UAV-assisted backscatter communication system security capacity optimization method and device

Abstract

The present invention discloses a method and device for optimizing the security capacity of a drone-assisted backscatter communication system, which relates to the field of wireless communication technology. The system includes one drone, K backscatter devices and M eavesdroppers, and includes the following steps: obtaining collected energy, inputting the collected energy into the backscatter device for backscatter communication, optimizing time allocation, drone trajectory and backscatter device scheduling based on preset energy constraints and security performance constraints of backscatter communication; solving time allocation, drone trajectory and backscatter device scheduling respectively to obtain three optimal solutions, performing global iteration on the three optimal solutions to obtain a global optimal solution as the security capacity optimization result, thereby maximizing the security capacity of the minimum link.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and device for optimizing the security capacity of a drone-assisted backscatter communication system. Background Art

[0002] Backscatter communication technology, with its low cost and low power consumption, can be widely used in large-scale IoT applications. IoT sensors are widely distributed, and some remote areas are beyond the reach of mobile networks. Introducing drones into wireless networks can improve network coverage and increase spectrum efficiency. However, wireless communication signals are susceptible to eavesdropping, potentially exposing user information to leaks. Summary of the Invention

[0003] In order to address the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a method and device for optimizing the safety capacity of a drone-assisted backscatter communication system, which maximizes the safety capacity through minimum backscattering under the constraints of energy causality and the safety performance of backscatter communication.

[0004] In a first aspect, the purpose of the present invention can be achieved by the following technical solution: a method for optimizing the security capacity of a drone-assisted backscatter communication system, the system including one drone, K backscatter devices, and M eavesdroppers, the method comprising the following steps:

[0005] Acquire collected energy and input it into the backscatter device for backscatter communication. Based on the preset energy constraints and the safety performance constraints of backscatter communication, optimize the time allocation, UAV trajectory and backscatter device scheduling;

[0006] The time allocation, drone trajectory and backscatter device scheduling are solved separately to obtain three optimal solutions. The three optimal solutions are globally iterated to obtain the global optimal solution as the safety capacity optimization result, thereby maximizing the safety capacity of the minimum link.

[0007] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: a prerequisite for the security capacity optimization is that the backscatter capacity is greater than the eavesdropping capacity.

[0008] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the problem of optimizing time allocation, drone trajectory, and backscatter device scheduling is a safety capacity optimization problem;

[0009] Among them, the linear programming method is used to find the optimal solution of the time allocation sub-problem;

[0010] The optimal solution of the UAV trajectory subproblem is solved using the continuous convex approximation method;

[0011] The branch and bound method is used to find the optimal solution to the backscatter device scheduling subproblem.

[0012] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: defining the safety capacity optimization problem as P1:

[0013]

[0014] stC1:a k [n][R uk [n]-R km [n]]≥0

[0015] C2:||u[n+1]-u[n]||≤V max t, u[1]=u[N]

[0016]

[0017] C4:t1[n]+t2[n]=t

[0018] C5:0≤t1[n]≤1

[0019] C6:0≤t2[n]≤1

[0020]

[0021] Among them, variable a k [n] refers to BD k Working status, a k [n]=0 means it is in dormant state, a k [n]=1 means it is in working state; R uk [n] refers to the time between UAV and BD in backscattering k Normalized backscatter communication capacity between km [n] refers to the backscattering time, ED m and BD k The normalized eavesdropping capacity between them; C1 is the backscattering capacity between the drone and the backscattering device in any time slot is always greater than the eavesdropping capacity of the eavesdropping device; C2 is the drone trajectory constraint, u[n]=(x n ,y n ) is the position of the UAV in any time slot, t = T / N is the time slot length, V max is the maximum flight speed of the UAV; C3 is the user scheduling variable constraint. Since the UAV only communicates with one BD in any time slot, a k [n] need to meet C4, C5, and C6 are all time constraints, t1[n] is the energy acquisition time, t2[n] is the backscattering time; C7 is the energy causality requirement, E min It is the minimum energy required to keep the backscatter device working in a time slot.

