论文标题
使用双Q学习的无人机-BSS的能源和服务优先级的轨迹设计
Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning
论文作者
论文摘要
下一代移动网络已提出将无人机（UAV）作为空中站（UAV-BS）的整合以服务地面节点。尽管使用UAV-BSS具有优势，但它们对车载有限的电池的依赖会阻碍其服务连续性。较短的轨迹可以节省飞行能源，但是，由于节点的服务要求并不总是相同的，因此无人机也必须根据其服务优先级提供节点。在本文中，我们为无人机辅助的物联网系统提供了节能轨迹优化，其中UAV-BS考虑了物联网节点在做出运动决策时的优先级。我们使用双Q学习算法解决了轨迹优化问题。仿真结果表明，基于Q学习的优化轨迹优于基准算法，即贪婪的算法，即减少UAV-BS的平均能源消耗以及高优先级节点的服务延迟。
Next-generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite having advantages of using UAV-BSs, their dependence on the on-board, limited-capacity battery hinders their service continuity. Shorter trajectories can save flying energy, however, UAV-BSs must also serve nodes based on their service priority since nodes' service requirements are not always the same. In this paper, we present an energy-efficient trajectory optimization for a UAV assisted IoT system in which the UAV-BS considers the IoT nodes' service priorities in making its movement decisions. We solve the trajectory optimization problem using Double Q-Learning algorithm. Simulation results reveal that the Q-Learning based optimized trajectory outperforms a benchmark algorithm, namely Greedily-served algorithm, in terms of reducing the average energy consumption of the UAV-BS as well as the service delay for high priority nodes.
