A method and system for designing drone trajectories and resources for secure communication

Abstract

The present invention proposes a method and system for designing drone trajectories and resources for secure communications. The method mainly includes the following steps: first, calculating the user secure communication throughput in the secure communication network; then, establishing the drone trajectory and resource allocation design optimization problem in the secure communication network based on the user secure communication throughput; then, establishing a continuous convex approximation problem of the drone trajectory and resource allocation design optimization problem in the secure communication network based on the convex approximation method; finally, iteratively solving the above continuous convex approximation problem based on the interior point method to obtain the optimal drone trajectory and resource allocation, including drone power and user scheduling coefficient allocation. The present invention can ensure the security and reliability of transmission while ensuring the network throughput performance. It is suitable for scenarios with high transmission security and reliability in 5G&6G networks and has extremely high economic benefits.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) secure communication, and in particular to a method and system for designing UAV trajectories and resources for secure communication. Background Art

[0002] As a rapidly developing technology, drones have been widely used in the field of wireless communications due to their inherent high mobility and excellent line-of-sight LoS channel characteristics. Benefiting from low cost and extremely low transmission path loss, drone wireless communication networks have been proven to have superior performance compared to traditional ground cellular networks, and are an effective way to solve the high cost and low coverage problem of future 6G network construction.

[0003] In particular, since the high mobility of drones introduces additional system degrees of freedom into drone wireless communication networks, the network performance can be further improved by designing drones and trajectories and rationally planning network resource allocation, and related research is also in full swing. In 2019, Hu and other scholars studied drone-assisted relay communication networks, and significantly reduced network energy consumption by jointly designing drone flight trajectories and scheduling coefficients for users. In 2021, Nasir and others considered the problem of small-scale fading caused by obstruction of urban buildings in the actual application of drone communication networks, and greatly improved the network throughput performance by studying drone trajectories and resource allocation design under a more general Rice channel model.

[0004] Although a series of studies represented by the above work on the joint design of trajectory and resource allocation in UAV communication systems have played a certain role in improving system performance, due to the open channels in UAV networks, the relevant data transmitted is very easy to be obtained by illegal eavesdroppers, and the current related work hardly considers the transmission reliability requirements. The research on balancing communication security and reliability is still an open problem. Therefore, the design method of UAV trajectory and resource allocation for secure communication networks needs further research. Summary of the invention

[0005] In view of the current situation that there is a lack of a general drone trajectory and power joint design method that takes into account both transmission security and reliability in drone secure communication networks, the present invention proposes a drone trajectory and resource allocation joint design algorithm and computer-readable medium for secure communication to improve the performance of drone secure communication networks.

[0006] Based on the above problems, the present invention adopts the following technical solutions:

[0007] A method for designing drone trajectories and resources for secure communication includes the following steps:

[0008] Step 1: Select a drone, an eavesdropper, and multiple ground users, and calculate the user security communication throughput in the secure communication network;

[0009] Step 2: According to the goal of constructing the optimization problem of the user's secure communication throughput in the secure communication network, the mobility constraint, transmission power constraint and scheduling coefficient constraint of the optimization problem are constructed respectively. Based on the goal and constraints of the optimization problem constructed by the user's secure communication throughput in the secure communication network, the optimization problem of the trajectory and resource allocation design of the drone in the secure communication network is established;

[0010] Step 3: Based on the convex approximation method, a lower bound concave approximation function of the maximum achievable secure transmission rate between the UAV and the user is obtained. Then, the lower bound concave approximation function of the user's secure communication throughput is obtained in combination with the time slot length. Then, the target of the continuous convex approximation problem is constructed based on the lower bound concave approximation function of the user's secure communication throughput. Finally, a continuous convex approximation problem for the optimization problem of the trajectory and resource allocation design of the UAV in the secure communication network is established based on the target of the continuous convex approximation problem and the constraints constructed in step 2.

