Methods and systems for designing drone trajectories under conditions of eavesdropping collusion

Abstract

This invention discloses a method and system for designing UAV trajectories under conditions of eavesdropping collusion. Addressing the issue of multiple potential eavesdropping collusions with uncertain locations in cognitive radio networks, the method optimizes UAV flight trajectories with the objective of maximizing the system's average secure transmission rate. First, an optimization problem fitting the scenario is constructed. Then, based on the Block Coordinate Descent (BCD) method, the original optimization problem is decoupled into subproblems concerning UAV power and UAV flight trajectory. For each non-convex subproblem, a convex approximation is performed. Finally, an iterative algorithm is used to solve the problem, finding the global suboptimal solution and the optimal UAV flight trajectory.

Description

Technical Field

[0001] This invention is mainly applied to UAV-assisted wireless communication scenarios. Specifically, it combines the advantages of shared spectrum resources of cognitive radio networks with the flexible and maneuverable characteristics of UAVs to construct a UAV-assisted cognitive wireless communication system. Through theoretical derivation and simulation verification, the flight trajectory of the cognitive UAV is designed to maximize the transmission rate of information security in the cognitive network. Background Technology

[0002] With the increasing level of information technology, high speed and large data volume have become basic requirements for modern communication. Traditional land communication can hardly meet the needs of social information transmission. As a core node of future mobile communication networks, unmanned aerial vehicles (UAVs) have a wide range of applications and prospects, and are therefore attracting attention from all walks of life. For example, the literature [Chen Xinying, Sheng Min, Li Bo, Zhao Nan. A review of UAV communication for 6G[J]. Journal of Electronics and Information Technology, 2022, 44(0):1-9.] points out that with the high mobility, flexible networking methods and low cost of UAVs, UAVs provide strong support for building an all-round integrated communication network of air, land, sea and air. According to the different roles played by UAVs in wireless communication networks, UAV-assisted wireless communication systems are classified into base station UAV-assisted communication systems, relay UAV-assisted communication systems, and UAV-assisted communication systems for airborne users. For example, the typical base station-based UAV-assisted communication system proposed in the literature [Amine Bejaoui, Ki-Hong Park, Mohamed-Slim Alouini. A QoS-Oriented trajectory optimization in swarming Unmanned-Aerial-Vehicles communications[J].IEEE Wireless Communications Letters,2020,9(6):791-794.] maximizes the system transmission rate by jointly optimizing the user fair scheduling coefficient, UAV transmission power and flight trajectory. The literature [Chao Wang, Xinying Chen, Jianping An, Zehui Xiong, Chengwen Xing, Nan Zhao, Dusit Niyato. Covert communication assisted by UAV-IRS[J].IEEE Transactions on Communications, 2022, doi:10.1109 / TCOMM.2022.3220903 1-1.] addresses the issue of imperfect location and status information for Willie by utilizing a UAV carrying an IRS as a communication node. By jointly optimizing the phase shift of the IRS, the transmission power of the ground base station, and the flight trajectory of the UAV, the covert transmission rate of the communication system is maximized.The literature [Weiran Luo, Yanyan Shen, Bo Yang, Shuqiang Wang, Xinping Guan. Joint 3-D trajectory and resource optimization in Multi-UAV-Enabled IoT networks with wireless power transfer[J].IEEE Internet of Things Journal, 2021, 8(10): 7833-7848.] studies the maximum and minimum data acquisition of aerial user UAVs in the design of flight trajectories for the Internet of Things.

[0003] The aforementioned studies lacked research on the secure transmission of information during wireless communication, particularly neglecting the threat posed by eavesdropping at uncertain locations. This invention, based on information theory and cognitive radio theory, investigates the impact of multiple active eavesdropping collusion conditions on the trajectory of a cognitive UAV. In the considered wireless communication system, active eavesdropping colludes, threatening the security of cognitive network information. By constructing a wireless communication problem that maximizes the cognitive network, and utilizing convex optimization theory, the transmission power and flight trajectory of the cognitive UAV are optimized, thereby maximizing the system's average security rate. Summary of the Invention

[0004] To address the threat to secure wireless communication network information transmission posed by multiple, uncertainly located eavesdropping conspiracies within cognitive networks, this invention proposes a joint optimization method based on iterative principles. This method jointly optimizes the flight trajectory and power of the UAV (Unmanned Aerial Vehicle) to maximize the system's security rate.

