Trajectory and power design method and system for UAV covert relay communication network

Abstract

This invention proposes a trajectory and power design method and system for a drone covert relay communication network. The method primarily comprises the following steps: first, deriving the channel capacity formulas between users and drones, and between drones and base stations in the covert relay network; then, deriving the minimum detection error rate for a listener in the covert relay network to detect user and drone transmissions; then, establishing a trajectory and power design optimization problem for the drone covert relay communication network based on the derived formulas; and finally, iteratively solving the above problem using a continuous convex approximation algorithm to obtain the optimal drone trajectory, drone transmission power, and user transmission power. This invention achieves highly concealed transmission while ensuring network throughput performance, making it suitable for scenarios requiring high transmission security and offering extremely high economic benefits.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) wireless communications, and in particular relates to a trajectory and power design method and system for a UAV covert relay communication network. Background Art

[0002] Drone-assisted communication technology offers a solution to a range of challenges in 5G network infrastructure construction and ensuring network service quality. Because drone-supported wireless communication networks have a high probability of high-quality line of sight in the air-to-ground (A2G) channels, integrating drone-supported wireless communication systems into terrestrial cellular networks can significantly improve network coverage and throughput. Specifically, drones can serve as mobile base stations for aerial relay communications. By hovering in the air and providing a stable communication connection, they can compensate for the blind spots and coverage limitations of ground base stations. The use of drones in relay communications also helps expand network capacity. During large-scale events, gatherings, or emergencies, the simultaneous use of large numbers of users can lead to network congestion. Drones can provide additional communication capacity, share the network load, and ensure communication quality.

[0003] Furthermore, because the high mobility of drones adds additional degrees of freedom to the network, the performance of drone-supported relay communication systems can be significantly improved by jointly optimizing communication resources such as drone trajectories and transmit power. Currently, scholars in related fields at home and abroad have carried out a series of research. In 2022, researchers such as S. Zhang focused on drone-supported communication systems. By designing and optimizing drone trajectories, they significantly reduced mission completion time and improved drone mission efficiency. In 2023, Z. He et al. considered energy transmission and communication design in drone relay communication networks, minimizing system energy loss by optimizing drone trajectories and transmit power.

[0004] While research on trajectory design in UAV communication systems, such as the aforementioned work, has significantly improved system performance, these approaches fail to consider communication security. Because communication via UAV air-to-ground channels is highly susceptible to detection and targeted attack by unauthorized users, it is crucial to consider communication concealment during system design. Therefore, further research is needed on trajectory and power design methods for covert UAV relay communication networks. Summary of the Invention

[0005] In response to the current situation where there is a lack of a general method for jointly designing drone trajectories and power in drone covert relay communication networks, the present invention proposes a trajectory and power design method for drone covert relay networks and a computer-readable medium to improve the covert communication performance of relay networks.

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

[0007] The trajectory and power design method of the UAV covert relay communication network includes the following steps:

[0008] Step 1: Based on the UAV covert relay communication network scenario, calculate the total signal received by the UAV, and obtain the channel capacity between the user and the UAV based on the total signal received by the UAV, and obtain the channel capacity between the UAV and the base station based on the total signal received by the base station;

[0009] Step 2: Calculate the total signal power of the listener in the user transmission band and the drone transmission band respectively. Based on the total signal power of the listener in the user transmission band and the drone transmission band, calculate the minimum detection error rate of the listener for detecting user transmission and drone transmission;

[0010] Step 3: Based on the channel capacities between users and drones and between drones and base stations, construct the objective function and data transmission constraints for the optimization problem. Furthermore, set the mobility constraints, transmission power constraints, and covert communication constraints for the optimization problem. Based on the constructed objective function and constraints, establish the trajectory and power design optimization problem for the drone covert relay communication network.

[0011] Step 4: Iteratively solve the optimization problem based on the continuous convex approximation algorithm to obtain the optimal UAV trajectory, UAV transmission power, and user transmission power.