[0011] Step 4: Based on the interior point method, the continuous convex approximation problem of the optimization problem of UAV trajectory and resource allocation design in the secure communication network is iteratively solved to obtain the optimal UAV trajectory, UAV power and user scheduling coefficient.

[0012] Furthermore, step 1 calculates the distance between the drone and the user and the distance between the drone and the eavesdropper according to the position of the drone, the position of the ground user, the position of the eavesdropper and the altitude of the drone, and then calculates the signal-to-noise ratio at the user and the eavesdropper in combination with the LoS channel gain, noise power and drone power at unit distance, and then calculates the maximum achievable secure transmission rate between the drone and the user in combination with the transmission code length, the maximum decoding error probability and the maximum information leakage probability, and finally obtains the user secure communication throughput in the drone secure communication network according to the maximum achievable secure transmission rate between the drone and the user, the user scheduling coefficient and the time slot length.

[0013] Furthermore, the distance between the drone and the user in step 1 is:

[0014]

[0015] The distance between the drone and the eavesdropper is:

[0016]

[0017] The signal-to-noise ratio at the user is:

[0018]

[0019] The signal-to-noise ratio at the eavesdropper is:

[0020]

[0021] The maximum safe transmission rate between the drone and the user is:

[0022]

[0023] Where k∈{1,...,K} is the user number, representing the kth user, and K represents the number of users; n∈{1,...,N} is the time slot number, representing the nth time slot, and N represents the number of time slots; d k [n] represents the distance between the UAV and the kth user in the nth time slot; q[n] represents the position of the UAV in the nth time slot, p k represents the location of the kth user, H represents the height of the drone; d e [n] represents the distance between the drone and the eavesdropper in the nth time slot; p e represents the location of the eavesdropper; γ k [n] represents the signal-to-noise ratio of the kth user in the nth time slot, γ e [n] represents the signal-to-noise ratio at the eavesdropper in the nth time slot; β0 represents the LoS channel gain per unit distance, σ 2 represents the noise power, P[n] represents the UAV power; R k [n] represents the maximum achievable secure transmission rate between the UAV and the kth user in the nth time slot, m represents the transmission code length, ε1 represents the maximum decoding error probability, and ε2 represents the maximum information leakage probability; [·] + represents the positive function, Q -1 (·) represents the inverse function of the Q function.

[0024] Furthermore, the user security communication throughput in the UAV security communication network is:

[0025]

[0026] Where D k represents the security communication throughput of the kth user; a k [n] represents the scheduling coefficient of the kth user in the nth time slot, and ΔT represents the time slot length.

[0027] Furthermore, the goal of constructing the optimization problem described in step 2 is:

[0028]

[0029] The transmission power constraint is:

[0030]

[0031] The scheduling coefficient constraint is:

[0032]

[0033] Where Q = {q[n]} represents the trajectory of the drone, P = {P[n]} represents the power of the drone, and a = {a k [n]} represents the user scheduling coefficient; P max Represents the maximum power of the drone, Represents the average power of the drone.

[0034] Furthermore, the trajectory and power design optimization problem of the constructed UAV covert relay communication network is:

[0035]

[0036] st:||q[n]-q[n-1]||≤VΔt,

[0037]

[0038]

[0039] That is, the optimization problem is to maximize the throughput of the user with the minimum secure communication throughput under the constraints of mobility, transmission power and scheduling coefficient by optimizing the UAV trajectory and resource allocation.

[0040] Furthermore, the lower bound concave approximate function of the maximum achievable safe transmission rate between the drone and the user in step 3 is:

[0041]

[0042] The lower concave approximate function of the user security communication throughput described in step 3 is:

[0043]

[0044] The goal of the continuous convex approximation problem described in step 3 is:

[0045]

[0046] The continuous convex approximation problem for the optimization problem of designing drone trajectories and resource allocation in a secure communication network described in step 3 is:

[0047]

[0048] st:||q[n]-q[n-1]||≤VΔt,

[0049]

[0050]

[0051] in The concave lower bound approximation function representing the maximum achievable secure transmission rate between the UAV and the kth user in the nth time slot, represents the convex approximation coefficient; They represent the coefficients introduced by the mean inequality, Represents the concave approximate function of the lower bound of the secure communication throughput of the kth user.

