Unmanned aerial vehicle secure transmission method based on intelligent reflecting surface

Abstract

The invention relates to an unmanned aerial vehicle secure transmission method based on an intelligent reflecting surface, and belongs to the field of unmanned aerial vehicle secure communication, and the method comprises the following steps: S1, constructing an unmanned aerial vehicle secure transmission network communication system which comprises a single-antenna unmanned aerial vehicle, a single-antenna legal user, a single-antenna eavesdropper and an intelligent reflecting surface; the unmanned aerial vehicle serves as an air base station and directly communicates with a legal user, and the intelligent reflecting surface serves as a relay to assist the unmanned aerial vehicle in signal transmission; the eavesdropper tries to eavesdrop the data information transmitted to the legal user by the unmanned aerial vehicle; s2, considering that position information of an eavesdropper in a real scene has uncertainty and no-fly area factor limitation exists in the flight process of the unmanned aerial vehicle, and constructing a joint optimization problem with the purpose of maximizing the average secrecy rate under the worst condition; and S3, decomposing the joint optimization problem into an intelligent reflector shift optimization sub-problem, an unmanned aerial vehicle trajectory optimization sub-problem and an unmanned aerial vehicle transmitting power optimization sub-problem, and performing optimization solution on the three sub-problems.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned aerial vehicle (UAV) safety communication, and relates to a UAV safety transmission method based on an intelligent reflective surface. Background Art

[0002] Unmanned Aerial Vehicle (UAV) base stations are often used for emergency communications in disaster-stricken areas and emergency support tasks in densely populated areas due to their rapid deployment and wide coverage. However, due to the broadcast nature and strong line-of-sight link characteristics of UAV wireless channels, the data information transmitted to users is easily eavesdropped by eavesdroppers, thus threatening the information security of legitimate users. In order to solve the security problem of UAV communications, there are two common traditional methods: one is to use information encryption technology, but with the development of computer chip technology, encrypted information is easy to be cracked; the other is physical layer security technology based on information theory. Common methods include beamforming, artificial noise and cooperative interference, but these technologies will increase energy consumption for UAV communication systems, and when the legitimate channel and the eavesdropping channel are highly correlated in space, the achievable security rate is limited.

[0003] In recent years, smart reflective surfaces have attracted widespread attention in the industry. They are composed of a large number of passive reflective elements, each of which can be independently controlled in real time to change the amplitude and phase of the incident signal, thereby achieving the purpose of reshaping the radio electromagnetic environment. This characteristic of smart reflective surfaces is also widely used in the field of drone security communications. By adjusting the phase shift of the reflective unit, the direction of the reflected signal is changed, and its reflected signal is constructively superimposed with the non-reflected signal of the legitimate user, and destructively superimposed with the non-reflected signal of the eavesdropper, thereby improving the security rate of the communication system.

[0004] Existing work often optimizes the flight trajectory of drones to obtain the best flight path, thereby ensuring the maximum safety rate. However, for the optimization of flight trajectories, most studies assume that the location information of the eavesdropper is known. In complex real-world scenarios, the location information of the eavesdropper is difficult to obtain accurately. This uncertainty will affect the path planning of the drone and the size of the system's safety rate. At the same time, the existence of no-fly zones will also have some practical impacts on the trajectory planning of drones. For example, drones are prohibited from flying in some government and military bases. Therefore, the location information of the eavesdropper and the existence of no-fly zones have become two important factors affecting the safe transmission of drones. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a UAV secure transmission method based on an intelligent reflecting surface, taking into account the two influencing factors of the eavesdropper's location information and the no-fly zone, and maximizing the average confidentiality rate in the worst case by jointly optimizing the phase shift of the intelligent reflecting surface, the UAV flight trajectory and the UAV transmission power.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for safe transmission of unmanned aerial vehicles based on intelligent reflective surfaces comprises the following steps:

[0008] S1: Construct a UAV secure transmission network communication system, including a single-antenna UAV, a single-antenna legal user, a single-antenna eavesdropper, and a smart reflective surface; the UAV acts as an aerial base station to directly communicate with the legal user, and the smart reflective surface acts as a relay to assist the UAV in signal transmission; the eavesdropper attempts to eavesdrop on the data information transmitted by the UAV to the legal user;

[0009] S2: Considering the uncertainty of the eavesdropper's location information in real scenarios and the no-fly zones in the drone's flight process, a joint optimization problem is constructed to maximize the average confidentiality rate in the worst case.