[0012] Furthermore, the channel capacity between the user and the drone in step 1 is:

[0013]

[0014] Among them C su [n] is the channel capacity between the user and the drone in the nth time slot, n∈{1,...,N} is the time slot number, indicating the nth time slot, and N is the total number of time slots; β0 is the LoS channel gain, P s [n] is the power of the user in the nth time slot, σ 2 is the ambient noise power, d su [n] is the distance between the user and the UAV in the nth time slot, α2 is the path attenuation index of the LoS channel, P j is the interference power of the jammer, d ju [n] represents the distance between the jammer and the drone in the nth time slot.

[0015] Furthermore, the channel capacity between the UAV and the base station in step 1 is:

[0016]

[0017] Among them Cub [n] is the channel capacity between the UAV and the base station in the nth time slot, P u [n] is the power of the UAV in the nth time slot, x is the integral variable, μ is the correction coefficient of the Rayleigh channel gain, α1 is the path attenuation index of the Rayleigh channel, d ub [n] is the distance between the drone and the base station in the nth time slot, d jb is the distance between the jammer and the base station.

[0018] Furthermore, the total signal power of the user transmission frequency band listener in step 2 is:

[0019]

[0020] Where T se [n] is the total signal power of the frequency band listener transmitted by the user in the nth time slot; X je [n] is the interference signal power at the listener in the nth time slot, subject to the parameter Exponential distribution of Y se [n] is the user signal power at the nth time slot listener, subject to the parameter exponential distribution of Assumption that users do not communicate, Assume that users are communicating, d se is the distance between the user and the listener, d je is the distance between the jammer and the listener;

[0021] The total signal power of the drone transmission band listener is:

[0022]

[0023] Where T ue [n] is the total signal power of the frequency band listener transmitted by the drone in the nth time slot, d ue [n] is the distance between the drone and the listener; Assuming that the drone has no communication, Assume that the drone is communicating.

[0024] Furthermore, the minimum detection error rate of the listener detecting the user transmission in step 2 is:

[0025]

[0026] in The minimum detection error rate of the user transmission detected by the listener in the nth time slot;

[0027] The closed-form expression for the minimum detection error rate of a listener detecting a drone transmission is:

[0028]

[0029] in is the minimum detection error rate of the drone transmission detected by the listener in the nth time slot.

[0030] Furthermore, the trajectory and power design optimization problem of the UAV covert relay communication network constructed in step 3 is:

[0031]

[0032] st:||q[n]-q[n-1]||≤VΔt,

[0033]

[0034] 0≤P s [n]≤P s,max ,0≤P u [n]≤P u,max ,

[0035]

[0036] in, is the objective function; is the transmission data volume constraint; ||q[n]-q[n-1]||≤VΔt is the mobility constraint; is the transmission power constraint; To provide covert communication constraints;

[0037] Where i∈{1,...,n} is the auxiliary time slot number, is the i-th time slot, C su [i] is the channel capacity between the user and the drone in the i-th time slot, C ub [i] is the channel capacity between the UAV and the base station in the i-th time slot, q = {q[n]} is the trajectory of the UAV, where q[n] is the position of the UAV in the n-th time slot, q[n-1] is the position of the UAV in the n-1-th time slot, and V is the maximum speed of the UAV; P s ={P s [n]} is the user power, P u ={P u [n]} is the UAV power, Δt is the length of each time slot; P s,max is the maximum power of the user, P u,max is the maximum power of the UAV, P s is the average power of the user, is the average power of the UAV; 1-ρ is the covert communication constraint threshold.

[0038] Furthermore, the step 4 includes:

[0039] Step 4.1: Initialize the initial trajectory q of the drone (0) , User initial power and the initial power of the drone Set the iteration threshold ε and use the initial trajectory of the drone, the initial power of the user and the initial power of the drone as the local points of iteration

[0040] Step 4.2: At the local point of the iteration The trajectory and power design optimization problem of the constructed UAV covert relay communication network is approximated as a convex problem;

[0041] Step 4.3: Use the ellipsoid method to solve the convex problem obtained in step 4.2 and obtain the optimal solution for this iteration

[0042] Step 4.4: If the improvement of the objective function of this iteration compared to the previous objective function is less than the iteration threshold ε, then stop the iteration. The optimal solution of this iteration is the optimal trajectory of the drone, the optimal power of the user, and the optimal power of the drone. Otherwise it will be used as the local point for the next iteration Return to step 4.2.

