Unmanned aerial vehicle relay safety communication system and safety communication performance optimization method thereof

Abstract

The invention discloses an unmanned aerial vehicle relay security communication system and a security communication performance optimization method thereof, and the method comprises the steps: building a joint optimization problem of a channel bandwidth allocation parameter A, a transmitting power allocation parameter P and an unmanned aerial vehicle trajectory parameter Q, and taking the maximized minimum security communication rate as a target function; introducing a slack variable conversion problem and decomposing the slack variable conversion problem into a first sub-problem P3 of fixed Q optimization A and P and a second sub-problem P4 of fixed A and P optimization Q; the P3 and the P4 are solved through alternate optimization, Q is given and substituted into the P3 to obtain solutions of A and P, the solutions of A and P and the given Q are substituted into the P4 to be solved, and in the process, Taylor expansion is carried out on a sub-problem to be converted into a convex optimization problem to be solved to obtain a target function value; and judging whether the value meets a constraint condition or not, if so, ending, otherwise, continuing solving. According to the invention, the multi-hop network data transmission of the multi-unmanned aerial vehicle relay is balanced through the joint optimization scheme, and the end-to-end security communication rate from the base station to the user is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) relay communication, and in particular to a UAV relay secure communication system and a method for optimizing secure communication performance thereof. Background Art

[0002] Drones (UAVs), with their advantages of high mobility, low cost, wide coverage, and on-demand deployment, have been widely adopted in military and civilian applications such as search and rescue, inspection and surveillance, and cargo transportation. UAVs are becoming key platforms in modern communications infrastructure, leveraging their capabilities to provide enhanced line-of-sight (LoS) connectivity. Existing drone relays for secure air-ground collaborative network communications typically consist of a single UAV relay. However, utilizing multiple UAV relays as intermediaries for data transmission from base stations to users can significantly improve the service coverage and performance of secure air-ground collaborative communications.

[0003] “G. Zhang, Q. Wu, M. Cui, and R. Zhang, "Securing UAV Communicationsvia Joint Trajectory and Power Control," in IEEE Transactions on WirelessCommunications, vol. 18, no. 2, pp. 1376–1389, Feb. 2019.” considered a wireless communication system supporting UAVs. Under visual channel modeling, a single UAV collects data from a single node on the ground. There is an eavesdropper on the ground. By jointly optimizing the power allocation and trajectory of the UAVs, the confidentiality of air-to-ground and ground-to-air communications is improved. However, this study did not consider the situation of multiple UAVs and multiple potential eavesdroppers at different locations. "J. Fan, M. Cui, G.Zhang, and Y. Chen, "Throughput Improvement for Multi-Hop UAV Relaying," inIEEE Access, vol. 7, pp. 147732–147742, 2019." deploys multiple drones as multi-hop relays to assist in communication from the source to the destination. The end-to-end throughput from the source to the destination is maximized by jointly optimizing the transmit power of the source and all drone relays, the channel bandwidth, and the trajectory of the drones. However, this study does not consider potential eavesdroppers on the ground. "T. Liu, G. Zhang, and M. Cui "Task Completion Time Minimization for UAV-Enabled Data Collection in Rician Fading Channels," in IEEE Internet of Things Journal, vol. 10, pp. 1134 - 1148 Jan. 2023" describes a dual-UAV data acquisition system. In this system, UAVs collect data, and a UAV relay collaboratively assists in transmitting data from ground sensors to an information fusion center. However, this study does not consider multiple users and potential eavesdroppers on the ground. Therefore, there is an urgent need for a UAV relay secure communication system and its secure communication performance optimization method that can consider multiple users and potential eavesdroppers on the ground and jointly optimize UAV trajectory parameters, channel bandwidth allocation parameters, and transmit power allocation parameters to improve secure communication performance. Summary of the Invention

[0004] The purpose of the present invention is to provide a UAV relay secure communication system and a secure communication performance optimization method thereof that can take into account multiple users and potential ground eavesdroppers, and can jointly optimize UAV trajectory parameters, channel bandwidth allocation parameters and transmission power allocation parameters to improve secure communication performance.

[0005] To achieve the above objectives, the first aspect of the present invention provides a UAV relay secure communication system, comprising: a base station, two UAV relay nodes UAV1 and UAV2, a plurality of legitimate user nodes, potential ground eavesdropping nodes 1 and 2, and a secure communication performance optimization module; The UAV1 receives data from the base station and transmits the data to the UAV2; The UAV2 transmits the received data to multiple legal user nodes on the ground; The ground potential eavesdropping nodes 1 and 2 eavesdrop on the drone relay nodes UAV1 and UAV2 respectively; The safety communication performance optimization module is used to jointly optimize the drone trajectory parameters, channel bandwidth allocation parameters and transmission power allocation parameters to optimize the safety communication performance.