[0010] S3: Decompose the joint optimization problem into three sub-problems: intelligent reflection surface phase shift optimization, UAV trajectory optimization and UAV transmission power optimization, and optimize and solve the three sub-problems respectively.

[0011] Furthermore, in the UAV secure transmission network communication system, it is considered that the eavesdropper is located in a circular estimation area with a center and a radius, the center of the circle is the most likely location of the eavesdropper, the radius is the maximum estimation error, and the coordinates of the center of the circle are The error radius is R E ,in The lower bounds of the projection distances from the drone to the eavesdropper and from the smart reflective surface to the eavesdropper are obtained using the triangular inequality, as shown in the following formula:

[0012]

[0013] Where q[n] represents the horizontal position of the UAV at the nth time slot; W S , W E Represent the horizontal positions of the legitimate user and the eavesdropper respectively; W R Indicates the horizontal position of the smart reflective surface;

[0014] The no-fly zone is modeled as a cylindrical geometric model, and the coordinate of its projection center is W NFZ =(x NFZ ,y NFZ), the maximum projection radius is R NFZ , the position of the drone in each time slot must satisfy:

[0015]

[0016] Furthermore, the joint optimization problem aiming at maximizing the average confidentiality rate in the worst case is expressed as:

[0017]

[0018] Where Θ represents the phase shift vector of the smart reflector, M means that the smart reflective surface has a total of M reflective units, θ m [n] represents the phase shift of the mth reflection unit in the nth time slot; Q represents the UAV trajectory vector, P represents the UAV transmission power vector, P[n] represents the transmission power of the UAV in the nth time slot; C S [n] represents the acceptance rate of legitimate users in the nth time slot; represents the upper bound of the eavesdropping rate of the eavesdropper in the nth time slot; C1 represents the phase shift constraint of the intelligent reflective surface, C2-C4 represent the trajectory constraint of the UAV, C5 represents the trajectory constraint of the UAV to effectively stay away from the no-fly zone, C6-C7 represent the transmission power constraint of the UAV; D represents the maximum flight distance of the UAV per time slot, P max Indicates the maximum transmit power of the drone.

[0019] Furthermore, for the phase shift optimization sub-problem of the smart reflection surface, firstly, given the UAV trajectory and the UAV transmission power, the phase alignment method is used to coherently combine the signals from different paths at the legitimate user to maximize the receiving rate, thereby maximizing the average confidentiality rate of the system. The optimal phase shift of the mth reflection unit at the nth time slot can be expressed as:

[0020]

[0021] Where λ is the carrier wavelength, d is the distance between the reflection units, φ RS ,φ UR [n] represents the cosine of the arrival angle from the smart reflective surface to the legitimate user and from the drone to the smart reflective surface, respectively;

[0022] According to the obtained optimal phase shift, the rates of the legitimate user and the eavesdropper in the nth time slot are updated, which are expressed as:

[0023]

[0024] In the formula σ 2represents the power of additive complex Gaussian white noise, β is the channel gain at the reference distance of 1m, is the path loss index; d US [n] and They represent the lower bounds of the distance from the drone to the legitimate user and the distance from the drone to the eavesdropper, d UR [n], d RS and They represent the lower bounds of the distance from the UAV to the smart reflective surface, the distance from the smart reflective surface to the legitimate user, and the distance from the smart reflective surface to the eavesdropping user; φ RE Represents the cosine of the arrival angle from the smart reflective surface to the eavesdropper.

