Name
Title | ||
|---|---|---|
LoRaDRL: Deep Reinforcement Learning Based Adaptive PHY Layer Transmission Parameters Selection for LoRaWAN |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LCN48667.2020.9314772 | 2020 IEEE 45th Conference on Local Computer Networks (LCN) |
Keywords | DocType | ISSN |
state-of-the-art PHY layer transmission parameter assignment algorithms,learning-based technique,network performance,deep reinforcement learning-based PHY layer transmission parameter assignment algorithm,physical layer,leading LPWAN technology,rule-based PHY layer transmission parameters assignment algorithms,densely-deployed low-power wide-area networks,LoRaWAN,adaptive PHY layer transmission parameters selection | Conference | 0742-1303 |
ISBN | Citations | PageRank |
978-1-7281-7159-3 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Inaam Ilahi | 1 | 1 | 0.70 |
| Muhammad Usama | 2 | 18 | 5.49 |
| Muhammad Omer Farooq | 3 | 110 | 13.18 |
| Muhammad Umar Janjua | 4 | 1 | 0.70 |
| Junaid Qadir | 5 | 0 | 1.35 |