condition and the approximate optimal solution of the system safety and energy efficiency is obtained.

[0107] The energy efficiency solution device for converged communication systems provided in this invention differs from current approaches that only address single-user scenarios in converged communication systems. This application, based on the model architecture of the converged communication system, defines the optimal problem of system safety energy efficiency by jointly designing base station active beamforming and IRS passive beamforming. System safety energy efficiency is the ratio of the system's safe rate to its total power consumption. The safe rate is the sum of the safe rates of all users. The total system power includes at least the base station transmit power, base station circuit consumption, and IRS circuit consumption. The converged communication system is an IRS-assisted NOMA converged communication system. By introducing auxiliary variables, the optimal problem of system safety energy efficiency is transformed into an iteratively solved parameterized problem. An alternating optimization algorithm is used to decompose the iteratively solved parameterized problem into a design subproblem of base station active beamforming and a design subproblem of IRS passive beamforming. Semidefinite relaxation and a one-section Taylor approximation are used to iteratively solve the design subproblems of base station active beamforming and IRS passive beamforming until the system safety energy efficiency satisfies the iteration stopping condition, thus obtaining an approximate optimal solution for system safety energy efficiency. The entire process maximizes the safe energy consumption of the IRS-assisted NOMA system by jointly optimizing the active beamforming of the base station and the passive beamforming of the IRS. Under the premise of ensuring the basic safety and energy efficiency requirements of all users, it can achieve the optimal safety and energy efficiency of the converged communication system, providing an effective solution for building a safe and energy-saving converged communication system.

[0108] In specific application scenarios, the device further includes: The building unit is used to construct the model architecture of the converged communication system based on the model architecture of the converged communication system. Before defining the optimal problem of system security and energy efficiency by jointly designing active beamforming of the base station and passive beamforming of the IRS, the model architecture includes at least a base station equipped with multiple antennas and an IRS with multiple reflection units. The base station communicates with multiple user groups through the IRS. Each user group is multiplexed in the same resource block. The multiple user groups do not interfere with each other. There is an eavesdropping user attempting to eavesdrop on the user information in the user group. The users in the user group and the eavesdropping user have a single antenna.

[0109] In specific application scenarios, the defined unit is specifically used for: Based on the model architecture of the converged communication system, the transmitted signal is weighted through multi-antenna precoding technology to obtain active beamforming of the base station. The active beamforming of the base station is an active modulated transmitted signal represented by a vector. Based on the model architecture of the converged communication system, by dynamically adjusting the scent of each passive reflection unit, the reflected signals are superimposed in phase at the legitimate user and canceled out of phase at the eavesdropping user, thus obtaining IRS passive beamforming, which is a passive reconstructed channel represented by a diagonal matrix. By jointly optimizing the actively controlled transmission signal represented by the vector and the passively reconstructed channel represented by the diagonal matrix, the optimal problem of system security and energy efficiency is defined while satisfying the target constraints.

[0110] In specific application scenarios, the defining unit is further used for: Before defining the optimal problem of system security and energy efficiency by jointly optimizing the actively controlled transmitted signal represented by the vector and the passively reconstructed channel represented by the diagonal matrix, and on the basis of satisfying the set conditions, target constraints for solving the optimal problem of system security and energy efficiency are set in advance. The target constraints include at least one or more of the following: minimum user security rate requirement, maximum base station transmit power constraint, constraint on successful user serial interference cancellation, constraint on ensuring user performance fairness, and IRS reflection phase shift constraint.

[0111] In specific application scenarios, the conversion unit is specifically used for: The defined relaxation vectors are used to introduce basic auxiliary variables for legitimate users and eavesdropping users respectively, so as to process the coupling between channel and phase through the basic auxiliary variables; To address the nonlinear relationship arising from the signal-to-noise ratio calculation in the reachable rate of eavesdropping users, an auxiliary variable is added to transform the nonlinear relationship into a linear one, thereby transferring the nonlinear impact of the reachable rate of eavesdropping users to the linear constraints of the added auxiliary variable. By combining the basic auxiliary variables and the newly added auxiliary variables, the problem of optimizing the system's safety and energy efficiency is transformed into a parameterized problem that can be solved iteratively.

[0112] In specific application scenarios, the decomposition unit is specifically used for: The alternating optimization algorithm is used to fix the passive beamforming variables of the IRS so that the part of the total safe rate related to the fixed passive beamforming variables of the IRS becomes a known quantity, and only the part related to the active beamforming variables of the base station is retained. The parameterization problem solved iteratively is decomposed into the design sub-problem of the active beamforming variables of the base station. Based on the design subproblem of the active beamforming variables of the base station, the active beamforming variables of the base station are fixed by an alternating optimization algorithm so that the base station-related parts of the total safe rate and the total energy consumption of the base station become known quantities, and only the parts related to the fixed IRS passive beamforming variables are retained. The parameterized problem of the iterative solution is decomposed into the design subproblem of the passive beamforming variables of the IRS.

