A Secure Communication Method for a 3D Dynamic Position Selection Strategy of Unmanned Aerial Vehicles

Abstract

The present invention provides a secure communication method for the 3D dynamic position selection strategy of unmanned aerial vehicles (UAVs). By proposing a successive optimization solution to solve the original traversal problem, transforming the constraints of the moving distance and the movement of the spatial coordinates, and studying the anti-eavesdropping technology of UAVs based on the optimal flight trajectory, it is more in line with the actual situation and has practical application value. It extends the operating environment of UAVs from a 2D plane to a 3D space, making the flight trajectory of UAVs more in line with the actual situation; transforms the constraints of the moving distance and the movement of the spatial coordinates, and successively optimizes the solution to the original traversal problem, greatly reducing the complexity and saving computing resources and computing time. The present invention reduces the computational complexity, saves the computing resources of the processor, and thus optimizes the trajectory of UAVs in three-dimensional space.

Description

Technical Field

[0001] The present invention relates to the field of multi-point cooperative communication in wireless communication technology, and in particular to a secure communication method for unmanned aerial vehicles (UAVs). Aiming at the problems that the transmission power of UAVs in a UAV relay cooperative communication network is limited, the signal processing ability is poor, and it is easy to be attacked and eavesdropped by illegal users, a scheme is proposed to maximize the network overall security rate by optimizing the operation trajectory of UAVs in three-dimensional space. Background Art

[0002] The inherent mobility, flexibility and flight altitude of UAVs, which are suitable for urban building clusters and other characteristics, make them have great application potential in wireless communication systems. They are often used in communication networks for cooperative communication to enhance the coverage area, security rate, reliability and energy efficiency of wireless networks.

[0003] However, when UAVs are used as a link in the signal transmission process, they are also limited by their own storage space and are difficult to carry large-volume processors and endurance devices, resulting in weak transmission power and poor processing ability, and it is very easy to be attacked and secretly eavesdropped by illegal users. In this case, it is necessary to find an effective anti-eavesdropping technology to improve the security of UAV cooperative communication systems.

[0004] Reference 1 “A. Li, Q. Wu, and R. Zhang, UAV-enabled cooperative jamming for improving secrecy of ground wiretap channel, IEEE Wireless Commun. Lett., 2019.” enables the UAV to control and adjust its trajectory to fly close to the location of the eavesdropper, and uses the mobility of the UAV to interfere with illegal users, thereby improving the average security rate.

[0005] Reference 2 “H. Lee, S. Eom, J. Park, and I. Lee, UAV-aided secure communications with cooperative jamming, IEEE Trans. Veh. Technol., 2018.” enables a mobile UAV to send confidential information to multiple legitimate users. At the same time, a cooperative UAV for transmitting interference signals is also considered. By optimizing the transmission power of the UAV and scheduling users, the secure transmission rate between ground users is maximized.

[0006] Reference 3, "C. Zhong, J. Yao, and J. Xu. Secure UAV communication with cooperative jamming and trajectory control. IEEE Commun. Letters, vol. 23, no. 2, pp. 286-289, Feb. 2019." uses other nearby UAVs to apply artificial noise to defend against eavesdropping and protect the communication security of relay UAVs. At the same time, two UAVs adaptively adjust their positions with the change of the time scene to complete cooperative communication, thereby enhancing the security of the communication system.

[0007] In the UAV-assisted wireless communication network, the existing theoretical research mainly focuses on optimizing the movement trajectory of UAVs in a two-dimensional plane. However, in an actual communication system, UAVs should operate in a three-dimensional space. At the same time, when optimizing the movement trajectory of UAVs, the existing theoretical research usually requires repeated iteration to traverse all feasible solutions, consuming a large amount of computing resources and computing time. Summary of the Invention

[0008] In order to overcome the deficiencies of the prior art, the present invention provides a secure communication method for a UAV 3D dynamic position selection strategy. The purpose of the present invention is to provide a secure communication design scheme for a UAV 3D dynamic position selection strategy to solve the above existing technical problems. For the UAV cooperative communication network, the present invention will solve the security problem in UAV cooperative communication in an actual three-dimensional space scenario. By proposing a successive optimization solution to the original traversal problem, transforming the constraints of the moving distance and the movement of spatial coordinates, and studying the anti-eavesdropping technology of UAVs based on the optimal flight trajectory, it is more in line with the actual situation and has practical application value.

