Online Learning Based Efficient Resource Allocation for LoRaWAN Network

• URL: http://arxiv.org/abs/2509.10493v2
• Date: Tue, 16 Sep 2025 13:58:52 GMT
• Title: Online Learning Based Efficient Resource Allocation for LoRaWAN Network
• Authors: Ruiqi Wang, Wenjun Li, Jing Ren, Tongyu Song, Xiong Wang, Sheng Wang, Shizhong Xu,
• Abstract summary: Large-scale LoRaWAN networks need to optimize conflicting metrics like Packet Delivery Ratio (PDR) and Energy Efficiency (EE)<n>Existing methods often oversimplify this challenge, focusing on a single metric or lacking the adaptability needed for dynamic channel environments.<n>We propose two online learning-based resource allocation frameworks that intelligently navigate the PDR-EE trade-off.
• Score: 23.183952975438633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The deployment of large-scale LoRaWAN networks requires jointly optimizing conflicting metrics like Packet Delivery Ratio (PDR) and Energy Efficiency (EE) by dynamically allocating transmission parameters, including Carrier Frequency, Spreading Factor, and Transmission Power. Existing methods often oversimplify this challenge, focusing on a single metric or lacking the adaptability needed for dynamic channel environments, leading to suboptimal performance. To address this, we propose two online learning-based resource allocation frameworks that intelligently navigate the PDR-EE trade-off. Our foundational proposal, D-LoRa, is a fully distributed framework that models the problem as a Combinatorial Multi-Armed Bandit. By decomposing the joint parameter selection and employing specialized, disaggregated reward functions, D-LoRa dramatically reduces learning complexity and enables nodes to autonomously adapt to network dynamics. To further enhance performance in LoRaWAN networks, we introduce CD-LoRa, a hybrid framework that integrates a lightweight, centralized initialization phase to perform a one-time, quasi-optimal channel assignment and action space pruning, thereby accelerating subsequent distributed learning. Extensive simulations and real-world field experiments demonstrate the superiority of our frameworks, showing that D-LoRa excels in non-stationary environments while CD-LoRa achieves the fastest convergence in stationary conditions. In physical deployments, our methods outperform state-of-the-art baselines, improving PDR by up to 10.8% and EE by 26.1%, confirming their practical effectiveness for scalable and efficient LoRaWAN networks.

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