[0043] In another aspect, the trajectory and power design system for a UAV covert relay communication network of the present invention comprises:

[0044] Module 1: Based on the UAV covert relay communication network scenario, it is used to calculate the total signal received by the UAV, and obtain the channel capacity between the user and the UAV based on the total signal received by the UAV, and obtain the channel capacity between the UAV and the base station based on the total signal received by the base station;

[0045] Module 2: Calculates the total signal power of the listener in the user transmission band and the drone transmission band, and calculates the minimum detection error rate of the listener for detecting user transmission and drone transmission based on the total signal power of the listener in the user transmission band and the drone transmission band.

[0046] Module 3: This module constructs the objective function and transmission data volume constraints of the optimization problem based on the channel capacity between users and drones and between drones and base stations. It also sets the mobility constraints, transmission power constraints, and covert communication constraints of the optimization problem. Based on the constructed objective function and constraints, it establishes the trajectory and power design optimization problem of the drone covert relay communication network.

[0047] Module 4: It is used to iteratively solve the optimization problem based on the continuous convex approximation algorithm to obtain the optimal UAV trajectory, UAV transmission power and user transmission power.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] This paper proposes an algorithm for the joint design of drone trajectories, user power, and drone power in a UAV covert relay communication network. This algorithm maximizes the amount of data transmitted to the base station under constraints on data volume, mobility, transmission power, and covert communication. This algorithm significantly improves network throughput while ensuring high communication covertness and data transmission security. It is suitable for use in future drone-based Internet of Things (IoT) networks, where data transmission requires high confidentiality. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 This is a schematic diagram of a UAV covert relay communication network according to an embodiment of the present invention;

[0052] Figure 2 A flow chart of the method for implementing the present invention;

[0053] Figure 3 Design an optimized drone trajectory map for the real-time example of the present invention;

[0054] Figure 4 This is a graph showing how the objective function of an embodiment of the present invention changes with the number of iterations. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0056] Example 1

[0057] The following combination Figure 1-4 A method for designing the trajectory and power of a UAV covert relay communication network according to an embodiment of the present invention is described as follows:

[0058] Figure 1A specific embodiment of the present invention considers a drone covert relay communication network consisting of a drone, a user, a base station, and an unauthorized eavesdropper. The user uploads data to the base station by sending it to the drone, which then relays it to the base station. The data transmission between the user and the drone is monitored in real time by the eavesdropper. The drone's flight altitude is H, and its maximum speed is V. Improving the performance of the drone covert relay communication network requires jointly designing the drone's trajectory, user power, and drone power to maximize system throughput while ensuring covert communication performance. This is a complex problem to solve.

[0059] like Figure 2 The flowchart of the method of the present invention is shown, and the implementation process includes the following steps:

[0060] Step 1: Derive the channel capacity formulas between users and drones, and between drones and base stations in a covert relay network. First, based on the fixed locations of the ground users, base stations, jammers, and drones, the distances between the jammer and base station, the distance between users and drones, the distance between drones and base stations, and the distance between jammers and drones are calculated. Then, the channel capacity formulas between users and drones, and between drones and base stations, are calculated by combining the drone's altitude, the LoS channel gain per unit distance, the correction factor for the Rayleigh channel gain, the ambient noise power, the jammer's interference power, the Rayleigh channel path attenuation exponent, the LoS channel path attenuation exponent, and the power of users and drones.

[0061] The distance between the jammer and the base station in step 1 is:

[0062] d jb =||w j -w b ||

[0063] where d jb is the distance between the jammer and the base station; w j is the position of the jammer, w b is the location of the base station.