[0113] In specific application scenarios, the solving unit is specifically used for: With the IRS phase fixed, a semidefinite relaxation is used to solve the design subproblem of active beamforming for the base station, so as to maximize the system's safety and energy efficiency by optimizing the beamforming vector of the base station. For fixed base station beamforming, the design subproblem of passive beamforming of the IRS is solved iteratively using a first-order Taylor approximation to maximize system safety and energy efficiency by optimizing the phase offset of the IRS. Repeat the above alternating optimization process. When the difference between the system safety and energy efficiency of two consecutive complete iterations meets the preset threshold, the approximate optimal solution for system safety and energy efficiency is obtained.

[0114] It should be noted that other corresponding descriptions of the functional units involved in the energy efficiency solution device for a converged communication system provided in this embodiment can be found in [reference]. Figure 1-Figure 1 The corresponding description in 0 will not be repeated here.

[0115] Based on the above, Figure 1-Figure 1 Accordingly, this application embodiment also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method. Figure 1-Figure 1 The energy efficiency solution method for the converged communication system shown in Figure 0.

[0116] Based on this understanding, the technical solution of this application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or portable hard drive), and includes several instructions to cause a computer device (such as a personal computer, server, or network device) to execute the methods described in the various implementation scenarios of this application.

[0117] Based on the above, Figure 1-Figure 1 The method shown in 0, and Figure 11 To achieve the above objectives, this application also provides a physical device for solving the energy efficiency problem of a converged communication system, as illustrated in the virtual device embodiment. Specifically, this physical device can be a computer, smartphone, tablet, smartwatch, server, or network device, etc. The physical device includes a storage medium and a processor; the storage medium stores a computer program; the processor executes the computer program to achieve the above-described... Figure 1-Figure 1The energy efficiency solution method for the converged communication system shown in Figure 0.

[0118] Optionally, the physical device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0119] In an exemplary embodiment, see Figure 12 The aforementioned physical devices include a communication bus, a processor, a memory, and a communication interface. They may also include input / output interfaces and a display device. The various functional units can communicate with each other via the bus. The memory stores computer programs, and the processor executes the programs stored in the memory, performing the energy efficiency solution method for the converged communication system described in the above embodiments.

[0120] Those skilled in the art will understand that the physical device structure for solving the energy efficiency of a converged communication system provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0121] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the physical device for solving the energy efficiency problem of the aforementioned converged communication system, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented using software plus necessary general-purpose hardware platforms, or it can be implemented in hardware. By applying the technical solution of this application, compared with the existing methods, this application maximizes the safe energy consumption of the IRS-assisted NOMA system by jointly optimizing the active beamforming of the base station and the passive beamforming of the IRS. Under the premise of ensuring the basic safety and energy efficiency requirements of all users, it can achieve optimal safety and energy efficiency of the converged communication system, providing an effective solution for building a safe and energy-saving converged communication system.

[0123] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0124] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A method for solving the energy efficiency of a converged communication system, characterized in that, include: Based on the model architecture of the converged communication system, the optimal problem of system security and energy efficiency is defined by jointly designing base station active beamforming and IRS passive beamforming. The system security and energy efficiency is the ratio of the secure rate of the converged communication system to the total power consumption of the system. The secure rate is the sum of the secure rates of all users. The total power of the system includes at least the base station transmit power, base station circuit consumption and IRS circuit consumption. The converged communication system is an IRS-assisted NOMA converged communication system. By introducing auxiliary variables, the problem of optimizing the system's safety and energy efficiency is transformed into a parameterized problem that can be solved iteratively. The parameterized problem solved iteratively is decomposed into a design subproblem of active beamforming for base stations and a design subproblem of passive beamforming for IRS using an alternating optimization algorithm. The design subproblems of active beamforming for the base station and passive beamforming for the IRS are solved iteratively using semidefinite relaxation and a first-order Taylor approximation until the system safety and energy efficiency meet the iteration stopping condition, thus obtaining an approximate optimal solution for the system safety and energy efficiency.

2. The method according to claim 1, characterized in that, Before defining the optimal system security and energy efficiency based on the model architecture of the converged communication system by jointly designing base station active beamforming and IRS passive beamforming, the method further includes: A model architecture for a converged communication system is constructed. The model architecture includes at least a base station equipped with multiple antennas and an IRS with multiple reflective elements. The base station communicates with multiple user groups through the IRS. Each user group is multiplexed in the same resource block. The multiple user groups do not interfere with each other. There is an eavesdropping user attempting to eavesdrop on user information in the user group. The users in the user group and the eavesdropping user have a single antenna.

