A method for sensing and communicating integrated drone-assisted Internet of Vehicles

Abstract

The present invention belongs to the field of drone-assisted detection and communication, and proposes a method for drone-assisted vehicle networking with integrated perception and communication. The system of drone-assisted vehicle networking with integrated perception and communication includes a drone, a data collection center, and a ground stationary vehicle; the drone, as a dual-function aerial platform, gives ground vehicles the ability to perceive the surrounding environment and sends vehicle information to the data collection center to complete information collection and processing. By introducing the concept of radar mutual information and jointly optimizing the drone's task scheduling, detection range, transmission power, and flight trajectory, the drone's radar estimation rate can be maximized while effectively executing ISAC and data upload tasks. Compared with the single-function drone-assisted vehicle networking method, the present invention expands the application scenarios of drones, can provide continuous perception and communication services for ground vehicles, and has high application value.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) assisted detection and communication, and in particular to a method for unmanned aerial vehicle (UAV) assisted vehicle networking with integrated perception and communication. Background Art

[0002] In recent years, with the rapid development of Internet of Things technology, the Internet of Vehicles (IoV) has become a research hotspot in the field of intelligent transportation. By setting up roadside units and base stations, vehicles can obtain information about the surrounding environment and share information with surrounding vehicles, infrastructure and pedestrians. However, in practical applications, the commonly used roadside units and base stations at this stage face pain points that need to be solved urgently. For example, in crowded urban areas, there are problems of obstruction and signal fading in radar detection links and communication links; there is a high cost problem in deploying roadside units in remote areas; and there is a problem that roadside units cannot be quickly restored to operation after being damaged due to non-human factors. In order to solve these problems, the Internet of Vehicles assisted by dual-function unmanned aerial vehicles (UAVs) has entered people's field of vision and is regarded as an effective means to solve the problems faced by the land-based Internet of Vehicles system.

[0003] "F.Liu et al., "Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond," IEEE Journal on Selected Areas in Communications, vol. 40, no. 6, pp. 1728-1767, June, 2022." proposed the Integrated Sensing and Communications (ISAC), which allows radar detection equipment and communication perception equipment to be integrated into a whole. The radar detection signal and the communication signal are transmitted by a transmitter and will gradually merge, and finally share the spectrum and signal processing equipment. The application of integrated sensing technology on drones can, on the one hand, take advantage of the high mobility of drones to provide continuous services for vehicles in the target area. On the other hand, the higher flight altitude makes the channel condition between drones and vehicles better, and has better detection and communication conditions than ground equipment. At the same time, ISAC technology also reduces the load of drones and reduces the flight power consumption of drones, which can allow drones to have longer working time and higher work efficiency. Therefore, the combination of drones and integrated sensing and communication technology can better realize the functions of perception and communication, and better play the advantages of drones with low cost and on-demand deployment. Summary of the invention

[0004] In response to the problem of lack of perception capability in drone-assisted Internet of Vehicles, the present invention proposes a method for drone-assisted Internet of Vehicles with integrated perception and communication, introduces perception and communication integration technology on the drone platform, and combines the drone's task scheduling, detection range, transmission power and flight trajectory to effectively improve the drone's total radar estimation rate.

[0005] The technical scheme of the present invention is as follows: a method for a perception and communication integrated UAV-assisted vehicle network, wherein the perception and communication integrated UAV-assisted vehicle network system includes a UAV, a data collection center and a ground stationary vehicle; the UAV has dual functions of perception and communication, and simultaneously transmits radar detection signals and communication signals; the UAV, as a dual-function aerial platform, performs perception and communication integration and data uploading tasks over a target area at a fixed altitude H and flight period T; the flight period of the UAV is decomposed into N time slots, and each time slot should be small enough to ensure that the flight trajectory of the UAV in each time slot can be approximated as a shorter straight line; when there is a ground vehicle within the UAV radar detection range, the UAV transmits a radar detection signal to detect the environment around the vehicle, and sends the perception information contained in the radar echo to the target vehicle in real time through a downlink communication link; when there is no vehicle within the UAV radar detection range, the UAV sends the stored perception information to the data collection center through an uplink communication link;

