Work

Path Planning for the Dynamic UAV-Aided Wireless Systems Using Monte Carlo Tree Search

Year: 2022

Type: article

Abstract: For UAV-aided wireless systems, online path planning attracts much attention recently. To better adapt to the real-time dynamic environment, for the first time, we propose a Monte Carlo Tree Search (MCTS)-based path planning scheme. In details, we consider a single UAV acts as a mobile server to provide computation tasks offloading services for a set of mobile users on the ground, where the movement of ground users follows a Random Way Point model. Our model aims at maximizing the average throughput under energy consumption and user fairness constraints, and the proposed time-saving MCTS algorithm can further improve the performance. Simulation results show that the proposed algorithm achieves a larger average throughput and a faster convergence performance compared with the baseline algorithms of Q-learning and Deep Q-Network.

Source: IEEE Transactions on Vehicular Technology

DOI: 10.1109/tvt.2022.3160746

Authors: Yuwen Qian, Kexin Sheng, Chuan Ma, Jun Li, Ming Ding, Mahbub Hassan

Institutions: Nanjing University of Science and Technology, Southeast University, Data61, UNSW Sydney

Language: English

Topic: UAV Applications and Optimization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: National Natural Science Foundation of China