[0022] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the time allocation optimization sub-problem is defined as P2:

[0023]

[0024] The UAV trajectory optimization sub-problem is defined as P3:

[0025]

[0026] The backscatter device scheduling optimization subproblem is defined as P4:

[0027]

[0028] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the process of solving the optimal solution of the time allocation subproblem using a linear programming method:

[0029] By introducing an auxiliary variable, the minimum-maximization problem is transformed into a maximum-maximization problem, and the problem P2 is reformulated as P5:

[0030]

[0031] Use linear programming to find the optimal solution for time allocation.

[0032] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the process of solving the optimal solution of the drone trajectory subproblem using the continuous convex approximation method:

[0033] Using variable substitution, problem P3 is simplified and reformulated as P6 as follows:

[0034]

[0035] Among them, introduce a new variable q[n], let q[n]=H 2 +||u[n]-b k || 2 , b k =(x k ,y k ) is the ground BD k 's coordinates. c=P t β0(1-γ k)η, a, b, c are constants, e m =(x m ,y m ) is the ground ED m The coordinates of . Using the first-order Taylor expansion method, the approximate linear form of the inequality constraint is obtained;

[0036] The current optimal UAV trajectory is obtained through continuous convex approximation iterative solution.

[0037] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the process of solving the optimal solution of the backscatter device scheduling subproblem by using the branch and bound method:

[0038] First, branch P4 for the first time and consider the relaxation problem in branch 1. First, use the observation method to find a bound, find an integer solution x, substitute the solution x into the objective function, and then modify the bound continuously during the calculation.

[0039] If the optimal solution x0 for solving the relaxation problem of branch 1 happens to be an integer solution, then if x0>x, we need to redefine the bound and use x0 as the new bound.

[0040] According to the new bound, we can see whether the relaxation problem of branch 2 needs to be discussed, and find the optimal value of the objective function f corresponding to the optimal solution of branch 2. * ;

[0041] If the optimal value of branch 2 is less than the optimal value of branch 1, f * ≤f 0 , now branch 2 does not need to branch anymore.

[0042] In a second aspect, in order to achieve the above-mentioned object, the present invention discloses a device for optimizing the security capacity of a drone-assisted backscatter communication system, comprising:

[0043] The data problem optimization module is used to obtain the collected energy and input the collected energy into the backscatter device for backscatter communication. Based on the preset energy constraints and the safety performance constraints of the backscatter communication, the module optimizes the time allocation, the UAV trajectory and the backscatter device scheduling.

[0044] The safety capacity optimization module is used to solve time allocation, drone trajectory and backscatter device scheduling respectively to obtain three optimal solutions. The three optimal solutions are globally iterated to obtain the global optimal solution as the safety capacity optimization result, thereby maximizing the safety capacity of the minimum link.

[0045] In another aspect of the present invention, in order to achieve the above-mentioned purpose, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned drone-assisted backscatter communication system security capacity optimization method is adopted.

[0046] Beneficial effects of the present invention:

[0047] This paper constructs a UAV-assisted backscatter secure communication system model in the presence of multiple eavesdropping devices. The system consists of a UAV, K backscatter devices, and M eavesdropping devices. In any time slot, the UAV communicates only with one active backscatter device; the remaining devices are dormant. A mechanism is employed where energy is collected before backscatter communication. Furthermore, when the UAV receives backscatter signals on the uplink, they are also eavesdropped on by all eavesdropping devices. Under the constraints of energy causality and backscatter communication security, an optimization problem is formulated to maximize the minimum link average security capacity.