[0006] By introducing radar mutual information, the UAV's task scheduling, detection range, transmission power and flight trajectory are jointly optimized to maximize the UAV's radar estimation rate. The specific steps include:

[0007] Step 1: Build a perception and communication integrated drone-assisted Internet of Vehicles system, including network channel model, performance evaluation model and drone motion model;

[0008] Step 2: Design an optimization problem to maximize the radar estimation rate of the UAV and meet the UAV task scheduling constraints, detection range constraints, transmission power constraints and flight trajectory constraints;

[0009] Step 3: Decompose the optimization problem into three non-convex sub-problems, use the continuous convex approximation method to convert the non-convex sub-problems into convex problems, design an alternating optimization algorithm, and solve it using the CVX toolbox in MATLAB.

[0010] The network channel model in step 1 is specifically: the communication channel power gains from the UAV to the data collection center and vehicle k are respectively expressed as h c (n) and h k,com(n), where k = 1, 2, ... K, K represents the total number of vehicles; n = 1, 2, ... N; the channel between the UAV and the data collection center and vehicle k is a line-of-sight channel, and the free space fading model is used to simulate the channel between the UAV and the data collection center and the vehicle, that is,

[0011]

[0012] Among them, G t and G c Respectively represent the antenna gains of the UAV transmitter and communication receiver, d c (n) and d k (n) represents the distance from the UAV to the data collection center and vehicle k, respectively, λ=c / f c Indicates the signal wavelength, c and f c Represent the speed of light and the carrier frequency of the signal respectively;

[0013] The radar channel power gain from the UAV to vehicle k is expressed as

[0014]

[0015] Among them, G r represents the antenna gain of the UAV radar receiver, and σ represents the radar cross-sectional area of ​​the target vehicle;

[0016] The performance evaluation model in step 1 is specifically:

[0017] Considering the internal interference of the perception and communication integrated UAV-assisted vehicle network system, when the UAV performs the perception and communication integrated task on vehicle k, the signal-to-interference-to-noise ratio Γ of its communication channel is k,com (n) and the signal-to-interference-to-noise ratio Γ on the radar channel k,rad (n) is expressed as

[0018]

[0019] Where B represents the signal bandwidth, N0 represents the noise power spectral density, P com and P rad They represent UAV communication power and radar detection power respectively;

[0020] According to Shannon's formula, in the nth time slot, the UAV arrives at the data collection center R c The communication information rate of vehicle k and the communication information rate R k,com (n) is expressed as

[0021]

[0022] Among them, Pt (n) represents the transmission power of the UAV in the nth time slot;

[0023] In the nth time slot, the radar estimation rate of the UAV for vehicle k is expressed as

[0024]

[0025] The total radar estimation rate of the UAV in one flight cycle is expressed as

[0026]

[0027] Among them, ω k (n) is a binary discrete variable used to represent the scheduling of the sensing and communication integration task, ω k When (n) is equal to 0, it means that the UAV does not perform the perception and communication integration task, ω k When (n) is equal to 1, it means that the UAV is performing the perception and communication integration task on vehicle k at this time;

[0028] To ensure that the UAV can effectively perform the corresponding tasks, the amount of information transmitted by the UAV to the vehicle is greater than the amount of information detected by the radar in each perception and communication integration task time slot; in a flight cycle, the amount of information transmitted by the UAV to the data collection center is greater than the amount of information detected by the radar; the UAV data transmission constraint is expressed as

[0029] R k,rad (n)≤R k,com (n) (10)

[0030]

[0031] in, It is used to indicate the scheduling of data upload tasks. When b(n) is equal to 0, it means that the UAV is not performing the data upload task. When b(n) is equal to 1, it means that the UAV is sending the stored perception information to the data collection center.

