A resource-constrained unmanned aerial vehicle (UAV) can be used as a flying LoRa gateway (GW) to move inside the target area for efficient data collection and LoRa resource management. In this work, we propose deep reinforcement learning (DRL) to optimize the energy efficiency (EE) in wireless LoRa networks composed of LoRa end devices (EDs) and a flying GW to extend the network lifetime. The trained DRL agent can efficiently allocate the spreading factors (SFs) and transmission powers (TPs) to EDs while considering the air-to-ground wireless link and the availability of SFs. In addition, we allow the flying GW to adjust its optimal policy onboard and perform online resource allocation. This is accomplished through retraining the DRL agent using reduced action space. Simulation results demonstrate that our proposed DRL-based online resource allocation scheme can achieve higher EE in LoRa networks over three benchmark schemes.

翻译：资源受限的无人飞行器（UAV）可作为飞行LoRa网关（GW）在目标区域内移动，以实现高效数据采集与LoRa资源管理。本文提出利用深度强化学习（DRL）优化由LoRa终端设备（EDs）与飞行网关组成的无线LoRa网络中的能量效率（EE），从而延长网络生命周期。训练后的DRL智能体可在考虑空对地无线链路及扩频因子（SFs）可用性的同时，为终端设备高效分配扩频因子与发射功率（TPs）。此外，我们允许飞行网关在线调整其最优策略并执行在线资源分配，这是通过使用缩减动作空间对DRL智能体进行再训练实现的。仿真结果表明，与三种基准方案相比，本文提出的基于DRL的在线资源分配方案能够在LoRa网络中实现更高的能量效率。