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

arXiv: https://arxiv.org/abs/2202.02457 · HTML: https://mlsanity.com/paper/2202.02457/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2022.

## Built on by (most cited first, 4 of 4)

- [BEDI: A Comprehensive Benchmark for Evaluating Embodied Agents on UAVs](https://mlsanity.com/paper/2505.18229/) (2025)
- [Look-Ahead Task Offloading for Multi-User Mobile Augmented Reality in Edge-Cloud Computing](https://mlsanity.com/paper/2305.19558/) (2023)
- [Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges](https://mlsanity.com/paper/2508.09561/) (2025)
- [Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing](https://mlsanity.com/paper/2309.02193/) (2023)

## Abstract (arXiv)

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.