Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning

TL;DR

This paper introduces LoRaDRL, a deep reinforcement learning-based resource allocation algorithm for dense LoRa networks, significantly enhancing packet delivery ratio, supporting mobility, reducing power consumption, and outperforming existing methods especially under jamming attacks.

Contribution

The paper presents a novel deep reinforcement learning algorithm for multi-channel resource management in dense LoRa networks, shifting complexity to the gateway and improving performance and robustness.

Findings

LoRaDRL significantly improves packet delivery ratio (PDR).

Supports mobile end-devices with lower power consumption.

Outperforms state-of-the-art techniques by over 500% in PDR.

Abstract

The anticipated increase in the count of IoT devices in the coming years motivates the development of efficient algorithms that can help in their effective management while keeping the power consumption low. In this paper, we propose an intelligent multi-channel resource allocation algorithm for dense LoRa networks termed LoRaDRL and provide a detailed performance evaluation. Our results demonstrate that the proposed algorithm not only significantly improves LoRaWAN's packet delivery ratio (PDR) but is also able to support mobile end-devices (EDs) while ensuring lower power consumption hence increasing both the lifetime and capacity of the network.} Most previous works focus on proposing different MAC protocols for improving the network capacity, i.e., LoRaWAN, delay before transmit etc. We show that through the use of LoRaDRL, we can achieve the same efficiency with ALOHA \textcolor{black}{compared to LoRaSim, and LoRa-MAB while moving the complexity from EDs to the gateway thus making the EDs simpler and cheaper. Furthermore, we test the performance of LoRaDRL under large-scale frequency jamming attacks and show its adaptiveness to the changes in the environment. We show that LoRaDRL's output improves the performance of state-of-the-art techniques resulting in some cases an improvement of more than 500\% in terms of PDR compared to learning-based techniques.