Receding Horizon Optimization for Energy-Efficient UAV Communication

TL;DR

This paper introduces a receding horizon optimization approach to enhance energy efficiency in UAV communication systems, reducing computational complexity while optimizing UAV trajectories for data collection from ground nodes.

Contribution

The paper proposes a novel receding horizon optimization method for UAV energy-efficient communication, improving computational efficiency over traditional discretization techniques.

Findings

The proposed method effectively maximizes UAV energy efficiency.

Simulation results demonstrate reduced computational complexity.

The approach improves UAV data collection performance.

Abstract

In this letter, we study a wireless communication system with a fixed-wing unmanned aerial vehicle (UAV) employed to collect information from a group of ground nodes (GNs). Our objective is to maximize the UAV's energy efficiency (EE), which is defined as the achievable rate among all GNs per unit propulsion energy consumption of the UAV. To efficiently solve this problem with continuous-time functions, we propose a new method based on receding horizon optimization (RHO), which significantly reduces the computational complexity compared to the conventional time discretization method. Specifically, we sequentially solve the EE maximization problem over a moving time-window of finite duration, for each of which the number of optimization variables is greatly reduced. Simulation results are provided to show the effectiveness of the proposed method.