US 12,333,950 B2
Unmanned aerial vehicle-aided over-the-air computing system based on full-duplex relay and trajectory and power optimization method thereof
Song Li, Xuzhou (CN); Yanjing Sun, Xuzhou (CN); Bowen Wang, Xuzhou (CN); Yu Zhou, Xuzhou (CN); Xiao Yun, Xuzhou (CN); Ruirui Chen, Xuzhou (CN); and Jiaqi Li, Xuzhou (CN)
Assigned to China University of Mining and Technology, Xuzhou (CN)
Filed by China University of Mining and Technology, Xuzhou (CN)
Filed on Mar. 22, 2023, as Appl. No. 18/124,751.
Application 18/124,751 is a continuation of application No. PCT/CN2022/105164, filed on Jul. 12, 2022.
Claims priority of application No. 202111561590.1 (CN), filed on Dec. 16, 2021.
Prior Publication US 2024/0105064 A1, Mar. 28, 2024
Int. Cl. G08G 5/30 (2025.01); G08G 5/26 (2025.01)
CPC G08G 5/30 (2025.01) [G08G 5/26 (2025.01)] 6 Claims
OG exemplary drawing
 
1. A trajectory and power optimization method of an unmanned aerial vehicle (UAV)-aided over-the-air computing system based on full-duplex relay, comprising one base station, one UAV and multiple sensors placed on a ground, the method comprising the following steps:
S1, establishing a coordinate system with an initial position of UAV flight as an origin, jointly optimizing sensor transmitting power, a UAV flight trajectory and a denoising factor under constraints of transmitting power of sensors and the UAV and an information transmission rate, establishing an optimization problem with an aim at minimizing an time average mean square error of the over-the-air computing system and decomposing the optimization problem into a denoising factor n[n] optimization sub-problem, a sensor transmitting power pk[n] optimization sub-problem, a UAV transmitting power P[n] optimization sub-problem, and a UAV flight position q[n] optimization sub-problem, wherein the optimization problem is established in the coordinate system, and an expression of the optimization problem is:

OG Complex Work Unit Math
wherein MSE is the time average mean square error of the UAV-aided over-the-air computing system and is related to pk[n], P[n], q[n], and η[n], pk[n] is the transmitting power of the sensors k in a time slot n, P[n] is the UAV transmitting power in the time slot n, q[n] is UAV flight position in the time slot n, η[n] is the denoising factor in the time slot n; Wk is a fixed horizontal position of the sensors, w is a horizontal position of a base station, β0 is a channel gain per unit distance, σ2 is an additive white Gaussian noise power, βu is a self-interference cancellation coefficient, L is a distance from a sending end to a receiving end of the UAV, H is a lowest flight altitude not to be ascended and descended in flight by the UAV, Gmin is a minimum information transmission rate between the UAV and the base station (BS), α is a path loss index and α≥2; N is a number of time slots in a duration T, T=Nδ, wherein δ denotes a time step; and vmax is a maximum flight speed of the UAV;
S2, solving each optimization sub-problem step by step by adopting an iterative optimization algorithm; and
S3, obtaining an optimal denoising factor η[n] the sensor transmitting power pk[n], the UAV transmission power P[n] and the UAV flight position q[n] according to the S2; and
flying the UAV according to the optimized flight trajectory.