论文标题
使用深度加固学习的无人机移动边缘计算的路径计划
Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learning
论文作者
论文摘要
在这封信中，我们研究了一个无人驾驶飞机（UAV）安装的移动边缘计算网络，在该网络中，无人机执行从移动终端用户（TUS）卸载的计算任务，每个TU的运动都遵循高斯 - 马尔科夫随机模型。为了确保每个TU的服务质量（QoS），具有有限能量的无人机根据移动TU的位置动态计划其轨迹。为此，我们将问题提出为马尔可夫决策过程，其中将无人机轨迹和无人机结合建模为要优化的参数。为了最大程度地提高系统奖励并满足QoS约束，我们基于双重Q网络制定了拟议算法中的基于QoS的动作选择策略。模拟表明，所提出的算法比传统算法更快地收敛，并获得更高的总和吞吐量。
In this letter, we study an unmanned aerial vehicle (UAV)-mounted mobile edge computing network, where the UAV executes computational tasks offloaded from mobile terminal users (TUs) and the motion of each TU follows a Gauss-Markov random model. To ensure the quality-of-service (QoS) of each TU, the UAV with limited energy dynamically plans its trajectory according to the locations of mobile TUs. Towards this end, we formulate the problem as a Markov decision process, wherein the UAV trajectory and UAV-TU association are modeled as the parameters to be optimized. To maximize the system reward and meet the QoS constraint, we develop a QoS-based action selection policy in the proposed algorithm based on double deep Q-network. Simulations show that the proposed algorithm converges more quickly and achieves a higher sum throughput than conventional algorithms.
