Unmanned aerial vehicle intelligent metasurface assisted satellite-to-ground physical layer security transmission method and system

Abstract

The application discloses a kind of unmanned vehicle intelligent metasurface assisted satellite-ground physical layer security transmission method and system, construct signal transmission model based on communication system, obtain the instantaneous signal-to-interference-and-noise ratio of communication terminal and system instantaneous and security rate, form joint optimization satellite base station transmission precoding matrix, satellite-borne passive intelligent metasurface phase shift matrix, airborne active intelligent metasurface diagonal reflection matrix and unmanned vehicle flight path, transmission optimization problem model with the goal of maximizing system average and security rate;Through the analysis of problem structure, propose the solution framework of hierarchical processing, in inner layer, successive convex approximation, manifold optimization based on punishment and semidefinite programming algorithm are used to process transmission signal and intelligent metasurface reflection respectively;In outer layer, the best flight path design is obtained by using unmanned vehicle flight path algorithm based on deep deterministic policy gradient, and the security performance of satellite-ground communication system is improved by solving the optimization problem of inner and outer layers.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of wireless communication optimization, and particularly relates to a method and system for satellite-to-ground physical layer security transmission assisted by unmanned aerial vehicle intelligent metasurface, which discloses the potential of the proposed model and related optimization algorithm in enhancing satellite-to-ground security transmission. BACKGROUND

[0002] In recent years, mobile communication networks have made remarkable achievements in densely populated areas such as cities, thanks to their high speed, low latency, and large-scale connectivity. However, in remote areas (such as mountains, deserts, etc.) and sparsely populated areas, the coverage of ground communication networks still faces many challenges. Limited by the lack of infrastructure and geographical conditions, the deployment and maintenance of ground base stations are costly, and it is difficult to improve communication coverage and capacity. In contrast, satellite communication is not limited by ground environment and can provide communication services in the above-mentioned areas at a lower cost, realizing seamless coverage and ubiquitous connectivity of global communication networks. In addition, thanks to its rapid deployment characteristics, satellite communication has become an indispensable technology in emergency communication, natural disaster response, and military use. In the future development of global communication networks, satellite communication is expected to play an increasingly important role.

[0003] Thanks to the rapid development of metamaterial theory and interface electromagnetics, intelligent metasurfaces, as a cross-disciplinary achievement, have received widespread attention in the interdisciplinary application of communication in recent years. Intelligent metasurfaces can reshape the wireless channel environment by manipulating electromagnetic waves, thereby providing support for coverage blind area elimination, security communication enhancement, multi-stream transmission rank enhancement, and energy-carrying communication transmission, etc. With the continuous development of these technologies, intelligent metasurfaces have become one of the key technologies in the sixth generation of mobile communication networks, and will play an important role in improving system performance, reducing energy consumption, and enhancing communication security in the future.

[0004] Existing research on intelligent metasurfaces mainly focuses on wide-area coverage and communication enhancement, and more research is biased towards a single type of intelligent metasurface. The potential of different types of intelligent metasurfaces in cooperatively achieving wide-area coverage and communication enhancement has not been revealed. In addition, the deployment location of intelligent metasurfaces is set to the surface of buildings in most studies, which greatly limits the effect of their assisted communication. Inspired by existing research, deploying intelligent metasurfaces on unmanned aerial vehicles to assist satellite-to-ground security communication not only guarantees the wide-area coverage of communication, but also achieves security enhancement of communication. In addition, the dynamic position adjustment of unmanned aerial vehicles also improves the degree of freedom of system security design.

[0005] In addition, existing intelligent metasurface assisted communication mostly uses a solution scheme based on convex optimization, but optimization of dynamic positions will bring huge demand for computing resources and complexity of solution. Therefore, recent research has also mentioned learning technology to obtain a more efficient solution. In this regard, machine learning as a powerful tool to solve complex non-convex optimization can effectively obtain the optimal design of the UAV flight path and the corresponding safe transmission strategy, providing real-time adaptability that traditional optimization methods cannot achieve. By combining convex optimization with learning-based flight path design, an efficient and low-complexity solution framework can be expected to be used to complete the processing of the constructed optimization problem. SUMMARY

[0006] The technical problem to be solved by the present application is to provide an unmanned aerial vehicle intelligent metasurface assisted satellite-to-ground physical layer security transmission method and system to solve the technical problem of low security performance of existing satellite-to-ground communication systems.

[0007] The application adopts the following technical solutions:

[0008] The unmanned aerial vehicle intelligent metasurface assisted satellite-to-ground physical layer security transmission method comprises the following steps:

[0009] S1, an air-space-ground integrated secure communication system assisted by a satellite-borne passive intelligent metasurface and an airborne active intelligent metasurface is established, the air-space-ground integrated secure communication system comprises a space-based network composed of a satellite base station and a satellite-borne passive intelligent metasurface, an air-based network composed of an unmanned aerial vehicle and an airborne active intelligent metasurface, and a ground-based network composed of a plurality of users and eavesdroppers;

[0010] S2, a signal transmission model of the air-space-ground integrated secure communication system is derived to obtain an instantaneous signal-to-interference-and-noise ratio of a communication terminal and a system instantaneous and security rate, and then an average and security rate of the satellite-to-ground communication system is represented;

[0011] S3, a transmission optimization problem model is formed to maximize the average and security rate of the system, in which a satellite base station transmission precoding matrix, a satellite-borne passive intelligent metasurface phase shift matrix, an airborne active intelligent metasurface diagonal reflection matrix and an unmanned aerial vehicle flight path are jointly optimized;

[0012] S4, the transmission optimization problem model is analyzed to give a hierarchical solution framework, in which a convex optimization technology is used in the inner layer to obtain a satellite-to-ground security transmission strategy, and a learning-based algorithm is used in the outer layer to design an unmanned aerial vehicle flight path;

[0013] S5, the iterative solution of the inner problem is completed under the block coordinate descent framework, the solution of the satellite transmission precoding matrix depends on the successive convex approximation algorithm, the design of the satellite-borne passive intelligent metasurface reflection matrix is completed through the manifold optimization algorithm based on punishment, and the diagonal reflection matrix of the airborne active intelligent metasurface is solved by using the semi-positive definite programming algorithm, and the three optimization variables are jointly iterated under the block coordinate descent framework to obtain the local optimal solution of the inner safe transmission problem;

[0014] S6, the outer unmanned aerial vehicle flight path is designed by using a learning algorithm based on deep deterministic policy gradient; the airborne active intelligent metasurface acts as an intelligent agent and interacts with the environment through a certain strategy; in each time slot, the intelligent agent selects an action based on the current state and moves to a new position, and the space-ground integrated safe communication system is then changed to a new state, and an instant reward is generated; the goal of the intelligent agent is to maximize the long-term discount return by learning the strategy; through continuous interaction, the intelligent agent realizes the optimal flight path design of the space-ground integrated safe communication system.

[0015] S7, the optimal flight path design of the unmanned aerial vehicle and the corresponding satellite-ground safe transmission strategy are output, and the average and safe rate of the space-ground integrated safe communication system is obtained.

[0016] Preferably, in step S1, passive intelligent metasurfaces are deployed on the satellite side to introduce a reflection link to enhance transmission; N1 passive reflection elements arranged in a uniform linear array are integrated on the satellite-borne passive intelligent metasurface, and the corresponding reflection coefficient matrix can be represented as

[0017] The unmanned aerial vehicle carrying active intelligent metasurfaces is deployed as a space-based relay node; the airborne active intelligent metasurface is equipped with N2 active reflection elements, and its diagonal reflection matrix can be represented as

[0018] Preferably, in step S2, the average and safe rate R of the satellite-ground communication system is:

[0019]

[0020] wherein N is the total number of flight time slots, is a set of flight time slots, R[n] is the instantaneous and safe rate of the system corresponding to the n time, and q is a covered user. is a set of covered users, R q [n] is the instantaneous safe rate of the covered user q at the n time, is a set of non-covered users, R k [n] is the instantaneous safe rate of the covered user k at the n time.

[0021] Preferably, in step S3, the transmission optimization problem model is specifically:

[0022]

[0023] where P S , and P A denote the nominal transmit power of the satellite base station, the maximum amplification factor of the airborne active intelligent metasurface, and the nominal amplified power, respectively, W denotes the satellite transmission precoding matrix, denote the system average and safety rate, Θ P denotes the on-board passive intelligent metasurface phase shift matrix, Θ A denotes the airborne active intelligent metasurface diagonal reflection matrix, q A denotes the UAV position, w denotes the beamforming vector corresponding to the jth user, P S denotes the nominal transmit power of the satellite base station, denotes the set of flight time slots, denotes the ith element of the on-board passive intelligent metasurface phase shift matrix at time n, N P denotes the set of elements of the on-board passive intelligent metasurface phase shift matrix, denotes the ith element of the airborne active intelligent metasurface diagonal matrix at time n, N A denotes the set of elements of the airborne active intelligent metasurface diagonal matrix, Θ A denotes the airborne active intelligent metasurface diagonal matrix at time n, denotes the set of users, H PA denotes the channel from the on-board passive intelligent metasurface to the airborne active intelligent metasurface at time n, Θ P denotes the channel from the satellite base station to the on-board passive intelligent metasurface at time n, H SP denotes the channel from the satellite base station to the airborne active intelligent metasurface at time n, H SA denotes the channel from the satellite base station to the airborne active intelligent metasurface at time n, w j denotes the beamforming vector corresponding to the jth user at time n, denotes the thermal noise introduced by the airborne active intelligent metasurface, q A denotes the initial UAV position at time 0, q0 denotes the position of the UAV at time τ = 0, q A denotes the final UAV position at time N, q F denotes the position of the UAV at time τ = T / N, denotes the area in which the UAV can move, V max denotes the maximum flight speed of the UAV, τ denotes the flight time slot.