[0006] Preferably, the communication links from the base station to UAV1, UAV1 to UAV2, UAV2 to legitimate user nodes, and UAV relay nodes to ground potential eavesdropping nodes are all line-of-sight LoS channels.

[0007] Preferably, the UAV trajectory parameters meet the following conditions: The UAV is kept in constant flight at a height of H meters above the ground. The height H is determined based on key safety factors, including terrain clearance requirements and obstacle avoidance. The total flight time T is evenly divided into N equal time periods, i.e. , Represents the duration of each time period. The UAV trajectory sequence consists of N discrete position points in equal time periods. The drone needs to meet the following maneuverability constraints:

[0008] in, 、 They represent the positions of UAV1 and UAV2 in the nth time period, i.e. time slot n, respectively. 、 Represent the initial position and final position of UAV1 and UAV2 respectively, Indicates the maximum flight distance of the UAV in each time slot, Indicates the maximum flight speed of the drone; The distance between UAV1 and UAV2 needs to meet the anti-collision constraints:

[0009] in, represents the minimum Euclidean distance between two UAVs at any time slot t ∈ {1, 2, ..., N}.

[0010] Preferably, the secure communication system divides the total bandwidth B into several mutually orthogonal sub-channels based on frequency division multiplexing. During the transmission process of time slot n, the bandwidth is The sub-channel of Hz is allocated to the first hop transmission from the base station to UAV1, and the bandwidth is The sub-channel of Hz is allocated to the second hop transmission from UAV1 to UAV2, and the bandwidth is The sub-channel of Hz is allocated to the last hop transmission from UAV2 to the legitimate user. The sum of the bandwidths of each sub-channel does not exceed the total bandwidth B, and the bandwidth of each sub-channel is non-negative. Each sub-channel in the channel bandwidth allocation parameters must meet the channel bandwidth allocation constraints:

[0011] The unit of the total bandwidth B and the bandwidth of each sub-channel is Hz.

[0012] Preferably, the transmit power allocation parameters should satisfy the average power constraint and the peak power constraint:

[0013] in, 、 and are the average transmission power limits of the base station, drone relay 1, and drone relay 2, respectively. 、 and are the peak transmit powers of the base station, drone relay 1, and drone relay 2, respectively.

[0014] Preferably, drone relays 1 and 2 need to satisfy the information causality constraint. If the processing delay of drone relays 1 and 2 is 1 time slot, the expression of the information causality constraint is:

[0015] in, Indicates i The achievable communication rate between UAV1 and UAV2 during the time slot, Indicates that i The achievable communication rate between UAV2 and the legal user node during the time slot, Indicates i The achievable communication rate between the base station and UAV1 during the time slot; Under the constraints of information causality, system transmission must comply with the following timing restrictions: the base station is inactive from time slot N-1 to time slot N; UAV relay 1 prohibits data transmission in the initial and final time slots; UAV relay 2 remains silent from time slot 1 to time slot 2; therefore, the channel bandwidth allocation parameters and transmit power allocation parameters must also meet the following constraints:

[0016] in, 、 Respectively represent the transmission power of the base station and UAV1 in time slot N, represents the transmit power of UAV2 in time slot 1, 、 They represent the channel bandwidth of the base station and UAV1 in time slot N respectively, Indicates the channel bandwidth of UAV2 in time slot 1.

[0017] To achieve the purpose of the present invention, a second aspect provides a method for optimizing the secure communication performance of a UAV relay secure communication system, comprising constructing a joint optimization problem of channel bandwidth allocation parameters, transmit power allocation parameters, and UAV trajectory parameters, and maximizing the minimum end-to-end secure communication rate as the objective function of the joint optimization problem; The non-convex joint optimization problem is transformed by introducing slack variables. The transformed problem is then decomposed into the first and second subproblems using the block coordinate descent method. The first subproblem is to optimize the channel bandwidth allocation parameters and the transmit power allocation parameters with fixed UAV trajectory parameters, while the second subproblem is to optimize the UAV trajectory parameters with fixed channel bandwidth allocation parameters and transmit power allocation parameters. Use the alternating optimization algorithm to solve the first and second subproblems: Given the UAV trajectory parameters, substitute them into the first subproblem to obtain the solution to the channel bandwidth allocation parameters and transmit power allocation parameters in the first subproblem. Substitute the solution to the channel bandwidth allocation parameters and transmit power allocation parameters and the UAV trajectory parameters into the second subproblem to solve it. During the solution process, the subproblem is expanded by a first-order Taylor expansion using the continuous convex approximation method. The non-convex subproblem is converted into a solvable convex optimization problem and solved using the standard interior point method to obtain the objective function value. Determine whether the objective function value satisfies the objective function value constraint. If so, end the solution; otherwise, continue the iterative solution.

