Backscatter communication (BC) becomes a promising energy-efficient solution for future wireless sensor networks (WSNs). Unmanned aerial vehicles (UAVs) enable flexible data collection from remote backscatter devices (BDs), yet conventional UAVs rely on omni-directional fixed-position antennas (FPAs), limiting channel gain and prolonging data collection time. To address this issue, we consider equipping a UAV with a directional movable antenna (MA) with high directivity and flexibility. The MA enhances channel gain by precisely aiming its main lobe at each BD, focusing transmission power for efficient communication. Our goal is to minimize the total data collection time by jointly optimizing the UAV's trajectory and the MA's orientation. We develop a deep reinforcement learning (DRL)-based strategy using the azimuth angle and distance between the UAV and each BD to simplify the agent's observation space. To ensure stability during training, we adopt Soft Actor-Critic (SAC) algorithm that balances exploration with reward maximization for efficient and reliable learning. Simulation results demonstrate that our proposed MA-equipped UAV with SAC outperforms both FPA-equipped UAVs and other RL methods, achieving significant reductions in both data collection time and energy consumption.

翻译：反向散射通信（BC）已成为未来无线传感器网络（WSNs）中一种极具前景的能效解决方案。无人机（UAVs）能够灵活地从远程反向散射设备（BDs）收集数据，但传统无人机依赖全向固定位置天线（FPAs），这限制了信道增益并延长了数据收集时间。为解决此问题，我们考虑为无人机配备具有高方向性和灵活性的定向可移动天线（MA）。该天线通过将其主瓣精确对准每个BD来增强信道增益，集中发射功率以实现高效通信。我们的目标是通过联合优化无人机的轨迹和天线的朝向，最小化总数据收集时间。我们开发了一种基于深度强化学习（DRL）的策略，利用无人机与每个BD之间的方位角和距离来简化智能体的观测空间。为确保训练稳定性，我们采用Soft Actor-Critic（SAC）算法，该算法在探索与奖励最大化之间取得平衡，以实现高效可靠的学习。仿真结果表明，我们提出的搭载SAC的MA无人机方案优于配备FPA的无人机及其他RL方法，在数据收集时间和能耗方面均实现了显著降低。