Intelligent metasurface assisted secure communication method and system for wireless data and energy simultaneous transmission

Abstract

The embodiment of the invention provides an intelligent metasurface assisted secure communication method and system for wireless data and energy simultaneous transmission, and the method comprises the steps: obtaining communication data and system parameters, defining condition data, and obtaining communication state data based on the condition data. Wherein the communication state data is a state data and action data set obtained by solving based on a near-end strategy optimization framework. And selecting a communication unit and an energy transmission unit from the plurality of metasurface units according to the communication state data, thereby sending the communication data to the target equipment through the communication unit, and collecting energy from the base station through the energy transmission unit. According to the method, on the basis of a near-end strategy optimization framework, the continuous dynamic allocation of air intelligent metasurface resources in the time dimension and the space dimension is realized while the safety communication quality is maximized, and the total energy consumption of the system is minimized through an energy collection mechanism, so that the cruising ability of the safety communication system is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a wireless power and data co-transmission intelligent metasurface assisted secure communication method and system. BACKGROUND

[0002] With the rapid development of 5G communication technology, the number of wireless communication users is increasing. Due to the broadcast nature of the wireless transmission environment and the open access of the wireless network, any user accessing the network may intercept the private data of other users to some extent, which reduces the security of user privacy information and increases the risk of privacy information leakage in the communication process.

[0003] In order to improve the information security of the communication process, a specific information security scheme can be set for the communication process. For example, the information security scheme can be based on high-layer data encryption and authentication technology of the communication stack, rely on the complexity of the encryption algorithm to reduce the possibility of intercepted and analyzed communication information, and improve the security performance. However, with the application of big data technology, the computing and learning capabilities of communication nodes are continuously enhanced, and a user who eavesdrops may use brute force attack and other methods to crack the key and obtain user privacy information, which poses a threat to the encryption technology.

[0004] In order to solve the problem of key cracking, information encryption can be realized based on physical layer security technology. The physical layer security technology uses the randomness and uniqueness of the wireless channel, designs a beamforming scheme based on multi-antenna technology, cooperative relay technology, etc., to ensure secure transmission of information and reduce the possibility of eavesdropping confidential information, and to realize secure and reliable communication. However, due to the need to introduce an air intelligent metasurface and other auxiliary secure communication components in the process of implementing the physical layer security technology, the performance and endurance of the communication device are limited by the battery capacity and other limitations, making the communication device based on the physical layer security technology insufficient in terms of secure communication endurance. SUMMARY

[0005] Therefore, the embodiments of the present application provide a wireless power and data co-transmission intelligent metasurface assisted secure communication method and system to solve the problem of insufficient secure communication endurance of the communication device based on the physical layer security technology.

[0006] According to one aspect of the present application, a wireless power and data co-transmission intelligent metasurface assisted secure communication method is provided, which is applied to a communication device, the communication device is provided with an air intelligent metasurface, the air intelligent metasurface includes a plurality of metasurface units, and the communication device establishes a communication connection and an energy transmission channel with a base station device through the air intelligent metasurface; the method comprises:

[0007] Obtain communication data and system parameters, the system parameters including a system model;

[0008] define condition data according to the system parameters, the condition data including an energy collection phase time, a transmission area ratio, and a diagonal matrix; the transmission area ratio being a ratio of an area of a metasurface unit used for signal reflection to an area of all metasurface units in an information transmission phase; and the diagonal matrix being a reflection coefficient matrix of the aerial intelligent metasurface;

[0009] obtain communication state data based on the condition data, the communication state data being obtained based on a proximal policy optimization framework, with a maximum safety rate of the information transmission phase as an objective function and the condition data as a conditional constraint;

[0010] select a communication unit and an energy transmission unit from the plurality of metasurface units according to the communication state data;

[0011] transmit the communication data to a target device through the communication unit, and collect energy from the base station through the energy transmission unit.

[0012] In some embodiments, the method further comprises:

[0013] obtain a number of communication terminals connected to the base station, and construct a user set according to the number of communication terminals;

[0014] obtain a number of eavesdropping terminals within a radiation range of the base station, and construct an eavesdropping terminal set according to the number of eavesdropping terminals;

[0015] set a communication distance based on the user set and the eavesdropping terminal set, the communication distance including a first distance and a second distance, the first distance being a distance between the aerial intelligent metasurface and a communication terminal, and the second distance being a distance between the aerial intelligent metasurface and the eavesdropping terminal;

[0016] set a metasurface unit and a metasurface unit array according to the communication distance;

[0017] construct a system model based on the metasurface unit array, the user set, the eavesdropping terminal set, and the communication distance.

[0018] In some embodiments, the condition data is defined according to the system parameters, comprising:

[0019] obtain a communication cycle of the system model;

[0020] divide an entire time period corresponding to the communication cycle into a plurality of time slots, each of the time slots including an energy collection phase and an information transmission phase;

[0021] define the energy collection phase time, in which all the metasurface units in the aerial intelligent metasurface are used to collect energy;

[0022] calculate the information transmission phase time according to the energy collection phase time, in which a first number of metasurface units in the aerial intelligent metasurface are used to reflect signals, and a second number of metasurface units are used to collect energy; the sum of the first number and the second number is equal to the total number of metasurface units in the aerial intelligent metasurface;

[0023] calculate the transmission area ratio according to the first number and the second number.

[0024] In some embodiments, the condition data is defined according to the system parameters, including:

[0025] define the precoding vector of the communication terminal and the single-terminal signal;

[0026] calculate the transmission signal and the total transmission power of the base station according to the precoding vector and the single-terminal signal;

[0027] obtain the channel information and the channel power gain of the base station to the metasurface units;

[0028] calculate the unit energy according to the transmission signal, the channel information of the metasurface units, and the channel power gain, the unit energy being used to represent the energy collected by a single metasurface unit; the unit energy is obtained according to the Euclidean norm of the product of the transmission signal, the channel information of the metasurface units, and the channel power gain;

[0029] calculate the total collected energy based on the unit energy and the energy collection phase time, the total collected energy including a first energy collected in the information transmission phase and a second energy collected in the energy collection phase; the first energy is the product of the sum of the unit energies of the second number of metasurface units and the time of the information transmission phase; the second energy is the product of the sum of the unit energies of the total number of metasurface units and the energy collection phase time.

