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首页> 外文会议>Asilomar Conference on Signals, Systems, and Computers >Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots
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Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots

机译：学习休息：带着着陆斑的飞行基站轨迹设计的Q学习方法

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摘要

We consider the problem of trajectory optimization for an autonomous UAV-mounted base station that provides communication services to ground users with the aid of landing spots (LSs). Recently, the concept of LSs was introduced to alleviate the problem of short mission durations arising from the limited on-board battery budget of the UAV, which severely limits network performance. In this work, using Q-learning, a model-free reinforcement learning (RL) technique, we train a neural network (NN) to make movement decisions for the UAV that maximize the data collected from the ground users while minimizing power consumption by exploiting the landing spots. We show that the system intelligently integrates landing spots into the trajectory to extend flying time and is able to learn the topology of the network over several flying epochs without any explicit information about the environment.
机译：我们考虑借助着陆点（LSS）为接地用户提供通信服务的自主无人机安装基站的轨迹优化问题。最近，引入了LSS的概念，以缓解无人机的有限电池预算产生的短期使命持续时间，这严重限制了网络性能。在这项工作中，使用Q-Learning，一种无模型加强学习（RL）技术，我们训练神经网络（NN）为UAV进行运动决策，从而最大限度地通过利用来最大限度地减少功耗的电力消耗着陆点。我们表明系统智能地将着陆点与轨迹集成到轨道中以扩展飞行时间，并且能够在没有任何关于环境的明确信息的情况下学习网络的拓扑。

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