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arXiv 2502.03377cs.NIcs.LG
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低功耗无人机辅助LoRa网关:多智能体优化方法

Energy-Efficient UAV-assisted LoRa Gateways: A Multi-Agent Optimization Approach

College of Computing, Mohammed VI Polytechnic University (UM6P)(穆莱·易斯六世理工学院计算机学院);Department of Computer Science, Khalifa University(哈利法大学计算机科学系);Concordia Institute for Information Systems Engineering, Concordia University(康科迪亚大学信息系统工程研究所)

Abdullahi Isa Ahmed, Jamal Bentahar, El Mehdi Amhoud

AI总结:

本文研究多无人机支持的LoRa网络在上行数据采集中的能量效率优化,通过多智能体强化学习框架实现传输功率、扩频因子等参数的联合优化。

摘要:

随着下一代物联网(NG-IoT)网络的不断发展,连接设备数量迅速增加,其能耗需求也随之上升,给资源管理和可持续性带来挑战。能量高效的通信,尤其是对电力有限的物联网设备而言,成为关键研究方向。本文研究由多个无人机支持的LoRa网络在上行数据采集场景中的性能。我们的目标是通过联合优化传输功率、扩频因子、带宽和用户关联来最大化系统能量效率。为解决这一挑战性问题,我们首先将其建模为部分可观测随机游戏(POSG),以考虑动态信道条件、终端设备移动性和每个无人机的局部可观测性。然后提出一个两阶段解决方案:一种面向信道的终端设备-无人机关联算法和一种基于集中训练与分散执行(CTDE)的协同多智能体强化学习(MARL)多智能体近端策略优化(MAPPO)框架。仿真结果表明,所提出的方法显著优于传统非策略和策略MARL算法。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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