[0024] Preferably, in step S4, the inner-layer safety transmission problem is formulated as:

[0025]

[0026] The outer unmanned aerial vehicle trajectory design problem is expressed as:

[0027]

[0028] wherein W is a satellite transmission precoding matrix, Θ P is a spaceborne passive intelligent metasurface phase shift matrix, Θ A is an airborne active intelligent metasurface diagonal reflection matrix, R q is a safety rate of a user q, R k is a safety rate of a user k, is a covered user set, is a non-covered user set, w j is a beamforming vector corresponding to the jth user, P S is a rated transmission power of a satellite base station, is an ith element of a spaceborne passive intelligent metasurface phase shift matrix, N P is an element set of a spaceborne passive intelligent metasurface phase shift matrix, is an ith element of an airborne active intelligent metasurface diagonal matrix, is a rated amplification factor of an airborne active intelligent metasurface, N A is an element set of an airborne active intelligent metasurface diagonal matrix, H PA is a channel from a spaceborne passive intelligent metasurface to an airborne active intelligent metasurface, H SP is a channel from a satellite base station to a spaceborne passive intelligent metasurface, H SA is a channel from a satellite base station to an airborne active intelligent metasurface, is thermal noise introduced by an airborne active intelligent metasurface, P A is a rated amplification power of an airborne active intelligent metasurface, is a system and average safety rate, q A is a position of an unmanned aerial vehicle, τ is a flight time slot, V max is a maximum flight speed of an unmanned aerial vehicle, is a, q A [n] is a position of an unmanned aerial vehicle at an nth time slot, q F is a position of an unmanned aerial vehicle at τ = T / N, is a region in which an unmanned aerial vehicle can move.

[0029] Preferably, step S5 is specifically:

[0030] S501, fixing a spaceborne passive intelligent metasurface phase shift matrix and an airborne active intelligent metasurface diagonal reflection matrix, and optimizing a source precoding matrix: regarding the reflection matrices of the spaceborne passive intelligent metasurface and the airborne active intelligent metasurface as known, a source precoding matrix is optimized by using successive convex approximation and semi-definite programming.

[0031] S502, fix the transmitter precoding matrix and the onboard active intelligent metasurface diagonal reflection matrix, and optimize the satellite-borne passive intelligent metasurface phase shift matrix: fix the transmitter precoding matrix and the onboard active intelligent metasurface diagonal reflection matrix, and obtain a local optimal solution of the satellite-borne passive intelligent metasurface phase shift matrix based on a penalty function and manifold optimization;

[0032] S503, fix the transmitter precoding matrix and the satellite-borne passive intelligent metasurface phase shift matrix, and optimize the onboard active intelligent metasurface diagonal reflection matrix: given the transmitter precoding matrix and the satellite-borne passive intelligent metasurface phase shift matrix, complete the optimization and solution of the onboard active intelligent metasurface diagonal reflection matrix by using semi-definite programming;

[0033] S504, in each iteration step, sequentially solve the above three sub-problems, and approximate the optimal solution of the original problem by updating the to-be-optimized variables step by step, and the iteration process stops after converging to a stable solution meeting the accuracy requirement.

[0034] Preferably, for the sub-problem of the satellite-borne passive intelligent metasurface phase shift matrix, the constraints include a single-mode constraint of the passive intelligent metasurface phase shift matrix and an amplification multiple constraint and an amplification power constraint of the onboard active intelligent metasurface, and a penalty factor and The sub-problem is converted into a problem containing only a single-mode constraint, and is expressed as follows:

[0035]

[0036] For the penalty factor, the secondary gradient is used for updating:

[0037]

[0038] wherein, and π 3 is an amplification coefficient, L is a penalty function, is the value of the penalty factor λ at the t+1th iteration, is the value of the penalty factor β at the t+1th iteration, is the value of the penalty factor at the t+1th iteration, Θ P is a satellite-borne passive intelligent metasurface phase shift matrix, is the i-th element of the satellite-borne passive intelligent metasurface phase shift matrix, N P is an element set of the satellite-borne passive intelligent metasurface phase shift matrix, is the value of the penalty factor λ matrix at the m-th row and the q-th column at the t+1th iteration, is the value of the penalty factor β matrix at the m-th row and the k-th column at the t+1th iteration, is Θ Poptimal solution, eavesdropping rate constraint for eavesdropper m to eavesdrop on user q, eavesdropping rate constraint for eavesdropper m to eavesdrop on user k, Q is the set of covered users, M is the set of eavesdroppers, K is the set of non-covered users;

[0039] Finally, the optimal passive smart metasurface phase shift matrix is obtained through the joint iteration of the inner and outer layers of the penalty factor and the passive smart metasurface phase shift matrix.

[0040] Preferably, for the sub-problem of the airborne active smart metasurface phase shift matrix, a new optimization variable θ A is defined A , v A = [1 θ A ] T and The objective function and the constraint are expressed in the form of Φ A , and the non-convex -log(tr(BΦ A )) is converted into a convex constraint:

[0041] For a given arbitrary integer d and any matrix E satisfying E≥0, |E|=1 The function f(S) = -tr(SE) + log|S| + d is constructed, and the following is obtained:

[0042]

[0043] The optimal solution S * = E -1 ;

[0044] After the above transformation, a solvable convex problem about Φ A is obtained:

[0045]

[0046]

[0047] wherein, is the transformed system instantaneous and security rate, F is the amplification matrix after the power equivalent transformation of the airborne active smart metasurface method, is the i-th element of the diagonal matrix of the airborne active smart metasurface, is the rated amplification factor of the airborne active smart metasurface, is the element set of the diagonal matrix of the airborne active smart metasurface, is the i-th element of the diagonal matrix of the airborne active smart metasurface, N A is the element set of the diagonal matrix of the airborne active smart metasurface, Φ AP is the reconstructed airborne active metasurface reflection matrix for the introduced new optimization variable, P A r is the rated amplification power of the airborne active metasurface, Q is the maximum eavesdropping rate of the eavesdropper on the covered user q, is the composite eavesdropping channel of the eavesdropper m on the user i, is the thermal noise introduced by the airborne active metasurface, is an intermediate variable obtained by matrix transformation, is the background noise at the eavesdropper, η q,m r is an auxiliary variable introduced to process the eavesdropping rate constraint of the eavesdropper m on the covered user q, K is the maximum eavesdropping rate of the eavesdropper on the covered user k;

[0048] Optimization is alternately performed on the introduced auxiliary variable and Φ A to obtain the optimal Then, singular value decomposition is performed to obtain the optimal ν A , and the first element is normalized, and the values of the remaining dimensions are extracted as the active reflection parameter θ A .

[0049] Preferably, in step S6, the UAV trajectory design problem is modeled as a Markov decision process, and a trajectory design algorithm based on deep deterministic policy gradient is used. After sufficient training, under the given initial communication system state, the agent will generate the corresponding optimized action using the Actor network according to the network feedback at each stage, and finally obtain the converged optimal UAV trajectory design strategy.

[0050] In a second aspect, the embodiments of the present application provide an unmanned aerial vehicle intelligent metasurface assisted satellite-ground physical layer security transmission system, comprising:

[0051] A network module establishes an air-space-ground integrated secure communication system assisted by a spaceborne passive intelligent metasurface and an airborne active intelligent metasurface. The air-space-ground integrated secure communication system includes a space-based network composed of a satellite base station and a spaceborne passive intelligent metasurface, an air-based network composed of an unmanned aerial vehicle and an airborne active intelligent metasurface, and a ground-based network composed of a plurality of users and eavesdroppers.

[0052] A problem module derives a signal transmission model of the air-space-ground integrated secure communication system to obtain the instantaneous signal-to-interference-and-noise ratio of the communication terminal and the instantaneous and secure rate of the system, and then represents the average and secure rate of the satellite-ground communication system. A transmission optimization problem model is formed, which jointly optimizes the satellite base station transmission precoding matrix, the spaceborne passive intelligent metasurface phase shift matrix, the airborne active intelligent metasurface diagonal reflection matrix, and the unmanned aerial vehicle trajectory, with the objective of maximizing the average and secure rate of the system.