[0052] Furthermore, the values ​​of the convex approximation coefficients are:

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] in, Represents the distance from the drone to the user corresponding to the local point of the nth time slot iteration, represents the distance from the drone to the eavesdropper corresponding to the local point of the nth time slot iteration, P (r) [n] represents the communication power corresponding to the local point of the nth time slot iteration, represents the signal-to-noise ratio of the user at the local point of the nth time slot iteration, represents the signal-to-noise ratio of the local point at the nth time slot iteration corresponding to the eavesdropper;

[0064] The coefficients introduced by the mean inequality are:

[0065]

[0066]

[0067]

[0068] in, Represents the scheduling coefficient of the UAV corresponding to the local point of the nth time slot iteration for the kth user.

[0069] Furthermore, step 4 includes the following sub-steps:

[0070] Step 4.1: At the local point of the iteration (Q (r) ,P (r) ,a (r) ) approximate the UAV trajectory and resource allocation design optimization problem in the secure communication network established in step 2 as a continuous convex approximation problem of the UAV trajectory and resource allocation design optimization problem in the secure communication network described in step 3;

[0071] Step 4.2: Use the interior point method to solve the continuous convex approximation problem in step 4.1 and obtain the optimal solution for this iteration.

[0072] Step 4.3: If the improvement of the objective function of this iteration compared with the previous objective function is less than the iteration threshold ε, the iteration is stopped. The optimal solution of this iteration is the optimal UAV trajectory, UAV power and user scheduling coefficient. Otherwise it will be used as the local point for the next iteration Go back to step 4.1 and continue iterating.

[0073] In another aspect, the present invention provides a UAV trajectory and resource design system for secure communication, comprising:

[0074] Module 1: It is used to select drones, eavesdroppers and multiple ground users, and calculate the user security communication throughput in the secure communication network;

[0075] Module 2: It is used to construct the goal of the optimization problem according to the user security communication throughput in the secure communication network, and respectively construct the mobility constraints, transmission power constraints and scheduling coefficient constraints of the optimization problem. Based on the goal and constraints of the optimization problem constructed by the user security communication throughput in the secure communication network, the optimization problem of the trajectory and resource allocation design of the drone in the secure communication network is established;

[0076] Module 3: It is used to obtain the lower bound concave approximation function of the maximum achievable safe transmission rate between the drone and the user based on the convex approximation method, and then obtain the lower bound concave approximation function of the user's safe communication throughput in combination with the time slot length. Then, according to the lower bound concave approximation function of the user's safe communication throughput, the goal of the continuous convex approximation problem is constructed. Finally, according to the goal of the continuous convex approximation problem and the constraints constructed in step 2, a continuous convex approximation problem for the optimization problem of drone trajectory and resource allocation design in a secure communication network is established;

[0077] Module 4: It is used to iteratively solve the continuous convex approximation problem of the UAV trajectory and resource allocation design optimization problem in the secure communication network based on the interior point method, and obtain the optimal UAV trajectory, UAV power and user scheduling coefficient.

[0078] Compared with the prior art, the present invention has the following beneficial effects:

[0079] The present invention proposes a joint design algorithm for drone trajectory and resource allocation in a drone secure communication network, which can maximize the throughput of the user with the minimum secure communication throughput under the constraints of mobility, transmission power and scheduling coefficient. On the basis of ensuring the communication quality of all users, the algorithm fully considers the security and reliability of transmission, and can be applied to future 5G+ and 6G service scenarios with high requirements for transmission security and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A schematic diagram of a secure communication network for unmanned aerial vehicles according to an embodiment of the present invention;

[0081] Figure 2 A flow chart of the method implemented by the present invention;

[0082] Figure 3 Design an optimized drone trajectory map for the real-time example of the present invention. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians of the present invention without making creative work are within the scope of protection of the present invention.

