LoRaDRL: Deep Reinforcement Learning Based Adaptive PHY Layer Transmission Parameters Selection for LoRaWAN

Autor:	Inaam Ilahi, Junaid Qadir, Muhammad Umar Janjua, Muhammad Omer Farooq, Muhammad Usama
Rok vydání:	2020
Předmět:	LPWAN Transmission (telecommunications) Computer science Network packet business.industry ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Physical layer Reinforcement learning Network performance business Interference (wave propagation) Algorithm Computer network
Zdroj:	LCN
Popis:	The performance of densely-deployed low-power wide-area networks (LPWANs) can significantly deteriorate due to packets collisions, and one of the main reasons for that is the rule-based PHY layer transmission parameters assignment algorithms. LoRaWAN is a leading LPWAN technology where LoRa serves as the physical layer. Here, we propose and evaluate a deep reinforcement learning (DRL)-based PHY layer transmission parameter assignment algorithm for LoRaWAN. Our algorithm ensures fewer collisions and better network performance compared to the existing state-of-the-art PHY layer transmission parameter assignment algorithms for LoRaWAN. Our algorithm outperforms the state of the art learning-based technique achieving up to 500% improvement of PDR in some cases.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1bc116e5066a510c7d3a425df8e15402 https://doi.org/10.1109/lcn48667.2020.9314772 Zobrazit plný text záznamu