[0053] an analysis module, which analyzes a transmission optimization problem model, gives a framework of layered solving, and obtains a satellite-ground safe transmission strategy by using a convex optimization technique in an inner layer and designs a UAV flight path by using a learning-based algorithm in an outer layer; an iterative solution of the inner layer problem is completed under a block coordinate descent framework, a satellite transmission precoding matrix is solved by using a continuous convex approximation algorithm, a spaceborne passive intelligent metasurface reflection matrix is designed by using a manifold optimization algorithm based on punishment, and an airborne active intelligent metasurface diagonal reflection matrix is solved by using a semi-definite programming algorithm, and three to-be-optimized variables are jointly iterated under the block coordinate descent framework to obtain a local optimal solution of the inner layer safe transmission problem;

[0054] a design module, which designs the outer layer UAV flight path by using a deep deterministic policy gradient learning algorithm; the airborne active intelligent metasurface interacts with the environment by a certain strategy; in each time slot, the intelligent agent selects an action based on the current state, moves to a new position, and the space-air-ground integrated safe communication system is then changed into a new state, and an instant reward is generated; the goal of the intelligent agent is to maximize the long-term discount reward by learning the strategy; through continuous interaction, the intelligent agent realizes the design of the flight path of the space-air-ground integrated safe communication system with an optimal safety rate;

[0055] an output module, which outputs the optimal UAV flight path design and the corresponding satellite-ground safe transmission strategy, and obtains the average and safety rate of the space-air-ground integrated safe communication system.

[0056] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the UAV intelligent metasurface assisted satellite-ground physical layer safe transmission method.

[0057] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium including a computer program, and the computer program is executed by a processor to implement the steps of the UAV intelligent metasurface assisted satellite-ground physical layer safe transmission method.

[0058] In a fifth aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the UAV intelligent metasurface assisted satellite-ground physical layer safe transmission method.

[0059] In a sixth aspect, an embodiment of the present application provides an electronic device including a computer program, and the computer program is executed by the electronic device to implement the steps of the UAV intelligent metasurface assisted satellite-ground physical layer safe transmission method.

[0060] Compared with the prior art, the present application has at least the following beneficial effects:

[0061] The application discloses a UAV intelligent metasurface assisted satellite-ground physical layer security transmission method, considers an integrated space-ground-ground integrated security communication system assisted by a satellite-borne passive intelligent metasurface and an airborne active intelligent metasurface in a communication area, and the system is composed of three layers of sub-networks, namely, a space-based network composed of a satellite base station and a satellite-borne passive intelligent metasurface, an air-based network composed of a UAV and an airborne active intelligent metasurface, and a ground-based network composed of a plurality of users and eavesdroppers. The problem is constructed as a transmission optimization problem for jointly optimizing a satellite base station transmission precoding matrix, a satellite-borne passive intelligent metasurface phase shift matrix, an airborne active intelligent metasurface diagonal reflection matrix and a UAV flight path, and maximizing the system average and security rate as the target. Since the problem is non-convex and the variables are highly coupled, the optimization variables exist in fractional form in the problem, and the design of the dynamic position of the UAV requires a large amount of computing resources. Based on this, a hierarchical processing solution framework is proposed, in which the transmission signal and the intelligent metasurface reflection are processed by using successive convex approximation, manifold optimization based on punishment and semi-definite programming algorithm in the inner layer; and the optimal flight path design is obtained by using a UAV flight path algorithm based on a deep deterministic policy gradient in the outer layer. Through the optimization and solution of the inner and outer layers, the eavesdropping system is effectively suppressed, and the security performance of the satellite-ground communication system is finally improved. Numerical results show that the method can significantly enhance the security performance of the satellite-ground communication system.

[0062] Further, the communication system containing a plurality of users and eavesdroppers fully reflects the complexity and randomness of the satellite-ground communication scene, and also conforms to the general scene characteristics in wireless communication, and is easy to be popularized to a more general real communication environment. Meanwhile, the deployment of the satellite-borne passive intelligent metasurface and the airborne active intelligent metasurface assisted communication in order to enhance the transmission and improve the system security performance can effectively solve the communication security risk caused by the broadcast characteristics of the satellite-ground communication.

[0063] Further, for the downlink communication scene from the satellite base station to the communication terminal, a signal transmission model is derived, the instantaneous signal-to-interference-and-noise ratio at the communication terminal and the system instantaneous and security rate are represented, and finally the system average and security rate is obtained, which can clearly show the specific form of the communication signal at each communication node, and provides a theoretical basis for subsequent problem modeling.

[0064] Further, the satellite base station transmission precoding matrix, the satellite-borne passive intelligent metasurface reflection matrix, the airborne active intelligent metasurface diagonal reflection matrix and the UAV flight path are jointly optimized to maximize the system average and security rate, and an optimization problem is constructed. By listing the optimization variables, the optimization constraints are determined, the optimization target is formulated, and the optimization direction and means of the communication system are clearly determined in a simple form, which provides favorable conditions for subsequent problem solving.

[0065] Further, by analyzing the problem structure, an optimization framework for hierarchical solution is given, and the inner and outer layers respectively use convex optimization techniques and learning-based algorithms to obtain satellite-ground safe transmission strategy and UAV trajectory design, and the specific framework for solving the problem is given. The proposed framework effectively decomposes the complex optimization problem through the inner and outer layer structure, and further efficiently solves the optimization of the proposed problem. At the same time, through the analysis of the inner and outer layer problems, a solution scheme suitable for each sub-problem is proposed to complete the specific solution of the entire optimization problem with low complexity.

[0066] Further, the inner problem involves optimization of satellite base station transmit precoding matrix, satellite-borne passive intelligent metasurface reflection matrix, and airborne active reconfigurable intelligent metasurface diagonal reflection matrix. Through the analysis of this problem, a block coordinate descent solution framework is proposed, and continuous convex approximation, manifold optimization algorithm based on punishment, and semi-definite programming are used to realize the alternating iterative optimization of the inner sub-problem variables. This scheme realizes the decoupling of the highly non-convex coupled optimization problem by optimizing a specific optimization variable in a single dimension, and further realizes the optimization solution of the variable. The given scheme decomposes the inner sub-problem and gives an effective solution to obtain a local optimal solution of the inner sub-problem with low complexity

[0067] Further, for the satellite-borne passive intelligent metasurface phase shift matrix sub-problem, considering that the single-mode constraint is difficult to handle, the traditional scheme increases the complexity of the overall algorithm, therefore, a manifold optimization algorithm based on punishment is proposed. The handling of the penalty factor uses the sub-gradient descent algorithm to realize efficient convergence, and the manifold optimization is used to solve the satellite-borne passive intelligent metasurface phase shift matrix. This method does not need to handle the single-mode constraint, and through gradient descent on the Riemannian manifold, a local optimal solution of the satellite-borne passive intelligent metasurface phase shift matrix can be obtained. The proposed algorithm does not need to operate on the problem structure too much, while ensuring the convergence and optimality of the problem solution.

[0068] Further, for the airborne active reconfigurable intelligent metasurface intelligent metasurface phase shift matrix sub-problem, first consider the diagonal matrix form of the optimization variable, and introduce new variables to reconstruct the sub-problem. Then, referring to the lemma given in the related research, an equation is introduced to perform non-convex convex operation, process the objective function and related constraints, and obtain a solvable convex optimization problem. In this process, the problem form is simplified by reconstructing the sub-problem, and the non-convex convex operation ensures the solvability of the problem. The algorithm considers the form of the original optimization variable, introduces new variables to reconstruct the problem, and then performs non-convex convex operation to ensure the convex optimization form of the problem. Finally, based on the relationship between the new variable and the original optimization variable, the optimal airborne active reconfigurable intelligent metasurface intelligent metasurface phase shift matrix is obtained. This operation not only ensures the equivalence of the solution, but also effectively reduces the difficulty of solving.

[0069] Further, the unmanned aerial vehicle trajectory design algorithm based on DDPG is adopted, the unmanned aerial vehicle is used as an intelligent agent to interact with the environment, and a trajectory capable of realizing optimal system safety rate is gradually learned. Compared with the traditional convex optimization technology, the learning-based trajectory design algorithm can better process the optimization problem under the time-varying channel, has lower calculation complexity, and can complete the collaborative design of the inner safety transmission strategy and the unmanned aerial vehicle trajectory through interaction with the inner safety transmission problem.

[0070] It can be understood that the beneficial effects of the above-mentioned second aspect to the sixth aspect can be referred to the related description in the first aspect, which will not be repeated here.

[0071] In summary, the present application proposes an airborne intelligent metasurface assisted satellite-ground safety transmission model and a corresponding solving framework, which combines convex optimization and machine learning methods to obtain the inner safety transmission strategy and the outer unmanned aerial vehicle trajectory design, respectively, and completes the joint optimization solution through the hierarchical solving framework, so as to achieve the system average and safety rate maximization target.

[0072] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. DETAILED DESCRIPTION

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments of the present application will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0074] Figure 1 The airborne intelligent metasurface assisted satellite-ground safety transmission model constructed by the present application is shown in the figure;

[0075] Figure 2 The flowchart of the present application is shown in the figure;

[0076] Figure 3 The relationship between the system average and safety rate and the number of intelligent metasurface elements of the present application is shown in the figure;

[0077] Figure 4 The schematic diagram of the computer device provided by an embodiment of the present application is shown in the figure;

[0078] Figure 5 The block diagram of a chip provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0079] Clearly, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort are within the protection scope of the present application.

[0080] In the description of the present application, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0081] It should also be understood that the terms used in the present application specification are only for the purpose of describing particular embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0082] It should be further understood that the term "and / or" used in the present application specification is intended to mean one or more of any combination of the associated listed items and all possible combinations thereof, and includes these combinations, for example, A and / or B can mean the existence of A alone, the existence of B alone, or the existence of both A and B. In addition, the character " / " in the present application generally represents an "or" relationship between the front and rear associated objects.

