Power communication system high energy efficiency optimization method and system based on security

Abstract

The invention belongs to the technical field of electric power communication optimization, and discloses a safety-based electric power communication system high-energy-efficiency optimization method and system, and the method comprises the steps: constructing an electric power wireless communication system model, and converting a safety energy efficiency maximization problem into a base station power minimization problem; based on the converted problem and an IRS phase shift matrix in a fixed system, the optimization problem of a beam forming vector and an artificial noise vector is converted into a convex optimization problem, non-convex constraint is converted into convex constraint through first-order Taylor approximation, and second-order cone programming SOCP is adopted for solving; a beam forming vector and an artificial noise vector are fixed, the optimization problem of an IRS phase shift matrix is converted into a feasibility problem, non-convex constraints are processed through first-order Taylor approximation and a penalty function, a convex optimization tool is adopted for solving until the safety energy efficiency performance reaches convergence, and balance optimization of the secrecy rate and energy consumption is achieved. The security problem is considered, the mutual restriction problem of the transmission rate and the power consumption is also considered, and the secrecy energy efficiency is improved.

Description

Technical Field

[0001] This invention belongs to the field of power communication optimization technology, specifically relating to a high-efficiency optimization method and system for a secure power communication system. Background Technology

[0002] With the development of smart grids, power communication has been widely applied in all aspects of power grid production control, management, and operation, and has become an integral part of the power system. Currently, relay protection, safety automatic devices, and automation systems commonly use optical communication technology, greatly improving channel reliability and freeing them from the constraints of traditional communication conditions such as bandwidth, latency, and reliability. This makes cross-regional control possible and cross-system monitoring and analysis a reality. With the widespread adoption of new power grid control technologies such as differential current protection and new EMS systems, power communication and power grid production are becoming increasingly integrated. From the perspective of power grid requirements for communication, the current power communication system urgently needs to address the issues of bandwidth, reliability, and security of the communication network. With the increasing number of wireless connections in power communication systems, eavesdropping on information during transmission has become possible. Currently, physical layer technologies are primarily used to improve system security, with common techniques including beamforming, zero-forcing precoding, artificial noise, and coordinated interference. However, these techniques have two main drawbacks: first, deploying active repeaters or other auxiliary equipment to ensure secure transmission leads to high hardware costs and significant energy consumption; second, in harsh wireless transmission environments, it is difficult to guarantee confidentiality, even with artificial noise or interference signals.

[0003] In recent years, Intelligent Reflector (IRS) technology has emerged. IRS, through a software controller, can optimize and improve the wireless propagation environment. Since IRS does not use a radio frequency chain, its power consumption is only used for phase control of the reflecting unit, resulting in lower power consumption. The IRS reflected signal can be superimposed on the line-of-sight link signal. By jointly optimizing the base station's transmit beamforming and the IRS's reflected beamforming, the interference power of the line-of-sight link signal can be reduced, thus lowering the signal power received by eavesdropping users. Therefore, IRS has great potential to improve the energy efficiency and security of power line wireless communication systems.

[0004] The security of IRS-assisted power line wireless communication systems has received increasing attention in recent years. Appropriate configuration of the IRS allows for dynamic phase adjustment, thereby achieving optimal security. In existing technologies, to improve system security, researchers have studied the joint optimization of transmit beamforming and the IRS phase shift matrix based on block coordinate descent and Majorize-Minimize (MM) techniques, presenting two suboptimal algorithms. Furthermore, researchers have extended the discrete phase of the IRS, maximizing the security of the MISO system under transmit power and IRS phase shift constraints. Literature describes an AO algorithm based on single-antenna eavesdropping users, extending it to scenarios with multiple antenna eavesdropping users. Artificial noise (AN) technology is an effective means of enhancing physical layer security. Although it consumes additional power, designing a reasonable covariance matrix can better suppress unauthorized eavesdropping, thereby improving the performance of the secure communication system and reducing overall system power consumption. For IRS-assisted MISO secure communication with AN transmission at the transmitter, existing technology provides an optimization algorithm that jointly optimizes the transmit beamforming matrix, AN covariance matrix, and IRS reflection beamforming, aiming to maximize system confidentiality. The difference lies in that the former focuses on scenarios involving a single legitimate user and multiple eavesdropping users, while the latter considers scenarios involving multiple legitimate users and a single eavesdropping user.

[0005] All of the above approaches prioritize maximizing system security. However, excessively pursuing high transmission rates leads to excessive energy consumption, which is detrimental to energy-constrained devices. Conversely, solely focusing on minimizing transmission power will also negatively impact transmission rates. Summary of the Invention

[0006] The purpose of this invention is to provide a high-efficiency optimization method and system for secure power communication systems to solve the problem of mutual constraints between transmission rate and power consumption.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a high-efficiency optimization method for a secure power communication system, comprising: A power wireless communication system model is constructed, and the ratio of the security rate of the power wireless communication system model to the total power consumption of the system is used as the security energy efficiency index. A security energy efficiency optimization problem model is established, and the problem of maximizing security energy efficiency is transformed into the problem of minimizing base station power. Based on the transformed problem, an alternating optimization approach is adopted: First, with the IRS phase shift matrix fixed, the optimization problem of the beamforming vector and artificial noise vector is transformed into a convex optimization problem. The non-convex constraints are transformed into convex constraints using a first-order Taylor approximation, and the transformed problem is solved using second-order cone programming (SOCP). Second, with the beamforming vector and artificial noise vector fixed, the optimization problem of the IRS phase shift matrix is ​​transformed into a feasibility problem. The non-convex constraints are handled using a first-order Taylor approximation and a penalty function, and the transformed problem is solved using convex optimization tools until the security and energy efficiency performance converges, achieving a balance between security and energy consumption.

[0008] Furthermore, the construction of the power wireless communication system model includes: The power wireless communication system model includes a base station, an intelligent reflector (IRS), legitimate users, and eavesdropping users. The base station has M antennas, the IRS has N reflectors, and there are K legitimate users with single antennas and L eavesdropping users with single antennas in the system. The base station transmits signals, including beamforming vectors, information symbols, and artificial noise vectors. The phase shift matrix of the IRS is defined.