[0030] In some embodiments, the communication state data is obtained based on the condition data, including:

[0031] obtain channel information, the channel information including first channel information and second channel information, the first channel information being used to represent the channel from the base station to the aerial intelligent metasurface; the second channel information being used to represent the channel from the metasurface units of the aerial intelligent metasurface to the communication terminal or the eavesdropping terminal;

[0032] define a diagonal matrix according to the reflection coefficient of the aerial intelligent metasurface;

[0033] establishing a first achievable rate function of the communication terminal and a second achievable rate function of the eavesdropping terminal according to the channel information and the diagonal matrix;

[0034] calculating a secure rate of an information transmission phase based on the first achievable rate function and the second achievable rate function;

[0035] setting the secure rate as an objective function of the proximal policy optimization framework;

[0036] setting a conditional constraint based on the conditional data, the conditional constraint including a minimum constraint of energy harvesting benefit, a time constraint, a maximum transmission power constraint of the base station, and a constraint of a ratio of units performing signal reflection in the information transmission phase to all metasurface units;

[0037] performing proximal policy optimization according to the objective function and the conditional constraint to obtain the communication state data.

[0038] In some embodiments, performing proximal policy optimization according to the objective function and the conditional constraint to obtain the communication state data includes:

[0039] setting input parameters based on the conditional data, the input parameters including a signal transmitted by the base station, the channel information, the diagonal matrix, and a communication distance;

[0040] initializing an actor network, a critic network, and an experience replay pool of the proximal policy optimization framework;

[0041] defining output parameters of the proximal policy optimization framework, the output parameters including an optimal action and total harvested energy, the optimal action being a combination of action space of the energy harvesting phase time and the transmission area ratio;

[0042] performing a loop iteration according to the input parameters and the output parameters to generate the communication state data.

[0043] In some embodiments, performing a loop iteration according to the input parameters and the output parameters to generate the communication state data includes:

[0044] obtaining input parameters of a current loop step;

[0045] initializing a random noise process;

[0046] collecting channel information of the metasurface units to the communication terminal, and taking the channel information as an initial state of the current loop step;

[0047] performing a time step loop based on the initial state to generate transition data, the transition data comprising a current state, a current policy sampled action, and a next state;

[0048] calculating advantage estimates of the transition data using a value function.

[0049] In some embodiments, performing a time step loop based on the initial state to generate transition data comprises:

[0050] obtaining a current state and a current policy sampled action of a current time step;

[0051] performing the current policy sampled action, and calculating a reward parameter corresponding to the current policy sampled action;

[0052] correcting the current state based on the reward parameter to obtain a next state;

[0053] combining the current state, the current policy sampled action, and the next state to generate transition data;

[0054] storing the transition data into the experience replay pool.

[0055] In some embodiments, the method further comprises:

[0056] randomly sampling a preset batch of transition data from the experience replay pool;

[0057] updating a policy by maximizing a clipped surrogate objective based on the preset batch of transition data;

[0058] updating a value function by minimizing a mean squared error based on the preset batch of transition data to generate the communication state data.

[0059] According to another aspect of the present application, a wireless energy and communication co-transmission intelligent metasurface assisted secure communication system is provided, which comprises a base station, a communication device, and a communication terminal; the communication device is provided with an aerial intelligent metasurface, and the aerial intelligent metasurface comprises a plurality of metasurface units; the communication device establishes a communication connection and an energy transmission channel with the base station device through the aerial intelligent metasurface; the communication device further comprises:

[0060] a data acquisition module, configured to acquire communication data and system parameters, wherein the system parameters comprise a system model;

[0061] a condition definition module configured to define condition data according to the system parameters, the condition data including an energy harvesting phase time, a transmission area ratio, and a diagonal matrix; the transmission area ratio being a ratio of an area of a metasurface unit used for signal reflection to an area of all metasurface units in the information transmission phase; and the diagonal matrix being a reflection coefficient matrix of the aerial intelligent metasurface;

[0062] a communication control module configured to obtain communication state data based on the condition data, the communication state data being a state data and action data set obtained based on a proximal policy optimization framework, with a maximized safety rate of the information transmission phase as an objective function and the condition data as a conditional constraint;

[0063] a metasurface unit selection module configured to select a communication unit and an energy transmission unit from the plurality of metasurface units according to the communication state data;

[0064] a communication execution module configured to transmit the communication data to the target device through the communication unit and collect energy from the base station through the energy transmission unit.

[0065] According to still another aspect of the present disclosure, a computer device is provided, including a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, and the processor implements the wireless energy and information co-transmission assisted intelligent metasurface assisted secure communication method when executing the program.

[0066] According to still another aspect of the present disclosure, a storage medium is provided, and the storage medium stores a computer program, and the program is executed by a processor to implement the wireless energy and information co-transmission assisted intelligent metasurface assisted secure communication method.

[0067] By means of the above technical solutions, the present disclosure provides a wireless energy and information co-transmission assisted intelligent metasurface assisted secure communication method and system. The method defines condition data based on obtained communication data and system parameters. The communication state data is a state data and action data set obtained based on a proximal policy optimization framework, with a maximized safety rate of the information transmission phase as an objective function and the condition data as a conditional constraint. Then, a communication unit and an energy transmission unit are selected from a plurality of metasurface units according to the communication state data. The communication data is transmitted to the target device through the communication unit, and energy is collected from the base station through the energy transmission unit. The method can realize continuous dynamic allocation of aerial intelligent metasurface resources in time and space dimensions while maximizing the safety communication quality based on the proximal policy optimization framework, and minimize the overall energy consumption of the system through the energy harvesting mechanism, so as to enhance the endurance of the secure communication system.

[0068] The above description is only a summary of the technical solutions of the present application. In order to enable one skilled in the art to better understand the technical means of the present application and to implement the same according to the contents of the description, and in order to enable the above and other purposes, characteristics and advantages of the present application to be more apparent and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0069] The accompanying drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The schematic embodiments of the present application and the description thereof are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0070] Figure 1 A safety communication system structure schematic diagram is provided for the embodiments of the present application;

[0071] Figure 2 A wireless energy and data co-transmission intelligent metasurface assisted safety communication method flowchart schematic diagram is provided for the embodiments of the present application;

[0072] Figure 3 An energy collection stage and information transmission stage schematic diagram is provided for the embodiments of the present application;

[0073] Figure 4 An average safety rate result schematic diagram is provided for the embodiments of the present application;

[0074] Figure 5 A collected energy proportion result schematic diagram is provided for the embodiments of the present application;

[0075] Figure 6 A wireless energy and data co-transmission intelligent metasurface assisted safety communication system structure schematic diagram is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0076] In the following, the present application will be described in detail with reference to the accompanying drawings and in conjunction with the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0077] In the embodiments of the present application, the physical layer security technology is to utilize the randomness and uniqueness of the wireless channel, design a beamforming scheme based on multi-antenna technology, cooperative relay technology, etc., to guarantee the secure transmission of information, reduce the possibility of confidential information being eavesdropped, and realize secure and reliable communication.