[0009] In the present invention, the operating environment of the UAV is extended from a 2D plane to a 3D space, making the flight trajectory of the UAV more in line with the actual situation. By transforming the constraints of the moving distance and the movement of spatial coordinates, and successively optimizing the solution to the original traversal problem, the complexity is greatly reduced, saving computing resources and computing time.

[0010] The technical solution adopted by the present invention to solve its technical problems includes the following steps:

[0011] Step 1: Establish a system model;

[0012] In the system model, it includes a transmitting node S, a destination node D, an eavesdropping node E, and a UAV relay U. Let L U [n] = [x(n), y(n), h(n)] T, n ∈ {1, 2...., N} represents the position of the UAV, and x(n), y(n), and h(n) represent the coordinate values corresponding to a certain point in the three-dimensional coordinate system. The positions of the transmitting node S, the destination node D, and the eavesdropping node E are known to the UAV relay, and the preset initial position u of the UAV relay I and the destination position u F ; The transmission of information is completed in two time slots;

[0013] In the first time slot, the transmitting node S sends out a signal i , and the receiving node sends out a noise signal S j . The signals received by the UAV relay and the eavesdropping end are y U [n] and y E1 [n] respectively, which are expressed as:

[0014] y U [n] = h SU [n]x i +h UD [n]x j +n u (1)

[0015] y E1 [n] = g SE x i +g UE [n]x j +n e1 (2)

[0016] Among them, are the signals finally transmitted by the transmitting node and the receiving node respectively; n u is the noise received by the UAV relay during the communication process, and n e1 is the noise received by the eavesdropping end in the first stage; is the free space loss of the channel power from the transmitting node S to the UAV relay U, is the free space loss of the channel power from the UAV relay U to the receiving node D, is the free space loss of the channel power from the transmitting node S to the eavesdropping node E, is the free space loss of the channel power from the UAV relay U to the eavesdropping node E, where ρ0 represents the channel gain of the system when the reference distance d0 = 1m, and L S , L D , L E are the positions of the transmitting node, the receiving node, and the eavesdropping point in space respectively, and d SU , d UD , d SE , d UEdenote the distances between the transmitting node S, the destination node D, the eavesdropping node E, and the UAV relay U pairwise, and P S , P D are the transmission powers of the transmitting node and the receiving node respectively;

[0017] In the second time slot, the UAV relay forwards the signal x u [n], and the signal received by the receiving node is y D [n], and the signal eavesdropped by the eavesdropping node is y E2 [n], which are respectively expressed as:

[0018]

[0019]

[0020] Among them, P U is the UAV transmission power, and n d is part of the noise received by the receiving node during communication, and n e2 is part of the noise received by the eavesdropping end in the time slot;

[0021] The signal obtained by the receiving end using the self-cancellation technique for the received signal is y D '[n], and the signal finally eavesdropped by the eavesdropping node is y E [n], which are respectively expressed as:

[0022]

[0023]

[0024] At this time, the signal-to-noise ratio of the receiving node is SNR d , and the signal-to-noise ratio of the listening node is SNR e , which are respectively expressed as:

[0025]

[0026]

[0027] Among them, σ 2 is the additive white Gaussian noise on the receiving node;

[0028] In the UAV cooperative communication system, the achievable secure rate C S [n] is expressed as:

[0029] C S [n]=log2(1 + SNR d ) - log2(1 + SNR e ) (9)

[0030] System security rate C S [n] and the position L of the UAV U There is a complex functional relationship between [n] and the position L of the UAV. To maximize the system security rate, it is necessary to optimize the UAV flight trajectory; the original optimization problem of the communication system is expressed as:

[0031]

[0032] Among them, the four constraint sub-conditions are respectively:

[0033]

[0034]

[0035] L U [N]=u F (13)

[0036] C S [n]≥C S [0],n∈{1,.....,N - 1} (14)