[0009] Furthermore, the security rate and total power consumption of the power wireless communication system model include: The channel from the base station to the smart reflector is represented as follows: The channel from the intelligent reflector to the kth legitimate user is represented as: The channel from the intelligent reflector to the l-th eavesdropping user is represented as: The channels from the base station to the k-th legitimate user and to the l-th eavesdropping user are respectively denoted as... and The phase shift matrix of the IRS is expressed as: ,in , or The transmitted signal at the base station is represented as shown in formula (1): (1) In formula (1) This is the beamforming vector that the base station transmits to the k-th legitimate user. The information symbol sent by the base station to the k-th user, and satisfying , The vector represents artificial noise and follows a circularly symmetric complex Gaussian distribution, i.e., it follows... ,in It is the covariance matrix of AN. The signal sent by the base station is received at the IRS and then reflected by the IRS to the receiving end. The signal received at the receiving end includes two aspects: one is the signal of the cascaded link, which is the signal sent by the base station and reflected by the IRS to the user at the receiving end for reception; the other is the signal of the direct link, which is the signal sent by the base station directly to the user at the receiving end for reception. Assuming that the l-th eavesdropping user is trying to eavesdrop on the information of the k-th legitimate user, the signals received by the legitimate user k and the eavesdropping user l are represented as shown in formula (2) and formula (3) respectively: (2) (3) in, Additive white Gaussian noise for legitimate users, and obeys ; To eavesdrop on users' additive white Gaussian noise, and obey According to Shannon's formula, the information rates of the kth legitimate user and the lth eavesdropping user are shown in formulas (4) and (5), respectively: (4) (5) At this point, based on formulas (4) and (5), the system's security rate is expressed as shown in formula (6): (6); The total power consumption of the system consists of the base station's transmission power and the hardware power consumed by the circuit. The total power consumption of the system is shown in formulas (7) and (8): (7) (8) in, For power amplifier coefficient, This refers to the base station's transmission power. For the hardware power consumption of the base station, This refers to the static power consumption of the IRS. For the user's hardware power consumption.

[0010] Furthermore, the establishment of a safety and energy efficiency optimization problem model transforms the safety and energy efficiency maximization problem into a base station power minimization problem, including: Under the constraints of user safety rate and base station total transmission power, the safety energy efficiency maximization model is established as shown in formula (9): (9) In the formula, For the system's minimum security rate, This represents the maximum transmission power of the base station. The original problem of maximizing security energy efficiency is transformed into a problem of minimizing base station power. Under the condition of minimizing base station transmission power, the goal of maximizing energy efficiency is achieved by ensuring that the security rate and energy consumption are balanced. The corresponding optimization problem is described by formula (10): (10) In the optimization model, constraint C1 in formula (9) is transformed into the corresponding signal-to-interference-plus-noise ratio (SIR) constraints for legitimate users and eavesdropping users, as shown in formula (11): (11) In formula (11), Minimum signal-to-interference-plus-noise ratio for legitimate users. To eavesdrop on users at the maximum signal-to-interference-plus-noise ratio, Through transformation, the problem model is reformulated as shown in formula (12): (12).

[0011] Furthermore, based on the transformed problem, with the IRS phase shift matrix fixed in the system, the optimization problem of the beamforming vector and artificial noise vector is transformed into a convex optimization problem. The non-convex constraints are transformed into convex constraints through a first-order Taylor approximation, and solved using second-order cone programming (SOCP), including: Using a fixed phase shift matrix, the beamforming vector and artificial noise vector are optimized. With the IRS phase shift matrix fixed, the design problem of the beamforming vector and artificial noise is expressed as shown in Equation (13): (13) The goal will be to transform non-convex problems into convex problems for solution, making , Therefore, the constraints C1 and C2 in formula (12) are represented as shown in formulas (14) and (15), respectively: (14) (15) Based on the above transformation, formula (13) can be expressed as formula (16): (16) The above two formulas can be written as shown in formulas (17) and (18): (17) (18) At this point, the original non-convex constraint conditions are transformed into convex constraints. The C1 and C2 constraints in formula (12) are transformed into formulas (17) and (18). The original problem is now transformed into a convex problem for solution, as shown in formula (19): (19) The second-order cone programming problem of formula (19) is solved using the CVX toolbox.

[0012] Furthermore, the fixed beamforming vector and artificial noise vector transform the optimization problem of the IRS phase shift matrix into a feasibility problem. Non-convex constraints are handled using a first-order Taylor approximation and a penalty function, and a convex optimization tool is employed to solve the problem until security and energy efficiency performance converges, achieving a balance between security and energy consumption. This includes: Using fixed beamforming and artificial noise vectors, the IRS phase shift matrix is ​​optimized. Under these fixed conditions, the optimization problem is transformed into an IRS phase shift matrix design problem. Before addressing the feasibility verification, the complex parameters in the problem are equivalently replaced, allowing... , , , Similarly, , , , , , At this point, the feasibility problem is described as shown in formula (20): (20) In formula (20), Except for C3, the other three constraints are non-convex constraints. C1 and C2 are transformed into convex forms by first-order Taylor approximation, as shown in formulas (21) and (22). (twenty one) (twenty two) At this point, two of the three non-convex constraints have been transformed from non-convex to convex forms. As for the last non-convex constraint... The semi-definite relaxation (SDR) technique was used to relax the constraint to Form; construct a and The equivalent convex constraint conditions are then constructed as shown in formula (23): (twenty three) In formula (23), For matrix The sum of all singular values, For matrix The maximum singular value, Using a penalty function, the transformed optimization problem is shown in equation (24): (twenty four) Assumption The optimal solution to formula (24) has a corresponding objective value that is less than The corresponding target value, assuming To find the optimal solution to equation (24), equation (25) is used to verify the process of iteration until the target value converges: (25) The optimal solution to problem formula (24) is obtained by using the CVX solver.

[0013] Secondly, the present invention provides a high-efficiency optimization system based on a secure power communication system, comprising: The model building module is used to build a model of a power wireless communication system. The ratio of the security rate of the power wireless communication system model to the total power consumption of the system is used as a security energy efficiency index to establish a security energy efficiency optimization problem model, which transforms the security energy efficiency maximization problem into the base station power minimization problem. The solution module is used to solve the transformed problem using alternating optimization: First, with the IRS phase shift matrix fixed, the optimization problem of the beamforming vector and artificial noise vector is transformed into a convex optimization problem. Then, the non-convex constraints are transformed into convex constraints using a first-order Taylor approximation, and the transformed problem is solved using second-order cone programming (SOCP). Second, with the beamforming vector and artificial noise vector fixed, the optimization problem of the IRS phase shift matrix is ​​transformed into a feasibility problem. The non-convex constraints are handled using a first-order Taylor approximation and a penalty function, and the transformed problem is solved using convex optimization tools until the security and energy efficiency performance converges, achieving a balance between security and energy consumption.

[0014] Furthermore, in the model building module, the construction of the power wireless communication system model includes: The system includes a base station, an intelligent reflector (IRS), legitimate users, and eavesdropping users. The base station has M antennas, the IRS has N reflectors, and there are K legitimate users with single antennas and L eavesdropping users with single antennas. The system defines the base station's transmitted signals, including beamforming vectors, information symbols, and artificial noise vectors. The system also defines the phase shift matrix of the IRS.

