As next-generation Internet of Things (NG-IoT) networks continue to grow, the number of connected devices is rapidly increasing, along with their energy demands, creating challenges for resource management and sustainability. Energy-efficient communication, particularly for power-limited IoT devices, is therefore a key research focus. In this paper, we study Long Range (LoRa) networks supported by multiple unmanned aerial vehicles (UAVs) in an uplink data collection scenario. Our objective is to maximize system energy efficiency by jointly optimizing transmission power, spreading factor, bandwidth, and user association. To address this challenging problem, we first model it as a partially observable stochastic game (POSG) to account for dynamic channel conditions, end device mobility, and partial observability at each UAV. We then propose a two-stage solution: a channel-aware matching algorithm for ED-UAV association and a cooperative multi-agent reinforcement learning (MARL) based multi-agent proximal policy optimization (MAPPO) framework for resource allocation under centralized training with decentralized execution (CTDE). Simulation results show that our proposed approach significantly outperforms conventional off-policy and on-policy MARL algorithms.

翻译：随着下一代物联网（NG-IoT）网络的持续扩张，连接设备数量及其能耗需求正迅速增长，这给资源管理与可持续性带来了挑战。因此，能量高效通信，特别是对于功率受限的物联网设备，成为一个关键的研究焦点。本文研究了在上行数据收集场景中由多架无人机（UAV）支持的长距离（LoRa）网络。我们的目标是通过联合优化传输功率、扩频因子、带宽以及用户关联，最大化系统能量效率。为应对这一具有挑战性的问题，我们首先将其建模为一个部分可观测随机博弈（POSG），以考虑动态信道条件、终端设备移动性以及每架无人机的部分可观测性。随后，我们提出了一种两阶段解决方案：一个用于终端设备-无人机关联的信道感知匹配算法，以及一个基于协作多智能体强化学习（MARL）的多智能体近端策略优化（MAPPO）框架，用于在集中训练分散执行（CTDE）模式下进行资源分配。仿真结果表明，我们提出的方法显著优于传统的离策略和同策略MARL算法。