[0048] Then, a joint optimization algorithm for time allocation, UAV trajectory and backscatter device scheduling is proposed. Since the original optimization problem is a non-convex optimization problem, the present invention decomposes it into three optimization sub-problems based on the BCD algorithm. The time allocation sub-problem is a convex optimization problem, and linear programming is used to obtain the optimal time. For the non-convex UAV trajectory optimization sub-problem, the original formula is first simplified by variable substitution, and then solved using continuous convex approximation (SCA). The scheduling sub-problem of the backscatter device is a 0-1 integer programming problem, so it is solved using the branch and bound method. After the three optimization sub-problems are solved separately, the optimal solutions for time allocation, UAV trajectory and backscatter device scheduling are finally obtained through global iteration. The present invention can effectively improve the safety capacity of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0050] Figure 1 It is a schematic flow chart of the method of the present invention;

[0051] Figure 2 Schematic diagram of a system model corresponding to a method for optimizing the security capacity of a backscatter communication system assisted by a drone according to an embodiment of the present invention;

[0052] Figure 3Schematic diagram of a time allocation model corresponding to a method for optimizing the security capacity of a backscatter communication system assisted by a drone according to an embodiment of the present invention;

[0053] Figure 4 Schematic diagram of safety capacity simulation under different comparison algorithms of a safety capacity optimization method of a backscatter communication system assisted by a drone according to an embodiment of the present invention;

[0054] Figure 5 This is a schematic diagram of a simulation of average optimal time allocation under different transmit powers for a method for optimizing the security capacity of a backscatter communication system assisted by a drone according to an embodiment of the present invention;

[0055] Figure 6 Schematic diagram of safety capacity simulation under different transmission powers of a safety capacity optimization method of a backscatter communication system assisted by a drone according to an embodiment of the present invention;

[0056] Figure 7 Schematic diagram of security capacity simulation of a method for optimizing security capacity of a backscatter communication system assisted by a drone under different numbers of eavesdropping devices according to an embodiment of the present invention;

[0057] Figure 8 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] Example 1:

[0060] The following is an introduction to the relevant terms involved in the embodiments of this application:

[0061] Drone: Unmanned aerial vehicle (UAV) is an unmanned aircraft that is controlled by a radio remote control device and a self-contained program control device, or is operated completely or intermittently autonomously by an onboard computer.

[0062] Backscatter communication: Backscatter communication uses the principle of backscattering of radio frequency signals to design an extremely low-power modulation and transmission technology. Since a part of the radio frequency signal will be reflected when it reaches the surface of an object, the sending node adjusts the matching between the receiving antenna and the impedance according to the information to be sent, enhances the reflection of the incident radio frequency signal, and modulates the perception data obtained by itself onto the reflected signal to complete the data transmission.

[0063] like Figure 1 As shown in Figure 1, a method for optimizing the security capacity of a drone-assisted backscatter communication system includes one drone, K backscatter devices, and M eavesdroppers. The drone communicates with a working backscatter device. The backscatter device first harvests energy and then performs backscatter communication. During the backscatter communication period, the device can be eavesdropped by the eavesdropper. The method includes the following steps:

[0064] S101: Acquire collected energy and input the collected energy into the backscatter device for backscatter communication. Based on the preset energy constraints and the safety performance constraints of the backscatter communication, optimize the time allocation, UAV trajectory and backscatter device scheduling;

[0065] S102: Solve time allocation, drone trajectory, and backscatter device scheduling respectively to obtain three optimal solutions. Perform global iteration on the three optimal solutions to obtain a global optimal solution as the safety capacity optimization result, thereby maximizing the safety capacity of the minimum link.

[0066] The prerequisite for security capacity optimization is that the backscatter capacity is greater than the eavesdropping capacity.

[0067] The problem of optimizing time allocation, drone trajectory and backscatter device scheduling is a safety capacity optimization problem;

[0068] Among them, the linear programming method is used to find the optimal solution of the time allocation sub-problem;

[0069] The optimal solution of the UAV trajectory subproblem is solved using the continuous convex approximation method;

[0070] The branch and bound method is used to find the optimal solution to the backscatter device scheduling subproblem.

