An unmanned aerial vehicle assisted air-ground backscattering communication resource optimization method

Abstract

The application provides a kind of unmanned aerial vehicle assisted air-ground backscattering communication resource optimization method, belongs to backscattering communication network field, it is related to a kind of design scheme using high mobility and flexibility of unmanned aerial vehicle to improve backscattering communication performance.Unmanned aerial vehicle transmits information to ground user and backscattering device through LOS (Line-of-Sight) link, backscattering transmitter receives information from unmanned aerial vehicle, and is transmitted to backscattering receiver simultaneously, and specific scheme is as shown in schematic diagram 1.Based on this model, the application provides a kind of design method for jointly optimizing unmanned aerial vehicle trajectory, unmanned aerial vehicle transmission power and ground user scheduling, which can generate the optimal motion trajectory of unmanned aerial vehicle and the transmission power of unmanned aerial vehicle in each time slot according to model parameters to maximize the average sum rate of backscattering user.

Description

Technical Field

[0001] This invention belongs to the field of Ambient Backscatter Communication (A2C), and relates to a design method for improving backscatter communication performance by leveraging the high mobility and flexibility of UAVs. Specifically, it refers to a method that jointly optimizes the UAV trajectory, UAV transmission power, and ground user scheduling when the UAV communicates with backscattering devices, thereby maximizing the minimum average backscattering rate for backscattering users. Background Technology

[0002] In recent years, with the continuous development of communication technology, people's production and lifestyles have undergone tremendous changes, and wireless networks have permeated every corner of daily life. Research on unmanned aerial vehicle (UAV) technology has also been continuously improving along with the development of wireless communication technology. Compared with ground-based wireless communication, UAVs have advantages such as low cost and high mobility. At the same time, UAV technology provides the possibility of establishing new dedicated ground networks. UAVs can also be used to establish short-range line-of-sight communication links with ground users, making the link performance more reliable. However, wireless networks are characterized by large-scale user access, high power consumption, and high throughput; therefore, energy consumption is also a significant factor limiting network development.

[0003] Backscattering, in physics, refers to the reflection of waves, particles, or signals from the direction they originate. In simpler terms, it means that communication devices do not generate signals themselves, but rather achieve information exchange by reflecting signals transmitted from the environment. The technology works by generating modulated reflections in incident radio frequency (RF) waves, which are typically acquired by a receiver designed as a reader. Backscattering communication technology has proven particularly effective for large-scale, low-cost IoT devices. Typically, backscattering allows backscattering devices to transmit information by reflecting incident RF waves (i.e., carrier waves), and also harvests energy from the incident RF signal, all without the need for complex and power-consuming active RF components. This new technology utilizes existing receivers to generate carrier signals. The most advanced backscattering technology involves the design of a novel backscattering tag that modulates carrier signals using the tag, providing long-range communication while consuming only microwatts of power. Backscattering communication is also considered a spectrum and energy-saving technology for the IoT, particularly offering significant advantages in improving the spectrum utilization and energy efficiency of battery-constrained devices.

[0004] Despite advancements in UAV-assisted backscatter communication technology, this invention focuses on reducing energy consumption and improving communication quality by analyzing communication link conditions and optimizing UAV trajectories, transmission power, and ground user scheduling. Therefore, optimizing the design to improve communication system performance is crucial. This invention optimizes the UAV air-to-ground system with the goal of maximizing the minimum average rate for backscattering users. Summary of the Invention

[0005] The purpose of this invention is to solve the problem of maximizing the data rate of UAV-assisted backscatter communication. The UAV transmits information to ground users and backscatter devices via a Line-of-Sight (LOS) link. The backscatter transmitter receives signals from the UAV and simultaneously transmits them to the backscatter receiver. A specific scheme is illustrated below. Figure 1 As shown. Based on this model, this invention provides a design method for jointly optimizing UAV trajectory, UAV transmission power, and ground user scheduling. This method can generate the optimal UAV trajectory and the UAV transmission power in each time slot according to set parameters, so as to maximize the average sum rate of backscattering users.