[0025] Furthermore, for the sub-problem of drone trajectory optimization, firstly, given the drone transmission power and the phase shift of the smart reflector, the following optimization problem is solved:

[0026]

[0027] in and After simplification:

[0028]

[0029] in Introducing slack variables and The problem can be restated as:

[0030]

[0031] Where ζ=P[n] / σ 2 , For the non-convexity of S[n] in C8, a slack variable is introduced When x[n]≥d 2 US [n],y[n]≥d 2 UR [n], we get S * [n] is a convex function relative to x[n] and y[n], and the successive convex approximation method is used to obtain an effective approximate solution, using the feasible point x obtained in the given kth iteration k [n] and y k [n],S * The first-order Taylor expansion of [n] is written as:

[0032]

[0033] The successive convex approximation method is used to obtain an effective approximate solution. H is the flight altitude of the UAV, h is the height of the intelligent reflective surface, and the feasible point q obtained in the given kth iteration is k [n] and (g2[n]) k ,||q[n]-W NFZ || 2 , and 2 UR The first-order Taylor expansion of [n] is written as:

[0034] ||q[n]-W NFZ || 2 ≥||q k [n]-W NFZ || 2 +2(q k [n]-W NFZ ) T (q[n]-q k [n])

[0035]

[0036] Substituting the above formula into the model, the problem can be restated as:

[0037]

[0038] The optimization problem is a convex optimization problem and is solved using the convex optimization solver CVX toolbox.

[0039] Furthermore, for the sub-problem of optimizing the UAV transmission power, according to the obtained phase shift matrix and the flight trajectory of the UAV, the optimization of the UAV transmission power is written as:

[0040]

[0041] in The eavesdropper rate is calculated by using the successive convex approximation method at the last iteration point P. k The first-order Taylor expansion is performed at [n], as follows:

[0042]

[0043] The optimization problem is approximately:

[0044]

[0045] The optimization problem is a convex optimization problem and is solved using the convex optimization solver CVX toolbox.

[0046] The beneficial effects of the present invention are as follows: the present method mainly takes into account two influencing factors: the location information of the eavesdropper and the no-fly zone. By jointly optimizing the phase shift of the intelligent reflection surface, the trajectory of the UAV and the transmission power of the UAV to maximize the average confidentiality rate in the worst case, it is more in line with the actual scenario and provides important theoretical and technical references for ensuring the safe transmission performance of the UAV.

[0047] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0049] Figure 1 Schematic diagram of intelligent reflective surface assisting UAV in safe transmission;

[0050] Figure 2 It is the trajectory map of the UAV;

[0051] Figure 3 It is an iteration performance comparison chart;

[0052] Figure 4 This is a comparison chart of the average confidentiality rate under different numbers of reflection units;

[0053] Figure 5 This is a comparison chart of the average confidentiality rate under different radius errors. DETAILED DESCRIPTION

[0054] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0055] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0056] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0057] The present invention relates to a safe transmission method for unmanned aerial vehicles based on an intelligent reflective surface, the method comprising the following steps:

[0058] Step 1: Build a secure drone transmission network based on intelligent reflective surfaces Figure 1 In the figure, the position of the smart reflective surface is considered to be fixed, and its lower left corner element is used as the reference point. The three-dimensional coordinates are (W R ,h), where W R =(x R ,y R ), h is the height of the intelligent reflective surface; the legitimate user and the eavesdropper are deployed on the ground, and their three-dimensional coordinates are (W S ,0),(W E ,0), where W S =(x S ,y S ),W E =(x E ,y E ); The flight period of the drone is T. For the convenience of analysis, the entire flight time T is discretized into N time slots, and the length of each time slot is Right now At the nth time slot, the three-dimensional coordinates of the drone are (q[n], H), where q[n] = (x[n], y[n]), and H is the flight altitude of the drone;

[0059] Assume that the channel from the drone to the legitimate user is β is the channel gain at the reference distance of 1m, d US [n] represents the distance from the drone to the legitimate user. Due to the obstruction of obstacles, it is assumed that the channel from the drone to the eavesdropper obeys the Rayleigh fading model, and the channel is expressed as d UE [n] represents the distance from the drone to the eavesdropper, is a cyclically symmetric complex Gaussian distribution with zero mean and unit variance; assuming that the channels from the drone to the smart reflective surface, from the smart reflective surface to the legitimate user, and from the smart reflective surface to the eavesdropper are line-of-sight link channel models, they can be expressed as:

[0060]

[0061] λ represents the carrier wavelength, d is the distance between the reflection units, is the path loss index; d UR [n], d RS and d RE They represent the distances from the UAV to the smart reflective surface, from the smart reflective surface to the legitimate user, and from the smart reflective surface to the eavesdropping user; φ UR [n], φ RS and φ RE They represent the cosine of the arrival angles from the UAV to the smart reflective surface, from the smart reflective surface to the legitimate user, and from the smart reflective surface to the eavesdropper respectively;

[0062] Step 2: Construct a joint optimization problem with the goal of maximizing the average confidentiality rate in the worst case. According to the receiving rate of the legitimate user:

[0063]

[0064] In the formula P[n] is the transmission power of the UAV in the nth time slot; σ 2 represents the power of additive complex Gaussian white noise;

[0065] Since it is difficult for the smart reflective surface to obtain the small-scale fading between the smart reflective surface and the eavesdropper, the upper bound of the eavesdropping rate is obtained by solving Jensen's inequality. Therefore, the upper bound of the eavesdropping rate of the eavesdropper is:

[0066]

[0067] In the formula E h is the expectation operator.

[0068] In the communication system, the horizontal position of the UAV in the nth time slot is represented by q[n], and the horizontal positions of the legitimate user and the eavesdropper are represented by W S , W E The horizontal position of the smart reflective surface is represented by W R However, in actual scenarios, due to the occlusion of obstacles, the location information of the eavesdropper cannot be accurately obtained. Therefore, the eavesdropper is considered to be located in a circular estimation area with a center and a radius. The center of the circle is the most likely location of the eavesdropper, the radius is the maximum estimation error, and the coordinates of the center of the circle are The error radius is R E ,in The triangle inequality can be used to obtain the lower bounds of the projection distances from the drone to the eavesdropper and from the smart reflective surface to the eavesdropper, thereby dealing with the uncertainty of their location information, as shown in the following formula:

[0069]

[0070] According to the lower bound of the projection distance, the maximum eavesdropping rate of the eavesdropper can be obtained as:

[0071]

[0072] in

[0073] The existence of no-fly zones will also bring some practical impacts. For example, drones are prohibited from flying in some government and military bases. In order to effectively stay away from no-fly zones, the no-fly zones are modeled as cylindrical geometric models with three-dimensional coordinates (W NFZ ,H NFZ ),R NFZ Indicates the maximum projection radius. Due to the existence of no-fly zones, the position of the drone in each time slot must meet the following requirements:

[0074]

[0075] Phase shifting by joint optimization of smart reflectors Drone tracks And the UAV transmission power The following optimization problem is established:

[0076]

[0077] Θ represents the phase shift vector of the smart reflector, M means that the smart reflective surface has a total of M reflective units, θ m [n] represents the phase shift of the mth reflection unit in the nth time slot; Q represents the UAV trajectory vector, P represents the UAV transmission power vector, P[n] represents the transmission power of the UAV in the nth time slot; C S [n] represents the acceptance rate of legitimate users in the nth time slot; represents the upper bound of the eavesdropping rate of the eavesdropper in the nth time slot; C1 represents the phase shift constraint of the intelligent reflective surface, C2-C4 represent the trajectory constraint of the drone, C5 represents the trajectory constraint of the drone to effectively stay away from the no-fly zone, C6-C7 represent the transmission power constraint of the drone; D represents the maximum flight distance of the drone per time slot, P max Indicates the maximum transmission power of the drone;

[0078] Step 3: Solve the three sub-problems separately. Due to the non-convexity of the objective function, in order to obtain the optimal solution of the objective function, the alternating optimization algorithm is used to decompose the original non-convex problem into three sub-optimization problems, namely the phase shift of the smart reflector, the trajectory of the drone, and the transmission power of the drone. By fixing two variables and solving the other variable, the three sub-problems are solved iteratively until the optimization problem converges.