[0064] The distance between the user and the drone in step 1 is:

[0065]

[0066] Where n∈{1,...,N} is the time slot number, is the nth time slot, and N is the total number of time slots; d su [n] is the distance between the user and the drone in the nth time slot, w s is the user's position, and q[n] is the position of the drone in the nth time slot.

[0067] The distance between the drone and the base station in step 1 is:

[0068]

[0069] where d ub [n] is the distance between the drone and the base station in the nth time slot, w b is the location of the base station.

[0070] The distance between the jammer and the drone in step 1 is:

[0071]

[0072] where d ju [n] is the distance between the jammer and the UAV in the nth time slot, w j is the location of the jammer.

[0073] According to the above distance formula, the total signal expression received by the drone is:

[0074]

[0075] where y u [n] is the total signal received by the drone in the nth time slot, is the useful signal received by the drone in the nth time slot, is the noise signal received by the UAV in the nth time slot; β0 is the LoS channel gain, P j is the interference power of the jammer, α2 is the path attenuation index of the LoS channel, P s [n] is the power of the user in the nth time slot; is the signal sent by the user in the nth time slot, is the signal sent by the interferer in the nth time slot, is the environmental noise signal, where σ 2 is the ambient noise power.

[0076] According to the total signal expression received by the UAV above, the channel capacity formula between the user and the UAV in step 1 is obtained as follows:

[0077]

[0078] Among them C su [n] is the channel capacity between the user and the UAV in the nth time slot, represents the entropy operation, and p(·) represents the probability density function.

[0079] According to the above distance formula, the total signal expression received by the base station is:

[0080]

[0081] where y b[n] is the total signal received by the base station in the nth time slot, is the useful signal received by the base station in the nth time slot, is the noise signal received by the base station in the nth time slot; μ is the correction coefficient of the Rayleigh channel gain, α1 is the path attenuation index of the Rayleigh channel, P u [n] is the power of the UAV in the nth time slot; is the Rayleigh channel scattering gain, The signal sent by the drone in the nth time slot.

[0082] According to the total signal expression received by the base station above, the channel capacity formula between the drone and the base station in step 1 is:

[0083]

[0084] Among them C ub [n] is the channel capacity between the UAV and the base station in the nth time slot, and x is the integral variable.

[0085] Step 2: Derivation of the minimum detection error rate (DER) for a listener to detect user and drone transmissions in a covert relay network. First, the distances between the user and the listener, the distances between the jammer and the listener, and the distances between the drone and the listener are calculated based on the fixed locations of the ground user, base station, jammer, and the drone. The total signal power of the listener is calculated in the user and drone transmission bands, respectively. Finally, a closed-form expression for the minimum DER for the listener to detect user and drone transmissions is derived.

[0086] The distance between the user and the listener in step 2 is:

[0087] d se =||w s -w e ||

[0088] where d se is the distance between the user and the listener, w e The location of the listener.

[0089] The distance between the jammer and the listener in step 2 is:

[0090] d je =||w j -w e ||

[0091] where d je is the distance between the jammer and the listener.

[0092] The distance between the drone and the listener in step 2 is:

[0093] due [n]=||q[n]-w e ||

[0094] where d ue [n] is the distance between the drone and the listener in the nth time slot.

[0095] The total signal power of the frequency band listener transmitted by the user in step 2 is:

[0096]

[0097] Where T se [n] is the total signal power of the frequency band listener transmitted by the user in the nth time slot; X je [n] is the interference signal power at the listener in the nth time slot, subject to the parameter Exponential distribution of Y se [n] is the user signal power at the nth time slot listener, subject to the parameter exponential distribution of Assumption that users do not communicate, Assumption that users are communicating.

[0098] According to the total signal power expression of the user transmission frequency band listener above, the false alarm rate of the user transmission frequency band listener is obtained as follows:

[0099]

[0100] in is the false alarm rate of the user transmission band listener in the nth time slot, is the decision result being transmitted by the user, τ se [n] is the detection threshold for the listener in the user transmission band of the nth time slot.