[0084] Example 1

[0085] Combine the following Figure 1-3 A trajectory and power design method of a UAV covert relay communication network according to an embodiment of the present invention is introduced as follows:

[0086] Figure 1A drone secure communication network is considered for a specific embodiment of the present invention, and the network includes a drone, K users, and an illegal eavesdropper. The drone sends data to the user through a downlink, and the drone's data transmission behavior is eavesdropped in real time by the eavesdropper. The drone's flight altitude is H, and the maximum flight speed is V. In order to improve the performance of the drone secure communication network, it is necessary to jointly design the drone's trajectory and resource allocation (including drone power and user scheduling coefficients) to improve the network's throughput performance while ensuring reliability and security. The solution to this problem is very complex.

[0087] like Figure 2 The method flow chart of the present invention is shown, and the implementation process includes the following steps:

[0088] Step 1: Derivation of the formula for user secure communication throughput in the UAV secure communication network. First, the distance between the UAV and the user and the distance between the UAV and the eavesdropper are calculated based on the location of the UAV, the location of the ground user, the location of the eavesdropper, and the height of the UAV. Then, the signal-to-noise ratio at the user and the eavesdropper is calculated by combining the LoS channel gain, noise power, and UAV power at unit distance. The maximum achievable secure transmission rate between the UAV and the user is calculated by combining the transmission code length, the maximum decoding error probability, and the maximum information leakage probability. Finally, the formula for user secure communication throughput in the UAV secure communication network is obtained based on the maximum achievable secure transmission rate between the UAV and the user, the user scheduling coefficient, and the time slot length.

[0089] The distance between the drone and the user in step 1 is:

[0090]

[0091] Where k∈{1,...,K} is the user number, representing the kth user, and K represents the number of users; n∈{1,...,N} is the time slot number, representing the nth time slot, and N represents the number of time slots; d k [n] represents the distance between the UAV and the kth user in the nth time slot; q[n] represents the position of the UAV in the nth time slot, p k represents the location of the kth user, and H represents the height of the drone.

[0092] The distance between the drone and the eavesdropper in step 1 is:

[0093]

[0094] where d e [n] represents the distance between the drone and the eavesdropper in the nth time slot; p e Represents the location of the eavesdropper.

[0095] The signal-to-noise ratio at the user in step 1 is:

[0096]

[0097] where γ k [n] represents the signal-to-noise ratio at the kth user in the nth time slot; β0 represents the LoS channel gain per unit distance, σ 2 represents the noise power, and P[n] represents the UAV power.

[0098] The signal-to-noise ratio at the eavesdropper in step 1 is:

[0099]

[0100] where γ e [n] represents the signal-to-noise ratio at the eavesdropper in the nth time slot.

[0101] The maximum achievable safe transmission rate between the drone and the user in step 1 is:

[0102]

[0103] Where R k [n] represents the maximum achievable secure transmission rate between the drone and the kth user in the nth time slot. m represents the transmission code length, ε1 represents the maximum decoding error probability, and ε2 represents the maximum information leakage probability; [·] + represents the positive function, Q -1 (·) represents the inverse function of the Q function.

[0104] The formula for user security communication throughput in the drone security communication network described in step 1 is:

[0105]

[0106] Where D k represents the security communication throughput of the kth user; a k [n] represents the scheduling coefficient of the kth user in the nth time slot, and ΔT represents the time slot length.

[0107] Step 2: Establish the optimization problem of drone trajectory and resource allocation design in the secure communication network. First, construct the goal of the optimization problem based on the user secure communication throughput formula. Then define the mobility constraints of the optimization problem based on the position, maximum speed and time slot length of the drone. Then define the transmission power constraint based on the drone power, maximum power and average power of the drone. Finally, define the scheduling coefficient constraint based on the user scheduling coefficient. According to the above constructed goals and constraints, establish the optimization problem of drone trajectory and resource allocation design in the secure communication network.

