论文标题
无人机安装的无线网络中的智能轨迹计划：量子启发的增强学习观点
Intelligent Trajectory Planning in UAV-mounted Wireless Networks: A Quantum-Inspired Reinforcement Learning Perspective
论文作者
论文摘要
在本文中，我们考虑了无线上行链路传输方案，其中无人机（UAV）用作航空站，从地面用户收集数据。为了优化预期的上行链路传输速率，没有任何地面用户的任何先验知识（例如，位置，渠道状态信息和传输功率），轨迹计划问题通过量子启发的增强学习（QIRL）方法优化。具体而言，QIRL方法采用了新颖的概率行动选择政策和新的增强策略，这些策略的灵感来自量子计算理论中的崩溃现象和振幅扩增。数值结果表明，与传统的强化学习方法相比，提出的QIRL解决方案可以通过排名可能的动作的崩溃概率在勘探和剥削之间提供自然平衡，这些方法高度依赖于调谐探索参数。
In this paper, we consider a wireless uplink transmission scenario in which an unmanned aerial vehicle (UAV) serves as an aerial base station collecting data from ground users. To optimize the expected sum uplink transmit rate without any prior knowledge of ground users (e.g., locations, channel state information and transmit power), the trajectory planning problem is optimized via the quantum-inspired reinforcement learning (QiRL) approach. Specifically, the QiRL method adopts novel probabilistic action selection policy and new reinforcement strategy, which are inspired by the collapse phenomenon and amplitude amplification in quantum computation theory, respectively. Numerical results demonstrate that the proposed QiRL solution can offer natural balancing between exploration and exploitation via ranking collapse probabilities of possible actions, compared to the traditional reinforcement learning approaches which are highly dependent on tuned exploration parameters.