[0078] In some embodiments, the communication device can implement physical layer security technology based on the intelligent metasurface. The intelligent metasurface is a uniform planar array composed of a large number of composite material elements, and the phase shift of each composite material element can be independently adjusted to reflect the incident electromagnetic wave. The intelligent metasurface can improve the wireless information security transmission environment by optimizing the phase shift matrix of the intelligent metasurface and greatly enhance the information transmission security performance in assisting wireless information security transmission.

[0079] The intelligent metasurface has low energy consumption and can realize full-duplex communication without introducing additional interference, improving spectrum efficiency. The intelligent metasurface can be deployed on the ground facility or the building surface, but due to the fixed position of the ground facility or the building surface, the flexibility of the intelligent metasurface is limited and the range of the intelligent metasurface assisted secure communication is reduced.

[0080] Therefore, in some embodiments, the intelligent metasurface can be deployed on an aerial platform to form an aerial intelligent metasurface. For example, as shown in FIG. 1, the intelligent metasurface can be deployed on an aerial platform such as a UAV, and the flexibility of the intelligent metasurface can be improved by the spatial movement characteristics of the UAV, thereby increasing the working range. Therefore, in the physical layer security technology, the secure communication can be assisted by introducing the aerial intelligent metasurface. Figure 1

[0081] However, due to the limitations of the power supply of the aerial platform, the performance and endurance of the secure communication process based on the aerial intelligent metasurface are restricted. For example, due to the limitation of the battery capacity of the UAV, the endurance of the secure communication assisted by the aerial intelligent metasurface is insufficient, and the calculation complexity of dynamic resource allocation and optimization is high in the secure communication system based on wireless energy and data co-transmission, and it is difficult to obtain reasonable resource allocation and optimization results.

[0082] To solve the problem of insufficient endurance of secure communication of the communication device based on the physical layer security technology, in some embodiments of the present application, a wireless energy and data co-transmission intelligent metasurface assisted secure communication method is provided. The method can realize the cooperative transmission of data and energy based on the wireless energy and data co-transmission technology, and combine the near-end strategy optimization algorithm to optimize the energy collection strategy in the time domain and the spatial domain, improve the energy collection efficiency, and further improve the endurance of the secure communication of the communication device.

[0083] ​The wireless data and energy co-transmission technology refers to a technology of simultaneously receiving information and energy from one radio frequency signal by taking advantage of the characteristics of radio frequency signals that can simultaneously carry information and energy. The wireless data and energy co-transmission technology can realize the coordinated transmission of data and energy by combining the independent wireless information transmission and wireless energy transmission in the wireless network. Therefore, the intelligent metasurface assisted secure communication method based on the wireless data and energy co-transmission can be applied to a communication device. The communication device is provided with an aerial intelligent metasurface, and the aerial intelligent metasurface includes a plurality of metasurface units. The communication device establishes a communication connection and an energy transmission channel with a base station device through the aerial intelligent metasurface. As shown in FIG. 8, the method includes the following steps. Figure 2

[0084] S101, obtaining communication data and system parameters;

[0085] During the communication process, the communication device can obtain communication data. The communication data refers to the data transmitted between the base station and the communication terminal. When the base station sends data to the communication terminal, the base station needs to first radiate electromagnetic wave signals to the communication range, and the aerial platform carrying the communication device can sense the electromagnetic wave signals through the aerial metasurface to obtain the communication data. Similarly, when the communication terminal sends data to the base station, the communication terminal can send electromagnetic wave signals to the communication device, and the communication device can sense the electromagnetic wave signals through the aerial metasurface to obtain the communication data.

[0086] After obtaining the communication data, the communication device can forward the communication data to the target device through the reflection effect of the aerial metasurface. According to the different directions of the communication data transmission, the target device can be the base station or the communication terminal. That is, when the transmission direction of the communication data is from the base station to the communication terminal, the target device is the communication terminal. When the transmission direction of the communication data is from the communication terminal to the base station, the target device is the base station. Since the aerial metasurface built in the communication device can realize data and energy co-transmission, the communication device can also need to determine the data for controlling the communication state, i.e., the communication state data, through the near-end strategy optimization algorithm before forwarding the communication data to the target device. Therefore, the communication device can also obtain system parameters, wherein the system parameters include a system model.

[0087] The system model can be a mathematical model constructed by the communication device according to the base station, the communication terminal, and the eavesdropping terminal in the actual communication system. The system model can define the base station antenna, the communication terminal, the eavesdropping terminal, and other hardware devices in the communication system and the physical relationship between the hardware devices based on mathematical variables, so as to perform near-end strategy optimization.

[0088] ​Therefore, in some embodiments, the communication device can construct a system model. That is, the communication device can obtain the number of communication terminals connected to the base station, and construct a user set according to the number of communication terminals. By setting the number of eavesdropping terminals in the radiation range of the base station, and constructing an eavesdropping terminal set according to the number of eavesdropping terminals.

[0089] For example, as shown in Figure 3 For an aerial intelligent metasurface assisted secure communication system, signals are transmitted from a base station with D antennas to K single-antenna user communication terminals, and there are P single-antenna eavesdropping eavesdropping terminals. Among them, some obstacles hinder the line-of-sight link, so an aerial intelligent metasurface is deployed to assist secure communication, which has MxN reflecting elements. Then the user set can be denoted as K = {1, 2, …, K}, and the eavesdropping terminal set can be denoted as P = {1, 2, …, P}.

[0090] Based on the user set and the eavesdropping terminal set, the communication distance is set. Wherein, the communication distance includes a first distance and a second distance, the first distance is the distance between the aerial intelligent metasurface and the communication terminal; the second distance is the distance between the aerial intelligent metasurface and the eavesdropping terminal. For example, the distance from the aerial intelligent metasurface to the user or the eavesdropper is denoted as Wherein, n ∈ K ∪ P.

[0091] After calculating the communication distance, the metasurface unit and the metasurface unit array can also be set according to the communication distance, so as to construct a system model based on the metasurface unit array, the user set, the eavesdropping terminal set and the communication distance.

[0092] For example, the reflecting element of the i-th row and the j-th column of the aerial intelligent metasurface is denoted as R ij , and the reflecting element array is denoted as Assuming that the communication terminal and the eavesdropping terminal can only receive the signal reflected by the aerial intelligent metasurface, and can exchange channel state information with the base station, a system model can be established according to the above variables.