[0037] Equation (11) means that the relative movement distance of the UAV relay between any two adjacent time slots does not exceed d s , Equation (12) means that the relative distance between the position point where the UAV relay stays in the first time slot and the starting position does not exceed d s , Equation (13) means that the UAV reaches the target position set by the flight program at the last time point, and Equation (14) means that the secure transmission rate at all times during the signal transmission process should be greater than the secure transmission rate threshold;

[0038] In the theoretical method for solving the above original problem, due to fewer constraint conditions, in the operable space of the UAV, the number of flight trajectories that meet the constraint conditions is a very large number. If all the trajectories are to be marked out and the optimal solution of the flight trajectory that maximizes the secure transmission rate of the communication system is to be solved, it will waste a lot of processor performance and waiting calculation time. Therefore, the present invention adds multiple movement constraints for optimization and, on this basis, relaxes some problems in the original optimization problem to obtain the lower bound problem of solving the secure rate;

[0039] Step 2: Add multiple constraint conditions;

[0040] During the simulation of the original problem, only the movement distance constraint is added, and the trajectory obtained by the simulation program is too complex, the number of trajectories is too large, and the operation time is too long, which is not suitable for practical applications. Therefore, the following coordinate movement direction constraints are newly added to the original optimization problem formula (10) for simulation:

[0041] x(n+1)=x(n)+v(n)sinθ(n)cosψ(n)t s (15)

[0042] y(n+1)=y(n)+v(n)sinθ(n)sinψ(n)t s (16)

[0043] h(n+1)=h(n)+v(n)cosθ(n)t s (17)

[0044] in θ(n), The UAV will not turn back on the x-axis, y-axis, and z-axis during its flight from the starting position to the destination position. Through simulation, it is concluded that adding constraints on the coordinate movement direction does not have much impact on the safety rate of the wireless communication system. At the same time, this will greatly reduce the amount of calculation and save processor computing resources.

[0045] After adding multiple constraints, the original problem is transformed into the P1 problem:

[0046]

[0047] Step 3: Find the lower bound of the secure transmission rate of the communication system;

[0048] According to ln(1+e x ) to obtain the lower bound of the safety capacity of the destination node According to ln(1+e x ) inequality, and find the upper bound of the eavesdropping channel capacity At this point, the lower limit of the secure transmission rate of the communication system can be obtained have:

[0049]

[0050] Step 4: Relax the problem;

[0051] On the basis of finding the lower bound of the system, we further relax the original problem by maximizing the lower bound of the average safety rate of the system, reduce the computational complexity of the P1 problem, and introduce the relaxation variable b n , c n Redefine for Thus, the P1 problem is transformed into the P2 problem:

[0052]

[0053] Finally, substitute the set parameters back into the system model in Step 1 to simulate and generate a sample space, and repeat Steps 2-3, that is, obtain the optimal flight path of the UAV based on the moving distance constraint and the coordinate movement constraint.

[0054] The beneficial effects of the present invention are as follows:

[0055] First, the operating environment of the UAV is further extended from a 2D plane with only the x-axis and y-axis to a 3D space with the addition of a third-axis height h-axis, making the flight trajectory of the UAV more in line with the actual situation. Second, based on the moving distance constraint, a constraint on spatial coordinate movement is added, the lower bound value of the system safety rate is solved and relaxed, and the original traversal problem is gradually optimized, reducing the computational complexity and saving the computing resources of the processor, thereby optimizing the trajectory of the UAV in the three-dimensional space. Description of the Drawings

[0056] Figure 1 is the system model of the UAV relay cooperative communication of the present invention.

[0057] Figure 2 is the simulation structure flow chart of the system of the present invention.

[0058] Figure 3 is the flight trajectory of the UAV when the signal-to-noise ratio of the transmission power of the UAV is 10 dB and the safety transmission rate threshold is 2.1 bit / s under only the moving distance constraint.

[0059] Figure 4 is the optimal flight trajectory of the UAV when the signal-to-noise ratio of the transmission power of the UAV is 10 dB and the safety transmission rate threshold is 2.1 bit / s under multiple moving constraints.

[0060] Figure 5 is the relationship diagram between the signal-to-noise ratio of the transmission power of the UAV and the average safety rate under multiple moving constraints.