3. The method according to claim 1, characterized in that, Based on the model architecture of the converged communication system, the optimal problem of system security and energy efficiency is defined by jointly designing base station active beamforming and IRS passive beamforming, including: Based on the model architecture of the converged communication system, the transmitted signal is weighted through multi-antenna precoding technology to obtain active beamforming of the base station. The active beamforming of the base station is an active modulated transmitted signal represented by a vector. Based on the model architecture of the converged communication system, by dynamically adjusting the scent of each passive reflection unit, the reflected signals are superimposed in phase at the legitimate user and canceled out of phase at the eavesdropping user, thus obtaining IRS passive beamforming, which is a passive reconstructed channel represented by a diagonal matrix. By jointly optimizing the actively controlled transmission signal represented by the vector and the passively reconstructed channel represented by the diagonal matrix, the optimal problem of system security and energy efficiency is defined while satisfying the target constraints.

4. The method according to claim 3, characterized in that, Before defining the optimal problem of system security and energy efficiency by jointly optimizing the actively modulated transmitted signal represented by the vector and the passively reconstructed channel represented by the diagonal matrix, based on satisfying set conditions, the method further includes: Pre-set the target constraints for solving the optimal problem of system safety and energy efficiency; The target constraints include at least one or more of the following: minimum user security rate requirement, maximum base station transmit power constraint, constraint on successful user serial interference cancellation, constraint on ensuring user performance fairness, and IRS reflection phase shift constraint.

5. The method according to claim 1, characterized in that, The process of introducing auxiliary variables to transform the optimal problem of system safety and energy efficiency into an iteratively solved parameterized problem includes: The defined relaxation vectors are used to introduce basic auxiliary variables for legitimate users and eavesdropping users respectively, so as to process the coupling between channel and phase through the basic auxiliary variables; To address the nonlinear relationship arising from the signal-to-noise ratio calculation in the reachable rate of eavesdropping users, an auxiliary variable is added to transform the nonlinear relationship into a linear one, thereby transferring the nonlinear impact of the reachable rate of eavesdropping users to the linear constraints of the added auxiliary variable. By combining the basic auxiliary variables and the newly added auxiliary variables, the problem of optimizing the system's safety and energy efficiency is transformed into a parameterized problem that can be solved iteratively.

6. The method according to any one of claims 1-5, characterized in that, The method of using an alternating optimization algorithm to decompose the iteratively solved parameterized problem into a design sub-problem of active beamforming for the base station and a design sub-problem of passive beamforming for the IRS includes: The alternating optimization algorithm is used to fix the passive beamforming variables of the IRS so that the part of the total safe rate related to the fixed passive beamforming variables of the IRS becomes a known quantity, and only the part related to the active beamforming variables of the base station is retained. The parameterization problem solved iteratively is decomposed into the design sub-problem of the active beamforming variables of the base station. Based on the design subproblem of the active beamforming variables of the base station, the active beamforming variables of the base station are fixed by an alternating optimization algorithm so that the base station-related parts of the total safe rate and the total energy consumption of the base station become known quantities, and only the parts related to the fixed IRS passive beamforming variables are retained. The parameterized problem of the iterative solution is decomposed into the design subproblem of the passive beamforming variables of the IRS.

7. The method according to any one of claims 1-5, characterized in that, The method of using semi-definite relaxation and a first-order Taylor approximation to iteratively solve the design subproblem of active beamforming for the base station and the design subproblem of passive beamforming for the IRS, until the system safety and energy efficiency meet the iteration stopping condition, yields an approximate optimal solution for the system safety and energy efficiency, including: With the IRS phase fixed, a semidefinite relaxation is used to solve the design subproblem of active beamforming for the base station, so as to maximize the system's safety and energy efficiency by optimizing the beamforming vector of the base station. For fixed base station beamforming, the design subproblem of passive beamforming of the IRS is solved iteratively using a first-order Taylor approximation to maximize system safety and energy efficiency by optimizing the phase offset of the IRS. Repeat the above alternating optimization process. When the difference between the system safety and energy efficiency of two consecutive complete iterations meets the preset threshold, the approximate optimal solution for system safety and energy efficiency is obtained.

8. An energy efficiency calculation device for a converged communication system, characterized in that, include: The definition unit is used to define the optimal problem of system security and energy efficiency based on the model architecture of the converged communication system by jointly designing base station active beamforming and IRS passive beamforming. The system security and energy efficiency is the ratio of the secure rate of the converged communication system to the total power consumption of the system. The secure rate is the sum of the secure rates of all users. The total power of the system includes at least the base station transmit power, base station circuit consumption and IRS circuit consumption. The converged communication system is an IRS-assisted NOMA converged communication system. The transformation unit is used to transform the optimal problem of system safety and energy efficiency into a parameterized problem that can be solved iteratively by introducing auxiliary variables; The decomposition unit is used to decompose the iteratively solved parameterized problem into a design subproblem of active beamforming for the base station and a design subproblem of passive beamforming for the IRS using an alternating optimization algorithm. The solution unit is used to iteratively solve the design subproblems of active beamforming of the base station and passive beamforming of the IRS using semidefinite relaxation and a section Taylor approximation until the system safety and energy efficiency meet the iteration stopping condition, thus obtaining an approximate optimal solution for the system safety and energy efficiency.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the energy efficiency solution method for the converged communication system according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the energy efficiency solution method for the converged communication system according to any one of claims 1 to 7.

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