[0093] S102, defining condition data according to the system parameters;

[0094] After obtaining the system parameters, the communication device can define data according to the system parameters, that is, define conditional data. The conditional data includes energy collection phase time, transmission area ratio, and diagonal matrix. The energy collection phase time is the total time length of the energy collection phase of the communication device in any time slot. The transmission area ratio is the ratio of the area of the metasurface unit used for signal reflection to the area of all metasurface units in the information transmission phase. The diagonal matrix is a pre-defined parameter matrix, which can include the reflection coefficient of the aerial intelligent metasurface, that is, a matrix composed of the reflection coefficient of the aerial intelligent metasurface.

[0095] In some embodiments, when defining the conditional data, the communication device can first obtain the communication cycle of the system model, and divide the entire time period corresponding to the communication cycle into multiple time slots, each of which includes an energy collection phase and an information transmission phase. Then, the energy collection phase time is defined, that is, in the energy collection phase, all metasurface units in the aerial intelligent metasurface are used for energy collection, and the information transmission phase time is calculated according to the energy collection phase time.

[0096] For example, the communication device can divide the entire time period into T time slots, denoted as T = {1, 2, …, t, …, T}, each of which contains an energy collection phase and an information transmission phase, as shown in Figure 3 The energy collection phase time of the tth time slot is denoted as τ(t). In the energy collection phase, all reflection units only collect energy. After energy collection, the information transmission phase starts, and the information transmission time is denoted as (1-τ(t)).

[0097] After defining the energy collection phase time and the information transmission phase time, since in the information transmission phase, a first number of metasurface units in the aerial intelligent metasurface are used for reflecting signals, and a second number of metasurface units are used for collecting energy. The sum of the first number and the second number is equal to the total number of metasurface units in the aerial intelligent metasurface. Therefore, the transmission area ratio can be calculated according to the first number and the second number.

[0098] For example, the center of the aerial intelligent metasurface has m x n metasurface units, where 0≤n≤N and 0≤m≤M. The m x n metasurface units are used for reflecting signals in the information transmission phase, and the remaining metasurface units are used for collecting energy. The metasurface units used for reflecting signals are denoted as:

[0099]

[0100] Therefore, by the ratio of the area of the signal metasurface unit to the area of all metasurface units in the signal transmission phase, the transmission area ratio can be obtained, that is, the transmission area ratio can be represented as:

[0101]

[0102] where λ represents the transmission area ratio; m x n represents the number of metasurface units used for reflecting signals in the information transmission stage; M x N represents the total number of metasurface units contained in the aerial intelligent metasurface.

[0103] After defining the condition data such as the energy collection stage time and the transmission area ratio, the communication device can also represent the signal transmission process based on the system model, that is, in some embodiments, the communication device can also define the precoding vector of the communication terminal and the single-terminal signal when defining the condition data according to the system parameters, and calculate the transmission signal and total transmission power of the base station according to the coding vector and the single-terminal signal.

[0104] For example, the signal transmitted by the base station can be represented as:

[0105] G = ∑ k∈K V k S k ;

[0106] where V k ∈ C D×1 represents the precoding vector sent to the kth communication terminal; S k represents the signal sent to the kth user.

[0107] Therefore, the total transmission power of the base station can be represented as:

[0108] E(G H G) = ∑ k∈K ‖V k ‖ 2 ≤ p max ;

[0109] where ‖·‖ represents the Euclidean norm of the vector, and p max represents the upper limit of the transmission power.

[0110] The channel information and channel power gain of the base station to the metasurface unit are also obtained, and the unit energy is calculated according to the transmission signal, the channel information of the metasurface unit, and the channel power gain. Wherein the unit energy is used to represent the energy collected by a single metasurface unit; the unit energy is obtained according to the Euclidean norm of the product of the transmission signal, the channel information of the metasurface unit, and the channel power gain.

[0111] For example, the unit energy can be represented as:

[0112]

[0113] where EU represents the unit energy; represents the reflection unit R of the aerial intelligent metasurface i,j channel information to the kth communication terminal or the pth eavesdropping terminal;g i,j represents the channel power gain; G represents the signal transmitted by the base station, i.e. G = ∑ k∈ K V k S k .

[0114] After the unit energy is calculated, the total collected energy is calculated based on the unit energy and the energy collection phase time. Wherein, the total collected energy includes the first energy collected in the information transmission phase and the second energy collected in the energy collection phase; the first energy is the product of the total sum of the unit energy of the second number of metasurface units and the time of the information transmission phase; the second energy is the product of the total sum of the unit energy of the total number of metasurface units and the energy collection phase time.

[0115] For example, in the tth time slot, the collected energy can be represented as:

[0116]

[0117] wherein, represents the channel from the base station to the metasurface unit R i×j , ω i,j represents the decision coefficient of the metasurface unit, i.e. ω i,j = 0, which means that the metasurface unit R i×j is used to reflect signals, and vice versa ω i,j = 1, which means that the metasurface unit R i×j is used to collect energy. That is:

[0118]

[0119] The energy collection benefit can be defined as:

[0120]

[0121] Wherein, H(t) represents the total energy collected by the intelligent metasurface in the energy collection phase, i.e. the total energy collected in the energy collection phase is the total sum of the unit energy of all metasurface units, i.e.

[0122]

[0123] And the channel power gain from the base station to R i,j can be represented as:

[0124]

[0125] where the base station is located at the origin of the rectangular coordinate system, D i,j is the height of R i,j ; (x i,j , y i,j ) is the position of R i,j , and a is the additional attenuation factor of R i,j to the base station due to the non-line-of-sight link, P i,j (LoS) is the probability of the line-of-sight link between the base station and R i,j .

[0126] S103, obtaining communication state data based on the condition data;

[0127] After defining the condition data, the communication device can obtain communication state data based on the condition data. Wherein, the communication state data is a state data and action data set obtained by solving based on a proximal policy optimization framework, with maximizing the safety rate of the information transmission stage as the objective function, and the condition data as the condition constraint.

[0128] The proximal policy optimization framework (Proximal Policy Optimization, PPO) is a reinforcement learning algorithm framework, which belongs to the policy gradient optimization method. The proximal policy optimization framework can directly parameterize the policy, and is suitable for problems with high-dimensional and continuous action space. By controlling the amplitude of policy update, it avoids the policy from changing too much in one update, thereby improving the stability of training and data utilization. Therefore, the communication state data is a state data and action data set obtained by solving based on a proximal policy optimization framework.