[0071] The safety capacity optimization problem is defined as P1:

[0072]

[0073] stC1:a k [n][R uk [n]-R km [n]]≥0

[0074] C2:||u[n+1]-u[n]||≤V max t, u[1]=u[N]

[0075]

[0076] C4:t1[n]+t2[n]=t

[0077] C5:0≤t1[n]≤1

[0078] C6:0≤t2[n]≤1

[0079]

[0080] Among them, variable a k [n] refers to BD k Working status, a k [n]=0 means it is in dormant state, a k [n]=1 means it is in working state; R uk [n] refers to the time between UAV and BD in backscattering k Normalized backscatter communication capacity between km [n] refers to the backscattering time, ED m and BD k The normalized eavesdropping capacity between them; C1 is the backscattering capacity between the drone and the backscattering device in any time slot is always greater than the eavesdropping capacity of the eavesdropping device; C2 is the drone trajectory constraint, u[n]=(x n ,y n ) is the position of the UAV in any time slot, t = T / N is the time slot length, V max is the maximum flight speed of the UAV; C3 is the user scheduling variable constraint. Since the UAV only communicates with one BD in any time slot, a k [n] need to meet C4, C5, and C6 are all time constraints, t1[n] is the energy acquisition time, t2[n] is the backscattering time; C7 is the energy causality requirement, E min It is the minimum energy required to keep the backscatter device working in a time slot.

[0081] The time allocation optimization sub-problem is defined as P2:

[0082]

[0083] The UAV trajectory optimization sub-problem is defined as P3:

[0084]

[0085] The backscatter device scheduling optimization subproblem is defined as P4:

[0086]

[0087] The process of finding the optimal solution to the time allocation subproblem using linear programming methods:

[0088]

[0089] in For time slot n UAV and ground BD k The normalized backscatter capacity of For ED m With BD k The normalized eavesdropping capacity between .

[0090] Observing subproblem P5, we can find that P5 is a linear optimization problem with respect to the optimization variable t2[n]. Therefore, according to C7, we can calculate the value range of the energy collection time t1[n], that is, The optimal value is obtained when the equation is taken, that is, the energy collected in one time slot is equal to the minimum energy required to maintain the backscatter device working in the time slot, that is, the optimal energy collection time is:

[0091]

[0092] According to the relationship t2 * [n] = t - t1 * [n] It can be obtained that the optimal time for backscatter communication is:

[0093]

[0094] The process of finding the optimal solution to the UAV trajectory subproblem using the continuous convex approximation method:

[0095] Using variable substitution, problem P3 is simplified and reformulated as P6:

[0096]

[0097] Among them, introduce a new variable q[n], let q[n]=H 2 +||u[n]-b k || 2 , b k =(x k ,y k ) is the ground BD k 's coordinates. c=P t β0(1-γ k )η, a, b, c are constants, e m =(x m ,y m ) is the ground ED m The coordinates of . Using the first-order Taylor expansion method, the approximate linear form of the inequality constraint is obtained;

[0098] It can be found that C9, C10 and C11 are still non-convex functions with respect to the variable q[n], so P6 is still a non-convex optimization problem. Next, we continue to solve P6 using the continuous convex approximation (SCA) method, replacing the functions in C9 and C10 with the functions in Performing a first-order Taylor expansion at point q[n] yields the following expression:

[0099]

[0100] Similarly, change the Performing a first-order Taylor expansion at point q[n] yields the following expression:

[0101]

[0102] Similarly, the C11 Performing a first-order Taylor expansion at point q[n] yields the following expression:

[0103]

[0104] By replacing Thus, subproblem P6 is transformed into:

[0105]

[0106] Among them, C12, C13 and C14 are all convex functions, so subproblem P7 is a convex optimization problem, which can be solved using the convex optimization toolbox CVX to obtain q * [n]. Then according to q[n]=H 2 +||u[n]-b k || 2 , we can calculate u * [n].

