Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks

TL;DR

This paper introduces MALoRa, a multiagent reinforcement learning algorithm with an attention mechanism, to optimize transmission parameters and significantly enhance energy efficiency in LoRa networks while maintaining acceptable packet delivery rates.

Contribution

The paper presents a novel MALoRa algorithm that uses attention-guided multiagent reinforcement learning for energy-efficient parameter allocation in LoRa networks.

Findings

MALoRa outperforms baseline algorithms in energy efficiency.

The attention mechanism improves learning effectiveness.

Packet delivery rate remains acceptable with MALoRa.

Abstract

Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale increases, the energy efficiency (EE) of LoRa networks decreases sharply due to severe packet collisions. To address this issue, it is essential to appropriately assign transmission parameters such as the spreading factor and transmission power for each end device (ED). However, due to the sporadic traffic and low duty cycle of LoRa networks, evaluating the system EE performance under different parameter settings is time-consuming. Therefore, we first formulate an analytical model to calculate the system EE. On this basis, we propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) with the aim of maximizing the system EE of LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED to better learn how much ''attention'' should be given to the parameter assignments for relevant EDs when seeking to improve the system EE. Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms with an acceptable degradation in packet delivery rate (PDR).