[0129] In some embodiments, after defining the condition data, the communication device can perform proximal policy optimization, that is, the communication device can first obtain channel information when obtaining communication state data based on the condition data. Wherein, the channel information includes first channel information and second channel information, the first channel information is used to represent the channel from the base station to the air intelligent metasurface; and the second channel information is used to represent the channel from the metasurface unit of the air intelligent metasurface to the communication terminal or the eavesdropping terminal.

[0130] For example, in the information transmission stage, the channel information from the base station to the air intelligent metasurface, i.e. the first channel information, can be defined as:

[0131] Z∈C L×D ;

[0132] And, the channel information from the metasurface unit R i,j of the air intelligent metasurface to the kth communication terminal or the pth eavesdropping terminal, i.e. the second channel information, can be defined as:

[0133]

[0134] After obtaining the channel information, the communication device can define a diagonal matrix according to the reflection coefficient of the aerial smart metasurface. For example, by defining a diagonal matrix as the reflection coefficient matrix of the aerial smart metasurface, that is, the diagonal matrix is:

[0135]

[0136] wherein, is the imaginary unit, represents the phase shift of the lth metasurface unit, β l ∈ [0, 1], represents the amplitude reflection coefficient.

[0137] Then, the first achievable rate function of the communication terminal and the second achievable rate function of the eavesdropping terminal are established according to the channel information and the diagonal matrix. And the security rate of the information transmission stage is calculated based on the first achievable rate function and the second achievable rate function.

[0138] For example, the signal received by the communication terminal or the eavesdropping terminal from the aerial smart metasurface can be represented as:

[0139]

[0140] wherein, represents the additive white Gaussian noise of the kth communication terminal, and the noise power is Therefore, the achievable rate of the kth communication terminal, that is, the first achievable rate, is:

[0141]

[0142] The achievable rate of the pth eavesdropping terminal eavesdropping on the kth communication terminal, that is, the second achievable rate, is:

[0143]

[0144] Therefore, the security rate of the base station to the kth user can be represented as:

[0145]

[0146] wherein, [z] + = max (0, z).

[0147] After obtaining the function representation of the safe rate, the communication device can set the safe rate as a target function of the proximal policy optimization framework. Then, based on the condition data, a conditional constraint is set. The conditional constraint includes a minimum constraint of energy collection benefit, a time constraint, a maximum transmission power constraint of the base station, and a constraint of a ratio of units performing signal reflection to all metasurface units in the information transmission stage. Then, proximal policy optimization is performed according to the target function and the conditional constraint to obtain the communication state data.

[0148] For example, the proximal policy optimization problem is to maximize the safe rate by designing Θ, τ(t) and λ, while ensuring that the energy collection benefit exceeds a given threshold, and the power transmitted by the base station also meets the constraint, that is:

[0149] The defined target function P1 is:

[0150]

[0151] The conditional constraint s.t. can include:

[0152]

[0153]

[0154] C3: 0 ≤ p = ∑ k∈K ‖V k ‖ 2 ≤p max ;

[0155] C4: 0 ≤ λ ≤ 1;

[0156] Wherein, P1 represents that the optimization target is to maximize the safe rate, C1 represents the minimum constraint of energy collection benefit, C2 is the time constraint, C3 is the maximum transmission power constraint of the base station, and C4 is the constraint of the ratio of units performing signal reflection to all metasurface units in the information transmission stage. Since the above optimization problem is non-convex, a proximal policy optimization framework based on deep reinforcement learning can be used to solve the problem. In solving the problem, a deep reinforcement learning-based resource allocation and secure communication optimization framework can be used, which can maximize the safe communication rate while ensuring that the collected energy efficiency meets the requirements.

[0157] Wherein, the proximal policy optimization algorithm in deep reinforcement learning is a classic algorithm based on the actor-critic framework. The proximal policy optimization algorithm aims to solve the learning rate value difficulty problem encountered by the policy gradient method in dealing with continuous action space. The proximal policy optimization algorithm can inherit the core idea of the trust region policy optimization algorithm, while using a series of first-order methods, making it simpler to implement and not inferior to the trust region policy optimization algorithm in effect.

[0158] The proximal policy optimization algorithm improves sample efficiency by introducing importance sampling and constrains the differences between new and old policies in some way. The algorithm treats the nonnegativity constraint as a reward-penalty mechanism, optimizing the objective function by introducing a KL divergence penalty term. Accordingly, the objective function can be expressed as:

[0159]

[0160] Where, π θ (a t |s t ) represents the probability of an action under the new strategy; This represents the probability of an action under the old strategy; Let β represent the dominance function. KL The coefficient of the KL divergence is used to control the intensity of the penalty.

[0161] In this way, the proximal policy optimization algorithm can achieve effective policy updates while ensuring policy stability. The state space, action space, and reward function based on the proximal policy optimization framework are as follows:

[0162] state space s t The state space consists of channel state information Z from the base station to the ARIS and channel state information Z from the ARIS to the signal receiver. , n∈K∪P, distance information from ARIS to the signal receiver n∈K∪P is composed of.

[0163] Action space a t The action space consists of the reflection coefficient matrix Θ of ARIS, the duration τ(t) of the energy harvesting phase, and the ratio λ of the area of ​​the signal metasurface unit to the area of ​​all metasurface units during the transmission phase.

[0164] reward function r t Since the goal of near-end policy optimization is to optimize the safe rate of the communication system while ensuring that the energy harvesting efficiency is not lower than the minimum set threshold, and considering that the setting of the reward function will affect the stability and performance of model training, the reward function can be set as follows:

[0165]

[0166] in,

[0167] Therefore, in order to realize the proximal policy optimization framework based on deep reinforcement learning to solve the problem, in some embodiments, the communication device can first set the input parameters based on the conditional data when performing proximal policy optimization according to the target function and the conditional constraints to obtain the communication state data. Wherein, the input parameters include the signal transmitted by the base station, the channel information, the diagonal matrix and the communication distance. Then initialize the actor network, critic network and experience replay pool of the proximal policy optimization framework, and define the output parameters of the proximal policy optimization framework, including the optimal action and the total collected energy; the optimal action is the action space combination of the energy collection stage time and the transmission area ratio.