[0005] The technical solution adopted in this invention is as follows: a method for designing drone trajectories under conditions of eavesdropping collusion, comprising the following steps:

[0006] (1) Construct a communication system model: Set up UAV S as the airborne base station of the cognitive network, and initialize the flight trajectory of S so that it serves K cognitive users on the ground. While the cognitive network is working, consider the existence of M eavesdropping users who cooperate with each other, share information, and continuously interfere with and obtain the confidential information of the cognitive network.

[0007] (2) To maximize the system's secure transmission rate, an optimization mathematical model is constructed with constraints on the UAV's transmission power and flight trajectory.

[0008] (3) The mathematical model described in step (2) is decoupled into subproblems based on the BCD method, which are only related to the power of the UAV and the flight trajectory of the UAV. For each non-convex subproblem, a convex approximation fitting method is used to convert it into a convex problem for solution.

[0009] (4) Use iterative algorithms to solve for the UAV's transmit power and flight trajectory.

[0010] Based on the above methods, the present invention also provides a drone trajectory system under conditions of eavesdropping conspiracy, comprising:

[0011] Configure drone S as an aerial base station for the cognitive network and initialize its flight trajectory to serve K cognitive users and R primary users U on the ground. r (r = 1, 2...R), K cognitive network users D k (k = 1, 2, ..., K), and M eavesdropping users E m (m=1,2...M), M eavesdropping users cooperate with each other, share information, and continuously interfere with and obtain confidential information of the cognitive network;

[0012] The mathematical model module is used to construct an optimization mathematical model with constraints such as UAV transmission power and UAV flight trajectory, with the goal of maximizing the system's safe transmission rate.

[0013] The mathematical model decoupling module is used to decouple the mathematical model based on the BCD method into subproblems that only concern the UAV power and the UAV flight trajectory. For each non-convex subproblem, a convex approximation fitting method is used to convert it into a convex problem for solution.

[0014] The module for solving UAV transmission power and flight trajectory is used to solve for the UAV transmission power and flight trajectory using an iterative algorithm.

[0015] This invention addresses the reality of multiple unpredictable eavesdropping entities colluding in cognitive radio networks. With the goal of maximizing the system's secure transmission rate, it dynamically plans and designs the transmission power and flight trajectory of UAVs (Underground Virtual Air Vehicles) in the airspace of cognitive networks.

[0016] Step one: Based on the fundamental theories of wireless communication and physical layer security, construct a model of a drone-assisted wireless communication system. This system model contains M undetermined eavesdropping devices, E. m (m=1,2...M), the robustness of the problem implementation needs to be considered, that is, the problem is equivalently transformed into the worst-case dual UAV-assisted communication problem.

[0017] Steps two and three refine the mathematical theoretical derivation and problem-solving analysis in the model building process. Addressing the non-convexity and high coupling of the optimization problem corresponding to the system model, an approximate fitting method based on the Block Coordinate Descent (BCD) algorithm and continuous convex approximation is used to decouple and transform the original optimization problem. (For details, please refer to the specific implementation of this invention).

[0018] Step four: Based on the idea of ​​iteration, the sub-problems obtained in steps two and three are iterated in a loop to make the system's secure transmission rate continuously approach a fixed value. This fixed value is the maximum system secure transmission rate required by this invention. Attached Figure Description

[0019] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration:

[0020] Figure 1 This is a communication system model of the present invention;

[0021] Figure 2 This is a flowchart illustrating the operation of the present invention.