[0018] Preferably, the joint optimization problem needs to satisfy the following constraints: UAV maneuverability constraints:

[0019] in, 、 They represent the positions of UAV1 and UAV2 in the nth time period, i.e. time slot n, respectively. 、 Represent the initial position and final position of UAV1 and UAV2 respectively, Indicates the maximum flight distance of the UAV in each time slot, Indicates the maximum flight speed of the drone; The anti-collision constraints between UAV1 and UAV2 are:

[0020] in, represents the minimum Euclidean distance between two UAVs at any time slot t ∈ {1, 2, ..., N}; Channel bandwidth allocation constraints:

[0021] The unit of the total bandwidth B and the bandwidth of each sub-channel is Hz; Transmit power allocation constraints:

[0022] in, 、 and are the average transmission power limits of the base station, drone relay 1, and drone relay 2, respectively. 、 and are the peak transmit powers of the base station, drone relay 1, and drone relay 2, respectively. 、 Respectively represent the transmission power of the base station and UAV1 in time slot N, represents the transmit power of UAV2 in time slot 1, 、 They represent the channel bandwidth of the base station and UAV1 in time slot N respectively, represents the channel bandwidth of UAV2 in time slot 1; Information Causality Constraints:

[0023] in, Indicates i The achievable communication rate between UAV1 and UAV2 during the time slot, Indicates that i The achievable communication rate between UAV2 and the legal user node during the time slot, Indicates i The achievable communication rate between the base station and UAV1 during the time slot.

[0024] Preferably, the first sub-problem is:

[0025] in, 、 、 They represent the achievable secure communication rates between the base station and UAV1, between UAV1 and UAV2, and between UAV2 and legitimate user nodes; 、 、 、 They represent the channel power gains between UAV1 and UAV2, between UAV1 and potential ground monitoring node 1, between UAV2 and legitimate user node, and between UAV2 and potential ground monitoring node 2 respectively; and the first sub-problem also needs to satisfy the channel bandwidth allocation constraint and the transmission power allocation constraint.

[0026] Preferably, the second sub-problem is:

[0027] in, 、 、 、 They represent the positions of the base station, the legitimate user node, the ground potential monitoring node 1, and the ground potential monitoring node 2 respectively; and the second sub-problem also needs to meet the maneuverability constraints of the UAV.

[0028] Compared with the prior art, the present invention has the following beneficial effects: The secure communication system of the present invention uses dual UAV relays as data transmission intermediaries from base stations to users, greatly improving the service scope and performance of air-ground collaborative communication. At the same time, multiple eavesdroppers eavesdrop on channel information, which is closer to the real working environment. The channel bandwidth allocation, transmission power allocation and trajectory of the UAV relay are jointly optimized through the secure communication performance optimization module to improve the secure communication performance of the system, with more optimization freedom. The secure communication performance optimization method of the present invention transforms a non-convex joint optimization problem into a solvable convex optimization problem through methods such as relaxation variables, continuous convex approximation and block coordinate descent, reducing the complexity of optimization and effectively improving the secure communication performance of the air-ground collaborative UAV relay communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a schematic diagram of a drone relay secure communication system according to Example 1 of the present application; Figure 2 This is a flowchart of the steps of the secure communication performance optimization method according to Example 2 of the present application; Figure 3This is a flowchart of the alternating optimization algorithm of Example 2 of the present application; Figure 4 This is a schematic diagram of the trajectory of a drone over a period of time obtained using the method of the present invention and other methods in Example 3 of the present application; Figure 5 This is a graph showing the relationship between normalized bandwidth and time for drone relays 1 and 2 over a period of time in Example 3 of the present application; Figure 6 This is a graph showing the transmission power changes of drone relays 1 and 2 over a period of time in Example 3 of the present application; Figure 7 This is a graph showing how the achievable safe communication rate of the method of the present invention and other methods changes with the drone operation time T in Example 3 of the present application. DETAILED DESCRIPTION

[0030] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0031] Example 1 See also Figure 1 ,This embodiment 1 provides a UAV relay secure communication system, including: a base station, two UAV relay nodes UAV1 and UAV2, multiple legitimate user nodes, ground potential eavesdropping nodes 1 and 2, and a secure communication performance optimization module; The UAV1 receives data from the base station and transmits the data to the UAV2; The UAV2 transmits the received data to multiple legal user nodes on the ground; The ground potential eavesdropping nodes 1 and 2 eavesdrop on the drone relay nodes UAV1 and UAV2 respectively; The secure communication performance optimization module is used to jointly optimize the drone's trajectory parameters, channel bandwidth allocation parameters, and transmit power allocation parameters to optimize secure communication performance. The secure communication performance optimization module can be placed in a base station, integrated into a drone, or placed in an external device.