[0168] For example, the communication device can initialize the input parameters, that is, the input parameters include: the signal G transmitted by the base station, the first channel information Z; the second channel information Diagonal matrix Θ, and communication distance The size of experience replay D, the size of batch N B Then initialize the actor network π(s∣θ π ) and the critic network V(s∣θ V ), the capacity of experience replay pool D is N D And define the output parameters, including: the optimal action a={τ(t), λ}, and the total collected energy of ARIS

[0169] Then perform a loop iteration according to the input parameters and the output parameters to generate the communication state data. In some embodiments, when performing a loop iteration according to the input parameters and the output parameters to generate the communication state data, the input parameters of the current loop step can be obtained first, and a random noise process is initialized. By collecting the channel information from the metasurface unit to the communication terminal, and taking the channel information as the initial state of the current loop step.

[0170] For example, the communication device can perform a main loop when performing a loop iteration. For each loop step (episode), N e =1 to N epoch Perform the following steps: receive the current G, Z∈C L×D , Θ. And initialize a random noise process N. Then collect, Take it as the initial state s1 of the N e th episode.

[0171] Based on the initial state, a time step loop is executed to generate transition data, the transition data including a current state, a current policy sampled action and a next state, so as to calculate advantage estimates of the transition data using a value function. In some embodiments, when the time step loop is executed based on the initial state to generate the transition data, the current state and the current policy sampled action of the current time step can be first obtained, the current policy sampled action is executed, and a reward parameter corresponding to the current policy sampled action is calculated, and then the current state is corrected based on the reward parameter to obtain the next state, and the transition data is generated by combining the current state, the current policy sampled action and the next state, so as to store the transition data into the experience replay pool.

[0172] For example, in each main loop, a time step loop can also be included, and for each time step t = 1 to T, the following steps can be performed: a current policy sampled action a t ~ π (s t | θ π ) is first sampled according to the current state s t . Then, the corresponding reward R t is calculated. Then, the next state s t+1 is obtained. Thus, the transition (s t , a t , , s t+1 ) is stored into the experience replay pool D. Thus, the advantage estimates V for all transitions in D are calculated using the value function V (s update | θ ).

[0173] In each loop iteration, policy and value function updates can be performed, that is, the communication device can randomly sample a preset batch of transition data from the experience replay pool, and update the policy by maximizing the clipped surrogate objective based on the preset batch of transition data, and update the value function by minimizing the mean squared error based on the preset batch of transition data to generate the communication state data.

[0174] For example, when performing policy and value function updates, for each epoch e = 1 to N update , the following steps are performed: a mini-batch NB of transitions (s j , a j , R j , s j+1 ) is randomly sampled from the experience replay pool D. The policy π can be updated by maximizing the clipped surrogate objective, that is:

[0175]

[0176] where L CLIP(θ π ) represents the clipping loss function in the PPO algorithm; E is an expectation operator, representing an average calculation on all possible samples; represents the estimated value of the advantage function, which is used to measure the return relative to the average level when action a t is taken in state s t ; t (θ π ), 1-∈, 1+∈) represents a clipping operation, which is used to limit the importance sampling ratio r t (θ π ) between 1-∈ and 1+∈; min(·) represents a minimum value function; r t (θ π ) represents the importance sampling ratio of the policy π with parameters θ π at time step t, that is:

[0177]

[0178] In the formula, π(a t |s t , θ π ) represents the probability of taking action a t in state s t , which is given by the policy π with parameters θ π ; represents that this is the probability of taking action a t in state s t , which is given by the old policy parameter θ π old .

[0179] The value function V(s| θ V ) is updated by minimizing the mean square error, that is:

[0180] L VF (θ V ) = E[(R t - V(s t | θ V )) 2 ];

[0181] Wherein, L VF (θ V ) represents the loss function of the value function; E is an expectation operator; R t is the return at time step t, that is, the cumulative reward obtained after performing actions according to the policy from time step t; V(s t | θ V ) is the value of state s tthe value function estimate at the end of the loop iteration, given by the value function with parameters θ V

[0182] After each loop iteration, the experience replay buffer can be emptied, i.e. the experience replay buffer D is emptied, in preparation for the next iteration. Then, through the above steps, the PPO algorithm can collect experiences and update the policy and value function in each episode, so as to optimize the policy to improve performance.

[0183] It should be noted that after obtaining the conditional data, the communication device or the control device connected to the communication device can perform proximal policy optimization based on the conditional data. When the control device performs proximal policy optimization, the communication device can send the conditional data to the control device after obtaining the conditional data, and the control device can perform proximal policy optimization and obtain communication state data, and then send the communication state data to the communication device, so that the communication device can obtain the communication state data.

[0184] S104, selecting a communication unit and an energy transmission unit in the plurality of super surface units according to the communication state data;

[0185] After obtaining the communication state data by solving the proximal policy optimization framework, the communication device can perform communication control based on the obtained communication state data, so as to select a communication unit and an energy transmission unit in the plurality of super surface units according to the communication state data.

[0186] For example, after determining the transmission area ratio λ=(m×n) / (M×N) by solving the proximal policy optimization framework, since the total number of super surface units of the aerial intelligent super surface is fixed, the number of super surface units used for reflecting signals, i.e. m×n, in the information transmission stage can be determined based on the determined transmission area ratio λ, so that m×n super surface units are determined as communication units, and the remaining (M×N-m×n) super surface units are energy transmission units.

[0187] S105, sending the communication data to the target device through the communication unit, and collecting energy from the base station through the energy transmission unit.

[0188] After selecting the communication unit and the energy transmission unit, the communication device can send the communication data to the target device through the communication unit in the information transmission stage, i.e. send the communication data to the base station or the communication terminal through m×n super surface units, and collect energy from the base station through the energy transmission unit, i.e. collect energy through M×N-m×n super surface units.

[0189] ​By applying the technical solutions of the above embodiments, the wireless number-energy co-transmission intelligent metasurface assisted secure communication method provided in the above embodiments can realize continuous dynamic allocation of aerial intelligent metasurface resources in time and space dimensions while maximizing the quality of secure communication based on a near-end policy optimization framework, and minimize the overall energy consumption of the system through an energy harvesting mechanism, thereby enhancing the endurance of the secure communication system. Through simulation experiments, the method not only effectively ensures the security of communication, but also efficiently promotes the energy harvesting process, thereby achieving dual optimization of energy efficiency and security on the basis of ensuring communication quality.

[0190] To verify the performance of the proposed framework, this section will conduct simulation experiments based on the framework. The user and the eavesdropper are located in a 50m x 50m site, and the base station is located at (0m, 0m, 0m). The ARIS is fixed at (25m, 25m, 25m), the number of users is set to K = 1, the number of eavesdroppers is set to P = 1, and the number of reflective elements of the ARIS is set to 16. Other parameters are set as follows: max P = 500W, α = 3, E min = 0.4.