[0079] (1) Smart reflector phase shift optimization

[0080] When the drone trajectory and the drone transmission power are given, the phase shift of the intelligent reflection surface is optimized, and the phase alignment method is used to coherently combine the signals from different paths at the legitimate user. The receiving rate can reach the maximum value, thereby maximizing the average confidentiality rate of the system. The optimal phase shift of the mth reflection unit at the nth time slot can be expressed as:

[0081]

[0082] Where λ is the carrier wavelength, d is the distance between the reflection units, φ RS ,φ UR [n] represents the cosine of the arrival angle from the smart reflective surface to the legitimate user and from the drone to the smart reflective surface, respectively.

[0083] According to the obtained optimal phase shift, the rates of the legitimate user and the eavesdropper in the nth time slot are updated, which are expressed as:

[0084]

[0085]

[0086] In the formula σ 2 represents the power of additive complex Gaussian white noise, β is the channel gain at the reference distance of 1m, is the path loss index; d US [n] and They represent the lower bounds of the distance from the drone to the legitimate user and the distance from the drone to the eavesdropper, d UR [n], d RS and They represent the lower bounds of the distance from the UAV to the smart reflective surface, the distance from the smart reflective surface to the legitimate user, and the distance from the smart reflective surface to the eavesdropping user; φ RE represents the cosine of the arrival angle from the smart reflective surface to the eavesdropper;

[0087] (2) UAV trajectory optimization

[0088] When the UAV transmission power is given and the phase shift of the smart reflector obtained above is adopted, optimizing the flight trajectory of the UAV requires solving the following optimization problem:

[0089]

[0090] in and After simplification:

[0091]

[0092] in Since the objective function is non-convex with respect to the trajectory, in order to solve the optimization problem, a slack variable is introduced and The problem can be restated as:

[0093]

[0094] Where ζ=P[n] / σ 2 , For the non-convexity of S[n] in C8, a slack variable is introduced When x[n]≥d 2 US [n],y[n]≥d 2 UR [n], we get S * [n] is a convex function with respect to x[n] and y[n], so the successive convex approximation method is used to obtain an effective approximate solution, using the feasible point x obtained in the given kth iteration k [n] and y k [n],S * The first-order Taylor expansion of [n] can be written as:

[0095]

[0096] The left side of equation C5 is convex with respect to q[n], the right side of equation C9 is convex with respect to g2[n], and the right sides of equations C10 and C11 are convex with respect to q[n]. Therefore, the successive convex approximation method is used to obtain an effective approximate solution. H is the flight altitude of the drone, h is the height of the smart reflective surface, and the feasible point q obtained in the given kth iteration is k [n] and (g2[n]) k ,||q[n]-W NFZ || 2 , and 2 UR The first-order Taylor expansion of [n] can be written as:

[0097] ||q[n]-W NFZ || 2 ≥||q k [n]-W NFZ || 2 +2(q k [n]-W NFZ ) T (q[n]-q k [n])

[0098]

[0099] d 2 UR [n]≥||q k [n]-W R || 2 +2(q k [n]-W R ) T (q[n]-q k [n])+(Hh) 2

[0100] Bringing the above formula into the model, the problem can be restated as:

[0101]

[0102] The optimization problem is a convex optimization problem, which can be solved by the convex optimization solver CVX toolbox.

[0103] (3) UAV transmission power optimization

[0104] According to the phase shift matrix and the flight trajectory of the UAV obtained above, the optimization of the UAV transmission power can be written as:

[0105]

[0106] in This optimization problem is a non-convex optimization problem. In order to transform this problem into a convex optimization problem, the successive convex approximation method is used to convert the eavesdropper rate at the previous iteration point P k The first-order Taylor expansion is performed at [n], as follows:

[0107]

[0108] Then the optimization problem can be approximated as:

[0109]

[0110] The optimization problem is a convex optimization problem, which can be solved by the convex optimization solver CVX toolbox.