[0032] The UAV secure communication system is a multi-hop UAV secure communication system consisting of a base station (BS), dual UAV relay nodes UAV1 and UAV2, and multiple legitimate user nodes. There are also fixed-position potential eavesdropping nodes on the ground in the system.

[0033] Assume that the set of legal users is , in a three-dimensional Cartesian coordinate system, the fixed positions of the base station, user and eavesdropper are known to be 、 and The system has full-dimensional spatial perception capabilities, in which the three-dimensional coordinate information of the eavesdropper can be detected and tracked in real time through the optoelectronic sensing system carried by the drone.

[0034] Drone at a height above the ground H The altitude is the minimum safe flight altitude determined by comprehensive consideration of terrain clearance requirements, obstacle avoidance and other key safety factors. T Divide equally into N time periods of equal length, i.e. ,in, Indicates the duration of each time period. T The trajectory within can be approximated by the sequence Assume that the maximum flight speed of the UAV is , the maximum flight distance of the UAV in each time slot is The initial and final positions of the UAV are expressed as and (Unit: meter). The maneuverability constraint of the UAV can be expressed as

[0035] The anti-collision constraints of UAV relay 1 and UAV relay 2 can be expressed as

[0036] in, represents the minimum Euclidean distance between two UAVs at any time slot t ∈ {1, 2, ..., N}.

[0037] In order to avoid the possible co-frequency interference caused by the simultaneous transmission of the base station and the drone relay, the system adopts a resource allocation strategy based on frequency division multiplexing. B (Hz) is divided into M+1 mutually orthogonal sub-channels. During the transmission of time slot n: the bandwidth is The Hz subchannel is dedicated to the first hop transmission from the base station to the drone relay 1; the bandwidth is The sub-channel of Hz is allocated to the second hop transmission from UAV relay 1 to UAV relay 2; the bandwidth is The Hz sub-channel is allocated to the last hop transmission from UAV relay 2 to the end user. The bandwidth allocation of each channel must meet the constraints

[0038] In the time slot n middle, 、 and They represent the transmit power of the base station, drone relay 1, and drone relay 2, respectively. These powers must satisfy both the average power and peak power constraints:

[0039] in, 、 and are the average transmission power limits of the base station, drone relay 1, and drone relay 2, respectively, and 、 and are their peak transmit powers respectively.

[0040] Base station-drone relay 1 transmission model: In the base station to drone relay 1 scenario, the base station and drone relay 1 act as legal transmitters and receivers, respectively. The legal link from the base station to the drone is a line-of-sight (LoS) channel. In the nth time slot, the power gain of the LoS channel from the base station to the drone follows the free space path loss model and is expressed as:

[0041] represents the channel power gain at reference distance = 1m, which depends on the carrier frequency and the antenna gains of the transmitter and receiver; Indicates the distance from the base station to drone relay 1 within the time slot.

[0042] Assuming the total bandwidth of the system is B, in Hertz (Hz), the achievable rate between the base station and drone relay 1 can be expressed as follows:

[0043] in, , (Unit: W / Hz) represents the power spectral density of additive white Gaussian noise (AWGN) at the receiver.

[0044] In a total of N time slots, the achievable safe rate (in bps / Hz) of the communication channel from the base station to the drone relay 1 is

[0045] Drone relay 1-drone relay 2 transmission model: In the UAV1-UAV2 communication scenario, both the legitimate communication link from UAV Relay 1 to UAV Relay 2 and the eavesdropping link from UAV Relay 2 to Eavesdropper 2 (potential ground eavesdropping node 2) are line-of-sight (LoS) channels. The mathematical model for the channel gain in the nth time interval (time slot) is as follows:

[0046] Similarly, the channel power gain of the line-of-sight (LoS) eavesdropping link from UAV 1 to eavesdropper 1 in the nth time interval is expressed as

[0047] The achievable rate between drone relay 1 and drone relay 2 is:

[0048] The calculation formula for the achievable rate (bps / Hz) of UAV relay 1-eavesdropper 1 (ground potential eavesdropping node 1) is:

[0049] According to equations (10) and (11), the achievable safe rate of the UAV-UAV relay link (UAV1-UAV2) in N consecutive time slots in bps / Hz is defined as