[0022] Figure 3 The optimal flight path map for the drone;

[0023] Figure 4 This is the convergence graph of the algorithm's iterations. Detailed Implementation

[0024] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] (1) Constructing a communication system model: Set up UAV S as the airborne base station of the cognitive network, and initialize the flight trajectory of S to serve K cognitive users on the ground. While the cognitive network is working, consider the existence of M eavesdropping users who cooperate with each other, share information, and continuously interfere with and obtain the confidential information of the cognitive network. Figure 1 This is the system model diagram of the entire solution, which includes R main users (PUs). r (r = 1, 2...R), K cognitive network users CU k (k = 1, 2, ..., K), and M eavesdropping users E m (m=1,2...M), here we use and These represent the horizontal coordinates of the main network user, the cognitive network user, and the eavesdropping node, respectively. The flight altitude of the drone S is denoted by H. (Definition) This represents the coordinates of the center of the circle where the m-th eavesdropping user is located. This represents the radius of the area where the m-th eavesdropping node E is located, as obtained by S. The channel between S and the ground user is defined as legitimate, and the eavesdropping node E... m The associated channel is an eavesdropping link.

[0026] (2) To maximize the average safe transmission rate of the system, an optimization mathematical model is constructed with constraints on the UAV transmission power and the UAV flight trajectory.

[0027] Among them, the UAV S, as an airborne base station of the cognitive network, uses the same frequency band resources authorized by the cognitive main network to send confidential information to the ground cognitive user CU.

[0028] The signal-to-noise ratio and transmission rate of a legitimate receiver are as follows:

[0029]

[0030]

[0031] In the formula, P S (n) indicates that base station S serves CU. k The transmission power, This indicates that base station S and cognitive user CU k The channel gain coefficient, CU k With E m Channel gain between, σ 2 This represents the base station to user k-additive white Gaussian noise. This indicates the transmit power of the user being eavesdropped on. This represents the expectation of a random variable, where β represents the channel gain per unit distance. This indicates a large-scale decline between ground-based cognitive users and eavesdropping users.

[0032] The signal-to-noise ratio of the eavesdropping device is expressed as:

[0033]

[0034] This indicates the channel gain between the drone and the eavesdropping user. This represents the transmission power of the j-th eavesdropping user. This indicates the channel gain between eavesdropping users.

[0035] Considering the scenario of collusion among eavesdropping users, the eavesdropping users can eliminate interference caused by each other between eavesdropping nodes. The transmission rate of the eavesdropping end is expressed as:

[0036]

[0037] In the formula, P S (n) indicates that base station S serves CU. k The transmission power, This indicates that base station S and eavesdropping E are connected. m Channel gain, σ 2 This represents the base station to user k-additive white Gaussian noise. This indicates the signal-to-noise ratio of the eavesdropping device.

[0038] The secure transmission rate for cognitive radio networks is:

[0039]

[0040] R k (n) represents the reachability rate of the kth cognitive user.

[0041] To ensure that normal communication in the cognitive network does not interfere with the main network, a user PU is configured in the main network. r Tolerance threshold r constraint:

[0042]

[0043]

[0044] P represents the channel gain between the drone S and the primary user. E This indicates the transmit power of the user being eavesdropped on. The channel gain between the eavesdropping user and the master user is represented by S, and N represents the total flight time slots of the UAV S.

[0045] Construct the complete optimization problem:

[0046]

[0047] stC1:

[0048] C2:

[0049] C3:

[0050] C4:

[0051] In the above formula, q represents the maximum instantaneous power of the UAV J during flight. S (0) q represents the initial flight position of the drone S. S (n) δ represents the flight position of the UAV at the end of time S. t The length of each flight time slot for the drone is defined. The maximum flight speed of the drone at any given time is defined. P represents the drone's transmission power, and Q represents the drone's flight trajectory. q represents the final flight position of the drone. S(n-1) represents the flight position of the UAV in the (n-1)th time slot. Among them, C1 is the change in instantaneous power during the flight of the UAV, C2 ensures the normal operation of the UAV's cognitive network, and C3 to C4 limit the initial and final positions and the maximum flight distance of the UAV.

[0052] (3) The UAV trajectory design method based on cognitive radio network under the condition of eavesdropping collusion proposed in this invention is a multivariable coupled non-convex problem. For the constructed multivariable coupled original optimization problem, it is decoupled into subproblems only concerning UAV power and UAV flight trajectory based on the BlockCoordinate Descent (BCD) method. For each non-convex subproblem, a convex approximation fitting method is used to transform it into a convex problem for solution.