[0006] To achieve the above objectives, the technical solution adopted by the present invention to solve the technical problem is as follows:

[0007] A method for optimizing air-to-ground backscatter communication resources assisted by unmanned aerial vehicles (UAVs) includes the following steps:

[0008] The first step is to build a system model:

[0009] 1) In a UAV air-to-ground backscatter communication network, UAVs transmit air-to-ground information in roughly two ways: The first method involves the UAV sending air-to-ground communication signals to K ground users via time-division multiplexing. Users receive the signals and decode the information; the communication performance is determined by the UAV-user air-to-ground link. The second method involves the UAV sending radio frequency carrier signals to M ground backscattering devices. Each backscattering device receives the radio frequency carrier signal and performs further processing on its receiver; the communication performance is determined by the UAV-backscattering device air-to-ground link.

[0010] 2) In this system, the UAV flies at an altitude of H, and its entire flight cycle is T, divided into N time intervals. In each time interval, the UAV's horizontal coordinates on the ground are... The horizontal coordinates corresponding to ground users are The coordinates of the backscatter transmitter are The receiver coordinates are respectively Assuming the air-to-ground channel gain satisfies the free-space path loss model, the reference path loss coefficient at a distance of 1 meter is β0, and the power emitted by the UAV is p. n Since the backscatter communication power is very low, the interference it causes to air-to-ground communication is negligible. Therefore, the signal-to-noise ratio of the k-th ground user can be expressed as:

[0011]

[0012] Where, σ 2 This is the noise power. Correspondingly, the signal-to-interference-plus-noise ratio (SIN / N) for the m-th backscatter communication user is:

[0013]

[0014] Where d0 is the distance between the backscatter transmitter and the receiver. η is the terrestrial communication path loss exponent.

[0015] Let R k This represents the average communication rate of the k-th ground user:

[0016]

[0017] Where, α k,n Let R be a discrete 0, 1 variable, representing the drone serving the k-th user in the nth time slot. Similarly, let R... m The average communication rate of the m-th backscattering user is:

[0018]

[0019] The second step is to simplify the objective function and list the optimization problem:

[0020] The average communication and rate of the m-th backscattering user, as shown in Equation (4), is a function related to the UAV trajectory Q and the UAV transmit power P. The average communication and rate of the k-th ground user is a function related to the ground user scheduling A, the flight trajectory Q, and the UAV transmit power P. The optimization objective is the average communication and rate of the system. Based on this model, the following optimization problem can be constructed:

[0021]

[0022] In this optimization problem, C1 and C2 are constraints on ground user scheduling A, stipulating that each UAV can only serve one ground user per time slot, while other ground users are in non-communication mode. C3 is a constraint on the ground user rate, where R0 represents the minimum communication rate of the ground user. C4 is a constraint on the UAV's transmit power, where P0 represents the maximum average transmit power of the UAV. C5 is a constraint on the minimum power. C6 is the maximum distance the UAV can fly in each time slot that is less than a set value D. The initial and final positions of the UAV are defined in C7, and the UAV must eventually return to its initial position.

[0023] The third step is to design an algorithm to solve the optimization problem:

[0024] The above problem is difficult to solve because it is a multivariable mixed integer nonconvex problem. Therefore, an alternating iterative method is introduced, and the problem (5) is decomposed into three subproblems. In each subproblem, convex optimization theory and continuous convex approximation techniques are used to solve the problem, thereby obtaining the local optimal solution.

[0025] 1) Optimization of ground user scheduling A

[0026] The problem involving variable A is an integer optimization problem because of the introduced α. k,n It is a discrete variable; for ease of processing, the binary variable α is... k,n Relaxed to continuous variables within the interval [0,1] Linear programming problems can be solved using the interior-point method in convex optimization algorithms.