[0050] Drone Relay 2-User Transmission Model: In the UAV-relay two-to-user communication framework, the UAV acts as the transmitting node, and the ground user acts as the designated receiver. The wireless propagation environment between the UAV and the ground user maintains line-of-sight (LoS) characteristics. In the nth time interval, the corresponding channel gain of this legitimate link can be expressed as follows:

[0051] Similarly, at the nth time slot, the channel power gain of the line-of-sight (LoS) link from UAV relay 2 to eavesdropper 2 is calculated as

[0052] The achievable link rate (bps / Hz) between UAV relay 2 and the ground user can be calculated using the Shannon capacity expression:

[0053] Similarly, the achievable rates (in bps / Hz) of UAV Relay 2 and Eavesdropper 2 are calculated as follows:

[0054] According to equations (15) and (16), the achievable safe rate (in bps / Hz) of the drone-user link in N consecutive time slots is defined as

[0055] Since UAV relay 1 and UAV relay 2 can only forward received data, assuming that their processing delay is one time slot, the information causality constraints that UAV relays should satisfy are as follows:

[0056] Under the constraints of information causality, system transmission must comply with the following timing restrictions: the base station is inactive from time slot N-1 to time slot N; drone relay 1 prohibits data transmission in the initial and final time slots; drone relay 2 remains silent from time slot 1 to time slot 2. Therefore, the channel bandwidth allocation and transmit power allocation of the base station and drone relay must also meet the following constraints:

[0057] Example 2 This embodiment 2 is based on embodiment 1 and provides a method for optimizing the security communication performance of a drone relay security communication system. Figure 2 As shown, a drone relay secure communication system applied in Example 1 includes: S1: Construct a joint optimization problem for channel bandwidth allocation parameters, transmit power allocation parameters, and UAV trajectory parameters, and use maximizing the minimum end-to-end secure communication rate as the objective function of the joint optimization problem; S2: Slack variables are introduced to transform the non-convex joint optimization problem. The transformed problem is decomposed into the first and second subproblems using the block coordinate descent method. The first subproblem is to optimize the channel bandwidth allocation parameters and transmit power allocation parameters with fixed UAV trajectory parameters, and the second subproblem is to optimize the UAV trajectory parameters with fixed channel bandwidth allocation parameters and transmit power allocation parameters. S3: Use an alternating optimization algorithm to solve the first and second subproblems. Given the UAV trajectory parameters, substitute them into the first subproblem to obtain the solution for the channel bandwidth allocation parameters and transmit power allocation parameters in the first subproblem. Substitute the solutions for the channel bandwidth allocation parameters and transmit power allocation parameters and the UAV trajectory parameters into the second subproblem to solve it. During the solution process, use the continuous convex approximation method to perform a first-order Taylor expansion on the subproblems, transform the non-convex subproblems into solvable convex optimization problems, and solve them using the standard interior point method to obtain the objective function value. S4: Determine whether the objective function value satisfies the objective function value constraint condition. If so, the solution is terminated; otherwise, the solution is continued in a loop.

[0058] This embodiment 2 is dedicated to building a secure communication performance optimization framework for the UAV secure communication system. The core goal is to maximize the system's secure communication performance through multi-dimensional joint optimization. Specifically, by jointly optimizing the channel bandwidth allocation , transmit power allocation and the UAV trajectory parameter Q, maximizing the minimum end-to-end safe communication rate (achievable safe communication rate). The optimization variables are subject to the UAV maneuverability and collision avoidance constraints in Equations (1) and (2), the bandwidth allocation and transmit power allocation constraints in Equations (3), (4), and (20), and the information causality constraints in Equations (18) and (19). The corresponding joint optimization problem is modeled as follows:

[0059] The optimization problem is difficult to solve due to the presence of 0-1 correlated variables (A and P) and the high degree of coupling and non-convexity with the UAV trajectory variable (Q). Therefore, it is necessary to transform the optimization problem into a convex one by introducing slack variables, block coordinate descent, and continuous convex approximation, and then solve it using a standard interior point method.

[0060] Problem (P1) is essentially a non-convex optimization problem, which is due to the non-convex characteristics of constraints (2), (18), (19), (21b), and (21c). The optimization variables in constraints (18), (19), (21b), and (21c) are closely related to each other, which makes the solution of problem (P1) more difficult. In order to solve it, this embodiment 2 introduces slack variables , by reasonably relaxing constraints (18), (19), (21b) and (21c), the problem can be transformed into (P2):

[0061] After processing, formula (P2) has changed in form, but it is still a very challenging non-convex problem. This is mainly due to two key factors: first, the non-convex characteristics in the constraint subsets (2), (21b-c), and (22a, c-d) are difficult to eliminate; second, there are complex coupled parameter dependencies between the optimization variables, and the variables are intertwined and constrained by each other, further exacerbating the complexity of the problem. Therefore, the optimization problem (P2) needs to be decomposed into two sub-problems: the first sub-problem is to simultaneously optimize the parameters {A, P} under a fixed trajectory Q, while the second sub-problem is to optimize the trajectory Q with a fixed channel bandwidth allocation A and transmit power allocation P.