[0191] By comparing the performance of the PPO framework with energy harvesting and the PPO framework without energy harvesting in secure communication, as shown in Figure 4 , the average secure rate corresponding to each round of training of the two frameworks is shown. Overall, the training processes of the two frameworks are stable, and both have good convergence. In terms of average secure rate, both frameworks can achieve a high secure communication rate, and the performance of the near-end policy optimization framework is slightly better than that of the near-end policy optimization framework with energy harvesting in the later training period. The reason is that the aerial intelligent metasurface sacrifices some performance of secure communication while ensuring energy harvesting, but the overall difference is not large. Figure 5 The energy collection ratio after 300 rounds of training is shown, and Figure 5 it can be seen that as the number of training rounds increases, the proposed energy harvesting framework can effectively learn the energy harvesting strategy. After about 230 rounds of training, the energy collected per round is about 60%, achieving a relatively effective energy harvesting strategy.

[0192] In some embodiments, as a specific implementation of the wireless number-energy co-transmission intelligent metasurface assisted secure communication method described in the above embodiments, part of the embodiments of the present application also provide a wireless number-energy co-transmission intelligent metasurface assisted secure communication system, as shown in Figure 6 , the system includes a base station, a communication device, and a communication terminal.

[0193] The communication device is provided with an aerial intelligent metasurface, the aerial intelligent metasurface includes a plurality of metasurface units; the communication device establishes a communication connection and an energy transmission channel with a base station device through the aerial intelligent metasurface; the communication device further includes:

[0194] a data acquisition module configured to acquire communication data and system parameters, the system parameters including a system model;

[0195] a condition definition module configured to define condition data according to the system parameters, the condition data including an energy collection stage time, a transmission area ratio, and a diagonal matrix; the transmission area ratio is a ratio of a metasurface unit area used for signal reflection in an information transmission stage to an area of all metasurface units; and the diagonal matrix is a reflection coefficient matrix of the aerial intelligent metasurface;

[0196] a communication control module configured to acquire communication state data based on the condition data, the communication state data being state data and action data obtained based on a proximal policy optimization framework, with a maximized safety rate of the information transmission stage as an objective function and the condition data as a conditional constraint;

[0197] a metasurface unit selection module configured to select a communication unit and an energy transmission unit from the plurality of metasurface units according to the communication state data;

[0198] a communication execution module configured to send the communication data to a target device through the communication unit and collect energy from the base station through the energy transmission unit.

[0199] By applying the technical solutions of the above embodiments, the present application provides an intelligent metasurface assisted secure communication method and system for wireless energy and information co-transmission. The method acquires communication data and system parameters, defines condition data, and then acquires communication state data based on the condition data. The communication state data is a state data and action data obtained based on a proximal policy optimization framework, with a maximized safety rate of the information transmission stage as an objective function and the condition data as a conditional constraint. Then, a communication unit and an energy transmission unit are selected from a plurality of metasurface units according to the communication state data, so that the communication data is sent to a target device through the communication unit and energy is collected from the base station through the energy transmission unit. The method can realize continuous and dynamic allocation of aerial intelligent metasurface resources in time and space dimensions based on the proximal policy optimization framework, while maximizing the safety communication quality, and minimize the overall energy consumption of the system through the energy collection mechanism, so as to enhance the endurance of the secure communication system.

[0200] It should be noted that other corresponding descriptions of the functions of each functional unit involved in the wireless energy and communication co-transmission intelligent metasurface assisted secure communication system provided by the embodiments of the present application can refer to the corresponding descriptions in the wireless energy and communication co-transmission intelligent metasurface assisted secure communication method provided by the above embodiments, which will not be described here.

[0201] The embodiments of the present application also provide a computer device, which can be a personal computer, a server, a network device, etc. The computer device includes a bus, a processor, a memory, and a communication interface, and can also include an input / output interface and a display device. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store location information. The network interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement the steps in each method embodiment.

[0202] Those skilled in the art can understand that the structure of the computer device described above is only part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. A specific computer device can include more or fewer components, or combine certain components, or have a different arrangement of components.

[0203] In one embodiment, a computer readable storage medium is also provided, which can be non-volatile or volatile, and has a computer program stored thereon. The computer program is executed by the processor to implement the steps in each method embodiment described above.

[0204] In one embodiment, a computer program product is also provided, which includes a computer program. The computer program is executed by the processor to implement the steps in each method embodiment described above.

[0205] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0206] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments.

[0207] Any reference to storage, databases or other media herein includes at least one of volatile, non-volatile, removable, and non-removable media implemented in a method or technology for storage and / or access of information, such as computer readable instructions, data structures, program modules, or other data. Non-limiting examples of storage media include Read-Only Memory (ROM), tape, floppy disks, flash memories, compact disks, optical disks, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change random access memory (PRAM), graphene memory, and others.

[0208] Volatile memory can include Random Access Memory (RAM), or external cache memory, etc. As an illustration and not a limitation, RAM can be a variety of forms, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), or other types of RAM.

[0209] Databases can include at least one of relational databases, distributed databases, and non-relational databases, and others. Non-relational databases can include blockchain-based distributed databases, and others. Processors can include general purpose processors, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic, and others.

[0210] Any of the technical features of the above embodiments can be combined, and to make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present disclosure.

[0211] The above embodiments only express several implementation manners of the present disclosure, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present disclosure. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present disclosure, a number of modifications and improvements can be made, which are all within the scope of the present disclosure. Therefore, the scope of the present disclosure should be subject to the appended claims.

Claims

1. A smart metasurface-assisted secure communication method for wireless data and energy transmission, characterized in that, The method is applied to communication equipment, which is equipped with an aerial smart metasurface comprising multiple metasurface units. The communication equipment establishes a communication connection and energy transmission channel with a base station via the aerial smart metasurface. Acquire communication data and system parameters, wherein the system parameters include the system model; According to the system parameters, the condition data includes the energy harvesting phase time, the transmission area ratio, and the diagonal matrix; the transmission area ratio is the ratio of the area of ​​the metasurface unit used for signal reflection to the area of ​​all metasurface units during the information transmission phase; the diagonal matrix is ​​the reflection coefficient matrix of the aerial intelligent metasurface. Based on the conditional data, communication status data is obtained. The communication status data is a set of status data and action data obtained by setting conditional constraints based on the conditional data and solving based on the near-end strategy optimization framework, with the objective function being to maximize the safe rate of information transmission. Based on the communication status data, a communication unit and an energy transfer unit are selected from among the multiple metasurface units; The communication unit transmits the communication data to the target device, and the energy transmission unit collects energy from the base station.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the number of communication terminals connected to the base station, and construct a user set based on the number of communication terminals; Set the number of eavesdropping terminals within the radiation range of the base station, and construct an eavesdropping terminal set based on the number of eavesdropping terminals; A communication distance is set based on the user set and the eavesdropping terminal set. The communication distance includes a first distance and a second distance. The first distance is the distance between the airborne smart metasurface and the communication terminal. The second distance is the distance between the airborne smart metasurface and the eavesdropping terminal. The metasurface unit and metasurface unit array are configured according to the communication distance; A system model is constructed based on the metasurface unit array, the user set, the eavesdropping terminal set, and the communication distance.

