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

TL;DR

This paper introduces MIX-MAB, a reinforcement learning-based resource allocation algorithm for LoRaWAN that improves packet delivery ratio by optimizing transmission parameters in a distributed manner.

Contribution

The paper proposes a novel two-phase multi-armed bandit algorithm, MIX-MAB, combining EXP3 and SE, for distributed resource allocation in LoRaWAN.

Findings

MIX-MAB outperforms existing schemes in convergence time.

MIX-MAB achieves higher packet delivery ratio.

Simulation results validate the effectiveness of the proposed algorithm.

Abstract

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.