[0062] The first sub-problem: Fix the UAV trajectory parameter Q and optimize the channel bandwidth allocation parameter A and the transmission power allocation parameter P: Given the predetermined UAV trajectory parameter Q, first determine

[0063] So, 、 、 、 and Can be written as

[0064] Substituting (24) into the constraints (21), (22a), (22c) and (22d) of problem (P2), the first subproblem can be expressed as:

[0065] Since (25a), (25b), (25d), and (25e) are not convex functions, a first-order Taylor expansion is performed on them. The remaining constraints are all linear, while constraint (25c) is a convex function with respect to A and P. Therefore, the dual interior point method can be used to efficiently solve this problem.

[0066] The second sub-problem: Fixed channel bandwidth allocation parameter A and transmit power allocation parameter P, optimize the UAV trajectory parameter Q: Given the bandwidth allocation A and the transmit power P, let , and ,So , , , ,and can be re-expressed as follows:

[0067] Substituting Equation (26) into the constraints (21), (22a), (22c) and (22d) of problem (P2), the second subproblem can be expressed as follows:

[0068] Solve (27g) and in order to get the lower limit, change the variable and , perform first-order Taylor expansion, considering the variable for and The convexity of is expressed as follows:

[0069] Since (27a) and (27d) are non-convex, we can perform first-order Taylor expansion on them; (27b) and (27c) are non-convex, so we can perform first-order Taylor expansion on them. and Do the relaxation. (27e) and (27f) are respectively and The remaining constraints are all linear. Therefore, the second subproblem (P4) is now a convex programming problem that can be solved efficiently using the standard interior point algorithm.

[0070] like Figure 3 As shown, the steps of the alternating optimization algorithm in step S4 are as follows: (1) Initialize the trajectory of drone relay 1 , UAV relay 1 trajectory , the objective function , the number of loop iterations l0=0, the threshold .

[0071] (2) Use the given and , solve the problem (P3), and the solution is expressed as , , , , , , go to step (3).

[0072] (3) Use the given solution , , , , , ,as well as and , to solve the problem (P4) and obtain and Go to step (4).

[0073] (4) Obtain the end-to-end safety rate of the entire system, i.e., the objective function value , determine the objective function value constraints Is it satisfied? If so, end the loop iteration solution. Otherwise, , repeat step (2).

[0074] Example 3 This embodiment 3 is based on embodiment 1 and embodiment 2, and adopts the secure communication system of the present invention to conduct an experimental comparison between the secure communication performance optimization method of the present invention and other methods.

[0075] like Figure 4 As shown, Figure 4Figure 2 shows schematic diagrams of drone flight trajectories obtained using different algorithms. It can be seen that when the flight duration T is between 30 and 47 seconds, the trajectories generated by the APQ-UAV algorithm (i.e., the secure communication performance optimization method of the present invention) and the baseline Q-UAV algorithm differ significantly. In contrast, in the baseline algorithm, drone relay 1 primarily flies near the base station, while drone relay 2 flies near the initial trajectory, farther away from the ground user. This inevitably reduces communication efficiency. This difference is caused by the fact that during the transmission of base station signals received by drone 1 to drone 2, drone 1 primarily transmits information to drone 2. The increasing distance between drone 1 and eavesdropper 1 brings the two drones very close. At t = 54 seconds, drone 2 primarily communicates with the user, thus remaining very close to the user and far away from drone 1.