[0083] It should be understood that although the terms first, second, third, etc. can be used in the embodiments of the present application to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, the first preset range can also be referred to as the second preset range, and similarly, the second preset range can also be referred to as the first preset range without departing from the scope of the embodiments of the present application.

[0084] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting". Similarly, the phrase "if determined" or "if detecting (a stated condition or event)" can be interpreted to mean "when determined" or "in response to determining" or "when detecting (a stated condition or event)" or "in response to detecting (a stated condition or event)", depending on the context.

[0085] The various structural diagrams according to the disclosed embodiments of the present application are shown in the drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for clarity and others omitted. The shapes and relative sizes of the various regions, layers, and their relative positions illustrated in the drawings are merely exemplary and may deviate in actuality due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes, and relative positions can be additionally designed according to actual needs by those skilled in the art.

[0086] The present application provides a kind of unmanned aerial vehicle intelligent metasurface assisted star ground physical layer security transmission method, disclose the potential of model and related optimization method in enhancing star ground safe transmission;Consider a fusion satellite-borne passive intelligent metasurface and airborne active intelligent metasurface assisted space-ground integration security communication system, in view of the limited coverage of satellite communication, according to whether user is in coverage area, user is divided into coverage area user set and non-coverage area user set while assuming that all eavesdroppers are located in satellite communication coverage area. First, a signal transmission model based on the communication system is constructed, the instantaneous signal-to-interference-and-noise ratio of the communication terminal and the instantaneous and security rate of the system are obtained, and finally a transmission optimization problem model is formed, which optimizes the satellite base station transmission precoding matrix, the satellite-borne passive intelligent metasurface phase shift matrix, the airborne active intelligent metasurface diagonal reflection matrix and the unmanned aerial vehicle flight path, with the goal of maximizing the average and security rate of the system. Through the analysis of the structure of the problem, a hierarchical processing solution framework is proposed, which uses successive convex approximation, manifold optimization based on penalty and semidefinite programming algorithm to process the transmission signal and intelligent metasurface reflection respectively in the inner layer;In the outer layer, the optimal flight path design is obtained by using the unmanned aerial vehicle flight path algorithm based on deep deterministic policy gradient, and the eavesdropping system is effectively suppressed by solving the optimization problem of the inner and outer layers, thereby improving the security performance of the star-ground communication system.

[0087] Referring to Figure 2 The present application provides a kind of unmanned aerial vehicle intelligent metasurface assisted star ground physical layer security transmission method, comprising the following steps:

[0088] S1, establish a fusion satellite-borne passive intelligent metasurface and airborne active intelligent metasurface assisted space-ground integration security communication system, which includes a space-based network composed of satellite base station and satellite-borne passive intelligent metasurface, an air-based network composed of unmanned aerial vehicle and airborne active intelligent metasurface, and a ground-based network composed of multiple users and eavesdroppers, a total of three sub-networks;

[0089] Referring to Figure 1 The present application constructs the model diagram of airborne intelligent metasurface assisted star ground security transmission, considers in communication area The system includes a satellite-based passive intelligent metasurface and an airborne active intelligent metasurface assisted space-ground integration security communication system. The system is composed of three layers of sub-networks: a space-based network composed of a satellite base station and a satellite-based passive intelligent metasurface, an air-based network composed of a UAV and an airborne active intelligent metasurface, and a ground-based network composed of multiple users and eavesdroppers. Due to the limited coverage of satellite communication, users are divided into covered area user set and non-covered area user set according to whether they are in the coverage area, and all eavesdroppers are located in the satellite communication coverage area.

[0090] To improve the communication energy efficiency of the system, a passive intelligent metasurface is deployed on the satellite side to introduce a reflection link to enhance transmission. The satellite-based passive intelligent metasurface is integrated with N1 passive reflection units arranged in a uniform linear array, and its reflection coefficient matrix is represented as Although the deployment of the satellite-based passive intelligent metasurface introduces a reflection link to improve the transmission efficiency of the system, the communication needs of users in the non-covered area on the ground are still difficult to meet due to the limited coverage of satellite communication.

[0091] To achieve full coverage, an additional UAV carrying an active intelligent metasurface is deployed as an air-based relay node. The airborne active intelligent metasurface provides wide-area coverage and further enhances the security transmission capability of the system by reflecting signals. The airborne active intelligent metasurface is equipped with N2 active reflection elements, and its diagonal reflection matrix is represented as

[0092] S2, derive the signal transmission model based on the communication system, and then obtain the instantaneous signal-to-interference-and-noise ratio of the communication terminal and the instantaneous and secure rate of the system, and finally represent the average and secure rate of the system;

[0093] Considering the limited endurance time of the UAV, the total flight time T is divided into N time slots τ=T / N. Assuming that the time span of each time slot is very small, the position of the airborne active intelligent metasurface in each flight time slot can be regarded as fixed, denoted as q A [n]=[x A [n],y A [n]] represents the horizontal position of the airborne active intelligent metasurface in the nth time slot, and the height is fixed as H A ; the flight area of the UAV is limited to the communication area , i.e. , its initial position and final position are q[0]=q0, q[N]=q F .

[0094] In addition, the maximum flight speed of the UAV is set to V max , and the horizontal displacement of the airborne active intelligent metasurface between adjacent time slots must satisfy

[0095] The relevant channels are described as follows. The communication link between the satellite base station and the spaceborne passive intelligent metasurface is modeled as a LoS link. The communication links between the satellite base station and the airborne active intelligent metasurface, the ground-covered users and the eavesdroppers can be modeled as a shadowed Rician fading channel. The communication links between the spaceborne passive intelligent metasurface and the airborne active intelligent metasurface, the ground-covered users and the eavesdroppers can be modeled as a Rician fading channel. The airborne active intelligent metasurface reflects the signal in the air, so the communication links with the users and the eavesdroppers mainly rely on the LoS link, while considering the small-scale fading caused by the signal being blocked, so the relevant channel can be modeled as a Rician fading channel model.

[0096] Let W = [w1, w2, ···, w J ] represent the satellite linear precoding matrix, which can be regarded as each user being allocated a dedicated beamforming vector w j , The baseband transmission signal can be expressed as x = Ws, where s = [s1, s2, ···, s J ] T represents the data vector, s j represents the independent data symbol of zero mean and normalized power allocated to the jth user.

[0097] On this basis, considering that the users are divided into covered and non-covered cases, it is assumed that the interference between user signals only exists within the same set, and there is no mutual interference between users in different sets; for the eavesdroppers, it is also assumed that their interference mechanism is similar to that of the users.

[0098] Accordingly, at the nth time slot, the instantaneous signal expressions received at the covered area users, the non-covered area users and the eavesdroppers are respectively:

[0099]

[0100] where, represents the background noise at the covered users, the non-covered users and the eavesdroppers, respectively.

[0101] Let the noise at all communication terminal nodes be equal, i.e. where κ B = -1.380649 × 10 -23 J / K is the Boltzmann constant, B is the system communication bandwidth, and T B is the noise temperature.

[0102] The background noise power at the covered users and the non-covered users is not distinguished in the expression, and is represented as is the thermal noise introduced by the amplifier integrated by the airborne active intelligent metasurface.

[0103] Before deriving the signal-to-noise ratio at the user and the eavesdropper, the following definitions are first defined for simplicity of expression:

[0104]

[0105]

[0106] The instantaneous received signal-to-noise ratios at the users in the coverage area, the users in the non-coverage area and the eavesdropper are expressed as follows using the above definitions:

[0107]

[0108] The instantaneous secure rates at the users in the coverage area and the users in the non-coverage area are expressed as follows:

[0109]

[0110] The instantaneous and secure rates of the system can be derived as follows:

[0111]

[0112] The average and secure rates of the satellite-to-ground communication system are finally obtained as follows:

[0113]

[0114] S3, a transmission optimization problem model is formed for jointly optimizing the satellite base station transmission precoding matrix, the spaceborne passive intelligent metasurface phase shift matrix, the airborne active intelligent metasurface diagonal reflection matrix and the UAV flight path, with the objective of maximizing the average and secure rate of the system;

[0115] For the satellite-to-ground secure communication system and signal transmission model constructed as described above, the average secure rate of the system is maximized by jointly optimizing the transmission precoding matrix of the satellite, the phase shift matrix of the spaceborne passive intelligent metasurface, the diagonal reflection matrix of the airborne active intelligent metasurface and the UAV flight path. Under the constraints of the UAV flight area and flight time, while considering the transmission power limit of the satellite, the unit modulus constraint of the spaceborne passive intelligent metasurface and the power amplification limit of the airborne active intelligent metasurface, a joint optimization problem description based on an integrated framework is proposed for the terminal nodes in different communication areas as follows:

[0116]

[0117] where P S , and P A represent the rated transmission power of the satellite base station, the maximum amplification factor of the airborne active intelligent metasurface and the rated amplification power, respectively.

[0118] S4, analyze the problem and give a hierarchical solution framework, use convex optimization technology to get the satellite-ground safe transmission strategy in the inner layer, and use learning-based algorithm to design the UAV trajectory in the outer layer;

[0119] The joint optimization problem established in step S3 has both linear and fractional form coupling relationships between optimization variables, which belongs to highly non-convex optimization problem, and it is difficult to solve directly.

