Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning
This work provides a significant improvement in network efficiency and lifetime for LoRa network operators and users by enhancing packet delivery and reducing power consumption, especially in dense and mobile environments.
This paper addresses the challenge of efficient resource allocation in dense LoRa networks to manage the increasing number of IoT devices while maintaining low power consumption. The proposed LoRaDRL algorithm significantly improves the packet delivery ratio (PDR) by over 500% compared to learning-based techniques, supports mobile end-devices, and reduces power consumption.
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.