[0027] This invention fixes the UAV's transmit power P and flight trajectory Q. This subproblem is expressed in the following form:

[0028]

[0029]

[0030]

[0031] 2) Pre-optimization of UAV transmit power and flight trajectory

[0032] When optimizing these two variables, variable A needs to be kept constant. It can be noted that the optimization problem for variables P and Q is a non-convex optimization problem because the objective function and some constraints are non-convex. This invention requires the objective function and constraints to be transformed using convex optimization techniques. Considering the speed of problem convergence, this invention first performs a pre-optimization process on the UAV's transmit power and trajectory. In formula (2), the interference from the backscatter transmitter is not considered initially; the signal-to-noise ratio of the receiver is calculated first, i.e. Then the following constraints apply:

[0033]

[0034]

[0035] C4-C7.(7c) where the optimization objective is...

[0036] Furthermore, based on the two criteria that any convex function is a global lower bound of its first-order Taylor expansion at any point and any concave function is a global upper bound of its first-order Taylor expansion at any point, this invention can obtain the lower bound of the objective function (7a) by performing a partial Taylor expansion at a fixed point. Similarly, for constraint (7b), its optimization method is the same as (7a). Using this method, this invention can transform the above problem into the following form:

[0037]

[0038]

[0039] C4-C7.(8c) where yes Given the UAV transmit power P in the r-th iteration r and trajectory Q r The first-order Taylor expansion at [location]. The solution method is the same as above.

[0040] After processing the above issues, convex optimization tools, such as the CVX toolbox, can be used to solve them.

[0041] 3) Optimization of UAV transmit power P and flight trajectory Q

[0042] This optimization involves further refining the pre-optimization of the UAV's transmit power and flight trajectory, taking into account the interference from the backscatter transmitter, as shown in equation (2). This improves both the convergence of the algorithm and the effectiveness and accuracy of the design. Specifically:

[0043] Based on the signal-to-interference-plus-noise ratio (SIR) of the backscatter receiver, the optimization problem becomes:

[0044]

[0045]

[0046] C4-C7.(9c)

[0047] The optimization and pre-optimization approaches for the UAV's transmit power P and flight trajectory Q are similar. First, other variables are fixed, and the optimization problem is defined. For the non-convex objective function and constraints, auxiliary variables are introduced, and continuous convex approximation techniques are used to approximate the original problem as a solvable convex problem. We let:

[0048]

[0049] in:

[0050]

[0051]

[0052] for We utilize the drone's transmit power P r and trajectory Q r The lower bound of the first-order Taylor expansion at point is obtained. Similarly, by introducing slack variables... right Approximation is performed, and the constraint condition (9b) is handled in the same way as (7b).

[0053] The present invention can transform the above problems into the following form:

[0054]

[0055]

[0056] C4-C7.(13c)

[0057] 4) Iterative Algorithm Design

[0058] This invention proposes an iterative optimization algorithm to solve this non-convex problem. It approximates the problem using continuous convex subproblems, transforming it into a convex optimization problem. The problem is divided into three subproblems. Through an iterative algorithm, these three variable blocks are alternately optimized in each iteration, optimizing one variable block at a time while keeping the others unchanged. This process is continued iteratively until the result is less than a given threshold. The specific process is as follows:

[0059] 1) Set the initial UAV transmit power P 0 and trajectory Q 0 The number of iterations r = 0, and the iteration termination threshold ξ;

[0060] 2) Repetition;

[0061] 3) Using the given Q r and P r Solve the convex optimization problem (6) and express the solution as A. r+1 ;

[0062] 4) Using the given A r+1 Q r and P r Solve the convex optimization problem (8) and express the solution as and

[0063] 5) Using the given A r+1 , and Solve the convex optimization problem (13) and represent the solution as P r+1 and Q r+1 ;

[0064] 6) Calculate the increase in the objective value of problem (P1) after this iteration. If the value is greater than the threshold ξ, update the iteration number r = r + 1 and jump to step 2) for the next iteration optimization; if the increase in the objective value is less than the threshold ξ,

[0065] Then terminate the iteration and output the optimized variable values.

[0066] Therefore, by jointly optimizing user scheduling, UAV transmit power, and flight trajectory, we maximized the average backscatter communication rate.