[0120] To effectively cope with the above challenges, a reasonable optimization framework needs to be designed to reduce the complexity of solving. Combined with the structural characteristics of the target problem, the hierarchical optimization method is an effective strategy. Specifically, by problem decomposition, the original problem is divided into satellite-ground safe transmission design problem and UAV trajectory optimization problem at two levels. Under this hierarchical structure, the inner problem of transmission beam and intelligent surface reflection design and the outer problem of UAV trajectory optimization have relative independence, but their solving results are closely related and dependent on each other. The hierarchical framework decomposes the original complex problem into two relatively easy-to-handle sub-problems, and then uses the interaction characteristics to realize organic integration, so as to obtain the overall optimal solution.

[0121] In the hierarchical framework, the inner problem can be efficiently solved by the block coordinate descent method due to its form characteristics, and the satellite-ground transmission strategy under each UAV position can be quickly generated by combining convex optimization technology. The outer problem is highly non-convex and dynamic, and a deep reinforcement learning-based trajectory optimization algorithm is used to solve it. Through this hierarchical and collaborative processing method, the highly coupled system average safety rate maximization problem can be effectively solved, and the optimization solution can be obtained with low complexity, thereby improving the practicality and efficiency of the algorithm.

[0122] Specifically, the inner safe transmission problem is expressed as:

[0123]

[0124] The outer UAV trajectory design problem is expressed as:

[0125]

[0126] S5, the inner problem is solved iteratively under the block coordinate descent framework; specifically, the solution of the satellite transmission precoding matrix depends on the continuous convex approximation algorithm, the design of the star-borne passive intelligent surface reflection matrix is completed through the manifold optimization algorithm based on punishment, and the diagonal reflection matrix of the airborne active intelligent surface is solved by using the semi-definite programming algorithm. Finally, the three optimization variables are iteratively solved under the block coordinate descent framework to obtain the local optimal solution of the inner safe transmission problem.

[0127] The inner-layer secure transmission problem involves three to-be-optimized variables: satellite transmission precoding matrix W, satellite-borne passive intelligent metasurface reflection matrix Θ P and airborne active intelligent metasurface reflection matrix Θ A In the inner-layer sub-problem, the highly non-convex coupling of to-be-optimized variables and the related constraints of RIS lead to no standard method to optimally solve the problem. For this problem, a BCD-based joint optimization framework is an effective solution, which can gradually approach the optimal solution by decoupling the inner-layer sub-problem into three sub-problems about the transmitter precoding matrix, the satellite-borne passive intelligent metasurface reflection matrix and the airborne active intelligent metasurface reflection matrix, and iteratively optimizing under the BCD framework. The specific solving process is as follows:

[0128] S501, fix the satellite-borne passive intelligent metasurface phase shift matrix and the airborne active intelligent metasurface diagonal reflection matrix, and optimize the transmitter precoding matrix: In this subsection, the reflection matrices of the satellite-borne passive intelligent metasurface and the airborne active intelligent metasurface are regarded as known, and the transmitter precoding matrix is optimized by combining convex approximation and semi-definite programming;

[0129] S502, fix the transmitter precoding matrix and the airborne active intelligent metasurface diagonal reflection matrix, and optimize the satellite-borne passive intelligent metasurface phase shift matrix: In this problem, the transmitter precoding matrix and the airborne active intelligent metasurface diagonal reflection matrix are fixed, and the optimal solution of the satellite-borne passive intelligent metasurface phase shift matrix is obtained based on penalty function and manifold optimization;

[0130] S503, fix the transmitter precoding matrix and the satellite-borne passive intelligent metasurface phase shift matrix, and optimize the airborne active intelligent metasurface diagonal reflection matrix: In this stage, the transmitter precoding matrix and the satellite-borne passive intelligent metasurface phase shift matrix are given, and the optimization solution of the airborne active intelligent metasurface diagonal reflection matrix is completed by using semi-definite programming;

[0131] S504, in each iteration step, the above three sub-problems are solved in turn, and the optimal solution of the original problem is approached by gradually updating the to-be-optimized variables. The iteration process stops when it converges to a stable solution with the required precision.

[0132] Specifically, before optimizing and solving the problem, the target function needs to be processed to deal with the discontinuity of the target function caused by the maximum value, and the variable r Q is introduced K , and

[0133]

[0134] The target function can be expressed as

[0135]

[0136] Meanwhile, the constraints on r Q and r K will be introduced in the original problem.

[0137] The three sub-problems on the transmitter precoding matrix, the satellite-based passive ISM phase shift matrix and the airborne active ISM diagonal reflection matrix will be processed respectively in the following, and iteratively optimized under the BCD framework.

[0138] Firstly, for the sub-problem on the transmitter precoding matrix, we can refer to the theorem:

[0139]

[0140] where is the expansion point; the log(1+γ q ) and log(1+γ k ) terms in the objective function are expanded into first-order terms about w i at the corresponding points.

[0141] For the constraints on r Q and r K , we can get:

[0142]

[0143] The left side of the above two equations can be expressed as first-order terms about r Q and r K by Taylor expansion, and the fraction form on the right side can be expressed as first-order terms about the auxiliary variables and w i respectively by introducing auxiliary variables and doing Taylor expansion again.

[0144] The transmit power constraint and the airborne active ISM amplification power constraint are convex constraints about w i , which do not need to be further processed.

[0145] After the above operations, we get a solvable convex problem about w i , r Q and r K :

[0146]

[0147] where a m,q , b m,q , c m,k and d m,k are auxiliary variables introduced, are the corresponding expansion points;

[0148] X Uq , Y Uq , X Uk and YUk ,H A , For the problem, the equivalent definition is made for simplifying the expression in the process.

[0149] For the subproblem of the phase shift matrix of the spaceborne passive intelligent metasurface, the constraints in the problem mainly include the single-mode constraint of the phase shift matrix of the passive intelligent metasurface and the power and amplification constraints. By introducing a penalty factor and The subproblem can be converted into a problem containing only the single-mode constraint, and the problem is represented as:

[0150]

[0151] The solution can be obtained by manifold optimization.

[0152] And for the penalty factor, the subgradient is used for updating:

[0153]

[0154] Wherein, and π 3 is the amplification coefficient.

[0155] Finally, the optimal phase shift matrix of the passive intelligent metasurface is obtained through the joint iteration of the inner and outer layers.

[0156] For the subproblem of the phase shift matrix of the airborne active intelligent metasurface, a new optimization variable θ A is defined as diag(Θ A ), and ν A =[1θ A ] T and Further, the objective function and the constraint are expressed in the form of Φ A , and since the objective function and the constraint about r Q , r K in the form of Φ A will appear in the form of

[0157] log(tr(AΦ A ))-log(tr(BΦ A )), the non-convex -log(tr(BΦ A )) can be converted into a convex constraint by the following theorem.

[0158] For given arbitrary integer d and arbitrary matrix satisfying E≥0, |E|=1, the function f(S)=-tr(SE)+log|S|+d is constructed, and the following is obtained:

[0159]

[0160] where the optimal solution S * * = E -1 .

[0161] After the above transformation, the solvable convex problem about Φ A is obtained:

[0162]

[0163]

[0164] where:

[0165]

[0166] Optimizing the introduced auxiliary variables and and Φ A alternately for this problem can obtain the optimal Then, through singular value decomposition, the optimal ν A is obtained, and the first element is normalized, and the values of the remaining dimensions are extracted as the active reflection parameter θ A .

[0167] S6, the outer unmanned aerial vehicle trajectory design adopts a learning algorithm based on deep deterministic policy gradient; the airborne active intelligent surface acts as an agent and interacts with the environment through a certain strategy; in each time slot, the agent selects an action based on the current state and moves to a new position, and the system then changes to a new state, while generating an instant reward; the goal of the agent is to maximize the long-term discount reward by learning the strategy; through continuous interaction, the agent gradually learns the trajectory design that can achieve the optimal safety rate of the system;

[0168] The outer unmanned aerial vehicle trajectory design problem is proposed based on deep reinforcement learning due to the continuity of the ARIS trajectory and the high dependence of the inner and outer results. Through the interaction between the agent and the environment, the optimal trajectory design is efficiently generated, reducing the computational resource consumption in traditional methods. Specifically, first, the unmanned aerial vehicle trajectory design problem is modeled as a Markov decision process, and then a trajectory design algorithm based on deep deterministic policy gradient is used. After sufficient training, under the given initial communication system state, the agent will generate the corresponding optimized action according to the network feedback at each stage, and finally obtain the converged optimal unmanned aerial vehicle trajectory design strategy, thereby effectively improving the safety rate performance of the system.

[0169] S7, the joint optimization problem constructed by the optimization method is solved, and the optimal unmanned aerial vehicle trajectory design and the corresponding satellite-ground safe transmission strategy are output, and the system average and safety rate are obtained.

[0170] ​Those skilled in the art can understand that each aspect of the present application can be implemented as a system, a method or a program product. Therefore, each aspect of the present application can be specifically implemented as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "platform" here.