[0015] Furthermore, the security rate and total power consumption of the power wireless communication system model include: The channel from the base station to the smart reflector is represented as follows: The channel from the intelligent reflector to the kth legitimate user is represented as: The channel from the intelligent reflector to the l-th eavesdropping user is represented as: The channels from the base station to the k-th legitimate user and to the l-th eavesdropping user are respectively denoted as... and The phase shift matrix of the IRS is expressed as: ,in , or The transmitted signal at the base station is represented as shown in formula (1): (1) In formula (1) This is the beamforming vector that the base station transmits to the k-th legitimate user. The information symbol sent by the base station to the k-th user, and satisfying , The vector represents artificial noise and follows a circularly symmetric complex Gaussian distribution, i.e., it follows... ,in It is the covariance matrix of AN. The signal sent by the base station is received at the IRS and then reflected by the IRS to the receiving end. The signal received at the receiving end includes two aspects: one is the signal of the cascaded link, which is the signal sent by the base station and reflected by the IRS to the user at the receiving end for reception; the other is the signal of the direct link, which is the signal sent by the base station directly to the user at the receiving end for reception. Assuming that the l-th eavesdropping user is trying to eavesdrop on the information of the k-th legitimate user, the signals received by the legitimate user k and the eavesdropping user l are represented as shown in formula (2) and formula (3) respectively: (2) (3) in, Additive white Gaussian noise for legitimate users, and obeys ; To eavesdrop on users' additive white Gaussian noise, and obey According to Shannon's formula, the information rates of the kth legitimate user and the lth eavesdropping user are shown in formulas (4) and (5), respectively: (4) (5) At this point, based on formulas (4) and (5), the system's security rate is expressed as shown in formula (6): (6); The total power consumption of the system consists of the base station's transmission power and the hardware power consumed by the circuit. The total power consumption of the system is shown in formulas (7) and (8): (7) (8) in, For power amplifier coefficient, This refers to the base station's transmission power. For the hardware power consumption of the base station, This refers to the static power consumption of the IRS. For the user's hardware power consumption.

[0016] Furthermore, the establishment of a safety and energy efficiency optimization problem model transforms the safety and energy efficiency maximization problem into a base station power minimization problem, including: Under the constraints of user safety rate and base station total transmission power, the safety energy efficiency maximization model is established as shown in formula (9): (9) In the formula, For the system's minimum security rate, This represents the maximum transmission power of the base station. The original problem of maximizing security energy efficiency is transformed into a problem of minimizing base station power. Under the condition of minimizing base station transmission power, the goal of maximizing energy efficiency is achieved by ensuring that the security rate and energy consumption are balanced. The corresponding optimization problem is described by formula (10): (10) In the optimization model, constraint C1 in formula (9) is transformed into the corresponding signal-to-interference-plus-noise ratio (SIR) constraints for legitimate users and eavesdropping users, as shown in formula (11): (11) In formula (11), Minimum signal-to-interference-plus-noise ratio for legitimate users. To eavesdrop on users at the maximum signal-to-interference-plus-noise ratio, Through transformation, the problem model is reformulated as shown in formula (12): (12).

[0017] Furthermore, in the first solution module, based on the transformed problem, the IRS phase shift matrix in the system is fixed, and the optimization problem of the beamforming vector and artificial noise vector is transformed into a convex optimization problem. The non-convex constraints are transformed into convex constraints through a first-order Taylor approximation, and solved using second-order cone programming (SOCP), including: Using a fixed phase shift matrix, the beamforming vector and artificial noise vector are optimized. With the IRS phase shift matrix fixed, the design problem of the beamforming vector and artificial noise is expressed as shown in Equation (13): (13) The goal will be to transform non-convex problems into convex problems for solution, making , Therefore, the constraints C1 and C2 in formula (12) are represented as shown in formulas (14) and (15), respectively: (14) (15) Based on the above transformation, formula (13) can be expressed as formula (16): (16) The above two formulas can be written as shown in formulas (17) and (18): (17) (18) At this point, the original non-convex constraint conditions are transformed into convex constraints. The C1 and C2 constraints in formula (12) are transformed into formulas (17) and (18). The original problem is now transformed into a convex problem for solution, as shown in formula (19): (19) The second-order cone programming problem of formula (19) is solved using the CVX toolbox.

[0018] Furthermore, in the second solution module, the fixed beamforming vector and artificial noise vector transform the optimization problem of the IRS phase shift matrix into a feasibility problem. Non-convex constraints are handled using a first-order Taylor approximation and a penalty function, and a convex optimization tool is employed to solve the problem until security and energy efficiency performance converges, achieving a balance between security and energy consumption. This includes: Using fixed beamforming and artificial noise vectors, the IRS phase shift matrix is ​​optimized. Under these fixed conditions, the optimization problem is transformed into an IRS phase shift matrix design problem. Before addressing the feasibility verification, the complex parameters in the problem are equivalently replaced, allowing... , , , Similarly, , , , , , At this point, the feasibility problem is described as shown in formula (20): (20) In formula (20), Except for C3, the other three constraints are non-convex constraints. C1 and C2 are transformed into convex forms by first-order Taylor approximation, as shown in formulas (21) and (22). (twenty one) (twenty two) At this point, two of the three non-convex constraints have been transformed from non-convex to convex forms. As for the last non-convex constraint... The semi-definite relaxation (SDR) technique was used to relax the constraint to Form; construct a and The equivalent convex constraint conditions are then constructed as shown in formula (23): (twenty three) In formula (23), For matrix The sum of all singular values, For matrix The maximum singular value, Using a penalty function, the transformed optimization problem is shown in equation (24): (twenty four) Assumption The optimal solution to formula (24) has a corresponding objective value that is less than The corresponding target value, assuming To find the optimal solution to equation (24), equation (25) is used to verify the process of iteration until the target value converges: (25) The optimal solution to problem formula (24) is obtained by using the CVX solver.

[0019] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the high-efficiency optimization method for a secure power communication system.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for optimizing the high energy efficiency of a secure power communication system.

