Energy-Aware Trajectory Design for Outage Minimization in UAV-Assisted Communication Systems
Nishant Gupta, Deepak Mishra, Satyam Agarwal
Abstract
This paper studies the flight trajectory of unmanned aerial vehicles (UAV) to provide fifth-generation (5G) cellular service in a given area. We consider a single UAV launched from the fixed initial to the final location, during which it serves the ground users that are distributed in a circular field. UAV’s limited on-board energy has been a major concern affecting the system’s performance. Therefore, in this work, we minimize the average outage probability of the system by optimizing the three-dimensional (3D) trajectory of the UAV while considering the velocity and on-board energy as constraints. To solve this problem, we propose two approaches: the low-complexity <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vertex</i> and the high-performance <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sequential approach</i> . The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vertex approach</i> involves two steps. Firstly, it obtains the optimal solution while relaxing the velocity and on-board energy constraints. Secondly, a greedy solution is proposed for the original problem. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sequential approach</i> finds the optimal location in each time slot sequentially. Simulation results compare the two approaches and show that our proposed strategies provide on an average 41% improvement in average outage probability over the benchmark schemes. Additionally, we show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sequential approach</i> is better than the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vertex approach</i> , though at the cost of high computational complexity.