[0101] According to the total signal power expression of the user transmission frequency band listener above, the missed alarm rate of the user transmission frequency band listener is obtained as follows:

[0102]

[0103] in is the missed alarm rate of the user transmission band listener in the nth time slot, is the decision result that the user did not transmit, τ se [n] is the detection threshold for the listener in the user transmission band of the nth time slot.

[0104] According to the above expressions of false alarm rate and missed alarm rate of the user transmission frequency band monitor, the frequency band detection error rate of the user transmission is obtained as follows:

[0105]

[0106] where ξ se [n] is the detection error rate of the user transmission band listener in the nth time slot.

[0107] According to the above expression of the frequency band detection error rate of user transmission, the derivative and extreme value point of the frequency band detection error rate of user transmission with respect to the detection threshold are obtained as follows:

[0108]

[0109] where τ′ se [n] is the extreme value of the frequency band detection error rate of the user transmission in the nth time slot. Therefore, when the detection threshold τ se [n]∈[σ 2 ,τ′ se [n]), the frequency band detection error rate of user transmission decreases monotonically with respect to the detection threshold. When the detection threshold τ se [n]∈(τ′ se [n],+∞), the frequency band detection error rate of user transmission increases monotonically with respect to the detection threshold. Based on the above analysis, the optimal detection threshold of the frequency band of user transmission is:

[0110]

[0111] is the optimal detection threshold for the frequency band of user transmission in the nth time slot.

[0112] According to the above-mentioned frequency band detection error rate of user transmission and the optimal detection threshold expression, the closed-form expression of the minimum detection error rate of the listener for detecting user transmission in step 2 is obtained as follows:

[0113]

[0114] in The minimum detection error rate for user transmissions detected by the listener in the nth time slot.

[0115] The total signal power of the drone transmission frequency band listener in step 2 is:

[0116]

[0117] Where T ue [n] is the total signal power of the monitor in the frequency band transmitted by the drone in the nth time slot; Assuming that the drone has no communication, Assume that the drone is communicating.

[0118] According to the total signal power expression of the UAV transmission frequency band monitor, the false alarm rate of the UAV transmission frequency band monitor is obtained as follows:

[0119]

[0120] in is the false alarm rate of the user transmission band listener in the nth time slot, is the judgment result being transmitted by the drone, τ ue [n] is the detection threshold of the monitor in the drone transmission band in the nth time slot.

[0121] According to the total signal power expression of the UAV transmission frequency band monitor, the missed alarm rate of the UAV transmission frequency band monitor is obtained as follows:

[0122]

[0123] in is the missed alarm rate of the monitor in the UAV transmission frequency band in the nth time slot, is the judgment result that the drone did not transmit, τ ue [n] is the detection threshold of the monitor in the drone transmission band in the nth time slot.

[0124] According to the above expressions of false alarm rate and missed alarm rate of the drone transmission frequency band monitor, the detection error rate of the drone transmission frequency band is obtained as follows:

[0125]

[0126] where ξ ue [n] is the detection error rate of the monitor in the nth time slot UAV transmission frequency band. According to the above formula, when the detection threshold When the detection error rate of the UAV transmission frequency band decreases monotonically with respect to the detection threshold, When the frequency band of the drone transmission is detected

[0127] The error rate increases monotonically with respect to the detection threshold. Based on the above analysis, the optimal detection threshold for the frequency band of drone transmission is:

[0128]

[0129] is the optimal detection threshold for the frequency band of the UAV transmission in the nth time slot.

[0130] According to the above-mentioned UAV transmission frequency band detection error rate and the optimal detection threshold expression, the closed-form expression of the minimum detection error rate of the listener for detecting UAV transmission in step 2 is obtained as follows:

[0131]

[0132] in is the minimum detection error rate of the drone transmission detected by the listener in the nth time slot.