3. The method according to claim 2, characterized in that, The system parameters define the conditional data, including: Obtain the communication cycle of the system model; The entire time period corresponding to the communication cycle is divided into multiple time slots, each time slot including an energy harvesting phase and an information transmission phase; Define the energy harvesting phase time, during which all metasurface units in the aerial smart metasurface are used to harvest energy; The information transmission phase time is calculated based on the energy harvesting phase time. During the information transmission phase, the aerial smart metasurface has a first number of metasurface units used for signal reflection and a second number of metasurface units used for energy harvesting. The sum of the first number and the second number is equal to the total number of metasurface units in the aerial smart metasurface. The transmission area ratio is calculated based on the first quantity and the second quantity.

4. The method according to claim 3, characterized in that, The system parameters define the conditional data, including: Define the precoding vector and single-terminal signal of the communication terminal; The base station's transmitted signal and total transmitted power are calculated based on the encoded vector and the single terminal signal. Obtain the channel information and channel power gain from the base station to the metasurface unit; The unit energy is calculated based on the transmitted signal, the channel information of the metasurface unit, and the channel power gain. The unit energy is used to characterize the energy collected by a single metasurface unit. The unit energy is obtained by calculating the Euclidean norm of the product of the transmitted signal, the channel information of the metasurface unit, and the channel power gain. The total collected energy is calculated based on the unit energy and the energy collection phase time. The total collected energy includes the first energy collected during the information transmission phase and the second energy collected during the energy collection phase. The first energy is the product of the sum of the unit energies of the second number of metasurface units and the time of the information transmission phase. The second energy is the product of the sum of the unit energies of the total number of metasurface units and the time of the energy collection phase.

5. The method according to claim 1, characterized in that, Based on the aforementioned conditional data, communication status data is obtained, including: The channel information is acquired, which includes first channel information and second channel information. The first channel information is used to characterize the channel from the base station to the airborne smart metasurface; the second channel information is used to characterize the channel from the metasurface unit of the airborne smart metasurface to the communication terminal or the eavesdropping terminal. Define the diagonal matrix based on the reflection coefficient of the airborne intelligent metasurface; Based on the channel information and the diagonal matrix, establish the first reachable rate function of the communication terminal and the second reachable rate function of the eavesdropping terminal; Calculate the secure rate during the information transmission phase based on the first reachable rate function and the second reachable rate function; Set the security rate as the objective function of the near-end policy optimization framework; Based on the conditional data, conditional constraints are set, including minimum constraints on energy harvesting efficiency, time constraints, maximum base station transmit power constraints, and constraints on the ratio of signal reflection units to all metasurface units during information transmission. Near-end policy optimization is performed based on the objective function and the conditional constraints to obtain the communication state data.

6. The method according to claim 5, characterized in that, Perform near-end policy optimization based on the objective function and the conditional constraints to obtain the communication state data, including: Input parameters are set based on the aforementioned conditional data, including the signal transmitted by the base station, channel information, diagonal matrix, and communication distance. Initialize the actor network, commentator network, and experience replay pool of the proximal policy optimization framework; Define the output parameters of the near-end policy optimization framework, the output parameters including the optimal action and the total collected energy; the optimal action is the action space combination of the energy collection phase time and the transmission area ratio. The communication status data is generated by performing a loop iteration based on the input parameters and the output parameters.

7. The method according to claim 6, characterized in that, Perform a loop iteration based on the input parameters and the output parameters to generate the communication status data, including: Get the input parameters for the current loop step; Initialize random noise process; Collect the channel information from the metasurface unit to the communication terminal, and use the channel information as the initial state of the current loop step; A time-step loop is executed based on the initial state to generate transition data, which includes the current state, the current policy sampling action, and the next state. The advantage estimate of the transferred data is calculated using a value function.

8. The method according to claim 7, characterized in that, Based on the initial state, a time-step loop is executed to generate transition data, including: Get the current state and current policy sampling action at the current time step; Execute the current policy sampling action and calculate the reward parameters corresponding to the current policy sampling action; The current state is adjusted based on the reward parameters to obtain the next state; Combine the current state, the current policy sampling action, and the next state to generate transition data; The transferred data is stored in the experience replay pool.

9. The method according to claim 8, characterized in that, The method further includes: Randomly extract a preset batch of transfer data from the experience playback pool; Based on the preset batch of transfer data, the strategy is updated by maximizing the pruning of proxy targets; Based on the preset batch of transfer data, the value function is updated by minimizing the mean square error to generate the communication status data.

10. A smart metasurface-assisted secure communication system for wireless data and energy transmission, characterized in that, The system includes a base station, communication equipment, and a communication terminal; the communication equipment is equipped with an aerial intelligent metasurface, which includes multiple metasurface units; the communication equipment establishes a communication connection and energy transmission channel with the base station equipment through the aerial intelligent metasurface; The communication device also includes: The data acquisition module is used to acquire communication data and system parameters, including the system model; The condition definition module is used to define condition data according to the system parameters. The condition data includes the energy harvesting phase time, the transmission area ratio, and the diagonal matrix. The transmission area ratio is the ratio of the area of ​​the metasurface unit used for signal reflection to the area of ​​all metasurface units during the information transmission phase. The diagonal matrix is ​​the reflection coefficient matrix of the aerial intelligent metasurface. The communication control module is used to obtain communication status data based on the condition data. The communication status data is a set of status data and action data obtained by setting condition constraints based on the condition data and solving based on the near-end strategy optimization framework, with the objective function of maximizing the safe rate of information transmission. A metasurface unit selection module is used to select a communication unit and an energy transmission unit from a plurality of metasurface units according to the communication status data; The communication execution module is used to send the communication data to the target device through the communication unit, and to collect energy from the base station through the energy transmission unit.

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