用无人机优化低功耗物联网通信，提升系统能效
Energy-Efficient UAV-assisted LoRa Gateways: A Multi-Agent Optimization Approach
- 将无人机与终端关联和资源分配建模为部分可观测随机博弈
- 联合优化发射功率、扩频因子、带宽和用户关联，提升能效30%以上
- 适合研究智能无线网络与绿色物联网的工程师和学者
随着下一代物联网（NG-IoT）网络持续发展，连接设备数量迅速增加，能源需求也随之上升，给资源管理和可持续性带来挑战。本文研究在上行数据采集场景中，由多架无人机支持的长距离（LoRa）网络，目标是通过联合优化发射功率、扩频因子、带宽和用户关联，最大化系统能效。为应对这一难题，我们首先将问题建模为部分可观测随机博弈（POSG），以考虑动态信道条件、终端移动性以及各无人机的局部可观测性。随后提出两阶段解决方案：第一阶段采用信道感知匹配算法进行终端-无人机关联；第二阶段基于集中训练、分散执行（CTDE）框架，设计了合作式多智能体强化学习（MARL）方法——多智能体近端策略优化（MAPPO）。仿真结果表明，所提方法显著优于传统非策略和策略型MARL算法，在能效方面提升超过30%。
原文摘要 · Abstract (English)
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 end device-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.
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