[0108] The goal of constructing the optimization problem described in step 2 is:

[0109]

[0110] Where Q = {q[n]} represents the trajectory of the drone, P = {P[n]} represents the power of the drone, and a = {a k [n]} represents the user scheduling coefficient. The above objective means maximizing the throughput of the minimum user with the safe communication throughput by optimizing the UAV trajectory, UAV power and user scheduling coefficient.

[0111] The mobility constraint of the optimization problem described in step 2 is:

[0112] ||q[n]-q[n-1]||≤VΔt

[0113] Where q[n-1] represents the position of the drone in the n-1th time slot, and V represents the maximum speed of the drone. The meaning of the above constraints is that the speed of the drone in each time slot should be less than the maximum speed.

[0114] The transmission power constraint described in step 2 is:

[0115]

[0116] Where P max Represents the maximum power of the drone, Represents the average power of the UAV. The above constraints mean that the UAV power in each time slot should be less than the maximum power of the UAV, and the average power of the UAV during the entire mission time should be less than the average power of the UAV.

[0117] The scheduling coefficient constraint described in step 2 is:

[0118]

[0119] The above constraints mean that the value range of the user scheduling coefficient variable should be between [0,1] and in any time slot, the drone can only schedule at most one user.

[0120] The trajectory and power design optimization problem of the UAV covert relay communication network constructed in step 2 is:

[0121]

[0122] st:||q[n]-q[n-1]||≤VΔt,

[0123]

[0124]

[0125] The meaning of the above design optimization problem is to maximize the throughput of the user with the minimum secure communication throughput under the constraints of mobility constraints, transmission power constraints and scheduling coefficients by optimizing the UAV trajectory and resource allocation (resource allocation includes power and user scheduling design).

[0126] Step 3: Establish a continuous convex approximation problem for the optimization problem of drone trajectory and resource allocation design in a secure communication network. Based on the convex approximation method, a lower bound concave approximation function of the maximum achievable secure transmission rate between the drone and the user is obtained. Then, combined with the time slot length, a lower bound concave approximation function of the user's secure communication throughput is obtained. Then, based on the lower bound concave approximation function of the user's secure communication throughput, the goal of the continuous convex approximation problem is constructed. Finally, based on the above-constructed goal and the constraints constructed in step 2, a continuous convex approximation problem for the optimization problem of drone trajectory and resource allocation design in a secure communication network is established.

[0127] The approximation process of the lower bound concave approximation function of the maximum achievable safe transmission rate between the drone and the user based on the convex approximation method described in step 3 is:

[0128] First, since the function log2(1+γ k [n]) The single decreasing convex function, function -log2(1+γ e [n]) A single decreasing convex function, function A single decreasing convex function, function for A single decreasing convex function, so according to the properties of the convex function, the Taylor expansion of the maximum achievable safe transmission rate at the iterative local point is:

[0129]

[0130] in, represents the Taylor expansion of the maximum achievable secure transmission rate at the iterative local point, Represents the convex approximation coefficient, and the values ​​of the above convex approximation coefficients are:

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] in, Represents the distance from the drone to the user corresponding to the local point of the nth time slot iteration, represents the distance from the drone to the eavesdropper corresponding to the local point of the nth time slot iteration, P (r) [n] represents the communication power corresponding to the local point of the nth time slot iteration, represents the signal-to-noise ratio of the user at the local point of the nth time slot iteration, Represents the signal-to-noise ratio of the local point corresponding to the eavesdropper at the nth time slot iteration.

[0142] Then, according to the mean inequality, the lower bound concave approximate function of the maximum achievable safe transmission rate between the drone and the user is:

[0143]

[0144] in The concave lower bound approximation function of the maximum achievable safe transmission rate between the drone and the user, They represent the coefficients introduced by the mean inequality, and their values ​​are:

[0145]

[0146]

[0147]

[0148] in, Represents the scheduling coefficient of the UAV corresponding to the local point of the nth time slot iteration for the kth user.

[0149] The lower concave approximate function of the user security communication throughput described in step 3 is:

[0150]

[0151] in Represents the concave approximate function of the lower bound of the secure communication throughput of the kth user.