[0067] The beneficial effects of this invention are:

[0068] This invention identifies the locations of ground users and backscatter devices within the system. It proposes a deployment scheme that maximizes average communication rate through the rational design of ground user scheduling, UAV transmission power, and flight trajectories. This invention also provides a reference method for determining optimal UAV trajectories and maximizing user communication rates. Attached Figure Description

[0069] Figure 1 It is a system model of UAV air-to-ground communication and backscatter communication;

[0070] Figure 2 This is a diagram showing the initial trajectory of the UAV in this invention and its flight trajectory under different R0 conditions;

[0071] Figure 3 This is a transmission power diagram for each time slot under different transmission powers in this invention;

[0072] Figure 4 This is a graph showing the average sum rate of the backscatter receiver under different flight trajectories in this invention;

[0073] Figure 5 This is a graph showing the average sum rate of the backscatter receiver at different UAV transmit powers in this invention. Detailed Implementation

[0074] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0075] To better understand the above technical solution, a detailed analysis is provided below in conjunction with the accompanying drawings and specific implementation methods.

[0076] This invention proposes a novel method to improve backscatter communication performance by leveraging the high mobility of unmanned aerial vehicles (UAVs), maximizing the average communication and data rate of the backscatter receiver while ensuring the data rate for ground users. The specific steps are as follows:

[0077] The first step is to build a system model:

[0078] like Figure 1 As shown, in an air-to-ground communication network with K=4 ground users, M=3 backscatter devices, and one UAV, the network side length of the UAV's flight area is 2000m. The UAV's flight trajectory is pre-set as a circular trajectory, and its flight altitude is 50m. Ground users and backscatter devices are distributed in a rectangular network, with the backscatter communication receivers positioned on a circle with a radius of 10m centered on the transmitter. The air-to-ground communication carrier frequency is 915MHz, and the gain of the UAV and ground user receiving antennas and the backscatter receiver is 6dBi, while the antenna gain of the backscatter transmitter is 1.8dBi. The path loss index of the air-to-ground channel is α1, with a value of 2, and the path loss index of the ground link is α2, with a value of 2.7. The iteration termination threshold ξ=10. -4 Other parameters: σ 2 = -110dBm, V max =50m / s, P0=100mW.

[0079] The second step is to determine the objective function, list the constraints, and list the optimization problems based on the specific parameter settings in the first step.

[0080] The third step is to solve the optimization problem: We decompose problem (5) into three optimization subproblems, namely problem (6), problem (8) and problem (13). Then we iteratively solve each of the three subproblems until the increment of the objective function is less than a set threshold. Figure 2 This invention presents a UAV flight trajectory design that maximizes the average communication and data rate of the backscatter receiver. The initial UAV trajectory and the optimized trajectory were obtained. During communication, the UAV's trajectory gradually approaches the backscatter device, with some hovering over it. However, as the minimum communication rate for the ground user increases, the UAV's flight trajectory slowly shifts towards the ground user. Throughout this process, the UAV's flight speed remains relatively constant, achieving an optimal trade-off between the ground user's communication rate and the backscatter device's communication rate. Figure 3The paper presents the optimized power diagram of the UAV in each flight time slot. Theoretically, to improve the performance of the communication system, the UAV should increase its transmission power when approaching the backscattering device, resulting in a local transmission power peak; and it should also increase its transmission power when approaching the ground user to meet the minimum communication rate requirements of the ground user, resulting in a small peak. In this invention, a significant peak is clearly observed when the UAV approaches the backscattering device.

[0081] Figure 4 The paper presents graphs showing the average backscatter communication rate for different average maximum transmit power of UAVs. The performance of four UAV flight trajectory designs was compared: optimized UAV transmit power and flight trajectory with R0 = 1 bit / s / Hz; optimized UAV transmit power and flight trajectory with R0 = 3 bits / s / Hz; initial UAV transmit power and flight trajectory with R0 = 1 bit / s / Hz; and fixed UAV position at the center of gravity of all users with R0 = 1 bit / s / Hz. As the average maximum transmit power of the UAV increases, the average backscatter communication rate also increases. However, when the transmit power exceeds 30 dBm, the average communication rate change is not significant. More importantly, through iterative optimization, the system's communication rate is significantly improved, verifying the effectiveness and feasibility of this design. Simultaneously, as R0 increases, the UAV needs to meet higher speed requirements for ground users, resulting in a slight decrease in the backscatter communication rate.