[0107] The process of finding the optimal solution to the backscatter device scheduling subproblem using the branch and bound method:

[0108] (1) First, branch P4 for the first time. Assume that branch 1 is considered as a relaxation problem. Each time, a bound must be found for the problem. First, use the observation method to find a bound. Find an integer solution x, substitute the solution into the objective function, and then modify the bound continuously during the calculation.

[0109] (2) If the optimal solution x0 for solving the relaxation problem in branch 1 happens to be an integer solution, then if x0>x, it is necessary to redefine the bound and use x0 as the new bound for discussion;

[0110] (3) According to the new bound, whether the relaxation problem of branch 2 needs to be discussed, and the optimal value of the objective function f corresponding to the optimal solution of branch 2 is obtained. * .

[0111] (4) If the optimal value of branch 2 is less than the optimal value of branch 1, f * ≤f 0 , now branch 2 does not need to branch anymore.

[0112] Furthermore, the optimal solution for the time allocation, UAV trajectory, and ground backscatter device scheduling is obtained by alternately solving problems P2, P3, and P4, including the following steps:

[0113] (1) Initialize u[n], a k [n], t1[n], t2[n] and q0[n], set the number of iterations l and the accuracy ε

[0114] (2) Substitute u (l-1) [n], and a k (l-1) [n] Calculate the optimal solution of P2

[0115] (3) Substitute t1 (l) [n], t2 (l) [n]、a k (l-1) [n] and q0 (l) [n] Calculate the optimal solution of P3 and get q (l) [n], according to the formula q (l) [n]=H 2 +||u l [n]-b k || 2 , get u (l) [n];

[0116] (4) Substitute t1 (l) [n], t2 (l) [n]、u (l) [n] Calculate the optimal solution of P4 and get a k (l) [n];

[0117] (5) The number of iterations increases by one

[0118] (6) Check whether the termination condition is met; if the termination condition is met, the method ends; otherwise, return to step (2) and continue iterating;

[0119] The termination conditions include: reaching the maximum number of iterations, and the objective function value being less than or equal to the objective function value of the previous iteration.

[0120] Specifically, the present invention will be further described below through examples:

[0121] The security capacity optimization method system of the backscatter communication system based on the assistance of UAV is proposed in this invention. Figure 1 As shown in Figure 1, the system consists of one unmanned aerial vehicle (UAV), K backscatter devices on the ground, and M eavesdroppers. The UAV flies from the starting point for T time and then returns to the starting point, dividing the UAV's flight time T into N time slots, each of which is t long. Among them, within any time slot length t, BDk first performs energy collection and then performs backscatter communication. In time slot t, the energy collection time is recorded as t1[n], and the backscattering time is recorded as t2[n], satisfying t1[n]+t2[n]=t. The time distribution is as follows: Figure 3 shown.

[0122] The various model mechanisms involved in the present invention are as follows:

[0123] Communication model:

[0124] At the beginning of each time slot, the UAV sends a radio frequency signal in the downlink to charge the BDs on the ground. After collecting energy for t1[n] time, the UAV receives the backscattered signal from the BDs on the ground in the uplink. Assuming that the flight altitude of the UAV is fixed at H, the coordinates in any time slot are denoted as u[n] = (x n ,y n ). The kth backscatter device BD on the ground k The coordinates of b k , the mth eavesdropper ED m The coordinates of m , BD k and ED m The distance between them is d mk =||b k -e m ||. Given that the maximum flight speed of the UAV is V max , then the position of the UAV satisfies |u[n+1]-u[n]||≤V max ×t. In the nth time slot, UAV and BD k The distance is Therefore, the channel power gain is set to h uk [n] = β0d uk [n] -2 .