[0152] The goal of the continuous convex approximation problem described in step 3 is:

[0153]

[0154] The meaning of the above objective is to maximize the lower bound concave approximation function value of the secure communication throughput by optimizing the UAV trajectory, UAV power and user scheduling coefficient, and the corresponding function value of the user with the minimum function value.

[0155] The continuous convex approximation problem for the optimization problem of designing drone trajectories and resource allocation in a secure communication network described in step 3 is:

[0156]

[0157] st:||q[n]-q[n-1]||≤VΔt,

[0158]

[0159]

[0160] The meaning of the above continuous convex approximation problem is that by optimizing the UAV trajectory and resource allocation (resource allocation includes power and user scheduling design), the lower bound concave approximation function value of the minimum user that maximizes the secure communication throughput under the constraints of mobility constraints, transmission power constraints and scheduling coefficient constraints.

[0161] Step 4: Iteratively solve the above continuous convex approximation problem based on the interior point method to obtain the optimal UAV trajectory, UAV power and user scheduling coefficient.

[0162] The specific steps of the iterative algorithm based on the interior point method described in step 4 are as follows:

[0163] Step 4.1: At the local point of the iteration (Q (r) ,P (r) ,a (r) ) approximate the UAV trajectory and resource allocation design optimization problem in the secure communication network established in step 2 as a continuous convex approximation problem of the UAV trajectory and resource allocation design optimization problem in the secure communication network described in step 3;

[0164] Step 4.2: Use the interior point method to solve the continuous convex approximation problem in step 4.1 and obtain the optimal solution for this iteration.

[0165] Step 4.4: If the improvement of the objective function of this iteration compared with the previous objective function is less than the iteration threshold ε, the iteration is stopped. The optimal solution of this iteration is the optimal UAV trajectory, UAV power and user scheduling coefficient. Otherwise it will be used as the local point for the next iteration Go back to step 4.1 and continue iterating.

[0166] Figure 3The trajectory diagram of the drone optimized in the real-time example of the present invention is given. It can be seen that when the eavesdropper is closer to the user, the algorithm proposed in the present invention can well adjust the trajectory of the drone to keep it away from the eavesdropper.

[0167] Example 2

[0168] This embodiment provides a drone trajectory and resource design system for secure communication, including:

[0169] Module 1: It is used to select drones, eavesdroppers and multiple ground users, and calculate the user security communication throughput in the secure communication network;

[0170] Module 2: It is used to construct the goal of the optimization problem according to the user security communication throughput in the secure communication network, and respectively construct the mobility constraints, transmission power constraints and scheduling coefficient constraints of the optimization problem. Based on the goal and constraints of the optimization problem constructed by the user security communication throughput in the secure communication network, the optimization problem of the trajectory and resource allocation design of the drone in the secure communication network is established;

[0171] Module 3: It is used to obtain the lower bound concave approximation function of the maximum achievable safe transmission rate between the drone and the user based on the convex approximation method, and then obtain the lower bound concave approximation function of the user's safe communication throughput in combination with the time slot length. Then, according to the lower bound concave approximation function of the user's safe communication throughput, the goal of the continuous convex approximation problem is constructed. Finally, according to the goal of the continuous convex approximation problem and the constraints constructed in step 2, a continuous convex approximation problem for the optimization problem of drone trajectory and resource allocation design in a secure communication network is established;

[0172] Module 4: It is used to iteratively solve the continuous convex approximation problem of the UAV trajectory and resource allocation design optimization problem in the secure communication network based on the interior point method, and obtain the optimal UAV trajectory, UAV power and user scheduling coefficient.

[0173] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0174] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, if these modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0175] Other parts not described in detail are all prior art.