[0053] The solution to the problem of this invention will be based on the following steps, the specific steps of which are as follows:

[0054] (3.1) Based on the Block Coordinate Descent (BCD) algorithm, The problem is decoupled to obtain subproblems. as follows:

[0055]

[0056]

[0057]

[0058] in β0 represents the channel gain per unit distance.

[0059] Based on the conditions of the Lagrange equations, the KKT equations are derived:

[0060]

[0061] v * All three represent Lagrange multipliers.

[0062] To obtain optimal power:

[0063]

[0064] in:

[0065] Obviously The value depends on v (where v represents the Lagrange multiplier), and the value of v can be obtained through binary search. Obtain.

[0066] (3.2) Problem This is a subproblem of designing the flight trajectory of a UAV (drone S). The optimization variables include... The optimization problem can be represented as:

[0067]

[0068]

[0069]

[0070]

[0071] in: f2=ρ0P S (n), Indicates drones and eavesdropping user E m The distance between them E represents m The estimation error of the region is denoted by ρ0, which represents the normalized signal-to-noise ratio.

[0072] It is obvious It is a non-convex expression, which can be obtained by introducing new slack variables and using the SCA method. The lower bound.

[0073]

[0074] in, To represent the secure transmission rate, the slack variable must satisfy the following:

[0075]

[0076]

[0077] at this time It is a non-convex constraint, which can be rewritten using SCA to obtain:

[0078]

[0079] Indicates that user E was being eavesdropped on m Horizontal position, Γ r This represents the tolerance threshold for users on the main network.

[0080] The same method can be used to obtain the following for cognitive radio underlay constraints:

[0081]

[0082] In the above formula These are new slack variables that need to satisfy:

[0083]

[0084] Based on the above transformations, a new optimization problem can be obtained, expressed as:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] (C3),(C4)

[0091] at this time This is a standard convex optimization problem, which can be solved using the interior point method.

[0092] (4) Use iterative algorithms to solve for the UAV's transmit power and flight trajectory.

[0093] The iterative algorithm flow is designed to connect the above convex optimization problems in series. The specific steps are shown in Table 1.

[0094] Table 1

[0095]

[0096] Figure 1 This is a communication system model of the present invention;

[0097] Figure 2 This is a flowchart of the iterative algorithm proposed in this invention for solving the optimization problem. Its detailed process corresponds to the specific steps for solving the aforementioned optimization problem.

[0098] Figure 3 This invention is based on simulation verification of the proposed scheme, through... Figure 2 The process ultimately yields the following result: Figure 3 The optimal flight trajectory for the drone is shown.

[0099] Figure 4 Simulations verified the convergence of the proposed solution. The simulation results demonstrate the effectiveness of the proposed algorithm.

Claims

1. A method for designing trajectories of unmanned aerial vehicles under eavesdropping collusion conditions, characterized in that, Includes the following steps: (1) Construct a communication system model: set unmanned aerial vehicle as a cognitive network of air base station, while the initialization flight trajectory, so that it serves the ground existence cognitive user, based on cognitive network work while considering the existence eavesdropping user mutual cooperation, mutual information sharing, constantly interfere with and obtain the secret information of cognitive network; (2) To maximize the system's secure transmission rate, an optimization mathematical model is constructed, constrained by the UAV's transmission power and flight trajectory; the mathematical model is as follows: In the formula, Indicates drone The maximum instantaneous power during flight, , Indicates drone Initial flight position , Indicates drone Flight position at the last moment The length of each flight time slot for the drone is defined. The maximum flight speed of the drone at any given time is defined. Indicates the secure transmission rate of cognitive radio networks. express Serving cognitive users The transmission power, Indicates main network user The tolerance threshold, where P represents the drone's transmit power and Q represents the drone's flight trajectory. This represents the flight position of the drone in the (n-1)th time slot, where N represents the number of time slots the drone flies. This indicates the interference caused by the cognitive network to the main network; in, This refers to the instantaneous power change during the drone's flight. This ensures the normal operation of the drone cognitive network. This limits the initial and final positions and maximum flight distance of the drone; (3) The mathematical model described in step (2) is decoupled into subproblems based on the BCD method, which are only related to the power of the UAV and the flight trajectory of the UAV. For each non-convex subproblem, a convex approximation fitting method is used to convert it into a convex problem for solution. (4) Use iterative algorithms to solve for the UAV's transmit power and flight trajectory.