[0061] Figure 6 : Average safety rate comparison diagram. Detailed Embodiments

[0062] The present invention will be further described below in conjunction with the drawings and embodiments.

[0063] The UAV-assisted wireless communication network studied by the present invention is as Figure 1 shown, including a transmitting node S, a destination node D, an eavesdropping node E, and a UAV relay U. Let L U [n] = [x(n), y(n), h(n)] T , n ∈ {1, 2...., N} represent the position of the UAV. It is assumed that the positions of the transmitting node (S), the destination node (D), and the eavesdropper (E) are known to the UAV relay, and the preset initial position of the UAV relay (uI ) and the destination location (u F ). The transmission of information is completed in two time slots

[0064] In the first time slot, the transmitting node sends out the signal S i , and the receiving node sends out the noise signal S j . The signals received by the UAV relay and the eavesdropping end are y U [n] and y E1 [n] can be expressed as:

[0065] y U [n] = h SU [n]x i + h UD [n]x j + n u (21)

[0066] y E1 [n] = g SE x i + g UE [n]x j + n e1 (22)

[0067] Among them, are the signals finally transmitted by the transmitting node and the receiving node respectively; h SU is the free space loss of the channel power from the transmitting node (S) to the UAV relay (U), h UD is the free space loss of the channel power from the UAV relay (U) to the receiving node (D). Similarly, g SE , g UE are the free space losses of the channel power from the transmitting node (S) to the eavesdropping end (E) and from the UAV relay (U) to the eavesdropping end (E) respectively, n u is the noise received by the UAV relay during the communication process, n e1 is the noise received by the eavesdropping end in the first stage.

[0068] In the second time slot, the UAV relay forwards the signal x u [n] to the receiving node. The signals received by the receiving node and eavesdropped by the eavesdropping end are y D [n] and y E2 [n] respectively, and can be expressed as:

[0069]

[0070]

[0071] Among them, P Uis the transmission power of the UAV, n d is the partial noise received by the receiving node during communication, n e2 is the partial noise received by the eavesdropping end in the i-th time slot.

[0072] The signals received by the receiving end using self-cancellation technology and the signals finally eavesdropped by the eavesdropping end are, respectively,

[0073] y D '[n], y E [n], can be respectively expressed as:

[0074]

[0075]

[0076] At this time, the signal-to-noise ratios of the receiving node and the eavesdropping end are SNR d , SNR e , can be respectively expressed as:

[0077]

[0078]

[0079] where σ 2 is the additive white Gaussian noise on the receiving node.

[0080] The main channel capacity of the destination node (D) and the channel capacity C S1 [n], C S2 [n] are:

[0081] C S1 [n] = log2(1 + SNR d ) (29)

[0082] C S2 [n] = log2(1 + SNR e ) (30)

[0083] In the UAV cooperative communication system, the achievable secure rate C S [n] is expressed as:

[0084] C S [n] = log2(1 + SNR d ) - log2(1 + SNR e ) (31)

[0085] The system secure rate C S [n] and the position L of the UAV UThere is a complex functional relationship between [[n]]. To maximize the system security rate, it is necessary to optimize the UAV flight trajectory. The original optimization problem of the communication system can be expressed as:

[0086]

[0087] The present invention optimizes by adding multiple movement constraint conditions to the original problem. On this basis, the lower bound value of the system security rate is obtained and a relaxation variable is introduced to optimize the original problem step by step, reducing the computational complexity. It is described in three parts: adding multiple constraint conditions, obtaining the lower bound value of the secure transmission rate of the communication system, and performing relaxation processing on the problem.

[0088] Figure 2 is the simulation structure flow chart of the system of the present invention. The effects of the present invention can be further illustrated by the following simulations. All simulations are conducted using the additive white Gaussian noise channel model. The software configuration for the simulation is the programming languages Matlab and C++. The simulation platforms used are R2015a and sublime text3. In terms of hardware configuration: the CPU is an Intel(R) Core(TM) i5-7200U.