[0021] Compared with the prior art, the present invention has the following technical effects: This invention introduces intelligent reflector technology and artificial noise technology into power line wireless multi-user communication systems to address the secure communication problem in situations where multiple single-antenna eavesdropping users exist. Furthermore, it provides a high-energy-efficiency optimization method based on secure communication. This technical solution transforms the energy efficiency problem into a power problem for optimization, employing a SOCP-based approach to effectively solve the non-convex problem and non-convex constraints involved in the optimization model. In systems with deployed IRS and multiple eavesdropping instances, the introduction of artificial noise technology effectively deters eavesdropping, thereby enhancing the system's anti-interference capability. This method considers both security and the trade-off between transmission rate and power consumption—that is, the balance between security and energy consumption—thus improving both security and energy efficiency. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the system structure in the embodiment; Figure 2 This is a structural simulation diagram of the system in the embodiment; Figure 3 This is a schematic diagram illustrating the relationship between transmit power and the number of IRS reflective elements in the embodiment; Figure 4 This is a schematic diagram illustrating the relationship between transmission power and the number of eavesdropping users in the embodiment; Figure 5 This is a schematic diagram illustrating the relationship between the power allocated to beamforming and artificial noise and the maximum rate of the eavesdropping user in the embodiment. Figure 6 This is a schematic diagram illustrating the relationship between energy efficiency and minimum security rate in the embodiments.

[0023] Figure 7 This is a flowchart of the present invention. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments, but this is not intended to limit the scope of the invention.

[0025] Example 1, please refer to Figure 7 This invention provides a high-efficiency optimization method for a secure power communication system, comprising: A power wireless communication system model is constructed, and the ratio of the security rate of the power wireless communication system model to the total power consumption of the system is used as the security energy efficiency index. A security energy efficiency optimization problem model is established, and the problem of maximizing security energy efficiency is transformed into the problem of minimizing base station power. Based on the transformed problem, an alternating optimization approach is adopted: First, with the IRS phase shift matrix fixed, the optimization problem of the beamforming vector and artificial noise vector is transformed into a convex optimization problem. The non-convex constraints are transformed into convex constraints using a first-order Taylor approximation, and the transformed problem is solved using second-order cone programming (SOCP). Second, with the beamforming vector and artificial noise vector fixed, the optimization problem of the IRS phase shift matrix is ​​transformed into a feasibility problem. The non-convex constraints are handled using a first-order Taylor approximation and a penalty function, and the transformed problem is solved using convex optimization tools until the security and energy efficiency performance converges, achieving a balance between security and energy consumption.

[0026] Example 2: This invention provides a high-efficiency optimization method for a secure power communication system, comprising: 1) Constructing a system model: including: 1-1) System Description: such as Figure 1 As shown, assume a power wireless communication system consists of a base station, an IRS, and legitimate and illegitimate users. The base station has M antennas, and the smart reflector has N reflectors. There are K legitimate users with single antennas and L eavesdropping users with single antennas. Around each legitimate user, there is an eavesdropping user attempting to eavesdrop on useful information. Downlink communication includes direct and reflected links. To improve system security, the base station superimposes artificial noise AN into its transmitted signal to disrupt the eavesdropping user's signal. It is also assumed that both the base station and the IRS are aware of the Channel State Information (CSI) statistics. The channel from the base station to the smart reflector is represented as follows: The channel from the intelligent reflector to the kth legitimate user is represented as: The channel from the intelligent reflector to the l-th eavesdropping user is represented as: The channels from the base station to the k-th legitimate user and to the l-th eavesdropping user are respectively denoted as... and The phase shift matrix of the IRS is expressed as: ,in , or The transmitted signal at the base station can be represented as shown in formula (1): (1), In formula (1): This is the beamforming vector that the base station transmits to the k-th legitimate user. The information symbol sent by the base station to the k-th user, and satisfying , The vector represents artificial noise and follows a circularly symmetric complex Gaussian distribution, i.e., it follows... ,in It is the covariance matrix of AN. The signal sent by the base station is received at the IRS and then reflected back to the receiver by the IRS. Therefore, at the receiver, whether it is a legitimate user or an eavesdropping user, the signal they receive includes two aspects: one is the signal of the cascaded link, which is the signal sent by the base station and reflected back to the user at the receiver by the IRS; the other is the signal of the direct link, which is the signal sent by the base station directly reaches the user at the receiver. Assuming that the l-th eavesdropping user is trying to eavesdrop on the information of the k-th legitimate user, the signals received by the legitimate user k and the eavesdropping user l can be represented as shown in formula (2) and formula (3) respectively: (2), (3), in, Additive white Gaussian noise for legitimate users, and obeys ; To eavesdrop on users' additive white Gaussian noise, and obey According to Shannon's formula, the information rates of the kth legitimate user and the lth eavesdropping user are shown in formulas (4) and (5), respectively: (4), (5), At this point, based on formulas (4) and (5), the system's security rate can be expressed as shown in formula (6): (6); 1-2) Determine the power consumption model: The total power consumption of the system consists of two parts, namely the transmission power of the base station and the hardware power consumed by the circuit. Since the artificial noise added at the base station will also consume additional power, the total power consumption of the system is as shown in formulas (7) and (8): (7), (8), in, For power amplifier coefficient, This refers to the base station's transmission power. For the hardware power consumption of the base station, This refers to the static power consumption of the IRS. For the user's hardware power consumption; 2) Safety and energy efficiency optimization algorithm design: including: 2-1) Optimization Problem Model: The ratio of the system's security rate to the total power consumption of the system is defined as the system's security efficiency. Considering the user's security rate constraint and the base station's total transmission power constraint, the security efficiency maximization model is established as shown in formula (9): (9), In formula (9), For the system's minimum security rate, This represents the maximum transmission power of the base station. Formula (9) is a multivariable nonlinear optimization problem. Both the numerator and denominator contain optimization variables, which are quite complex and difficult to solve directly. It is also difficult to solve directly using fractional programming. Therefore, the original problem of maximizing security energy efficiency is transformed into a problem of minimizing base station power. Under the condition of minimizing base station transmission power, the security rate and energy consumption are kept in balance, thereby achieving the goal of maximizing energy efficiency. The corresponding optimization problem description is shown in Formula (10): (10) In the optimization model, constraint C1 in formula (9) can be transformed into the corresponding signal-to-interference-plus-noise ratio (SIR) constraints for legitimate users and eavesdropping users, as shown in formula (11): (11) In formula (11), Minimum signal-to-interference-plus-noise ratio for legitimate users. To eavesdrop on users at the maximum signal-to-interference-plus-noise ratio, Through transformation, the problem model can be reformulated as shown in formula (12): (12) Formula (12) is a non-convex problem, and the optimization variables are... and The coupling between the two problems is relatively high. The solution to the original problem is to decouple it into two non-convex subproblems and optimize them alternately until the energy efficiency performance converges. Therefore, the two non-convex subproblems are: fixing the phase shift matrix and optimizing the beamforming vector and artificial noise vector; fixing the beamforming vector and artificial noise vector and optimizing the IRS phase shift matrix.