[0032] The UAV motion model includes the relative position constraints of the UAV flight and the kinematic constraints of the UAV flight; the UAV satisfies the following relative position constraints during flight:

[0033] l u (1) = l u (N) (12)

[0034]

[0035] Among them, l u (n)=(x u (n),y u(n)) represents the position coordinates of the UAV at the nth time slot, l u (N) represents the position coordinates of the UAV at the Nth time slot; θ represents the maximum detection angle of the UAV radar, (x k ,y k ) represents the position coordinates of vehicle k;

[0036] The drone meets the following kinematic constraints during flight:

[0037]

[0038] Among them, V(n) represents the flight speed of the UAV in the nth time slot, Δt represents the duration of each time slot, and a max Indicates the maximum acceleration of the drone flight;

[0039] The energy consumption of the drone includes the consumption of the drone's transmission power and the consumption of the drone's propulsion power. In the nth time slot, the power P used by the drone for communication is com (n) and the power P used by the drone for radar detection rad (n) is expressed as

[0040]

[0041] Among them, 0≤α(n)≤1 represents the power allocation factor in each time slot; when the UAV performs the data uploading task, α(n)=1.

[0042] In order to maximize the radar estimation rate of the UAV, the optimization problem of step 2 is

[0043]

[0044] ω k (n)((x u (n)-x k ) 2 +(y u (n)-y k ) 2 )≤(Htanθ) 2 (18e)

[0045]

[0046] V(1)=V(N)(18j)

[0047] l u (1) = l u (N)(18k)

[0048]

[0049] Among them, Pavg Represents the average transmission power of the UAV, V max represents the maximum flight speed of the UAV; (18b)~(18d) indicate the task scheduling constraints. In each time slot, the UAV can only perform one task and can only provide services to one vehicle when performing the perception and communication integration task; (18e) ensures that the UAV provides perception and communication integration services to vehicles within its detection range; (15f) and (15g) restrict the radar detection and information transmission of the UAV so that the UAV can reasonably perform the perception and communication integration and data upload tasks; (18h) and (18i) restrict the transmission power of the UAV in each time slot to ensure that the UAV power is not overloaded; (18j)~(18n) restrict the flight parameters of the UAV to ensure the normal flight of the UAV.

[0050] Furthermore, the optimization problem proposed in step 2 is a non-convex optimization problem of mixed integer fractions, which is difficult to solve directly. Therefore, in step 3, the original problem is decomposed into three sub-problems, and an iterative algorithm of alternating optimization is designed. The CVX toolbox is used to cyclically optimize the task scheduling of the UAV, the transmission power of the UAV, and the flight trajectory of the UAV until the objective function value converges and the optimal solution of the optimization problem is obtained.

[0051] Beneficial effects of the invention: The invention proposes a method for sensing and communicating integrated drone-assisted Internet of Vehicles, which can maximize the radar estimation rate of drones while effectively executing ISAC and data upload tasks by jointly optimizing the drone's task scheduling, detection range, transmission power, and flight trajectory. Compared with the single-function drone-assisted Internet of Vehicles method, the invention expands the application scenarios of drones, gives ground vehicles the ability to perceive the surrounding environment, and has strong application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a system structure diagram of the perception-communication integrated drone-assisted Internet of Vehicles of the present invention.

[0053] Figure 2 It is the motion trajectory of the UAV in the present invention.

[0054] Figure 3 It is the flight speed change of the UAV in the present invention.

[0055] Figure 4 It is the convergence of the alternating optimization algorithm in the present invention at different radar maximum detection angles.

[0056] Figure 5 It is the change of the total radar estimation rate of the UAV with the flight altitude in the present invention.

[0057] In the figure: For communication links; is the trajectory of the drone; is the radar detection range; For ground vehicles. DETAILED DESCRIPTION

[0058] The present invention is further described below in conjunction with specific embodiments.

[0059] Figure 1 In the system of perception communication integrated drone-assisted vehicle networking shown in the figure, K = 12, T = 100s, Δt = 0.5s, H = 100m, V max =40m / s, a max =5m / s 2 ,θ=30°,P avg =1W,f c =3.5GHz, B=50MHz, N0=-160dBmW / Hz, σ=1m 2 , G t =17dBi, G r =17dBi, G c =0dBi. Ground vehicles are randomly distributed in a circle with a radius of 500m and the data collection center (0m, 0m, 0m) as the center.

