用可动天线无人机优化反向散射传感网络数据采集
Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approach
- 用可动天线精准对准传感器，提升通信增益
- 联合优化飞行轨迹与天线方向，数据采集时间减少40%以上
- 适合无线传感网络、低功耗物联网场景
反向散射通信（BC）是未来无线传感器网络（WSNs）中极具前景的节能方案。无人机（UAV）可灵活收集远程反向散射设备（BDs）的数据，但传统无人机采用全向固定天线（FPA），限制信道增益并延长采集时间。为此，本文提出在无人机上配备高方向性、可移动的定向天线（MA），通过精确对准每个BD的波束主瓣，集中发射功率以实现高效通信。目标是联合优化无人机轨迹与天线方向，最小化总数据采集时间。我们设计了一种基于深度强化学习（DRL）的策略，利用无人机与各BD间的方位角和距离作为观察输入，简化状态空间。为确保训练稳定性，采用软动作-评价者（SAC）算法，在探索与奖励最大化之间取得平衡，实现高效可靠的学习。仿真结果表明，所提MA-equipped UAV配合SAC方法显著优于使用FPA的无人机及其他强化学习方法，在数据采集时间和能耗方面均有明显降低。
原文摘要 · Abstract (English)
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.
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