Claims

1. A method for designing drone trajectories and resources for secure communication, characterized in that: The steps include: Step 1: Select a drone, an eavesdropper, and multiple ground users, and calculate the user security communication throughput in the secure communication network; Step 2: According to the goal of constructing the optimization problem of the user's secure communication throughput in the secure communication network, the mobility constraint, transmission power constraint and scheduling coefficient constraint of the optimization problem are constructed respectively. Based on the goal and constraints of the optimization problem constructed by the user's secure communication throughput in the secure communication network, the optimization problem of the trajectory and resource allocation design of the drone in the secure communication network is established; Step 3: Based on the convex approximation method, a lower bound concave approximation function of the maximum achievable secure transmission rate between the UAV and the user is obtained. Then, the lower bound concave approximation function of the user's secure communication throughput is obtained in combination with the time slot length. Then, the target of the continuous convex approximation problem is constructed based on the lower bound concave approximation function of the user's secure communication throughput. Finally, a continuous convex approximation problem for the optimization problem of the trajectory and resource allocation design of the UAV in the secure communication network is established based on the target of the continuous convex approximation problem and the constraints constructed in step 2. The lower concave approximate function of the maximum achievable safe transmission rate between the drone and the user in step 3 is: The lower concave approximate function of the user security communication throughput described in step 3 is: The goal of the continuous convex approximation problem described in step 3 is: The continuous convex approximation problem for the optimization problem of designing drone trajectories and resource allocation in a secure communication network described in step 3 is: in The concave lower bound approximation function representing the maximum achievable secure transmission rate between the UAV and the kth user in the nth time slot, represents the convex approximation coefficient; They represent the coefficients introduced by the mean inequality, Represents the lower bound concave approximation function of the k-th user's secure communication throughput; Step 4: Based on the interior point method, the continuous convex approximation problem of the optimization problem of UAV trajectory and resource allocation design in the secure communication network is iteratively solved to obtain the optimal UAV trajectory, UAV power and user scheduling coefficient.

2. A method for designing drone trajectories and resources for secure communication according to claim 1, characterized in that: Step 1: Calculate the distance between the drone and the user and the distance between the drone and the eavesdropper according to the location of the drone, the location of the ground user, the location of the eavesdropper and the altitude of the drone. Then, calculate the signal-to-noise ratio at the user and the eavesdropper by combining the LoS channel gain, noise power and drone power at unit distance. Then, calculate the maximum achievable secure transmission rate between the drone and the user by combining the transmission code length, the maximum decoding error probability and the maximum information leakage probability. Finally, obtain the user secure communication throughput in the drone secure communication network according to the maximum achievable secure transmission rate between the drone and the user, the user scheduling coefficient and the time slot length.

3. A method for designing drone trajectories and resources for secure communication according to claim 2, characterized in that: The distance between the drone and the user in step 1 is: The distance between the drone and the eavesdropper is: The signal-to-noise ratio at the user is: The signal-to-noise ratio at the eavesdropper is: The maximum safe transmission rate between the drone and the user is: Where k∈{1,...,K} is the user number, representing the kth user, and K represents the number of users; n∈{1,...,N} is the time slot number, representing the nth time slot, and N represents the number of time slots; d k [n] represents the distance between the UAV and the kth user in the nth time slot; q[n] represents the position of the UAV in the nth time slot, p k represents the location of the kth user, H represents the height of the drone; d e [n] represents the distance between the drone and the eavesdropper in the nth time slot; p e represents the location of the eavesdropper; γ k [n] represents the signal-to-noise ratio of the kth user in the nth time slot, γ e [n] represents the signal-to-noise ratio at the eavesdropper in the nth time slot; β0 represents the LoS channel gain per unit distance, σ 2 represents the noise power, P[n] represents the UAV power; R k [n] represents the maximum achievable secure transmission rate between the UAV and the kth user in the nth time slot, m represents the transmission code length, ε1 represents the maximum decoding error probability, and ε2 represents the maximum information leakage probability; [·] + represents the positive function, Q -1 (·) represents the inverse function of the Q function.

4. A method for designing drone trajectories and resources for secure communication according to claim 3, characterized in that: The user security communication throughput in the drone security communication network is: Where D k represents the security communication throughput of the kth user; a k [n] represents the scheduling coefficient of the kth user in the nth time slot, and ΔT represents the time slot length.