[0089] Step 1: Generation of the data set

[0090] Set the simulation parameters, and the coordinates of each node in the communication system are L S =[0, 0, 100] T , L D =[250, 250, 100] T , L E =[0, 250, 0] T , u I =[0, 0, 250] T and u F =[250, 0, 250] T ; the maximum flight speed v of the UAV is 5 m / s, the number of time slots N is 10, the channel gain (normalized) ρ0 is 1, the transmission power P of the transmitter S is 50 dBW, and the transmit power signal-to-noise ratio of the UAV is 10 to 30 (dB), and in this example, 10 dB is taken; the secure transmission rate threshold is C S [0]=2.1 (bit / s), (C S[0] The value is determined by the operating conditions of the communication system and can be adjusted). Substitute it into the established mathematical model of the communication system's security capacity to simulate a three-dimensional grid-like sample space with a size of 250*250*250 (m*m*m). Each coordinate point in the space has a security rate value, where the maximum value is 7.8 (bit / s) and the minimum value is 0.01 (bit / s). The setting of the security rate threshold discards some points with relatively low security rates, that is, the UAV relay does not need to consume energy and time to pass through these points with relatively low security rates.

[0091] Step 2: Construct the moving distance constraint

[0092] The moving distance constraint includes: the relative moving distance of the UAV relay between any two adjacent time slots does not exceed d s = V·t s , where V represents the maximum flight speed of the UAV, and t s represents the duration of one time slot, that is, when the UAV relay flies in the sample space, it flies from any vertex of the grid to the diagonal vertex at the farthest; the relative distance between the position point where the UAV relay stays in the first time slot and the starting position does not exceed d s , which can be satisfied by restricting the UAV relay to fly within a three-dimensional grid when it flies in the sample space; the UAV needs to reach the preset destination position at the last

[0093] time point; the secure transmission rate at all times during the signal transmission process should be greater than the secure transmission rate threshold. At this time, there are 26 paths when the UAV flies in each time slot. When N time slots are completed, it is equivalent to the UAV relay traversing all points in the space, and the number of flight trajectories is 26 10 paths, and the trajectories are tortuous and too complex, and the traditional traversal operation takes too long and is not suitable for practical applications.

[0094] Step 3: Add coordinate movement constraints

[0095] The simulated generated sample space can be regarded as a Cartesian coordinate system with an x-axis, a y-axis, and a z-axis. When the UAV relay flies in the sample space, the coordinate values on the x-axis, y-axis, and z-axis will increase or decrease. Use "1", "0", "-1" to represent the UAV relay's position moving forward, staying, and moving backward on the x-axis or y-axis or z-axis respectively, so there will be combinations. Here, take (1,1,1) as an example, which means the UAV relay flies in the direction of a 45° horizontal angle and a 45° elevation angle. Since the traditional method has high complexity, is not easy to process large-scale data, and consumes a large amount of computing resources, the following combinations are used to make the UAV relay fly:

[0096] {(1, 0, 1), (1, 0, 0), (1, 0, -1), (1, 1, 1), (1, 1, 0), (1, 1, -1), (0, 1, 1), (0, 1, 0), (0, 1, -1), (0, 0, 1), (0, 0, -1)}

[0097] At this time, the UAV relay will fly along the given horizontal direction angle and elevation angle, that is, during the flight of the UAV from the starting position to the destination position, there will be no turning back on the x-axis, y-axis, and z-axis. This will greatly save the computing resources of the processor, enabling the UAV to respond to eavesdropping phenomena faster, quickly adjust the trajectory, reduce the risk of information leakage, and enhance the security of the communication system.

[0098] Step Four: Relaxation Processing Optimization

[0099] First, according to the convexity and concavity of ln(1 + e x ), the lower bound of the security capacity of the destination node and the upper bound of the eavesdropping channel capacity are obtained, which are respectively

[0100]

[0101]

[0102] At this time, the lower bound value of the secure transmission rate of the communication system can be obtained

[0103]

[0104] Then introduce slack variables b n , c n They respectively satisfy b n ≤ (||L U [n] - L D ||) 2 , c n ≥ (||L U [n] - L E ||) 2

[0105]

[0106] Among them, γ is the Euler constant, γ = 0.57721566490153286060.

