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

TL;DR

This paper presents a multi-agent reinforcement learning approach to optimize energy efficiency in UAV-assisted LoRa IoT networks, addressing dynamic conditions and resource management challenges.

Contribution

It introduces a novel joint optimization framework using a POSG model and a MARL-based solution for UAV-assisted IoT networks, enhancing energy efficiency.

Findings

Significant energy efficiency improvements over baseline algorithms.

Effective handling of dynamic channel conditions and device mobility.

Robust multi-agent coordination in resource allocation.

Abstract

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.