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

TL;DR

This paper introduces a Monte Carlo Tree Search-based path planning method for UAV-assisted wireless systems, optimizing throughput and convergence speed in dynamic environments with mobile ground users.

Contribution

It is the first to apply MCTS for real-time UAV path planning in dynamic wireless systems, improving throughput and convergence over existing methods.

Findings

Achieves higher average throughput than baseline algorithms.

Faster convergence compared to Q-learning and Deep Q-Network.

Effective in dynamic environments with mobile users.

Abstract

For UAV-aided wireless systems, online path planning attracts much attention recently. To better adapt to the real-time dynamic environment, we, for the first time, 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 timesaving 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.