2. The method for designing drone trajectories under eavesdropping conspiracy conditions according to claim 1, characterized in that: Decoupling the mathematical model yields subproblems. and the problem As shown below: in , , , Channel gain per unit distance; Indicates the number of users who are aware of the information. Indicates base station With cognitive users The channel gain coefficient, where M represents the number of eavesdropping users. This indicates the transmit power of the user being eavesdropped on. This indicates a large-scale decline between ground-based cognitive users and eavesdropping users. This represents additive white Gaussian noise from the base station to the user. This indicates the channel gain between the drone and the eavesdropping user; in: , , , Indicates drones and eavesdropping users The distance between them express The estimation error of the region This represents the normalized signal-to-noise ratio. Indicates drone The flight altitude.

3. The method for designing drone trajectories under eavesdropping conspiracy conditions according to claim 2, characterized in that: The new optimization problem is denoted as: Indicates the secure transmission rate. , , As slack variables, This represents the channel gain between the eavesdropping user and the master user. The horizontal coordinates of the eavesdropping user. This represents the horizontal coordinates of the primary network user; at this time It is a standard convex optimization problem, which is solved using the interior point method.

4. The method for designing drone trajectories under eavesdropping conspiracy conditions according to claim 2, characterized in that: Regarding the sub-problems Based on the conditions of the Lagrange equations, the KKT equations are derived: , , All three represent Lagrange multipliers; To obtain optimal power: in: Indicates drone The channel gain between the primary user and the secondary user is obvious. The value depends on the Lagrange multipliers. , The value can be obtained through binary search. Obtain.

5. The method for designing drone trajectories under eavesdropping conspiracy conditions according to claim 2, characterized in that: Regarding the sub-problems By introducing new slack variables and using the SCA method, we obtain The lower bound: in, To represent the secure transmission rate, the slack variable must satisfy the following: at this time It is a non-convex constraint, which can be rewritten using SCA to obtain: The horizontal coordinates of the user being eavesdropped on; For cognitive radio underlay constraints, the same method is used to obtain: In the above formula These are new slack variables that need to satisfy: This represents the horizontal coordinates of the primary network user.

6. A drone trajectory system under conditions of eavesdropping conspiracy, characterized in that, include: drones Configure it as an aerial base station for the cognitive network and initialize it. Its flight trajectory enables it to serve ground-based applications. One cognitive user; individual main users , A cognitive network user and M eavesdropping users , Individual eavesdropping users collaborate with each other, share information, and continuously interfere with and obtain confidential information from cognitive networks; The mathematical model module is used to construct an optimization mathematical model with constraints of UAV transmission power and UAV flight trajectory, aiming to maximize the system's secure transmission rate; the mathematical model is as follows: In the formula, Indicates drone The maximum instantaneous power during flight, , Indicates drone Initial flight position , Indicates drone Flight position at the last moment The length of each flight time slot for the drone is defined. The maximum flight speed of the drone at any given time is defined. Indicates the secure transmission rate of cognitive radio networks. express Serving cognitive users The transmission power, Indicates main network user The tolerance threshold, where P represents the drone's transmit power and Q represents the drone's flight trajectory. This represents the flight position of the drone in the (n-1)th time slot, where N represents the number of time slots the drone flies. This indicates the interference caused by the cognitive network to the main network; in, This refers to the instantaneous power change during the drone's flight. This ensures the normal operation of the drone cognitive network. This limits the initial and final positions and maximum flight distance of the drone; The mathematical model decoupling module is used to decouple the mathematical model based on the BCD method into subproblems that only concern the UAV power and the UAV flight trajectory. For each non-convex subproblem, a convex approximation fitting method is used to convert it into a convex problem for solution. The module for solving UAV transmission power and flight trajectory is used to solve for UAV transmission power and UAV flight trajectory using iterative algorithms.

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