[0133] Step 3: Establish a trajectory and power optimization problem for the UAV covert relay communication network. First, construct the optimization objective and transmission data volume constraints based on the channel capacity formulas between users and UAVs and between UAVs and base stations. Then, define the mobility constraints of the optimization problem based on the position of the UAVs in each time slot. Finally, define the transmission power constraints based on the maximum and average power of the users and UAVs. Finally, define the covert communication constraints based on the minimum detection error rate of the eavesdropper for detecting user and UAV transmissions. Based on these established objectives and constraints, establish the trajectory and power optimization problem for the UAV covert relay communication network.

[0134] Step 3 constructs the optimization problem with the following goal:

[0135]

[0136] Where q = {q[n]} is the trajectory of the UAV, P s ={P s [n]} is the user power, P u ={P u [n]} is the UAV power, and Δt is the length of each time slot. The above objective is to maximize the amount of data uploaded to the base station by optimizing the UAV trajectory, user power, and UAV power.

[0137] The transmission data volume constraint in step 3 is:

[0138]

[0139] Where i∈{1,...,n} is the auxiliary time slot number, is the i-th time slot, C su [i] is the channel capacity between the user and the drone in the i-th time slot, C ub [i] is the channel capacity between the drone and the base station in the i-th time slot. The above constraint means that the amount of data transmitted by the drone to the base station should be less than the amount of data transmitted by the user to the drone at any time slot.

[0140] The mobility constraint of the optimization problem in step 3 is:

[0141] ||q[n]-q[n-1]||≤VΔt

[0142] Where q[n-1] is the position of the UAV in the n-1th time slot, and V is the maximum speed of the UAV. The above constraints mean that the speed of the UAV in each time slot should be less than the maximum speed.

[0143] The transmission power constraint in step 3 is:

[0144] 0≤Ps [n]≤P s,max ,0≤P u [n]≤P u,max

[0145]

[0146] Among them, P s,max is the maximum power of the user, P u,max is the maximum power of the drone, is the average power of the user, The above constraints mean that the power of the user and the UAV in each time slot should be less than the corresponding maximum power, and the average power during the entire mission time should be less than the average power.

[0147] The covert communication constraints in step 3 are:

[0148]

[0149] where 1-ρ is the covert communication constraint threshold. The above constraint means that the product of the minimum detection error rate of the eavesdropper for detecting user transmissions and drone transmissions should be greater than the covert communication constraint threshold.

[0150] The trajectory and power design optimization problem of the UAV covert relay communication network constructed in step 3 is:

[0151]

[0152] st:||q[n]-q[n-1]||≤VΔt,

[0153]

[0154] 0≤P s [n]≤P s,max ,0≤P u [n]≤P u,max ,

[0155]

[0156] The meaning of the above design optimization problem is to maximize the amount of data transmitted to the base station under the constraints of transmission data volume, mobility, transmission power and covert communication by optimizing the UAV trajectory, user power and UAV power.

[0157] Step 4: Iteratively solve the above problem based on the continuous convex approximation algorithm to obtain the optimal UAV trajectory, UAV transmission power, and user transmission power.

[0158] The specific steps of step 4 based on the continuous convex approximation algorithm are as follows:

[0159] Step 4.1: Initialize the initial trajectory q of the drone (0) , User initial power and the initial power of the drone Set the iteration threshold ε and use the initial trajectory of the drone, the initial power of the user and the initial power of the drone as the local points of iteration

[0160] Step 4.2: At the local point of the iteration The trajectory and power design optimization problem of the UAV covert relay communication network constructed in step 3 is approximated as a convex problem;

[0161] Step 4.3: Use the ellipsoid method to solve the convex problem obtained in step 4.2 and obtain the optimal solution for this iteration

[0162] Step 4.4: If the improvement of the objective function of this iteration compared to the previous objective function is less than the iteration threshold ε, then stop the iteration. The optimal solution of this iteration is the optimal trajectory of the drone, the optimal power of the user, and the optimal power of the drone. Otherwise it will be used as the local point for the next iteration Return to step 4.2.

[0163] The present invention also provides a computer-readable medium, which stores a computer program executed by an electronic device. When the computer program runs on the electronic device, the steps of the drone trajectory and power design method are executed.