[0171] In another embodiment of the present application, a UAV intelligent metasurface assisted satellite-ground physical layer secure transmission system is provided, which can be used to implement the above-mentioned UAV intelligent metasurface assisted satellite-ground physical layer secure transmission method. Specifically, the UAV intelligent metasurface assisted satellite-ground physical layer secure transmission system includes a network module, a problem module, an analysis module, a design module and an output module.

[0172] The network module establishes an air-space-ground integrated secure communication system assisted by satellite-borne passive intelligent metasurfaces and airborne active intelligent metasurfaces. The air-space-ground integrated secure communication system includes a space-based network composed of a satellite base station and a satellite-borne passive intelligent metasurface, an air-based network composed of a UAV and an airborne active intelligent metasurface, and a ground-based network composed of a plurality of users and eavesdroppers.

[0173] The problem module derives a signal transmission model of the air-space-ground integrated secure communication system, obtains the instantaneous signal-to-interference-and-noise ratio of the communication terminal and the instantaneous and secure rate of the system, and then represents the average and secure rate of the satellite-ground communication system. A transmission optimization problem model is formed to maximize the average and secure rate of the system, with the joint optimization of the satellite base station transmission precoding matrix, the satellite-borne passive intelligent metasurface phase shift matrix, the airborne active intelligent metasurface diagonal reflection matrix and the UAV flight path.

[0174] The analysis module analyzes the transmission optimization problem model and gives a hierarchical solution framework. In the inner layer, the convex optimization technique is used to obtain the satellite-ground secure transmission strategy, and in the outer layer, the UAV flight path is designed based on the learning algorithm. The iterative solution of the inner layer problem is completed under the block coordinate descent framework. The solution of the satellite transmission precoding matrix depends on the continuous convex approximation algorithm, the satellite-borne passive intelligent metasurface reflection matrix design is completed through the manifold optimization algorithm based on punishment, and the airborne active intelligent metasurface diagonal reflection matrix is solved by using the semi-definite programming algorithm. The three optimization variables are jointly iterated under the block coordinate descent framework to obtain the local optimal solution of the inner layer secure transmission problem.

[0175] The design module adopts a learning algorithm based on deep deterministic policy gradient to design an outer unmanned aerial vehicle flight path; the airborne active intelligent metasurface interacts with the environment through a certain strategy as an agent; at each time slot, the agent selects an action based on the current state and moves to a new position, and the space-air-ground integrated secure communication system is then changed to a new state while generating an instant reward; the goal of the agent is to maximize the long-term discount reward by learning a strategy; through continuous interaction, the agent realizes the optimal flight path design of the space-air-ground integrated secure communication system safety rate.

[0176] The output module outputs the optimal flight path design of the unmanned aerial vehicle and the corresponding satellite-to-ground secure transmission strategy to obtain the average and safety rate of the space-air-ground integrated secure communication system.

[0177] In another embodiment of the present application, a terminal device is provided, which includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to realize corresponding method processes or corresponding functions; the processor in the embodiment of the present application can be used for the operation of the satellite-to-ground physical layer secure transmission method assisted by the unmanned aerial vehicle intelligent metasurface, including:

[0178] The space-ground integration secure communication system is established by fusing the spaceborne passive intelligent metasurface and the airborne active intelligent metasurface, and the space-ground integration secure communication system comprises a space-based network composed of a satellite base station and a spaceborne passive intelligent metasurface, an air-based network composed of a UAV and an airborne active intelligent metasurface, and a ground-based network composed of a plurality of users and eavesdroppers; a signal transmission model of the space-ground integration secure communication system is derived, the instantaneous signal-to-interference-and-noise ratio of a communication terminal and the instantaneous and secure rate of the system are obtained, and then the average and secure rate of the satellite-ground communication system is represented; a transmission optimization problem model is formed by jointly optimizing the satellite base station transmission precoding matrix, the spaceborne passive intelligent metasurface phase shift matrix, the airborne active intelligent metasurface diagonal reflection matrix and the UAV flight path, and the transmission optimization problem model takes the maximum system average and secure rate as the target; the transmission optimization problem model is analyzed, and a hierarchical solving framework is given, the inner layer uses convex optimization technology to obtain the satellite-ground secure transmission strategy, and the outer layer designs the UAV flight path through a learning-based algorithm; the iterative solution of the inner layer problem is completed under the block coordinate descent framework, the solution of the satellite transmission precoding matrix depends on the continuous convex approximation algorithm, the spaceborne passive intelligent metasurface reflection matrix is designed through the manifold optimization algorithm based on punishment, and the airborne active intelligent metasurface diagonal reflection matrix is solved by using the semi-definite programming algorithm, and the local optimal solution of the inner layer secure transmission problem is obtained by jointly iterating the three optimization variables under the block coordinate descent framework; the learning algorithm based on deep deterministic policy gradient is used to design the outer layer UAV flight path; the airborne active intelligent metasurface interacts with the environment through a certain strategy; at each time slot, the agent selects an action based on the current state and moves to a new position, and the space-ground integration secure communication system is then changed to a new state, and an instant reward is generated; the goal of the agent is to maximize the long-term discount reward by learning the strategy; through continuous interaction, the agent realizes the optimal flight path design of the space-ground integration secure communication system security rate; and the optimal flight path design of the UAV and the corresponding satellite-ground secure transmission strategy are output, and the average and secure rate of the space-ground integration secure communication system is obtained.

[0179] Please refer to Figure 4 , the terminal device is a computer device, the computer device 60 of the embodiment comprises a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61, and the computer program 63 realizes the unmanned aerial vehicle intelligent metasurface assisted satellite-ground physical layer secure transmission method in the embodiment when executed by the processor 61, to avoid repetition, which will not be described here. Alternatively, the computer program 63 realizes the functions of each model / unit in the unmanned aerial vehicle intelligent metasurface assisted satellite-ground physical layer secure transmission system of the embodiment when executed by the processor 61, to avoid repetition, which will not be described here.

[0180] The computer device 60 can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The computer device 60 can include, but is not limited to, a processor 61, a memory 62. Those skilled in the art can understand that Figure 4 The computer device 60 is only an example and does not constitute a limitation on the computer device 60, and can include more or fewer components than shown, or combine certain components, or different components, for example, the computer device can also include an input / output device, a network access device, a bus, and the like.

[0181] The processor 61 can be a central processing unit (CPU), and can also be other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0182] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or a memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like.

[0183] Further, the memory 62 can include both an internal storage unit and an external storage device of the computer device 60. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0184] Please refer to Figure 5 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device can include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components including the storage unit 620 and the processing unit 610, a display unit 640, and the like.

[0185] The storage unit stores program code that can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the method part of this description according to various exemplary embodiments of the application. For example, the processing unit 610 can perform the steps as shown in Figure 2 the method part of this description according to various exemplary embodiments of the application. For example, the processing unit 610 can perform the steps as shown in

[0186] The storage unit 620 can include a readable medium in the form of volatile storage such as a random access memory (RAM) 6201 and / or cache memory 6202, and also possibly non-volatile storage such as read only memory (ROM) 6203.

[0187] The storage unit 620 can also include program / utility 6204 having a set of one or more program modules 6205, including an operating system, one or more application programs, other program modules, and program data, each of which

[0188] The bus 630 can represent one or more of several types of bus structures, including a storage bus or bus controller, a peripheral bus, a graphics bus (e.g., an Accelerated Graphics Port, or AGP bus) and a local bus using any of a variety of bus architectures.

[0189] The electronic device 600 can also communicate with one or more external devices 700 such as a keyboard or pointing device, using one or more communication ports 640. Communication ports 640 can also enable the electronic device 600 to communicate with one or more devices that enable user interaction with the electronic device 600 (for example, a display, a printer, a personal digital assistant, or the like) and / or one or more devices that enable communication between the electronic device 600 and other computing devices. This communication can occur via an input / output (I / O) interface 650. Still yet, the electronic device 600 can communicate with one or more networks (for example, a local area network (LAN), a wide area network (WAN), and / or the Internet) through a network adapter 660. The network adapter 660 can be any of a variety of modems, including cable modem, digital subscriber line (DSL), and / or the like. The network adapter 660 can be communicatively coupled to the other components of the electronic device 600 via a bus 630. It should be appreciated that the bus 630 can be one or more busses, and can be used to couple together various components of the electronic device 600, including the processing unit 610, the storage unit 620, the input / output (I / O) interface 650, and the network adapter 660. As will be appreciated, different buses can be used to carry different kinds of information, which can be prioritized.

[0190] In still another embodiment of the present application, the present application also provides a storage medium, specifically a computer readable storage medium, which is a memory device in the terminal device, used for storing programs and data. It can be understood that the computer readable storage medium herein can include the built-in storage medium in the terminal device, and of course can also include the extended storage medium supported by the terminal device. The computer readable storage medium provides a storage space which stores the operating system of the terminal. Moreover, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory.