[0125] The UAV's transmission power is P t ,BD k The received power is P r [n]=P t h uk [n], the backscattered power is P r [n]γ k h uk [n], where γ k It's BD kThis paper assumes that only one ground BD is working in any time slot and the other BDs are dormant, so we set BD k The working status is a k [n],a k [n]=0 means it is in dormant state, a k [n] = 1 means it is in working state. Since UAV only communicates with one BD in any time slot, a k [n] need to meet In the nth time slot, the UAV and a k [n] = 1 BD communicates. In the time slot length t, BD first collects energy. The time for collecting energy is t1[n]. After the energy collection meets the constraint conditions, backscattering is performed. The backscattering communication time is t2[n]. This paper assumes that the energy collected in a time slot can only be used in that time slot, and the energy between time slots is not accumulated. Therefore, it can be obtained that in the nth time slot BD k The harvested energy is expressed as:

[0126] E k [n] = a k [n]P t h uk [n](1-γ k )ηt1[n]

[0127] Where η∈[0,1] is the energy collection efficiency. During the backscattering time t2[n], the UAV and BD k The normalized backscatter communication capacity between is expressed as:

[0128]

[0129] During the backscattering time t2[n], M eavesdroppers will simultaneously eavesdrop on BD k , but here we only consider receiving the current BD k The eavesdropper with the largest signal SNR, that is, the one closest to the current BD k The nearest eavesdropper, therefore ED m and BD k The normalized eavesdropping capacity between is:

[0130]

[0131] Among them, h km It's BD k and distance from current BD k Recent eavesdropper ED m The channel gain between .

[0132] You can get the BD in working state in any time slotk The normalized safety capacity of backscatter communication between τ and UAV is expressed as:

[0133]

[0134] To verify the performance of the algorithm proposed in this invention, we consider comparing it with the following two algorithms:

[0135] Comparative Algorithm 1: There is no eavesdropping device in the scene. The drone trajectory is optimized but energy harvesting is not performed, and there is no time optimization.

[0136] Comparative Algorithm 2: It takes eavesdropping capacity into consideration and optimizes the UAV trajectory, but does not optimize the time allocation of energy collection and backscattering, and has no time optimization.

[0137] Figure 4 The security capacity of the algorithm proposed in this paper is compared with that of Comparative Algorithms 1 and 2. As shown in the figure, the security capacity of each algorithm increases with increasing drone transmit power, with the algorithm proposed in this paper outperforming the other two algorithms in terms of security capacity performance. This is because Comparative Algorithm 2 does not consider energy causality and, therefore, does not optimize time allocation, resulting in a lower security capacity than the algorithm proposed in this paper. Comparative Algorithm 1 does not consider communication security, does not involve eavesdropping devices in the scenario, optimizes communication capacity, and does not perform energy harvesting, so it does not optimize time allocation. Therefore, it has the worst security performance of the three algorithms.

[0138] Figure 5 The bar graph of the average energy collection time and average backscattering time as the UAV transmit power changes within 60 time slots when the slot length is 1s is given. The blue square represents the average energy collection time (s) and the yellow square represents the average backscattering time (s). By comparing different transmit powers P t The average time distribution under the condition of low transmission power can be found that the time used for energy collection is significantly longer than the backscattering time. t As , the energy collection time gradually decreases, the proportion of energy collection time in time slot t decreases, and the backscattering time increases accordingly.

[0139] Figure 6 Given the different transmission powers P t The curve of safety capacity changing with the flight period T of UAV is shown in the figure below. Figure 6It can be found that as the UAV's flight period T increases, the secure backscatter capacity will also increase, and the growth rate is faster in the early stage. This is because when the number of time slots remains unchanged, the increase in T increases the time slot length t, thereby increasing the energy collection and backscattering time in the corresponding time slot, resulting in an increase in backscattering capacity. The growth rate slows down in the later stage. This is because there is a threshold in the later stage, and the eavesdropping capacity will also increase. In order to ensure secure backscatter communication, the proportion of backscattering time will be slightly reduced compared to the early stage. In addition, when the UAV's transmission power P t When the transmission power increases, the safe backscatter capacity will also increase. Therefore, if you want to increase the target capacity, you can increase the transmission power P t And increase the flight period T of UAV to achieve it.

