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

arXiv 2502.03377 ↗First posted 5 Feb 2025cs.NI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 5 Feb 2025.

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6 of 16 references.

References · 6

  1. 2017Counterfactual Multi-Agent Policy Gradientscited by 751
  2. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  3. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  4. 2022A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challengescited by 12
  5. 2023Deep Reinforcement Learning-based Energy Efficiency Optimization For Flying LoRa Gatewayscited by 3
  6. 2021Multi-sided Matching for the Association of Space-Air-Ground Integrated Systemscited by 1

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  1. 2026Hetero-Net: An Energy-Efficient Resource Allocation and 3D Placement in Heterogeneous LoRa Networks via Multi-Agent Optimization
  2. 2025Wireless Laser Power Transfer for Low-altitude Uncrewed Aerial Vehicle-assisted Internet of Things: Paradigms, Challenges, and Solutions ↗
  3. 2025Integrated Communication and Control for Energy-Efficient UAV Swarms: A Multi-Agent Reinforcement Learning Approach
  4. 2025UAV-Assisted Resilience in 6G and Beyond Network Energy Saving: A Multi-Agent DRL Approach
  5. 2026Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning
  6. 2026Game-Theoretic Multi-Agent Reinforcement Learning for Swarm Trajectory Planning in Low-Altitude Wireless Networks
  7. 2025Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach
  8. 2024Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach