Trajectory Design for UAV Communications with No-Fly Zones by Deep Reinforcement Learning

Liu，Zhenrong1; Zeng，Yuan2; Zhang，Wei3; Gong，Yi4,5

10.1109/ICCWorkshops50388.2021.9473572

2021-06-01

2164-7038

978-1-7281-9442-4

2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings

1-5

14-23 June 2021

Montreal, QC, Canada

This paper studies the trajectory design problem for the cellular-connected unmanned aerial vehicle (UAV) with limited energy, which aims at maximizing the uplink transmission rate from multiple ground users in urban environments with no-fly zones (NFZs). We first argue that the successive convex approximation-based (SCA-based) conventional trajectory design method via formulating and solving optimization problems face challenges, and then we formulate the trajectory design problem for rate maximization as a Markov Decision Process and propose a deep reinforcement learning-based (DRL-based) solution. Simulation results show that the proposed DRL has similar performance to the SCA-based conventional method with regularly shaped NFZ constraints. Moreover, simulation results in a scenario with an irregular NFZ show that the designed trajectories of the proposed DRL can effectively serve users and detour the NFZ.

deep reinforcement learning no-fly zones trajectory design UAV communications

第一

英语

EI

20213410796365

Antennas ; Design ; Markov processes ; Reinforcement learning ; Trajectories ; Unmanned aerial vehicles (UAV) ; Vehicle transmissions

Mechanical Transmissions:602.2 ; Aircraft, General:652.1 ; Artificial Intelligence:723.4 ; Probability Theory:922.1

2-s2.0-85112800564

Scopus

1.Southern University of Science and Technology (SUSTech),Department of Electrical and Electronic Engineering,Shenzhen,China
2.Southern University of Science and Technology (SUSTech),Academy for Advanced Interdisciplinary Studies,Shenzhen,China
3.University of New South Wales (UNSW),School of Electrical Engineering and Telecommunications,Sydney,Australia
4.Southern University of Science and Technology (SUSTech),University Key Laboratory of Advanced Wireless Communications of Guangdong Province,China
5.Peng Cheng Laboratory,Shenzhen,China

Liu，Zhenrong,Zeng，Yuan,Zhang，Wei,et al. Trajectory Design for UAV Communications with No-Fly Zones by Deep Reinforcement Learning[C],2021:1-5.