[0027] 2-2) Beamforming vector and artificial noise vector design: Using a fixed phase shift matrix, the beamforming vector and artificial noise vector are optimized. Assuming the IRS phase shift matrix is ​​fixed, the beamforming vector and artificial noise design problem is expressed as shown in formula (13): (13) It is easy to see that the constraints C1 and C2 in formula (12) are non-convex. Next, we will focus on transforming the non-convex problem into a convex problem for solution. For the sake of simplification, let , Therefore, the constraints C1 and C2 in formula (12) are represented as shown in formulas (14) and (15), respectively: (14) (15) Therefore, based on the above transformation, formula (13) can be expressed as formula (16): (16) Observation reveals that formulas (14) and (15) are extremely similar to the first-order Taylor approximation of complex numbers, i.e. The two formulas above can be similarly written as shown in formulas (17) and (18): (17) (18) At this point, the original non-convex constraint conditions are transformed into convex constraints. The C1 and C2 constraints in formula (12) are transformed into formulas (17) and (18). The original problem is now transformed into a convex problem for solution, as shown in formula (19): (19) Formula (19) is a second-order cone programming problem. MATLAB has a dedicated convex optimization toolkit, and the CVX toolbox can be used to solve this problem directly. 2-3) IRS Phase Shift Matrix Design: Using a fixed beamforming vector and artificial noise vector, the IRS phase shift matrix is ​​optimized. Under the premise of a fixed beamforming vector and artificial noise vector, the optimization problem is transformed into the design problem of the IRS phase shift matrix. Observing formula (10), it can be seen that there is no term related to the phase shift matrix in the objective function of the original problem. Therefore, the original problem is transformed into a feasibility verification problem. Before solving the feasibility verification problem, the complex parameters in the problem are equivalently replaced, let , , , Similarly, , , , , , At this point, the feasibility problem is described as shown in formula (20): (20) In formula (20), Except for C3, the other three constraints are non-convex constraints. Similar to step 2-2), C1 and C2 are transformed into convex forms by first-order Taylor approximation, as shown in formulas (21) and (22). (twenty one), (twenty two), At this point, two of the three non-convex constraints have been transformed from non-convex to convex forms. As for the last non-convex constraint... The semi-definite relaxation (SDR) technique was used to relax the constraint to The form is also a convex expression, but after relaxation, it needs to be restored to conform to the form. The optimal solution requires extensive Gaussian randomization; even then, in existing technologies, the solution recovered using this method is only... Therefore, considering the high complexity of the above SDR method, we consider constructing a similar approach. The equivalent convex constraint conditions are then constructed as shown in formula (23): (twenty three), In formula (23), For matrix The sum of all singular values, For matrix The maximum singular value, To incorporate this constraint into the objective function of the feasibility problem, a penalty function is used, and the transformed optimization problem is shown in equation (24): (twenty four), Assumption The optimal solution to formula (24) has a corresponding objective value that is less than The corresponding target value, assuming To find the optimal solution to equation (24), equation (25) is used to verify the process of iteration until the target value converges: (25) The CVX solver can quickly and accurately obtain the optimal solution of problem formula (24).

[0028] In this example, the simulation results and analysis are as follows: The feasibility of the proposed method was verified through simulation. The proposed method was analyzed and compared with existing algorithms, and the impact of different environments on IRS deployment performance was also compared and analyzed. The channel model used was the SV geometric channel model, and the parameter settings in the simulation are shown in Table 1. Table 1 Simulation parameter settings

[0029] Simulation model diagram as follows Figure 2 As shown, the location deployment of base stations, IRS, legitimate users, and eavesdropping users in the system is displayed more intuitively in the simulation scenario. Figure 2On the left side of the center, assuming the base station location is (10,0,20) and the reference location of the IRS center point is (0,100,2), and K legitimate users are randomly distributed in a circle centered at (8,90,0) with a radius of... In a circle, L eavesdropping users are randomly distributed around a circle centered at (10, 100, 0) with a radius of... Within the circle; simulation scene Figure 2 In the middle right, the deployment locations of the base station and IRS are... Figure 2 The left side is the same. In addition, assume that K legitimate users are uniformly distributed along (10,90,0) to (10,110,0) and L eavesdropping users are uniformly distributed along (8,90,0) to (8,110,0).

[0030] Experiment 1: Relationship between transmit power and number of IRS reflectors: Simulation parameters are set as follows: transmitter antenna M=32, base station maximum transmit power. With the number of legitimate users K=3, the number of eavesdropping users L=2, and the number of IRS reflectors ranging from 10 to 90, this experiment analyzes and compares the proposed method with four algorithms in two different simulation scenarios: one based on SDR, the other on random phase shift (marked as "Random phase" in the figure), and the third without IRS deployment (marked as "No IRS" in the figure). Figure 3 As shown, with the increase in the number of IRS reflectors, the transmit power at the base station of both SDR-based and SOCP-based schemes decreases significantly, but the performance results are quite similar. Furthermore, the simulation scenario... Figure 2 The rate of power decrease in the left-hand side of the simulation scenario is faster than in the right-hand side. Figure 2 The right side is faster because compared to Figure 2 The scene on the right side of the middle, Figure 2 The legitimate users on the left side are closer to the IRS; therefore, the following experiments will follow the same scenario. Figure 2 The simulation was performed on the left side of the screen. It can also be seen that the performance gap between the IRS-assisted scheme and the scheme without IRS assistance increases with the increase of the number of reflectors in both scenarios. This is because deploying IRS can increase the line-of-sight link for signal transmission, and IRS reflectors can provide higher degrees of freedom for passive beamforming of IRS.

[0031] Experiment 2: Relationship between transmission power and the number of eavesdropping users: Simulation parameter settings: Transmitter antenna M=32, base station maximum transmit power The number of legitimate users K=3, the number of IRS reflective elements N=64, and the number of eavesdropping users varies from 1 to 8. This experiment primarily compares the method presented in this example with two other schemes: random phase shift and no IRS deployment. It also analyzes the changes in base station transmission power as the number of eavesdropping users increases, with and without the introduction of artificial noise. Figure 4 As shown, the transmission power increases with the number of eavesdropping users to meet the system's security requirements. However, compared to the other two benchmark schemes, the scheme with artificial noise in this example achieves lower transmission power (the transmission power involved in this experiment is only the average value taken from the set of feasible solutions). In addition, for the case where there are a large number of eavesdropping users in the system (i.e., L>4), the transmission power of the scheme with artificial noise is relatively lower than that of the scheme without artificial noise. This is a significant advantage of artificial noise. Furthermore, as the number of eavesdropping users increases, the system's security rate decreases. In other words, as the number of eavesdropping users in the system increases, adding artificial noise to the system to improve system security is an effective measure.