[0082] Figure 5 The average sum and rate of backscatter communication under different power levels were compared as the number of iterations increased. With increasing iterations, the average sum and rate of backscatter communication continuously increased, reaching convergence at iteration number 30. When the UAV's transmit power was relatively low, the rate variation range was large; however, as the transmit power increased, the increase in the average sum and rate of backscatter communication decreased. Therefore, while increasing the UAV's transmit power can improve the quality of the communication system, simply increasing it does not significantly improve system performance efficiency.

[0083] The above-described embodiments are merely illustrative of the implementation methods of the present invention, but should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the protection scope of the present invention.

Claims

1. A method for optimizing air-to-ground backscatter communication resources assisted by unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: The first step is to build a system model: 1) In a UAV air-to-ground backscatter communication network, UAVs transmit air-to-ground information in two ways: The first method involves the UAV sending air-to-ground communication signals to K ground users via time-division multiplexing. The users receive the signals from the UAV and decode the information. The communication performance is determined by the UAV-user air-to-ground link. The second method involves the UAV sending radio frequency carrier signals to M ground backscattering devices. Each backscattering device receives the radio frequency carrier signals and performs further processing on its receiver. The communication performance is determined by the UAV-backscattering device air-to-ground link. 2) In this system, the UAV's flight altitude is H, and its entire flight cycle is T, divided into N time intervals; in each time interval, the UAV's horizontal coordinate on the ground is... The horizontal coordinates corresponding to ground users are The coordinates of the backscatter transmitter are The receiver coordinates are respectively Assuming the air-to-ground channel gain satisfies the free-space path loss model, the reference path loss coefficient at a distance of 1 meter is β0, and the power emitted by the UAV is p. n Since the backscatter communication power is very low, the interference it causes to air-to-ground communication is negligible. Therefore, the signal-to-noise ratio of the k-th ground user can be expressed as: Where, σ 2 This is the noise power; correspondingly, the signal-to-interference-plus-noise ratio (SIN / N) for the m-th backscatter communication user is: Where d0 is the distance between the backscatter transmitter and the receiver; η is the ground communication path loss exponent; Let R k This represents the average communication rate of the k-th ground user: Where, α k,n Let R be a discrete 0, 1 variable, representing the drone serving the k-th user in the nth time slot; similarly, let R... m The average communication rate of the m-th backscattering user is: The second step is to simplify the objective function and list the optimization problem: The average communication and rate of the m-th backscattering user, as shown in Equation (4), is a function related to the UAV trajectory Q and the UAV transmit power P. The average communication and rate of the k-th ground user is a function related to the ground user scheduling A, the flight trajectory Q, and the UAV transmit power P. The optimization objective is the average communication and rate of the system. Based on this model, the following optimization problem is constructed: (P1)： C6:||q n+1 -q n ||≤D,n=1,2,…,N-1,C7:q1=q N . In this optimization problem, C1 and C2 are constraints on ground user scheduling A, stipulating that each UAV can only serve one ground user in each time slot, while other ground users are in non-communication mode; C3 is a constraint on the ground user rate, where R0 represents the minimum communication rate of the ground user; C4 is a constraint on the UAV's transmit power, where P0 represents the maximum average transmit power of the UAV; C5 is a constraint on the minimum power; C6 is that the maximum distance the UAV flies in each time slot is less than a set value D; the initial and final positions of the UAV are defined in C7, and the UAV must eventually return to its initial position. The third step is to design an algorithm to solve the optimization problem: By introducing an alternating iteration method, problem (5) is decomposed into three subproblems. In each subproblem, convex optimization theory and continuous convex approximation technique are used to solve the problem, thereby obtaining the local optimal solution of the problem.