[0060] Figure 2 The optimized trajectories of the two drones are shown. From the simulation results, it can be seen that when the vehicle is within the detection range of the drone, the drone has been performing the ISAC task, which is consistent with the goal of maximizing the radar estimation rate. During the flight, the drone will fly over the vehicle as much as possible to obtain better channel conditions and more detection information. When there is no ground vehicle within the detection range of the drone, the drone will perform the data upload task, and at this time the drone mostly flies in a straight line between vehicles to leave more time for the ISAC task.

[0061] Figure 3 The initial speed and optimized speed of the drone are shown. From the simulation results, it can be seen that, unlike the uniform speed in the initial state, the optimized speed fluctuates greatly, and the drone always flies at a lower speed when performing ISAC tasks, and often flies at full speed when performing data upload tasks. This is because the drone will reduce its speed when flying over the vehicle, so that it stays around the vehicle longer to obtain more ISAC task time and thus obtain more detection information.

[0062] Figure 4The convergence of the alternating optimization algorithm we designed under different maximum detection angles is demonstrated. From the simulation results, it can be seen that when the flight altitude remains unchanged, increasing the maximum detection angle of the drone can effectively improve the total radar detection rate. This is because as the maximum detection angle increases, the coverage of the drone radar gradually increases, and the drone has more time to perform ISAC tasks. In addition, the convergence speed of the algorithm will gradually increase with the increase of the maximum detection angle. This is because when the detection range of the drone radar is small, the drone flight trajectory has greater uncertainty and more iterations are required to obtain a stable trajectory.

[0063] Figure 5 The relationship between the radar estimation rate and the flight altitude is shown. From the simulation results, it can be seen that as the flight altitude increases, the total radar estimation rate first increases to a maximum value at 120m and then decreases. This is because as the UAV's flight altitude increases, the UAV's detection range on the ground will be larger, thereby obtaining more detection information. However, as the flight altitude increases, the channel conditions between the UAV and the aircraft deteriorate, which offsets the positive gain of the larger detection range and ultimately leads to a decrease in system performance.