[0191] The one or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the unmanned aerial vehicle intelligent metasurface assisted satellite-ground physical layer secure transmission method in the above embodiments; the one or more instructions in the computer readable storage medium are loaded and executed by the processor as follows:

[0192] The application establishes an air-space-ground integrated secure communication system assisted by a satellite-borne passive intelligent metasurface and an airborne active intelligent metasurface, the air-space-ground integrated secure communication system comprises a space-based network composed of a satellite base station and a satellite-borne passive intelligent metasurface, an air-based network composed of a UAV and an airborne active intelligent metasurface, and a ground-based network composed of a plurality of users and eavesdroppers; a signal transmission model of the air-space-ground integrated secure communication system is derived, the instantaneous signal-to-interference-and-noise ratio of a communication terminal and the instantaneous and secure rate of the system are obtained, and the average and secure rate of the satellite-ground communication system is obtained; a transmission optimization problem model is formed, in which a satellite base station transmission precoding matrix, a satellite-borne passive intelligent metasurface phase shift matrix, an airborne active intelligent metasurface diagonal reflection matrix and a UAV flight path are jointly optimized, and the objective of the transmission optimization problem model is to maximize the average and secure rate of the system; the transmission optimization problem model is analyzed, and a hierarchical solving framework is given, in which the inner layer uses convex optimization technology to obtain a satellite-ground secure transmission strategy, and the outer layer designs a UAV flight path through a learning-based algorithm; the iterative solution of the inner layer problem is completed under the block coordinate descent framework, the solution of the satellite transmission precoding matrix depends on the continuous convex approximation algorithm, the satellite-borne passive intelligent metasurface reflection matrix is designed through the manifold optimization algorithm based on punishment, and the airborne active intelligent metasurface diagonal reflection matrix is solved by using the semi-definite programming algorithm, and the local optimal solution of the inner layer secure transmission problem is obtained through the joint iteration of the three optimization variables under the block coordinate descent framework; the outer layer UAV flight path is designed by using the deep deterministic policy gradient learning algorithm; the airborne active intelligent metasurface interacts with the environment through a certain strategy; at each time slot, the agent selects an action based on the current state and moves to a new position, and the air-space-ground integrated secure communication system is then changed to a new state, and an instant reward is generated; the goal of the agent is to maximize the long-term discount reward through learning strategy; through continuous interaction, the agent realizes the optimal flight path design of the air-space-ground integrated secure communication system security rate; the optimal flight path design of the UAV and the corresponding satellite-ground secure transmission strategy are output, and the average and secure rate of the air-space-ground integrated secure communication system is obtained.

[0193] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0194] Please refer to Figure 3The system average and the security rate change with the number of intelligent metasurface elements are given. Figure 3 As can be seen from the above, when the number of intelligent metasurface elements increases, the security rate of the scheme gradually increases, and is always better than that of other schemes. At the same time, compared with the fixed position scheme, the proposed scheme can obtain greater security rate and security rate growth rate by designing the UAV flight path, which also proves the effectiveness of the proposed scheme.

[0195] In summary, the UAV intelligent metasurface assisted satellite-to-ground physical layer security transmission method and system, the intelligent metasurface assisted satellite-to-ground security transmission model and the corresponding solving framework are proposed, the security transmission strategy of the inner layer and the UAV flight path design of the outer layer are obtained by combining convex optimization and machine learning method, and the joint optimization is solved through the hierarchical solving framework, so as to achieve the maximum system average and security rate.

[0196] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or software. In addition, the specific name of each functional unit and module is only for easy distinction, and does not limit the protection scope of the application. The specific working process of the unit and module in the above system can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

[0197] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0198] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0199] In the embodiments of the present application, it should be understood that the disclosed apparatus / terminal and method can be implemented in other manners. For example, the embodiments of the apparatus / terminal described above are merely schematic, and the division of the modules or units is merely logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0200] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0201] In addition, each functional unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can be a physically independent unit, or two or more units can be integrated into a unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0202] The integrated module / unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer readable medium can include or exclude content according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0203] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in one or more flow or blocks

[0204] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in one or more flow or blocks

[0205] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in one or more flow or blocks

[0206] The above merely provides the technical idea of the present application, and cannot be used to limit the protection scope of the present application. Any modification made according to the technical idea of the present application, on the basis of the technical solutions, falls within the protection scope of the claims of the present application.

Claims

1. A method for satellite-to-ground physical layer security transmission assisted by unmanned aerial vehicle intelligent metasurface, characterized in that, The method comprises the following steps: S1, establishing a space-ground integration security communication system assisted by fusion of spaceborne passive intelligent metasurface and airborne active intelligent metasurface, the space-ground integration security communication system comprising a space-based network composed of a satellite base station and a spaceborne passive intelligent metasurface, an air-based network composed of a UAV and an airborne active intelligent metasurface, and a ground-based network composed of a plurality of users and eavesdroppers; S2, deriving a signal transmission model of the space-ground integration security communication system, obtaining an instantaneous signal-to-interference-and-noise ratio of a communication terminal and a system instantaneous and security rate, and then representing an average and security rate of the space-ground communication system; S3, forming a transmission optimization problem model for jointly optimizing a satellite base station transmission precoding matrix, a spaceborne passive intelligent metasurface phase shift matrix, an airborne active intelligent metasurface diagonal reflection matrix and a UAV flight path, and taking maximizing the system average and security rate as an objective; S4, analyzing the transmission optimization problem model, and giving a framework for layered solution, wherein a convex optimization technique is used to obtain a space-ground security transmission strategy in an inner layer, and a learning-based algorithm is used to design the UAV flight path in an outer layer; S5, completing iterative solution of the inner layer problem under a block coordinate descent framework, wherein a continuous convex approximation algorithm is used to solve the satellite base station transmission precoding matrix, a manifold optimization algorithm based on punishment is used to design the spaceborne passive intelligent metasurface phase shift matrix, and a semi-definite programming algorithm is used to solve the airborne active intelligent metasurface diagonal reflection matrix, and the satellite base station transmission precoding matrix, the spaceborne passive intelligent metasurface phase shift matrix and the airborne active intelligent metasurface diagonal reflection matrix are jointly iterated under the block coordinate descent framework to obtain a local optimal solution of the inner layer security transmission problem; S6, using a learning algorithm based on deep deterministic policy gradient to design the outer layer UAV flight path; the airborne active intelligent metasurface interacts with the environment through a certain strategy; in each time slot, the intelligent agent selects an action based on the current state and moves to a new position, and the space-ground integration security communication system is then changed to a new state, while an instant reward is generated; The goal of the intelligent agent is to maximize the long-term discount reward by learning the strategy; Through continuous interaction, the intelligent agent realizes the optimal flight path design of the space-ground integration security communication system security rate; S7, outputting the optimal UAV flight path design and the corresponding space-ground security transmission strategy, and obtaining the average and security rate of the space-ground integration security communication system.

2. The UAV-intelligent metasurface-assisted satellite-to-ground physical layer secure transmission method according to claim 1, wherein, In step S1, deploying a passive intelligent metasurface on the satellite side introduces a reflection link to enhance transmission; a passive intelligent metasurface on the satellite is integrated with a passive reflection unit arranged in a uniform linear array, and the corresponding reflection coefficient matrix is represented as ; Deploying drones with active intelligent metasurfaces as base-relay nodes; the airborne active intelligent metasurface is equipped with an active reflection element whose diagonal reflection matrix is denoted as .

3. The UAV-intelligent metasurface-assisted satellite-to-ground physical layer secure transmission method according to claim 1, wherein, In step S2, the average and safe rate of the satellite-ground communication system is calculated as follows: is calculated as follows: wherein, is the total number of flight time slots, is the set of flight time slots, is the system instant and safety rate at time instant, is the covered user, is the set of covered users, is the instant safety rate of the covered user at time instant, is the set of non-covered users, is the instant safety rate of the covered user at time instant.

4. The UAV-intelligent metasurface-assisted satellite-to-ground physical layer secure transmission method according to claim 1, wherein, In step S3, the transmission optimization problem model is specifically: in, , and These represent the rated transmit power of the satellite base station, the maximum amplification factor of the airborne active smart metasurface, and the rated amplification power, respectively. This represents the satellite base station transmission precoding matrix. Indicates the system average and safe rate. This represents the phase shift matrix of a spaceborne passive intelligent metasurface. This represents the diagonal reflection matrix of an airborne active intelligent metasurface. Indicates the location of the drone. Indicates the corresponding number Beamforming vectors for each user This indicates the rated transmission power of the satellite base station. Represents the set of flight time slots. express The first moment of the spaceborne passive intelligent metasurface phase shift matrix One element, The set of elements representing the phase shift matrix of a spaceborne passive intelligent metasurface. express The first time the diagonal reflection matrix of the airborne active intelligent metasurface is... One element, This represents the set of elements of the diagonal reflection matrix of an airborne active intelligent metasurface. express Airborne active intelligent metasurface diagonal reflection matrix Represents a set of users. express The channel constantly transitions from spaceborne passive intelligent metasurfaces to airborne active intelligent metasurfaces. express time, express The constant flow of the channel from satellite base stations to spaceborne passive intelligent metasurfaces express The channel constantly connects satellite base stations to airborne active smart metasurfaces. express The time corresponding to the first Beamforming vectors for each user This indicates the thermal noise introduced by the airborne active intelligent metasurface. This indicates the initial position of the drone. Indicates that drones are in Location at any given moment Indicates the drone's position at the final moment. Indicates that drones are in Location at any given moment Indicates the area where the drone can move. represents a maximum flight speed of the drone, represents a flight time slot.