[0032] Experiment 3: Relationship between the power allocated to beamforming and artificial noise and the maximum rate of the eavesdropping user: Simulation parameter settings: Transmitter antenna M=32, IRS reflector number N=64, base station maximum transmit power. The number of legitimate users K=3, the number of eavesdropping users L=2, and the maximum rate variation range of eavesdropping users is 1.5~5; This experiment primarily investigates how, under different maximum data rates for eavesdropping users, the base station allocates transmission power to artificial noise and beamforming operations, respectively. Figure 5 As shown, when the maximum rate of the eavesdropping user is low, the base station allocates most of its transmission power to artificial noise and a small portion to beamforming. In this case, the eavesdropping user's rate is low, thus ensuring a high level of system confidentiality and better security. When the maximum rate of the eavesdropping user is high, the base station allocates more power to beamforming, effectively utilizing the degrees of freedom of the base station antenna and IRS reflectors to improve beamforming capabilities, thereby increasing the system's confidentiality rate and improving system energy efficiency. Therefore, when the system's confidentiality rate requirement is high, selecting a suitable maximum rate for the eavesdropping user is also an important task.

[0033] Experiment 4: Relationship between energy efficiency and minimum secrecy level: Simulation parameter settings: Transmitter antenna M=32, IRS reflector number N=64, base station maximum transmit power. The number of legitimate users K=3, the number of eavesdropping users L=2, and the constraint range of the minimum security rate of the system is 0~9. This experiment mainly studies the energy efficiency changes of four different schemes when the minimum security level of the system changes continuously, such as... Figure 6 As shown: (1) In the “SR-based maximum” scheme, the energy efficiency performance remains basically at the same level. This can be explained by the fact that the base station consumes almost all available power to ensure that SR reaches its maximum value. Therefore, regardless of the minimum security rate of the system, (1) Regardless of the value, the SR value remains unchanged, thus the system safety efficiency of this scheme also remains unchanged; (2) In the "power-minimum" scheme, the system's energy efficiency first shows an upward trend, then decreases, when When the value is no more than 4.5, the growth rate of SR is greater than the growth rate of power consumption, therefore the system's energy efficiency shows an increasing trend. When the security level is greater than 4.5, the growth rate of SR is less than the growth rate of power consumption. Therefore, the energy efficiency of the system shows a downward trend. This phenomenon can be explained as follows: when the security level increases, the power cost required to improve the security level is greater than the benefits it brings to the system, resulting in lower energy efficiency. This also shows that there is a trade-off between energy efficiency and security level. (3) In the scheme based on "no IRS" and "random phase shift", the system energy efficiency performance first remains at a stable level, and then as The increase leads to a decrease, because when At lower levels, the higher SR achieved by these two schemes can help the system achieve higher energy efficiency, but when... When the rate exceeds these optimal rates, the system requires more energy to increase the SR to the minimum security rate constraint, resulting in reduced energy efficiency. Furthermore, when... When the values ​​are greater than 3.5 and 4 respectively, the curves for the no-IRS and random phase-shift schemes are not shown. This is because, even with the maximum base station transmit power to increase the rate, there is no feasible solution to meet the increased requirements. .

[0034] This example transforms the energy efficiency problem into a power problem for optimization. The SOCP-based approach effectively addresses the non-convex problem and non-convex constraints involved in the optimization model. Simulations demonstrate that in systems with IRS deployments and multiple eavesdropping attempts, introducing artificial noise technology effectively prevents eavesdropping and enhances the system's anti-interference capabilities. In other words, combining IRS and artificial noise technology is an effective measure to improve system security.

[0035] In another embodiment of the present invention, a high-efficiency optimization system for a secure power communication system is provided, which can be used to implement the above-mentioned high-efficiency optimization method for a secure power communication system. Specifically, the system includes: The model building module is used to build a model of a power wireless communication system. The ratio of the security rate of the power wireless communication system model to the total power consumption of the system is used as a security energy efficiency index to establish a security energy efficiency optimization problem model, which transforms the security energy efficiency maximization problem into the base station power minimization problem. The solution module is used to solve the transformed problem using alternating optimization: First, with the IRS phase shift matrix fixed, the optimization problem of the beamforming vector and artificial noise vector is transformed into a convex optimization problem. Then, the non-convex constraints are transformed into convex constraints using a first-order Taylor approximation, and the transformed problem is solved using second-order cone programming (SOCP). Second, with the beamforming vector and artificial noise vector fixed, the optimization problem of the IRS phase shift matrix is ​​transformed into a feasibility problem. The non-convex constraints are handled using a first-order Taylor approximation and a penalty function, and the transformed problem is solved using convex optimization tools until the security and energy efficiency performance converges, achieving a balance between security and energy consumption.

[0036] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0037] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a high-energy-efficiency optimization method for a secure power communication system.

[0038] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of the terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the energy-efficient optimization method for a secure power communication system described in the above embodiments.

[0039] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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.

[0040] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0041] 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.

[0042] 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.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A high-efficiency optimization method for a secure power communication system, characterized in that, include: A power wireless communication system model is constructed, and the ratio of the security rate of the power wireless communication system model to the total power consumption of the system is used as the security energy efficiency index. A security energy efficiency optimization problem model is established, and the problem of maximizing security energy efficiency is transformed into the problem of minimizing base station power. Based on the transformed problem, an alternating optimization approach is adopted: First, the IRS phase shift matrix in the system is fixed, and the optimization problem of the beamforming vector and artificial noise vector is transformed into a convex optimization problem. The non-convex constraints are transformed into convex constraints using a first-order Taylor approximation, and the transformed problem is solved using second-order cone programming (SOCP). Second, the beamforming vector and artificial noise vector are fixed, and the optimization problem of the IRS phase shift matrix is ​​transformed into a feasibility problem. The non-convex constraints are handled using a first-order Taylor approximation and a penalty function, and the transformed problem is solved using convex optimization tools. This alternating approach continues until the security and energy efficiency performance converges, achieving a balance between security and energy consumption.

2. The high-efficiency optimization method for a secure power communication system according to claim 1, characterized in that, The construction of the power wireless communication system model includes: The power wireless communication system model includes a base station, an intelligent reflector (IRS), legitimate users, and eavesdropping users. The base station has M antennas, the IRS has N reflectors, and there are K legitimate users with single antennas and L eavesdropping users with single antennas in the system. The base station transmits signals, including beamforming vectors, information symbols, and artificial noise vectors. The phase shift matrix of the IRS is defined.