[0107] Finally, substitute the set parameters back into the mathematical model, simulate and generate the sample space, and repeat steps two and three, that is, obtain the optimal path based on the moving distance constraint and the coordinate movement constraint.

[0108] Figure 3It means that the drone transmits only under the constraint of moving distance with a power signal-to-noise ratio of 10dB and a safe transmission rate threshold of 2.1bit / s. Figure 3 It can be seen that in the UAV-assisted wireless communication network, with only the constraints of the moving distance condition, the UAV flight trajectory is too complicated, the number of trajectories is too large, and as the UAV transmit power signal-to-noise ratio gradually increases, the UAV trajectory diagram becomes more and more complicated. At the same time, as the UAV transmit power signal-to-noise ratio gradually increases, the UAV flight trajectory tends to fly close to the receiver. Although in this process, the UAV is getting closer and closer to the eavesdropper, it is obvious that the benefits of being close to the receiver are greater than the risks of being close to the eavesdropper. The UAV will not gradually move away from the receiver until it is about to reach its destination.

[0109] Figure 4 It represents the optimal flight trajectory of the UAV optimized under the constraints of moving distance and coordinate movement. Here, the transmit power signal-to-noise ratio is set to 10dB and the safe transmission rate threshold is set to 2.1bit / s. It can be seen from the figure that under the action of the constraints, the UAV only navigates forward at a given flight angle during the flight from the preset initial point to the destination point, and there is no round trip during the flight.

[0110] Figure 5 Under multiple mobile constraints, the influence of different UAV transmission power signal-to-noise ratios on the average safety rate is observed. Here, the UAV transmission power signal-to-noise ratio is 5dB, 10dB, 15dB, 20dB, 25dB, and 30dB respectively. It can be seen from the figure that with the increase of the UAV transmission power signal-to-noise ratio, the average safety rate basically increases linearly.

[0111] Figure 6 This is a comparison diagram of the average safety rate schemes. It can be seen from the figure that compared with the case where the drone trajectory optimization is not considered, that is, the drone flies directly from the starting point to the end point in a straight line, the average safety rate of the communication system is greatly improved after optimizing the drone trajectory; the optimized trajectory obtained by only following the moving distance constraint can make the average safety rate of the communication system the highest, that is, the optimal path, but the simulation time when there is only a moving distance constraint is too long, consuming a lot of computing resources; after adding the coordinate movement constraint, the simulation time is greatly reduced, and it only takes more than ten minutes to simulate a transmitter power data, and the safety rate reduction is small. After evaluating the time benefit and the safety rate increase benefit, the non-return path scheme is better than the theoretical optimal path scheme in practicality.

[0112] Conclusion: For the UAV-assisted wireless communication relay network, the present invention proposes a scheme to optimize the flight trajectory of the UAV while fixing the transmission power of the UAV, so as to maximize the average security rate in three-dimensional space. By adding the constraint of spatial coordinate movement on the basis of the moving distance constraint, the lower bound of the system security rate is solved and relaxed, and the successive optimization solves the problems of high complexity, difficulty in processing large-scale data and consumption of a large amount of computing resources in the traditional method. The correctness and feasibility of the proposed scheme are verified by the simulation results.

