Tootfinder

GLo-MAPPO: A Multi-Agent Proximal Policy Optimization for Energy Efficiency in UAV-Assisted LoRa Networks
Abdullahi Isa Ahmed, Jamal Bentahar, El Mehdi Amhoud
https://arxiv.org/abs/2509.17676

GLo-MAPPO: A Multi-Agent Proximal Policy Optimization for Energy Efficiency in UAV-Assisted LoRa Networks
Long Range (LoRa) based low-power wide area networks (LPWANs) are crucial for enabling next-generation IoT (NG-IoT) applications in 5G/6G ecosystems due to their long-range, low-power, and low-cost characteristics. However, achieving high energy efficiency in such networks remains a critical challenge, particularly in large-scale or dynamically changing environments. Traditional terrestrial LoRa deployments often suffer from coverage gaps and non-line-of-sight (NLoS) propagation losses, while s…