[0140] Figure 7 The secure backscatter capacity is given as the transmit power P of the UAV under different numbers of eavesdropping devices. t As can be seen from the figure, as the number of eavesdropping devices increases, the secure backscatter capacity will decrease. This is because as the number of eavesdropping devices increases, the eavesdropping capacity will also increase during the backscatter communication time, which will reduce the corresponding secure backscatter capacity. In addition, by comparing Figure 6 、 Figure 7 From the simulation results, we can find that the UAV’s transmission power P t The impact on security capacity is greater than the number of eavesdropping devices, so more attention should be paid to the value of transmission power.

[0141] Example 2: The second aspect, as Figure 8 As shown, in order to achieve the above-mentioned purpose, the present invention discloses a device for optimizing the security capacity of a UAV-assisted backscatter communication system, comprising:

[0142] The data problem optimization module 11 is used to obtain collected energy, input the collected energy into the backscatter device for backscatter communication, and optimize the time allocation, UAV trajectory and backscatter device scheduling based on the preset energy constraints and the safety performance constraints of the backscatter communication;

[0143] The safety capacity optimization module 12 is used to solve the time allocation, drone trajectory and backscatter device scheduling respectively to obtain three optimal solutions, perform global iteration on the three optimal solutions, and obtain the global optimal solution as the safety capacity optimization result, thereby maximizing the safety capacity of the minimum link.

[0144] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0145] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which executes the above method when executed by a processor. The storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0146] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0147] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present disclosure. Various changes and improvements may be made to the present disclosure without departing from the spirit and scope of the present disclosure, and such changes and improvements shall fall within the scope of the present disclosure.

Claims

1. A method for optimizing the security capacity of a drone-assisted backscatter communication system. The system includes one drone, K backscatter devices, and M eavesdroppers. The method is characterized by: The method comprises the following steps: Acquire collected energy and input it into the backscatter device for backscatter communication. Based on the preset energy constraints and the safety performance constraints of backscatter communication, optimize the time allocation, UAV trajectory and backscatter device scheduling; The problem of optimizing time allocation, drone trajectory and backscatter device scheduling is a safety capacity optimization problem; Among them, the linear programming method is used to find the optimal solution of the time allocation sub-problem; The optimal solution of the UAV trajectory subproblem is solved using the continuous convex approximation method; The optimal solution of the backscatter device scheduling subproblem is solved using the branch and bound method; The three optimal solutions for time allocation, drone trajectory, and backscatter device scheduling are solved separately. These three optimal solutions are then globally iterated to obtain the global optimal solution, which is used as the safety capacity optimization result to maximize the safety capacity of the minimum link. The prerequisite for the security capacity optimization is that the backscatter capacity is greater than the eavesdropping capacity.

2. The method for optimizing the security capacity of a UAV-assisted backscatter communication system according to claim 1, wherein: The safety capacity optimization problem is defined as P1: s.t.C1:a k [n][R uk [n]-R km [n]]≥0 C2:||u[n+1]-u[n]||≤V max t,u[1]=u[N] C4:t1[n]+t2[n]=t C5:0≤t1[n]≤1 C6:0≤t2[n]≤1 Among them, variable a k [n] refers to BD k Working status, a k [n]=0 means it is in dormant state, a k [n]=1 means it is in working state; R uk [n] refers to the backscatter time between UAV and BD k Normalized backscatter communication capacity between km [n] refers to the backscattering time, ED m and BD k The normalized eavesdropping capacity between the two time slots, N is the number of time slots; C1 is the backscattering capacity between the drone and the backscattering device in any time slot is always greater than the eavesdropping capacity of the eavesdropping device; C2 is the drone trajectory constraint, u[n]=(x n ,y n ) is the position of the UAV in any time slot, t = T / N is the time slot length, T is the flight period, V max is the maximum flight speed of the UAV; C3 is the user scheduling variable constraint. Since the UAV only communicates with one BD in any time slot, a k [n] need to meet C4, C5, and C6 are all time constraints, t1[n] is the energy acquisition time, t2[n] is the backscattering time; C7 is the energy causality requirement, E min is the minimum energy required to maintain the backscatter device working in a time slot, H is the flight altitude of the UAV, b k For BD k Coordinates of BD k is the kth backscattering device on the ground, ED m There are m eavesdroppers, γ k It's BD k The reflection coefficient, η∈[0,1] is the energy collection efficiency, E k [n] is the nth time slot BD k The collected energy is expressed as P t is the transmission power of the UAV, and u[n] is the coordinate in any time slot.