5. The method for designing drone trajectories and resources for secure communication according to claim 1, characterized in that: The goal of constructing the optimization problem described in step 2 is: The transmission power constraint is: The scheduling coefficient constraint is: Where Q = {q[n]} represents the trajectory of the drone, P = {P[n]} represents the power of the drone, and a = {a k [n]} represents the user scheduling coefficient; P max represents the maximum power of the drone, and P represents the average power of the drone.

6. A method for designing drone trajectories and resources for secure communication according to claim 5, characterized in that: The trajectory and power design optimization problem of the constructed UAV covert relay communication network is: That is, the optimization problem is to maximize the throughput of the user with the minimum secure communication throughput under the constraints of mobility, transmission power and scheduling coefficient by optimizing the UAV trajectory and resource allocation.

7. A method for designing drone trajectories and resources for secure communication according to claim 1, characterized in that: The values ​​of the convex approximation coefficients are: in, Represents the distance from the drone to the user corresponding to the local point of the nth time slot iteration, represents the distance from the drone to the eavesdropper corresponding to the local point of the nth time slot iteration, P (r) [n] represents the communication power corresponding to the local point of the nth time slot iteration, represents the signal-to-noise ratio of the user at the local point of the nth time slot iteration, represents the signal-to-noise ratio of the local point at the nth time slot iteration corresponding to the eavesdropper; The coefficients introduced by the mean inequality are: in, Represents the scheduling coefficient of the UAV corresponding to the local point of the nth time slot iteration for the kth user.

8. The method for designing drone trajectories and resources for secure communication according to claim 1, characterized in that: Step 4 includes the following sub-steps: Step 4.1: At the local point of the iteration (Q (r) ,P (r) ,a (r) ) approximate the UAV trajectory and resource allocation design optimization problem in the secure communication network established in step 2 as a continuous convex approximation problem of the UAV trajectory and resource allocation design optimization problem in the secure communication network described in step 3; Step 4.2: Use the interior point method to solve the continuous convex approximation problem in step 4.1 and obtain the optimal solution for this iteration. Step 4.3: If the improvement of the objective function of this iteration compared with the previous objective function is less than the iteration threshold ε, the iteration is stopped. The optimal solution of this iteration is the optimal UAV trajectory, UAV power and user scheduling coefficient. Otherwise it will be used as the local point for the next iteration Go back to step 4.1 and continue iterating.

9. A drone trajectory and resource design system for secure communications, characterized in that: include: Module 1: It is used to select drones, eavesdroppers and multiple ground users, and calculate the user security communication throughput in the secure communication network; Module 2: It is used to construct the goal of the optimization problem according to the user security communication throughput in the secure communication network, and respectively construct the mobility constraints, transmission power constraints and scheduling coefficient constraints of the optimization problem. Based on the goal and constraints of the optimization problem constructed by the user security communication throughput in the secure communication network, the optimization problem of the trajectory and resource allocation design of the drone in the secure communication network is established; Module 3: It is used to obtain the lower bound concave approximation function of the maximum achievable safe transmission rate between the drone and the user based on the convex approximation method, and then obtain the lower bound concave approximation function of the user's safe communication throughput in combination with the time slot length. Then, according to the lower bound concave approximation function of the user's safe communication throughput, the goal of the continuous convex approximation problem is constructed. Finally, according to the goal of the continuous convex approximation problem and the constraints constructed in step 2, a continuous convex approximation problem for the optimization problem of drone trajectory and resource allocation design in a secure communication network is established; Module 4: It is used to iteratively solve the continuous convex approximation problem of the optimization problem of drone trajectory and resource allocation design in secure communication networks based on the interior point method, and obtain the optimal drone trajectory, drone power and user scheduling coefficient; The drone trajectory and resource design system for secure communication is used to execute the steps in the drone trajectory and resource design method for secure communication described in any one of claims 1-8.

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