5. The UAV-intelligent metasurface-assisted satellite-to-ground physical layer secure transmission method according to claim 1, wherein, In step S4, the inner layer security transmission problem is expressed as: The outer layer UAV flight path design problem is expressed as: in, Transmit the precoding matrix for the satellite base station. For spaceborne passive intelligent metasurface phase shift matrix, For airborne active intelligent metasurface diagonal reflection matrix, For users The safe speed, For users The safe speed, To cover the user set, For the non-coverage user set, For the corresponding number Beamforming vectors for each user This refers to the rated transmission power of the satellite base station. The first phase shift matrix of the spaceborne passive intelligent metasurface One element, This is the set of elements for the phase shift matrix of a spaceborne passive intelligent metasurface. The first of the diagonal reflection matrices of an airborne active intelligent metasurface One element, The rated amplification factor for airborne active intelligent metasurfaces, This is the set of elements of the diagonal reflection matrix of an airborne active intelligent metasurface. For the channel from spaceborne passive smart metasurfaces to airborne active smart metasurfaces, For the channel from satellite base station to spaceborne passive smart metasurface, For the channel from satellite base station to airborne active smart metasurface, Thermal noise introduced by airborne active intelligent metasurfaces For the rated amplification power of the airborne active intelligent metasurface, For system and average safety rate, Location of the drone. For flight time slots, The maximum flight speed of the drone, For flight time slot set, For drones in the The position of each time slot For drones Location at any given moment This refers to the area where drones can move.

6. The UAV-intelligent metasurface-assisted satellite-to-ground physical layer secure transmission method according to claim 1, wherein, Step S5 is specifically: S501, fixing the spaceborne passive intelligent metasurface phase shift matrix and the airborne active intelligent metasurface diagonal reflection matrix, and optimizing the satellite base station transmission precoding matrix: taking the spaceborne passive intelligent metasurface phase shift matrix and the airborne active intelligent metasurface diagonal reflection matrix as known, and using successive convex approximation to approximate the satellite base station transmission precoding matrix by combining convex approximation and semi-definite programming; S502, fix the satellite base station transmit precoding matrix and the airborne active intelligent metasurface diagonal reflection matrix, and optimize the spaceborne passive intelligent metasurface phase shift matrix: fix the satellite base station transmit precoding matrix and the airborne active intelligent metasurface diagonal reflection matrix, and obtain the local optimal solution of the spaceborne passive intelligent metasurface phase shift matrix based on the penalty function and manifold optimization; S503, fix the satellite base station transmit precoding matrix and the spaceborne passive intelligent metasurface phase shift matrix, and optimize the airborne active intelligent metasurface diagonal reflection matrix: given the satellite base station transmit precoding matrix and the spaceborne passive intelligent metasurface phase shift matrix, the optimization solution of the airborne active intelligent metasurface diagonal reflection matrix is completed by using semi-definite programming; S504, in each iteration step, the above three sub-problems are solved in turn, and the optimal solution of the original problem is approached by updating the to-be-optimized variables step by step, and the iteration process is stopped after converging to a stable solution meeting the accuracy requirement.

7. The UAV-intelligent metasurface-assisted satellite-to-ground physical layer secure transmission method according to claim 6, characterized in that, For the sub-problem of the phase shift matrix of the spaceborne passive ISM, the constraints include the single-mode constraint of the phase shift matrix of the passive ISM and the amplification factor constraint and the amplification power constraint of the airborne active ISM, and the sub-problem is converted into a problem containing only the single-mode constraint by introducing a penalty factor and as follows: For the penalty factor, the sub-gradient is used for updating: in, and This is the magnification factor. Let be the penalty function. For the first Penalty factor in the next iteration The value, For the first Penalty factor in the next iteration The value, For the first Penalty factor in the next iteration The value, For spaceborne passive intelligent metasurface phase shift matrix, The first phase shift matrix of the spaceborne passive intelligent metasurface One element, This is the set of elements for the phase shift matrix of a spaceborne passive intelligent metasurface. For the first Penalty factor in the next iteration Matrix number Line number The value of the column, For the first Penalty factor in the next iteration Matrix number Line number The value of the column, Obtained by manifold optimization Optimal solution eavesdropper eavesdropping on users The eavesdropping rate constraint, eavesdropper eavesdropping on users The eavesdropping rate constraint, To cover the user set, Gathering for eavesdroppers For non-covered user sets; Finally, the optimal passive intelligent metasurface phase shift matrix is obtained through the joint iteration of the penalty factor and the spaceborne passive intelligent metasurface phase shift matrix.

8. The UAV-intelligent metasurface-assisted satellite-to-ground physical layer secure transmission method according to claim 6, wherein, For the subproblem of the onboard active intelligent metasurface diagonal reflection matrix, define new optimization variables , and , express the objective function and constraints in the form of by matrix transformation, and convert the non-convex into convex constraints: For a given arbitrary integer and any matrix satisfying ; the constructor yields: optimal solution ; After the above transformation, we obtain the solvable convex problem about x = arg min x∈X f(x) in, The transformed instantaneous and safe rates of the system. This is the amplification matrix after the power equivalence transformation of the airborne active intelligent metasurface method. The first of the diagonal reflection matrices of an airborne active intelligent metasurface One element, The rated amplification factor for airborne active intelligent metasurfaces, This is the set of elements of the diagonal reflection matrix of an airborne active intelligent metasurface. The first of the diagonal reflection matrices of an airborne active intelligent metasurface One element, This is the set of elements of the diagonal reflection matrix of an airborne active intelligent metasurface. The newly introduced optimization variable, whose physical meaning is the reconstructed airborne active intelligent metasurface reflection matrix, For the rated amplification power of the airborne active intelligent metasurface, For eavesdroppers to cover users The maximum eavesdropping rate, eavesdropper For users Composite eavesdropping channel, Thermal noise introduced by airborne active intelligent metasurfaces These are intermediate variables obtained from matrix transformations. Background noise at the location of the eavesdropper. In order to deal with eavesdroppers For covered users The auxiliary variable introduced by the eavesdropping rate constraint, For eavesdroppers to cover users The maximum eavesdropping rate; Alternately apply the introduced auxiliary variables and Optimize to obtain the optimal result. Then, the optimal value is obtained through singular value decomposition. The first element is normalized, and the values ​​of the remaining dimensions are extracted as active reflection parameters. .

9. The UAV-intelligent metasurface-assisted satellite-to-ground physical layer secure transmission method according to claim 1, wherein, In step S6, the UAV trajectory design problem is modeled as a Markov decision process, and then a trajectory design algorithm based on deep deterministic policy gradient is used. After sufficient training, the agent will generate corresponding optimized actions according to the network feedback of each stage under the given initial communication system state, and finally obtain the converged optimal UAV trajectory design strategy.

10. An unmanned aerial vehicle intelligent metasurface-assisted satellite-to-ground physical layer security transmission system, characterized in that, Comprise: a network module for establishing an air-space-ground integrated secure communication system assisted by spaceborne passive intelligent metasurfaces and airborne active intelligent metasurfaces, the air-space-ground integrated secure communication system comprising a space-based network composed of a satellite base station and a spaceborne passive intelligent metasurface, an air-based network composed of a UAV and an airborne active intelligent metasurface, and a ground-based network composed of multiple users and eavesdroppers; a problem module for deriving a signal transmission model of the air-space-ground integrated secure communication system, obtaining the instantaneous signal-to-interference-and-noise ratio of the communication terminal and the instantaneous and secure rate of the system, and then representing the average and secure rate of the satellite-ground communication system; forming a transmission optimization problem model for jointly optimizing the satellite base station transmit precoding matrix, the spaceborne passive intelligent metasurface phase shift matrix, the airborne active intelligent metasurface diagonal reflection matrix, and the UAV trajectory, with the maximum system average and secure rate as the optimization objective; The analysis module analyzes the transmission optimization problem model, gives a hierarchical solving framework, uses convex optimization technology in the inner layer to obtain the satellite-ground safe transmission strategy, and uses a learning-based algorithm to design the UAV flight path in the outer layer; the iterative solution of the inner layer problem is completed under the block coordinate descent framework, the solution of the satellite base station transmission precoding matrix depends on the continuous convex approximation algorithm, the design of the on-board passive intelligent metasurface phase shift matrix is completed through the manifold optimization algorithm based on punishment, and the diagonal reflection matrix of the airborne active intelligent metasurface is solved by using the semi-positive definite programming algorithm, and the local optimal solution of the inner layer safe transmission problem is obtained by jointly iterating the satellite base station transmission precoding matrix, the on-board passive intelligent metasurface phase shift matrix and the airborne active intelligent metasurface diagonal reflection matrix under the block coordinate descent framework; The design module uses a learning algorithm based on deep deterministic policy gradient to design the outer layer UAV flight path; the airborne active intelligent metasurface interacts with the environment through a certain strategy as an intelligent agent; in each time slot, the intelligent agent selects an action based on the current state and moves to a new position, and the space-air-ground integrated safe communication system is then changed to a new state, while generating an instant reward; The goal of the intelligent agent is to maximize the long-term discount reward through learning strategy; Through continuous interaction, the intelligent agent realizes the optimal flight path design of the space-air-ground integrated safe communication system with the optimal safety rate; The output module outputs the optimal flight path design of the UAV and the corresponding satellite-ground safe transmission strategy, and obtains the average and safety rate of the space-air-ground integrated safe communication system.

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