[0164] Figure 3 The trajectory diagram of the drone designed and optimized in the real-time example of the present invention is given. It can be seen that in order to improve the concealment of communication, the drone tends to move away from the eavesdropper while flying towards the user. Figure 4 The real-time example of the present invention shows how the objective function value changes with the number of iterations under different covert communication constraint thresholds. It can be seen that as the number of iterations increases, the objective function value first increases and then converges, verifying the convergence of the proposed algorithm. On the other hand, as the covert communication constraint threshold increases, the objective function value increases, but at the same time, the probability of successful detection by an eavesdropper also increases, indicating a trade-off between system throughput and transmission security performance.

[0165] Example 2

[0166] This embodiment provides a trajectory and power design system for a UAV covert relay communication network, including:

[0167] Module 1: Based on the UAV covert relay communication network scenario, it is used to calculate the total signal received by the UAV, and obtain the channel capacity between the user and the UAV based on the total signal received by the UAV, and obtain the channel capacity between the UAV and the base station based on the total signal received by the base station;

[0168] Module 2: Calculates the total signal power of the listener in the user transmission band and the drone transmission band, and calculates the minimum detection error rate of the listener for detecting user transmission and drone transmission based on the total signal power of the listener in the user transmission band and the drone transmission band.

[0169] Module 3: This module constructs the objective function and transmission data volume constraints of the optimization problem based on the channel capacity between users and drones and between drones and base stations. It also sets the mobility constraints, transmission power constraints, and covert communication constraints of the optimization problem. Based on the constructed objective function and constraints, it establishes the trajectory and power design optimization problem of the drone covert relay communication network.

[0170] Module 4: It is used to iteratively solve the optimization problem based on the continuous convex approximation algorithm to obtain the optimal UAV trajectory, UAV transmission power and user transmission power.

[0171] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0172] It should be understood that the above description of the preferred embodiments is relatively detailed and cannot be considered as limiting the scope of protection of the present invention. It is not necessary and impossible to list all embodiments here. Under the guidance of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which fall within the scope of protection of the present invention. The scope of protection of the present invention shall be based on the attached claims.

Claims

1. A trajectory and power design method for a UAV covert relay communication network, characterized in that: The following steps are involved: Step 1: Based on the UAV covert relay communication network scenario, calculate the total signal received by the UAV, and obtain the channel capacity between the user and the UAV based on the total signal received by the UAV, and obtain the channel capacity between the UAV and the base station based on the total signal received by the base station; The channel capacity between the user and the drone is: in For the The channel capacity between users and drones in each time slot is is the time slot number, indicating the time slots, is the total number of time slots; is the LoS channel gain, For the The power of the user in each time slot, is the ambient noise power, For the The distance between the user and the drone in each time slot, is the path attenuation exponent of the LoS channel, is the jammer’s interference power, Representative The distance between the jammer and the drone in each time slot; The channel capacity between the drone and the base station is: in For the The channel capacity between the drone and the base station in each time slot, For the The power of the drone in each time slot, is the integration variable, is the correction coefficient of the Rayleigh channel gain, is the path attenuation exponent of the Rayleigh channel, For the The distance between the drone and the base station in each time slot, is the distance between the jammer and the base station; Step 2: Calculate the total signal power of the listener in the user transmission band and the drone transmission band respectively. Based on the total signal power of the listener in the user transmission band and the drone transmission band, calculate the minimum detection error rate of the listener for detecting user transmission and drone transmission; Step 3: Construct the objective function of the optimization problem and the transmission data volume constraint based on the channel capacity between the user and the UAV and the channel capacity between the UAV and the base station; The mobility constraints, transmission power constraints, and covert communication constraints of the optimization problem are set. Based on the constructed objective function and constraints, the trajectory and power design optimization problem of the UAV covert relay communication network is established. The trajectory and power design optimization problem of the constructed UAV covert relay communication network is: in, is the objective function; To constrain the amount of data transmitted; is the mobility constraint; is the transmission power constraint; To provide covert communication constraints; in is the auxiliary time slot number, time slots, For the The channel capacity between users and drones in each time slot is For the The channel capacity between the drone and the base station in each time slot, is the trajectory of the drone, where For the The position of the drone in each time slot, For the The position of the drone in each time slot, is the maximum speed of the drone; is the user power, is the UAV power, is the length of each time slot; is the maximum power of the user, is the maximum power of the drone, is the average power of the user, is the average power of the UAV; is the covert communication constraint threshold; For the The minimum detection error rate of user transmissions detected by a time slot listener; For the The minimum detection error rate of drone transmissions detected by a listener in each time slot; Step 4: Iteratively solve the optimization problem based on the continuous convex approximation algorithm to obtain the optimal UAV trajectory, UAV transmission power, and user transmission power.