[0111] Step 4: Simulation experiment

[0112] In order to verify the feasibility of the present invention, a simulation experiment is carried out on the method of the present invention, as follows:

[0113] 4.1 Experimental Data Setup

[0114] Legal user horizontal coordinate W S is [0,120] T , the horizontal coordinate of the center area where the eavesdropper is located Error radius R E is 10m, and the coordinates of the smart reflective surface are [0,150] T , the height h is 10m, the initial flight position and stop position of the drone are [-300,0] respectively T and [300,0] T , the flight altitude H is 100m, the maximum flight distance per time slot of the drone is D = 20m, the number of time slots N = 100, the average power of the drone P = 10dbm and the peak power P max =30dbm, the center coordinate of the no-fly zone is W NFZ =[-150,120] T , maximum projection radius R NFZ =100, noise power σ 2 =-80dbm, convergence accuracy ε = 10 -6 .

[0115] 4.2 Simulation results and analysis

[0116] In order to verify the feasibility of the method of the present invention, a simulation experiment was carried out on the method of the present invention, and the method of the present invention was compared with three benchmark schemes, which are respectively drone safe transmission without no-fly zone restrictions, drone safe transmission without intelligent reflective surface as relay, and drone safe transmission without trajectory optimization.

[0117] The simulation results are as follows Figures 2 to 5 shown. Figure 2 The figure shows the comparison of the flight trajectory of the drone of the method of the present invention and the benchmark schemes one, two and three. As can be seen from the figure, when the drone of the method of the present invention encounters a no-fly zone during flight, it will fly along the tangent direction of the no-fly zone, and gradually move away from the no-fly zone, fly to the legal user and the smart reflective surface, hover for a period of time, and then fly to the end point. As the number of reflective units of the smart reflective surface increases, the drone is closer to the smart reflective surface. This is because the influence of the reflection path of the smart reflective surface increases with the increase in the number of elements. Therefore, in order to effectively utilize the reflection path, the drone tries to obtain a better channel between itself and the smart reflective surface to maximize the confidentiality rate. Benchmark scheme one has no restrictions on no-fly zones. The drone flies directly between the legal user and the smart reflective surface, hovers for a period of time, and then flies to the end point. Benchmark scheme two drones gradually move away from the no-fly zone, fly to the legal user, hover for a period of time, and fly to the end point. Benchmark scheme three drones fly directly from the origin to the end point along a straight line path.

[0118] Figure 3It shows how the average confidentiality rate of the method of the present invention and benchmark schemes one, two and three changes with the number of iterations. As can be seen from the figure, the confidentiality rate of each scheme gradually converges with the increase in the number of iterations. Compared with benchmark scheme one, due to the existence of the no-fly zone, the drone of the method of the present invention must detour to avoid passing through the no-fly zone, which reduces the time for the drone to hover and effectively communicate, and the receiving rate of legitimate users will be reduced, but the confidentiality rate is only slightly lower than that of scheme one. Scheme two does not introduce smart reflective surfaces and does not utilize the reflection path of the smart reflective surface, and its confidentiality rate is lower than that of the scheme of the present invention. Benchmark scheme three does not perform trajectory optimization, and its performance is lower than all other schemes. In summary, the joint optimization of the phase shift of the smart reflective surface and the trajectory of the drone plays a vital role in secure transmission.

[0119] Figure 4 The figure shows the relationship between the average confidentiality rate of the method of the present invention and benchmark solutions one, two and three and the number of reflective units on the smart reflective surface. As can be seen from the figure, the average confidentiality rate of each solution increases with the increase in the number of reflective units, because more reflective units can introduce more controllable channels, thereby reconfiguring the wireless propagation environment. Benchmark solution one does not have a no-fly zone, and its confidentiality rate is slightly higher than that of the solution in this article. Benchmark solution two does not contain a smart reflective surface, so its confidentiality rate is not affected by the number of reflective units. The performance of benchmark solution three is lower than that of all other solutions. In summary, a higher average confidentiality rate can be achieved by deploying more passive reflective elements.