3. The high-efficiency optimization method for a secure power communication system according to claim 2, characterized in that, The security rate and total power consumption of the power wireless communication system model include: The channel from the base station to the smart reflector is represented as follows: The channel from the intelligent reflector to the kth legitimate user is represented as: The channel from the intelligent reflector to the l-th eavesdropping user is represented as: The channels from the base station to the k-th legitimate user and to the l-th eavesdropping user are respectively denoted as... and The phase shift matrix of the IRS is expressed as: ,in , or The transmitted signal at the base station is represented as shown in formula (1): （1） In formula (1) This is the beamforming vector that the base station transmits to the k-th legitimate user. The information symbol sent by the base station to the k-th user, and satisfying , The vector represents artificial noise and follows a circularly symmetric complex Gaussian distribution, i.e., it follows... ,in It is the covariance matrix of AN. The signal sent by the base station is received at the IRS and then reflected by the IRS to the receiving end. The signal received at the receiving end includes two aspects: one is the signal of the cascaded link, which is the signal sent by the base station and reflected by the IRS to the user at the receiving end for reception; the other is the signal of the direct link, which is the signal sent by the base station directly to the user at the receiving end for reception. Assuming that the l-th eavesdropping user is trying to eavesdrop on the information of the k-th legitimate user, the signals received by the legitimate user k and the eavesdropping user l are represented as shown in formula (2) and formula (3) respectively: （2） （3） in, Additive white Gaussian noise for legitimate users, and obeys ; To eavesdrop on users' additive white Gaussian noise, and obey According to Shannon's formula, the information rates of the kth legitimate user and the lth eavesdropping user are shown in formulas (4) and (5), respectively: （4） （5） At this point, based on formulas (4) and (5), the system's security rate is expressed as shown in formula (6): （6）； The total power consumption of the system consists of the base station's transmission power and the hardware power consumed by the circuit. The total power consumption of the system is shown in formulas (7) and (8): （7） （8） in, For power amplifier coefficient, This refers to the base station's transmission power. For the hardware power consumption of the base station, This refers to the static power consumption of the IRS. For the user's hardware power consumption.

4. The high-energy-efficiency optimization method for a secure power communication system according to claim 3, characterized in that, The establishment of a safety and energy efficiency optimization problem model transforms the safety and energy efficiency maximization problem into a base station power minimization problem, including: Under the constraints of user safety rate and base station total transmission power, the safety energy efficiency maximization model is established as shown in formula (9): （9） In the formula, For the system's minimum security rate, This represents the maximum transmission power of the base station. The original problem of maximizing security energy efficiency is transformed into a problem of minimizing base station power. Under the condition of minimizing base station transmission power, the goal of maximizing energy efficiency is achieved by ensuring that the security rate and energy consumption are balanced. The corresponding optimization problem is described by formula (10): （10） In the optimization model, constraint C1 in formula (9) is transformed into the corresponding signal-to-interference-plus-noise ratio (SIR) constraints for legitimate users and eavesdropping users, as shown in formula (11): （11） In formula (11), Minimum signal-to-interference-plus-noise ratio for legitimate users. To eavesdrop on users at the maximum signal-to-interference-plus-noise ratio, Through transformation, the problem model is reformulated as shown in formula (12): （12）。 5. The high-energy-efficiency optimization method for a secure power communication system according to claim 4, characterized in that, The transformed problem, with the IRS phase shift matrix fixed in the system, transforms the optimization problem of beamforming vector and artificial noise vector into a convex optimization problem. It then uses a first-order Taylor approximation to convert non-convex constraints into convex constraints and employs second-order cone programming (SOCP) to solve it, including: Using a fixed phase shift matrix, the beamforming vector and artificial noise vector are optimized. With the IRS phase shift matrix fixed, the design problem of the beamforming vector and artificial noise is expressed as shown in Equation (13): （13） The goal will be to transform non-convex problems into convex problems for solution, making , Therefore, the constraints C1 and C2 in formula (12) are represented as shown in formulas (14) and (15), respectively: （14） （15） Based on the above transformation, formula (13) can be expressed as formula (16): （16） The above two formulas can be written as shown in formulas (17) and (18): （17） （18） At this point, the original non-convex constraint conditions are transformed into convex constraints. The C1 and C2 constraints in formula (12) are transformed into formulas (17) and (18). The original problem is now transformed into a convex problem for solution, as shown in formula (19): （19） The second-order cone programming problem of formula (19) is solved using the CVX toolbox.

6. The high-energy-efficiency optimization method for a secure power communication system according to claim 4, characterized in that, The fixed beamforming vector and artificial noise vector transform the optimization problem of the IRS phase shift matrix into a feasibility problem. Non-convex constraints are handled using a first-order Taylor approximation and a penalty function, and a convex optimization tool is employed to solve the problem until security and energy efficiency performance converges, achieving a balance between security and energy consumption. This includes: Using fixed beamforming and artificial noise vectors, the IRS phase shift matrix is ​​optimized. Under these fixed conditions, the optimization problem is transformed into an IRS phase shift matrix design problem. Before addressing the feasibility verification, the complex parameters in the problem are equivalently replaced, allowing... , , , Similarly, , , , , , At this point, the feasibility problem is described as shown in formula (20): （20） In formula (20), Except for C3, the other three constraints are non-convex constraints. C1 and C2 are transformed into convex forms by first-order Taylor approximation, as shown in formulas (21) and (22). （21） （22） At this point, two of the three non-convex constraints have been transformed from non-convex to convex forms. As for the last non-convex constraint... The semi-definite relaxation (SDR) technique was used to relax the constraint to Form; construct a and The equivalent convex constraint conditions are then constructed as shown in formula (23): （23） In formula (23), For matrix The sum of all singular values, For matrix The maximum singular value, Using a penalty function, the transformed optimization problem is shown in equation (24): （24） Assumption The optimal solution to formula (24) has a corresponding objective value that is less than The corresponding target value, assuming To find the optimal solution to equation (24), equation (25) is used to verify the process of iteration until the target value converges: （25） The optimal solution to problem formula (24) is obtained by using the CVX solver.

7. A high-efficiency optimization system for a secure power communication system, characterized in that, include: The model building module is used to build a model of a power wireless communication system. The ratio of the security rate of the power wireless communication system model to the total power consumption of the system is used as a security energy efficiency index to establish a security energy efficiency optimization problem model, which transforms the security energy efficiency maximization problem into the base station power minimization problem. The solution module is used to solve the transformed problem using alternating optimization: First, with the IRS phase shift matrix fixed, the optimization problem of the beamforming vector and artificial noise vector is transformed into a convex optimization problem. Then, the non-convex constraints are transformed into convex constraints using a first-order Taylor approximation, and the transformed problem is solved using second-order cone programming (SOCP). Second, with the beamforming vector and artificial noise vector fixed, the optimization problem of the IRS phase shift matrix is ​​transformed into a feasibility problem. The non-convex constraints are handled using a first-order Taylor approximation and a penalty function, and the transformed problem is solved using convex optimization tools. This alternating process continues until the security and energy efficiency performance converges, achieving a balance between security and energy consumption.