3. The method for optimizing the security capacity of a UAV-assisted backscatter communication system according to claim 2, wherein: The time allocation subproblem is defined as P2: The UAV trajectory subproblem is defined as P3: The backscatter device scheduling subproblem is defined as P4:

4. The method for optimizing the security capacity of a UAV-assisted backscatter communication system according to claim 3, wherein: The process of using linear programming method to solve the optimal solution of the time allocation sub-problem is as follows: By introducing an auxiliary variable, the minimum-maximization problem is transformed into a maximum-maximization problem, and the problem P2 is reformulated as P5: Use linear programming to find the optimal solution for time allocation.

5. The method for optimizing the security capacity of a UAV-assisted backscatter communication system according to claim 4, wherein: The process of using the continuous convex approximation method to solve the optimal solution of the drone trajectory subproblem is as follows: Using variable substitution, problem P3 is simplified and reformulated as P6 as follows: Among them, introduce a new variable q[n], let q[n]=H 2 +||u[n]-b k || 2 , b k =(x k ,y k ) is the ground BD k The coordinates of c=P t β0(1-γ k )η, a, b, c are constants, e m =(x m ,y m ) is the ground ED m coordinates; The linear form of the inequality constraint is obtained by the first-order Taylor expansion method; The current optimal UAV trajectory is obtained through continuous convex approximation iterative solution.

6. The method for optimizing the security capacity of a UAV-assisted backscatter communication system according to claim 5, wherein: The process of using the branch and bound method to solve the optimal solution of the backscatter device scheduling subproblem is as follows: First, branch P4 for the first time and consider the relaxation problem in branch 1. First, use the observation method to find a bound, find an integer solution x, substitute the solution x into the objective function, and then modify the bound continuously during the calculation. If the optimal solution x0 for solving the relaxation problem of branch 1 happens to be an integer solution, then if x0>x, we need to redefine the bound and use x0 as the new bound. According to the new bound, we can see whether the relaxation problem of branch 2 needs to be discussed, and find the optimal value of the objective function f corresponding to the optimal solution of branch 2. * ; If the optimal value of branch 2 is less than the optimal value of branch 1, f * ≤f 0 , at this time branch 2 no longer branches.

7. UAV-assisted backscatter communication system security capacity optimization device, characterized in that: include: The data problem optimization module is used to obtain the collected energy and input the collected energy into the backscatter device for backscatter communication. Based on the preset energy constraints and the safety performance constraints of the backscatter communication, the module optimizes the time allocation, the UAV trajectory and the backscatter device scheduling. The safety capacity optimization module is used to solve time allocation, drone trajectory and backscatter device scheduling respectively to obtain three optimal solutions. The three optimal solutions are globally iterated to obtain the global optimal solution as the safety capacity optimization result, thereby maximizing the safety capacity of the minimum link.

8. A terminal device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on a processor. When the processor loads and executes the computer program, the method for optimizing the security capacity of a drone-assisted backscatter communication system according to any one of claims 1 to 6 is adopted.

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