2. The trajectory and power design method for a UAV covert relay communication network according to claim 1 is characterized in that: The total signal power of the user transmission frequency band monitor in step 2 is: in For the The total signal power of the frequency band listener transmitted by the user in the time slot; For the The interference signal power at the listener in each time slot is subject to the parameter exponential distribution of For the The user signal power at the time slot listener is subject to the parameter exponential distribution of Assumption that users do not communicate, Assuming that users are communicating, is the distance between the user and the listener, is the distance between the jammer and the listener; The total signal power of the drone transmission band listener is: in For the The total signal power of the listener in the frequency band of the drone transmission in the time slot, is the distance between the drone and the listener; Assuming that the drone has no communication, Assume that the drone is communicating.

3. The trajectory and power design method for a UAV covert relay communication network according to claim 2 is characterized in that: The minimum detection error rate of the listener in step 2 for detecting user transmission is: in For the The minimum detection error rate of user transmissions detected by a time slot listener; The closed-form expression for the minimum detection error rate of a listener detecting a drone transmission is: in For the The minimum detection error rate of a UAV transmission detected by a listener in each time slot.

4. The trajectory and power design method for a UAV covert relay communication network according to claim 1 is characterized in that: The step 4 comprises: Step 4.1: Initialize the initial trajectory of the drone , User initial power and the initial power of the drone , set the iteration threshold , and the initial trajectory of the drone, the initial power of the user and the initial power of the drone are used as the local points of iteration ; Step 4.2: At the local point of iteration The trajectory and power design optimization problem of the constructed UAV covert relay communication network is approximated as a convex problem; Step 4.3: Use the ellipsoid method to solve the convex problem obtained in step 4.2 and obtain the optimal solution for this iteration ; Step 4.4: If the improvement of the objective function of this iteration compared to the previous objective function is less than the iteration threshold , then the iteration is stopped, and the optimal solution of this iteration is the optimal trajectory of the drone, the optimal power of the user and the optimal power of the drone ; Otherwise it will be used as the local point for the next iteration , return to step 4.

2.

5. Trajectory and power design system for UAV covert relay communication network, characterized by: include: Module 1: Based on the UAV covert relay communication network scenario, it is used to calculate the total signal received by the UAV, and obtain the channel capacity between the user and the UAV based on the total signal received by the UAV, and obtain the channel capacity between the UAV and the base station based on the total signal received by the base station; Module 2: Calculates the total signal power of the listener in the user transmission band and the drone transmission band, and calculates the minimum detection error rate of the listener for detecting user transmission and drone transmission based on the total signal power of the listener in the user transmission band and the drone transmission band. Module 3: It is used to construct the objective function and transmission data volume constraints of the optimization problem based on the channel capacity between users and drones and the channel capacity between drones and base stations; The mobility constraints, transmission power constraints, and covert communication constraints of the optimization problem are set; based on the constructed objective function and constraints, the trajectory and power design optimization problem of the UAV covert relay communication network is established; Module 4: It is used to iteratively solve the optimization problem based on the continuous convex approximation algorithm to obtain the optimal UAV trajectory, UAV transmission power and user transmission power; The trajectory and power design system for a UAV covert relay communication network is used to execute the steps in the trajectory and power design method for a UAV covert relay communication network described in any one of claims 1-4.

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