2. The method for optimizing air-to-ground backscatter communication resources assisted by unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The specific steps of the third step are as follows: 1) Optimization of ground user scheduling A Introducing discrete variable α k,n , the binary variable α k,n Relaxed to continuous variables within the interval [0,1] The interior-point method in convex optimization algorithms is used to solve linear programming problems. Given a fixed UAV transmit power P and flight trajectory Q, this subproblem can be expressed in the following form: (P2)： 2) Pre-optimization of UAV transmit power and flight trajectory When optimizing these two variables, variable A needs to be kept constant; the objective function and constraints are transformed using convex optimization techniques; a pre-optimization process is first performed on the UAV's transmit power and trajectory. In formula (2), the interference from the backscatter transmitter is not considered at first, and the signal-to-noise ratio of the receiver is calculated first, i.e. Then the following constraints apply: (P3): C4-C7.(7c) where the optimization objective is... Furthermore, by performing a Taylor expansion of the objective function (7a) at a fixed point to obtain its lower bound, similarly, the optimization method for constraint (7b) is the same as that for (7a). Using this method, the above problem is transformed into the following form: (P4): C4-C7.(8c) where yes Given the UAV transmit power P in the r-th iteration r and trajectory Q r The first-order Taylor expansion at the given point; The solution method is the same as above; The above problems can be solved using convex optimization tools after processing the results. 3) Optimization of UAV transmit power P and flight trajectory Q This optimization involves further refining the pre-optimization of the UAV's transmit power and flight trajectory, taking into account the interference from the backscatter transmitter, as shown in formula (2); specifically as follows: Based on the signal-to-interference-plus-noise ratio (SIR) of the backscatter receiver, the optimization problem becomes: (P5): C4-C7.(9c) The optimization and pre-optimization approaches for UAV transmit power P and flight trajectory Q are similar; first, fix other variables and list the optimization problem; for non-convex objective functions and constraints, use the introduction of auxiliary variables and continuous convex approximation techniques to approximate the original problem as a solvable convex problem; make: in: for We utilize the drone's transmit power P r and trajectory Q r The lower bound of the first-order Taylor expansion at point is obtained. Similarly, by introducing slack variables... right Approximation is performed, and the constraint condition (9b) is handled in the same way as (7b); The above problem can be transformed into the following form: (P6)： C4-C7. (13c) 4) Design iterative algorithms An iterative optimization algorithm is proposed to solve this non-convex problem. It is approximated by continuous convex approximation subproblems to transform it into a convex optimization problem. The problem is divided into three subproblems, and then the iterative algorithm is used to alternately optimize these three variable blocks in each iteration, optimizing one variable block at a time while keeping the other variable blocks unchanged. Then, the algorithm is continuously iterated until the result is less than a given threshold.

3. The method for optimizing air-to-ground backscatter communication resources assisted by unmanned aerial vehicles (UAVs) according to claim 2, characterized in that, In step 4) of the third step, the specific process of designing the iterative algorithm is as follows: 1) Set the initial UAV transmit power P 0 and trajectory Q 0 The number of iterations r = 0, and the iteration termination threshold ξ; 2) Repetition; 3) Using the given Q r and P r Solve the convex optimization problem (6) and express the solution as A. r+1 ; 4) Using the given A r+1 Q r and P r Solve the convex optimization problem (8) and express the solution as and 5) Using the given A r+1 , and Solve the convex optimization problem (13) and represent the solution as P r+1 and Q r+1 ; 6) Calculate the increase in the objective value of problem (P1) after this iteration. If the value is greater than the threshold ξ, update the iteration number r = r + 1 and jump to step 2) for the next iteration optimization; if the increase in the objective value is less than the threshold ξ, Then terminate the iteration and output the optimized variable values; Ultimately, by jointly optimizing user scheduling, UAV launch power, and flight trajectory, the average backscatter communication rate is maximized.

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