# MIX-MAB: Reinforcement Learning-based Resource Allocation Algorithm for LoRaWAN

arXiv: https://arxiv.org/abs/2206.03401 · HTML: https://mlsanity.com/paper/2206.03401/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [Understanding the limits of LoRaWAN](https://mlsanity.com/paper/1607.08011/) (2016)
- [How Agile is the Adaptive Data Rate Mechanism of LoRaWAN?](https://mlsanity.com/paper/1808.09286/) (2018)
- [Long-range Low-power Wireless Networks and Sampling Strategies in Electricity Metering](https://mlsanity.com/paper/1803.02084/) (2018)

## Built on by (most cited first, 3 of 3)

- [Why Keep Your Doubts to Yourself? Trading Visual Uncertainties in Multi-Agent Bandit Systems](https://mlsanity.com/paper/2601.18735/) (2026)
- [Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2310.19069/) (2023)
- [Online Learning Based Efficient Resource Allocation for LoRaWAN Network](https://mlsanity.com/paper/2509.10493/) (2025)

## Abstract (arXiv)

This paper focuses on improving the resource allocation algorithm in terms of packet delivery ratio (PDR), i.e., the number of successfully received packets sent by end devices (EDs) in a long-range wide-area network (LoRaWAN). Setting the transmission parameters significantly affects the PDR. Employing reinforcement learning (RL), we propose a resource allocation algorithm that enables the EDs to configure their transmission parameters in a distributed manner. We model the resource allocation problem as a multi-armed bandit (MAB) and then address it by proposing a two-phase algorithm named MIX-MAB, which consists of the exponential weights for exploration and exploitation (EXP3) and successive elimination (SE) algorithms. We evaluate the MIX-MAB performance through simulation results and compare it with other existing approaches. Numerical results show that the proposed solution performs better than the existing schemes in terms of convergence time and PDR.