[0076] Under the conditions of T = 60s and P = 0.03 W, Figure 5 and Figure 6 The results of spectrum resource allocation and RF power allocation obtained through collaborative optimization of bandwidth, power, and trajectory are shown, respectively. In the previous figure, we see the channel bandwidth (normalized relative to the total bandwidth B) of three communication links as a function of time t. These links include the connection from the base station (BS) to UAV Relay 1, the relay communication from UAV Relay 1 to UAV Relay 2, and the final transmission path from UAV Relay 2 to the ground user. Figure 5 The data shows that from t = 0s to t = 17s, UAV Relay 1 and UAV Relay 2 were approaching the base station. During this period, both the spectrum allocation and power allocation to the base station and UAV Relay 1 were positive. The data demonstrates that power changes with the relay's movement during temporal dynamics. Between t = 18s and t = 46s, the power allocated to the base station decreased continuously, ultimately reaching zero. Meanwhile, the power allocated to UAV Relay 1, used to transmit data to UAV Relay 2, increased, while the bandwidth remained constant. During this operational phase, the collaborative optimization framework prioritized improving the backhaul between UAVs, increasing the bandwidth of the UAV-to-UAV 2 relay channel. From t = 47s to t = 53s, the UAV relays were in close proximity, with UAV Relay 2 also near the user. During this period, both UAV relays were allocated high power. The bandwidth of UAV Relay 1 decreased, while the bandwidth of the second UAV Relay increased, indicating that both UAV relays were transmitting data, as well as transmitting data from UAV Relay 2 to the user. From t=54s to t=60s, UAV relay 2 approaches the user. During this period, the power of UAV relay 1 is zero, and all power is allocated to UAV relay 2. The bandwidth of UAV relay 2 increases, indicating that only UAV relay 2 transmits data to the user during this period.

[0077] Figure 5The graph shows how the achievable safe communication rate for the proposed method and other methods varies with the drone's operating time T. As can be seen from the graph, the proposed algorithm consistently achieves an advantage in terms of the highest safe communication rate. This is due to more flexible resource allocation and higher data transmission efficiency.

[0078] In summary, the joint optimization scheme proposed in the present invention effectively improves the end-to-end secure communication rate from the base station to the ground user by balancing the data transmission of the multi-UAV relay multi-hop network.

[0079] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A drone relay secure communication system, characterized in that: include: Base station, two UAV relay nodes UAV1 and UAV2, multiple legitimate user nodes, ground potential eavesdropping nodes 1 and 2, and a secure communication performance optimization module; The UAV1 receives data from the base station and transmits the data to the UAV2; The UAV2 transmits the received data to multiple legal user nodes on the ground; The ground potential eavesdropping nodes 1 and 2 eavesdrop on the drone relay nodes UAV1 and UAV2 respectively; The safety communication performance optimization module is used to jointly optimize the drone trajectory parameters, channel bandwidth allocation parameters and transmission power allocation parameters to optimize the safety communication performance.

2. The secure communication system according to claim 1, wherein: The communication links from the base station to UAV1, UAV1 to UAV2, UAV2 to legitimate user nodes, and UAV relay nodes to potential ground eavesdropping nodes are all line-of-sight LoS channels.

3. The secure communication system according to claim 1, wherein: The UAV trajectory parameters meet the following conditions: The UAV is kept in constant flight at a height of H meters above the ground. The height H is determined based on key safety factors, including terrain clearance requirements and obstacle avoidance. The total flight time T is evenly divided into N equal time periods, i.e. , Represents the duration of each time period. The UAV trajectory sequence consists of N discrete position points in equal time periods. The drone needs to meet the following maneuverability constraints: in, 、 They represent the positions of UAV1 and UAV2 in the nth time period, i.e. time slot n, respectively. 、 Represent the initial position and final position of UAV1 and UAV2 respectively, Indicates the maximum flight distance of the UAV in each time slot, Indicates the maximum flight speed of the drone; The distance between UAV1 and UAV2 needs to meet the anti-collision constraints: in, represents the minimum Euclidean distance between two UAVs at any time slot t ∈ {1, 2, ..., N}.

4. The secure communication system according to claim 1, wherein: The secure communication system divides the total bandwidth B into several mutually orthogonal sub-channels based on frequency division multiplexing. During the transmission process of time slot n, the bandwidth is The sub-channel of Hz is allocated to the first hop transmission from the base station to UAV1, and the bandwidth is The sub-channel of Hz is allocated to the second hop transmission from UAV1 to UAV2, and the bandwidth is The sub-channel of Hz is allocated to the last hop transmission from UAV2 to the legitimate user. The sum of the bandwidths of each sub-channel does not exceed the total bandwidth B, and the bandwidth of each sub-channel is non-negative. Each sub-channel in the channel bandwidth allocation parameters must meet the channel bandwidth allocation constraints: The unit of the total bandwidth B and the bandwidth of each sub-channel is Hz.

5. The secure communication system according to claim 1, wherein: The transmit power allocation parameters should meet the average power constraint and peak power constraint: in, 、 and are the average transmission power limits of the base station, drone relay 1, and drone relay 2, respectively. 、 and are the peak transmit powers of the base station, drone relay 1, and drone relay 2, respectively.