Claims

1. A secure communication method for a 3D dynamic position selection strategy of an unmanned aerial vehicle, characterized in that It includes the following steps: Step 1: Establish a system model; In the system model, it includes a transmitting node S, a destination node D, a wiretapping node E, and a UAV relay U. Let L U [n] = [x(n), y(n), h(n)] T , where n ∈ {1, 2...., N} represents the position of the UAV, and x(n), y(n), and h(n) represent the coordinate values corresponding to a certain point in the three-dimensional coordinate system. The positions of the transmitting node S, the destination node D, and the wiretapping node E are known to the UAV relay, and the preset initial position u I and the destination position u F ; The transmission of information is completed in two time slots; In the first time slot, the transmitting node sends out signal S i , and the receiving node sends out noise signal S j . The signals received by the UAV relay and the eavesdropping end are y U [n] and y E1 [n] respectively, which are expressed as: wherein, are the signals finally transmitted by the transmitting node and the receiving node respectively; n u is the noise received by the UAV relay during the communication process, and n e1 is the noise received by the eavesdropping end in the first stage; is the free space loss of the channel power from the transmitting node S to the UAV relay U, is the free space loss of the channel power from the UAV relay U to the receiving node D, is the free space loss of the channel power from the transmitting node S to the eavesdropping node E, is the free space loss of the channel power from the UAV relay U to the eavesdropping node E, where ρ0 represents the channel gain of the system when the reference distance d0 = 1m, L S ,L D ,L E are the positions of the transmitting node, the receiving node and the eavesdropping point in space respectively, d SU ,d UD ,d SE ,d UE represents the distances between the transmitting node S, the destination node D, the eavesdropping node E and the UAV relay U pairwise, P S ,P D are the transmission powers of the transmitting node and the receiving node respectively; In the second time slot, the UAV relay forwards the signal x to the receiving node u [n], and the signal received by the receiving node is y D [n], and the signal eavesdropped by the eavesdropping node is y E2 [n], which are respectively expressed as: Among them, P U is the transmission power of the drone, n d is the partial noise received by the receiving node during the communication process, and n e2 is the partial noise received by the eavesdropping end in the i-th time slot; The signal obtained by the receiving end using self-cancellation technology for the received signal is y D '[n], the signal y finally eavesdropped by the eavesdropping node E [n], are respectively expressed as: At this time, the signal-to-noise ratio of the receiving node is SNR d , and the signal-to-noise ratio of the eavesdropping node is SNR e , which are respectively expressed as: where σ 2 is the additive white Gaussian noise at the receiving node; In a drone cooperative communication system, the achievable secure rate C S of the system is expressed as: C S [n] = log2(1 + SNR d ) - log2(1 + SNR e ) (9) System security rate C S [n] and the position L of the UAV U [n] has a complex functional relationship. To maximize the system security rate, it is necessary to optimize the UAV flight trajectory. The original optimization problem of the communication system is expressed as: Among them, the four constraint sub-conditions are respectively: L U [N] = u F (13) C S [n]≥C S [0],n∈{1,.....,N - 1} (14) Equation (11) indicates that the relative movement distance of the UAV relay between any two adjacent time slots does not exceed d s , Equation (12) indicates that the relative distance between the position where the UAV relay stays in the first time slot and the starting position does not exceed d s , Equation (13) indicates that the UAV reaches the destination position set by the flight program at the last time point, and Equation (14) indicates that the secure transmission rate at all times during the signal transmission process should be greater than the secure transmission rate threshold; Step 2: Add multiple constraint conditions; The following coordinate movement direction constraints are newly added to the original optimization problem formula (10) for simulation: x(n + 1) = x(n) + v(n)sinθ(n)cosψ(n)t s (15) y(n + 1)=y(n)+v(n)sinθ(n)sinψ(n)t s (16) h(n + 1)=h(n)+v(n)cosθ(n)t s (17) in θ(n), The UAV will not turn back on the x-axis, y-axis, and z-axis during its flight from the starting position to the destination position. Through simulation, it is concluded that adding constraints on the coordinate movement direction does not have much impact on the safety rate of the wireless communication system. At the same time, this will greatly reduce the amount of calculation and save processor computing resources. After adding multiple constraint conditions, the original problem is transformed into Problem P1: Step 3: Obtain the lower bound value of the secure transmission rate of the communication system; Based on the convexity of ln(1 + e x ), the lower bound of the secure capacity of the destination node is obtained Based on the concavity of the ln(1 + e x ) inequality, the upper bound of the eavesdropping channel capacity is obtained At this time, the lower bound value of the secure transmission rate of the communication system can be obtained There is: Step 4: Relax the problem; Based on obtaining the lower bound of the system, the original problem is further relaxed by maximizing the lower bound of the system's average security rate to reduce the computational complexity of Problem P1, and slack variables b n , c n are redefined as Thus, Problem P1 is transformed into Problem P2: Finally, substitute the set parameters back into the system model in Step 1, simulate to generate a sample space, and repeat Steps 2 - 3, that is, obtain the optimal flight path of the UAV based on the movement distance constraint and the coordinate movement constraint.

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