8. A high-efficiency optimization system for a secure power communication system according to claim 7, characterized in that, In the model building module, the construction of the power wireless communication system model includes: The power wireless communication system model includes a base station, an intelligent reflector (IRS), legitimate users, and eavesdropping users. The base station has M antennas, the IRS has N reflectors, and there are K legitimate users with single antennas and L eavesdropping users with single antennas in the system. The base station transmits signals, including beamforming vectors, information symbols, and artificial noise vectors. The phase shift matrix of the IRS is defined.

9. A high-efficiency optimization system for a secure power communication system according to claim 8, characterized in that, The security rate and total power consumption of the power wireless communication system model include: The channel from the base station to the smart reflector is represented as follows: The channel from the intelligent reflector to the kth legitimate user is represented as: The channel from the intelligent reflector to the l-th eavesdropping user is represented as: The channels from the base station to the k-th legitimate user and to the l-th eavesdropping user are respectively denoted as... and The phase shift matrix of the IRS is expressed as: ,in , or The transmitted signal at the base station is represented as shown in formula (1): （1） In formula (1) This is the beamforming vector that the base station transmits to the k-th legitimate user. The information symbol sent by the base station to the k-th user, and satisfying , The vector represents artificial noise and follows a circularly symmetric complex Gaussian distribution, i.e., it follows... ,in It is the covariance matrix of AN. The signal sent by the base station is received at the IRS and then reflected by the IRS to the receiving end. The signal received at the receiving end includes two aspects: one is the signal of the cascaded link, which is the signal sent by the base station and reflected by the IRS to the user at the receiving end for reception; the other is the signal of the direct link, which is the signal sent by the base station directly to the user at the receiving end for reception. Assuming that the l-th eavesdropping user is trying to eavesdrop on the information of the k-th legitimate user, the signals received by the legitimate user k and the eavesdropping user l are represented as shown in formula (2) and formula (3) respectively: （2） （3） in, Additive white Gaussian noise for legitimate users, and obeys ; To eavesdrop on users' additive white Gaussian noise, and obey According to Shannon's formula, the information rates of the kth legitimate user and the lth eavesdropping user are shown in formulas (4) and (5), respectively: （4） （5） At this point, based on formulas (4) and (5), the system's security rate is expressed as shown in formula (6): （6）； The total power consumption of the system consists of the base station's transmission power and the hardware power consumed by the circuit. The total power consumption of the system is shown in formulas (7) and (8): （7） （8） in, For power amplifier coefficient, This refers to the base station's transmission power. For the hardware power consumption of the base station, This refers to the static power consumption of the IRS. For the user's hardware power consumption.

10. A high-efficiency optimization system for a secure power communication system according to claim 9, characterized in that, The establishment of a safety and energy efficiency optimization problem model transforms the safety and energy efficiency maximization problem into a base station power minimization problem, including: Under the constraints of user safety rate and base station total transmission power, the safety energy efficiency maximization model is established as shown in formula (9): （9） In the formula, For the system's minimum security rate, This represents the maximum transmission power of the base station. The original problem of maximizing security energy efficiency is transformed into a problem of minimizing base station power. Under the condition of minimizing base station transmission power, the goal of maximizing energy efficiency is achieved by ensuring that the security rate and energy consumption are balanced. The corresponding optimization problem is described by formula (10): （10） In the optimization model, constraint C1 in formula (9) is transformed into the corresponding signal-to-interference-plus-noise ratio (SIR) constraints for legitimate users and eavesdropping users, as shown in formula (11): （11） In formula (11), Minimum signal-to-interference-plus-noise ratio for legitimate users. To eavesdrop on users at the maximum signal-to-interference-plus-noise ratio, Through transformation, the problem model is reformulated as shown in formula (12): （12）。 11. A high-efficiency optimization system for a secure power communication system according to claim 10, characterized in that, In the first solution module, based on the transformed problem, the IRS phase shift matrix in the system is fixed, and the optimization problem of beamforming vector and artificial noise vector is transformed into a convex optimization problem. The non-convex constraints are transformed into convex constraints through a first-order Taylor approximation, and then solved using second-order cone programming (SOCP), including: Using a fixed phase shift matrix, the beamforming vector and artificial noise vector are optimized. With the IRS phase shift matrix fixed, the design problem of the beamforming vector and artificial noise is expressed as shown in Equation (13): （13） The goal will be to transform non-convex problems into convex problems for solution, making , Therefore, the constraints C1 and C2 in formula (12) are represented as shown in formulas (14) and (15), respectively: （14） （15） Based on the above transformation, formula (13) can be expressed as formula (16): （16） The above two formulas can be written as shown in formulas (17) and (18): （17） （18） At this point, the original non-convex constraint conditions are transformed into convex constraints. The C1 and C2 constraints in formula (12) are transformed into formulas (17) and (18). The original problem is now transformed into a convex problem for solution, as shown in formula (19): （19） The second-order cone programming problem of formula (19) is solved using the CVX toolbox.

12. A high-efficiency optimization system for a secure power communication system according to claim 10, characterized in that, In the second solution module, the fixed beamforming vector and artificial noise vector transform the optimization problem of the IRS phase shift matrix into a feasibility problem. Non-convex constraints are handled using a first-order Taylor approximation and a penalty function, and a convex optimization tool is employed to solve the problem until security and energy efficiency performance converges, achieving a balance between security and energy consumption. This includes: Using fixed beamforming and artificial noise vectors, the IRS phase shift matrix is ​​optimized. Under these fixed conditions, the optimization problem is transformed into an IRS phase shift matrix design problem. Before addressing the feasibility verification, the complex parameters in the problem are equivalently replaced, allowing... , , , Similarly, , , , , , At this point, the feasibility problem is described as shown in formula (20): （20） In formula (20), Except for C3, the other three constraints are non-convex constraints. C1 and C2 are transformed into convex forms by first-order Taylor approximation, as shown in formulas (21) and (22). （21） （22） At this point, two of the three non-convex constraints have been transformed from non-convex to convex forms. As for the last non-convex constraint... The semi-definite relaxation (SDR) technique was used to relax the constraint to Form; construct a and The equivalent convex constraint conditions are then constructed as shown in formula (23): （23） In formula (23), For matrix The sum of all singular values, For matrix The maximum singular value, Using a penalty function, the transformed optimization problem is shown in equation (24): （24） Assumption The optimal solution to formula (24) has a corresponding objective value that is less than The corresponding target value, assuming To find the optimal solution to equation (24), equation (25) is used to verify the process of iteration until the target value converges: （25） The optimal solution to problem formula (24) is obtained by using the CVX solver.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the high-efficiency optimization method for a secure power communication system as described in any one of claims 1 to 6.

14. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the high-efficiency optimization method for a secure power communication system as described in any one of claims 1 to 6.

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