# Online Learning Based Efficient Resource Allocation for LoRaWAN Network

arXiv: https://arxiv.org/abs/2509.10493 · HTML: https://mlsanity.com/paper/2509.10493/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 31 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings](https://mlsanity.com/paper/1807.00491/) (2018)
- [A Survey on LoRa Networking: Research Problems, Current Solutions and Open Issues](https://mlsanity.com/paper/1908.10195/) (2019)
- [Understanding the limits of LoRaWAN](https://mlsanity.com/paper/1607.08011/) (2016)
- [Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching](https://mlsanity.com/paper/2001.03835/) (2020)
- [LoRa-RL: Deep Reinforcement Learning for Resource Management in Hybrid Energy LoRa Wireless Networks](https://mlsanity.com/paper/2109.02392/) (2021)
- [Self-organized Low-power IoT Networks: A Distributed Learning Approach](https://mlsanity.com/paper/1807.09333/) (2018)
- [Joint Allocation Strategies of Power and Spreading Factors with Imperfect Orthogonality in LoRa Networks](https://mlsanity.com/paper/1904.11303/) (2019)
- [MIX-MAB: Reinforcement Learning-based Resource Allocation Algorithm for LoRaWAN](https://mlsanity.com/paper/2206.03401/) (2022)
- [Design and Implementation of Open LoRa for IoT](https://mlsanity.com/paper/1812.09012/) (2018)
- [Matching-Driven Deep Reinforcement Learning for Energy-Efficient Transmission Parameter Allocation in Multi-Gateway LoRa Networks](https://mlsanity.com/paper/2407.13076/) (2024)
- [D-LoRa: a Distributed Parameter Adaptation Scheme for LoRa Network](https://mlsanity.com/paper/2501.12589/) (2025)

## Abstract (arXiv)

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.