[0120] Figure 5 It shows the relationship between the average confidentiality rate of the method of the present invention and the benchmark schemes one, two and three and the radius error of the eavesdropper. As can be seen from the figure, the size of the average confidentiality rate of each scheme decreases with the increase of the radius error. Because the radius error reflects the location information of the eavesdropper, its size has an impact on the confidentiality rate. As the radius error increases, the eavesdropper's location information that can be obtained becomes less and less, and the possibility of information leakage increases, resulting in an increase in the eavesdropping rate and a decrease in the average confidentiality rate. In summary, compared with scheme one, the confidentiality rate of the scheme of the present invention is slightly lower than that of scheme one due to the limitation of the no-fly zone. Compared with schemes two and three, the scheme of this paper simultaneously optimizes the phase shift of the intelligent reflector, the UAV transmission power and the UAV trajectory, which can effectively deal with the uncertainty of the eavesdropper's position.

[0121] The present invention mainly considers the location information of the eavesdropper and the physical layer security issues of the drone in the presence of a no-fly zone, and maximizes the average confidentiality rate of the communication system by jointly optimizing the phase shift of the intelligent reflector, the flight trajectory of the drone, and the transmission power of the drone. In order to facilitate the solution, the alternating optimization algorithm is used to decouple the joint optimization problem, and the system average confidentiality rate problem is decomposed into three sub-problems for alternating solution. Phase alignment is used to obtain the optimal phase shift value, and the successive convex approximation algorithm is used to optimize the flight trajectory and transmission power of the drone. The simulation results show that when the drone is at a certain flight altitude, the scheme in this paper has good convergence, can well cope with the uncertainty of the eavesdropper's location information and the restrictions of the no-fly zone, and has certain practical significance.

[0122] In the above embodiments, the description's reference to "this embodiment" indicates that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0123] In the above-described embodiments, although the invention has been described in conjunction with specific embodiments of the invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed. Embodiments of the invention are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0124] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented.

[0125] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0126] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0127] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0128] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0129] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0130] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0131] The present invention can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0132] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for safe transmission of unmanned aerial vehicles based on intelligent reflective surfaces, characterized in that: The following steps are involved: S1: Construct a UAV secure transmission network communication system, including a single-antenna UAV, a single-antenna legal user, a single-antenna eavesdropper, and a smart reflective surface; the UAV acts as an aerial base station to directly communicate with the legal user, and the smart reflective surface acts as a relay to assist the UAV in signal transmission; the eavesdropper attempts to eavesdrop on the data information transmitted by the UAV to the legal user; S2: Considering the uncertainty of the eavesdropper's location information in real scenarios and the no-fly zones in the drone's flight process, a joint optimization problem is constructed to maximize the average confidentiality rate in the worst case. S3: Decompose the joint optimization problem into three sub-problems: intelligent reflection surface phase shift optimization, UAV trajectory optimization and UAV transmission power optimization, and optimize and solve the three sub-problems respectively.

2. The method for safe transmission of unmanned aerial vehicles based on intelligent reflective surfaces according to claim 1 is characterized in that: In the UAV secure transmission network communication system, the eavesdropper is considered to be located in a circular estimation area with a center and a radius. The center is the most likely location of the eavesdropper, the radius is the maximum estimation error, and the coordinates of the center are The error radius is R E ,in The lower bounds of the projection distances from the drone to the eavesdropper and from the smart reflective surface to the eavesdropper are obtained using the triangular inequality, as shown in the following formula: Where q[n] represents the horizontal position of the UAV at the nth time slot; W S , W E Represent the horizontal positions of the legitimate user and the eavesdropper respectively; W R Indicates the horizontal position of the smart reflective surface; The no-fly zone is modeled as a cylindrical geometric model, and the coordinate of its projection center is W NFZ =(x NFZ ,y NFZ ), the maximum projection radius is R NFZ , the position of the drone in each time slot must satisfy:

3. The method for safe transmission of unmanned aerial vehicles based on intelligent reflective surfaces according to claim 1 is characterized in that: The joint optimization problem with the goal of maximizing the average confidentiality rate in the worst case is expressed as: Where Θ represents the phase shift vector of the smart reflector, M means that the smart reflective surface has a total of M reflective units, θ m [n] represents the phase shift of the mth reflection unit in the nth time slot; Q represents the UAV trajectory vector, P represents the UAV transmission power vector, P[n] represents the transmission power of the UAV in the nth time slot; C S [n] represents the acceptance rate of legitimate users in the nth time slot; represents the upper bound of the eavesdropping rate of the eavesdropper in the nth time slot; C1 represents the phase shift constraint of the intelligent reflective surface, C2-C4 represent the trajectory constraint of the UAV, C5 represents the trajectory constraint of the UAV to effectively stay away from the no-fly zone, C6-C7 represent the transmission power constraint of the UAV; D represents the maximum flight distance of the UAV per time slot, P max Indicates the maximum transmit power of the drone.

4. The method for safe transmission of unmanned aerial vehicles based on intelligent reflective surfaces according to claim 1 is characterized in that: For the phase shift optimization subproblem of the smart reflection surface, firstly, given the trajectory and transmit power of the UAV, the phase alignment method is used to coherently combine the signals from different paths at the legitimate user to maximize the receiving rate, thereby maximizing the average confidentiality rate of the system. The optimal phase shift of the mth reflection unit at the nth time slot can be expressed as: Where λ is the carrier wavelength, d is the distance between the reflection units, φ RS ,φ UR [n] represents the cosine of the arrival angle from the smart reflective surface to the legitimate user and from the drone to the smart reflective surface, respectively; According to the obtained optimal phase shift, the rates of the legitimate user and the eavesdropper in the nth time slot are updated, which are expressed as: In the formula σ 2 represents the power of additive complex Gaussian white noise, β is the channel gain at the reference distance of 1m, is the path loss index; d US [n] and They represent the lower bounds of the distance from the drone to the legitimate user and the distance from the drone to the eavesdropper, d UR [n], d RS and They represent the lower bounds of the distance from the UAV to the smart reflective surface, the distance from the smart reflective surface to the legitimate user, and the distance from the smart reflective surface to the eavesdropping user; φ RE Represents the cosine of the arrival angle from the smart reflective surface to the eavesdropper.

5. The method for safe transmission of unmanned aerial vehicles based on intelligent reflective surfaces according to claim 1 is characterized in that: For the UAV trajectory optimization subproblem, firstly, given the UAV transmission power and the phase shift of the smart reflector, the following optimization problem is solved: in and After simplification: in Introducing slack variables and The problem can be restated as: Where ζ=P[n] / σ 2 , For the non-convexity of S[n] in C8, slack variables are introduced When x[n]≥d 2 US [n],y[n]≥d 2 UR [n], we get S * [n] is a convex function relative to x[n] and y[n], and the successive convex approximation method is used to obtain an effective approximate solution, using the feasible point x obtained in the given kth iteration k [n] and y k [n],S * The first-order Taylor expansion of [n] is written as: The successive convex approximation method is used to obtain an effective approximate solution. H is the flight altitude of the UAV, h is the height of the intelligent reflective surface, and the feasible point q obtained in the given kth iteration is k [n] and (g2[n]) k ,||q[n]-W NFZ || 2 , and The first-order Taylor expansion of is written as: ||q[n]-W NFZ || 2 ≥||q k [n]-W NFZ || 2 +2(q k [n]-W NFZ ) T (q[n]-q k [n]) d 2 UR [n]≥||q k [n]-W R || 2 +2(q k [n]-W R ) T (q[n]-q k [n])+(H-h) 2 Substituting the above formula into the model, the problem can be restated as: The optimization problem is a convex optimization problem and is solved using the convex optimization solver CVX toolbox.

6. The method for safe transmission of unmanned aerial vehicles based on intelligent reflective surfaces according to claim 1, characterized in that: For the UAV transmission power optimization subproblem, according to the obtained phase shift matrix and the UAV flight trajectory, the optimization of the UAV transmission power is written as: in The eavesdropper rate is calculated by using the successive convex approximation method at the last iteration point P. k The first-order Taylor expansion is performed at [n], as follows: The optimization problem is approximately: The optimization problem is a convex optimization problem and is solved using the convex optimization solver CVX toolbox.

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