论文标题
使用Monte Carlo Tree搜索的动态无人机无线系统的路径规划
Path Planning for the Dynamic UAV-Aided Wireless Systems using Monte Carlo Tree Search
论文作者
论文摘要
对于无用的无线系统，在线路径计划最近引起了很多关注。为了更好地适应实时动态环境，我们首次提出了基于蒙特卡洛树搜索（MCTS）的路径计划计划。详细说明，我们考虑使用一个无人机作为移动服务器，以提供计算任务，为地面上的一组移动用户卸载服务，地面用户的移动遵循随机的方式点模型。我们的模型旨在最大程度地提高能源消耗和用户公平限制下的平均吞吐量，而提出的避免时间的MCT算法可以进一步提高性能。仿真结果表明，与Q学习和深Q网络的基线算法相比，所提出的算法可实现更大的平均吞吐量和更快的收敛性能。
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.