6. The secure communication system according to claim 1, wherein: UAV relays 1 and 2 need to satisfy the information causality constraint. If the processing delay of UAV relays 1 and 2 is 1 time slot, the expression of the information causality constraint is: in, Indicates i The achievable communication rate between UAV1 and UAV2 during the time slot, Indicates that i The achievable communication rate between UAV2 and the legal user node during the time slot, Indicates i The achievable communication rate between the base station and UAV1 during the time slot; Under the constraints of information causality, system transmission must comply with the following timing restrictions: the base station is inactive from time slot N-1 to time slot N; UAV relay 1 prohibits data transmission in the initial and final time slots; UAV relay 2 remains silent from time slot 1 to time slot 2; therefore, the channel bandwidth allocation parameters and transmit power allocation parameters must also meet the following constraints: in, 、 Respectively represent the transmission power of the base station and UAV1 in time slot N, represents the transmit power of UAV2 in time slot 1, 、 They represent the channel bandwidth of the base station and UAV1 in time slot N respectively, Indicates the channel bandwidth of UAV2 in time slot 1.

7. A method for optimizing the security communication performance of a drone relay security communication system, applied to a drone relay security communication system according to any one of claims 1 to 6, characterized in that: The secure communication performance optimization method comprises: A joint optimization problem of channel bandwidth allocation parameters, transmit power allocation parameters, and UAV trajectory parameters is constructed, with maximizing the minimum end-to-end secure communication rate as the objective function of the joint optimization problem. The non-convex joint optimization problem is transformed by introducing slack variables. The transformed problem is then decomposed into the first and second subproblems using the block coordinate descent method. The first subproblem is to optimize the channel bandwidth allocation parameters and the transmit power allocation parameters with fixed UAV trajectory parameters, while the second subproblem is to optimize the UAV trajectory parameters with fixed channel bandwidth allocation parameters and transmit power allocation parameters. The first and second subproblems are solved using an alternating optimization algorithm. Given the UAV trajectory parameters, they are substituted into the first subproblem to obtain the solution for the channel bandwidth allocation parameters and transmit power allocation parameters in the first subproblem. The solutions for the channel bandwidth allocation parameters and transmit power allocation parameters and the UAV trajectory parameters are then substituted into the second subproblem to solve it. During the solution process, a continuous convex approximation method is used to perform a first-order Taylor expansion on the subproblems. The non-convex subproblems are converted into solvable convex optimization problems and solved using a standard interior point method to obtain the objective function value. Determine whether the objective function value satisfies the objective function value constraint. If so, end the solution; otherwise, continue the iterative solution.

8. The optimization method according to claim 7, characterized in that: The joint optimization problem needs to satisfy the following constraints: UAV maneuverability constraints: in, 、 They represent the positions of UAV1 and UAV2 in the nth time period, i.e. time slot n, respectively. 、 Represent the initial position and final position of UAV1 and UAV2 respectively, Indicates the maximum flight distance of the UAV in each time slot, Indicates the maximum flight speed of the drone; The anti-collision constraints between UAV1 and UAV2 are: in, represents the minimum Euclidean distance between two UAVs at any time slot t ∈ {1, 2, ..., N}; Channel bandwidth allocation constraints: The unit of the total bandwidth B and the bandwidth of each sub-channel is Hz; Transmit power allocation constraints: Among them, 、 and are the average transmission power limits of the base station, drone relay 1, and drone relay 2, respectively. 、 and are the peak transmit powers of the base station, drone relay 1, and drone relay 2, respectively. 、 Respectively represent the transmission power of the base station and UAV1 in time slot N, represents the transmit power of UAV2 in time slot 1, 、 They represent the channel bandwidth of the base station and UAV1 in time slot N respectively, represents the channel bandwidth of UAV2 in time slot 1; Information Causality Constraints: in, Indicates i The achievable communication rate between UAV1 and UAV2 during the time slot, Indicates that i The achievable communication rate between UAV2 and the legal user node during the time slot, Indicates i The achievable communication rate between the base station and UAV1 during the time slot.

9. The optimization method according to claim 8, characterized in that: The first sub-problem is: in, 、 、 They represent the achievable secure communication rates between the base station and UAV1, between UAV1 and UAV2, and between UAV2 and legitimate user nodes; 、 、 、 They represent the LoS channel power gains between UAV1 and UAV2, between UAV1 and potential ground monitoring node 1, between UAV2 and the legitimate user node, and between UAV2 and potential ground monitoring node 2, respectively. Furthermore, the first sub-problem also needs to satisfy the channel bandwidth allocation constraints and the transmit power allocation constraints.

10. The optimization method according to claim 8, characterized in that: The second sub-problem is: in, 、 、 、 They represent the coordinates of the base station, the legitimate user node, the ground potential monitoring node 1, and the ground potential monitoring node 2 respectively; and the second sub-problem also needs to satisfy the maneuverability constraint of the UAV.

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