[0064] The above-described embodiments merely express the implementation methods of the present invention, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for sensing and communicating integrated drone-assisted Internet of Vehicles, characterized in that: The system of the perception-communication-integrated UAV-assisted vehicle networking method includes a UAV, a data collection center, and a ground stationary vehicle; The UAV has the dual functions of perception and communication, and transmits radar detection signals and communication signals at the same time; as a dual-function aerial platform, the UAV performs perception, communication integration and data upload tasks over the target area at a fixed flight altitude H and flight period T; the flight period of the UAV is decomposed into N time slots, and the flight trajectory of the UAV in each time slot is regarded as a short straight line; when there is a ground vehicle within the UAV radar detection range, the UAV transmits a radar detection signal to detect the environment around the vehicle, and sends the perception information contained in the radar echo to the target vehicle in real time through the downlink communication link; when there is no vehicle within the UAV radar detection range, the UAV sends the stored perception information to the data collection center through the uplink communication link; By introducing radar mutual information, the UAV's task scheduling, detection range, transmission power and flight trajectory are jointly optimized to maximize the UAV's radar estimation rate. The specific steps include: Step 1: Build a perception and communication integrated drone-assisted Internet of Vehicles system, including network channel model, performance evaluation model and drone motion model; Step 2: Design an optimization problem to maximize the radar estimation rate of the UAV and meet the UAV task scheduling constraints, detection range constraints, transmission power constraints and flight trajectory constraints; Step 3: Decompose the optimization problem into three non-convex sub-problems, use the continuous convex approximation method to transform the non-convex sub-problems into convex problems, design an alternating optimization algorithm, and solve it using the CVX toolbox in MATLAB; The network channel model in step 1 is specifically: the communication channel power gains from the UAV to the data collection center and vehicle k are respectively expressed as h c (n) and h k,com (n), where k = 1, 2, ... K, K represents the total number of vehicles; n = 1, 2, ... N; the channel between the UAV and the data collection center and vehicle k is a line-of-sight channel, and the free space fading model is used to simulate the channel between the UAV and the data collection center and the vehicle, that is, Among them, G t and G c Respectively represent the antenna gains of the UAV transmitter and communication receiver, d c (n) and d k (n) represents the distance from the UAV to the data collection center and vehicle k, respectively, λ=c / f c Indicates the signal wavelength, c and f c Represent the speed of light and the carrier frequency of the signal respectively; The radar channel power gain from the UAV to vehicle k is expressed as Among them, G r represents the antenna gain of the UAV radar receiver, and σ represents the radar cross-sectional area of ​​the target vehicle; The performance evaluation model in step 1 is specifically: Considering the internal interference of the perception and communication integrated UAV-assisted vehicle network system, when the UAV performs the perception and communication integrated task on vehicle k, the signal-to-interference-to-noise ratio Γ of its communication channel is k,com (n) and the signal-to-interference-to-noise ratio Γ on the radar channel k,rad (n) is expressed as Where B represents the signal bandwidth, N0 represents the noise power spectral density, P com and P rad They represent UAV communication power and radar detection power respectively; According to Shannon's formula, in the nth time slot, the UAV arrives at the data collection center R c The communication information rate of vehicle k and the communication information rate R k,com (n) is expressed as Among them, P t (n) represents the transmission power of the UAV in the nth time slot; In the nth time slot, the radar estimation rate of the UAV for vehicle k is expressed as The total radar estimation rate of the UAV in one flight cycle is expressed as Among them, ω k (n) is a binary discrete variable used to represent the scheduling of the sensing and communication integration task, ω k When (n) is equal to 0, it means that the UAV does not perform the perception and communication integration task, ω k When (n) is equal to 1, it means that the UAV is performing the perception and communication integration task on vehicle k at this time; To ensure that the UAV can effectively perform the corresponding tasks, the amount of information transmitted by the UAV to the vehicle is greater than the amount of information detected by the radar in each perception and communication integration task time slot; in a flight cycle, the amount of information transmitted by the UAV to the data collection center is greater than the amount of information detected by the radar; the UAV data transmission constraint is expressed as R k,rad (n)≤R k,com (n) (10) in, It is used to indicate the scheduling of data upload tasks. When b(n) is equal to 0, it means that the UAV is not performing the data upload task. When b(n) is equal to 1, it means that the UAV is sending the stored perception information to the data collection center at this time. The UAV motion model includes the relative position constraints of the UAV flight and the kinematic constraints of the UAV flight; the UAV satisfies the following relative position constraints during flight: l u (1)＝l u (N) (12) Among them, l u (n)=(x u (n),y u (n)) represents the position coordinates of the UAV at the nth time slot, l u (N) represents the position coordinates of the UAV at the Nth time slot; θ represents the maximum detection angle of the UAV radar, (x k ,y k ) represents the position coordinates of vehicle k; The drone meets the following kinematic constraints during flight: Among them, V(n) represents the flight speed of the UAV in the nth time slot, Δt represents the duration of each time slot, and a max Indicates the maximum acceleration of the drone flight; The energy consumption of the drone includes the consumption of the drone's transmission power and the consumption of the drone's propulsion power. In the nth time slot, the power P used by the drone for communication is com (n) and the power P used by the drone for radar detection rad (n) is expressed as Among them, 0≤α(n)≤1 represents the power allocation factor in each time slot; when the UAV performs the data uploading task, α(n)=1; The optimization problem of step 2 is V(1)=V(N)(18j)l u (1)=l u (N)(18k) Among them, P avg Represents the average transmission power of the UAV, V max represents the maximum flight speed of the UAV; (18b)~(18d) indicate the task scheduling constraints. In each time slot, the UAV can only perform one task and can only provide services to one vehicle when performing the perception and communication integration task; (18e) ensures that the UAV provides perception and communication integration services to vehicles within its detection range; (15f) and (15g) restrict the radar detection and information transmission of the UAV so that the UAV can perform perception and communication integration and data upload tasks; (18h) and (18i) restrict the transmission power of the UAV in each time slot to ensure that the UAV power is not overloaded; (18j)~(18n) restrict the flight parameters of the UAV to ensure the normal flight of the UAV.

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