Autor: |
Inaam Ilahi, Junaid Qadir, Muhammad Umar Janjua, Muhammad Omer Farooq, Muhammad Usama |
Rok vydání: |
2020 |
Předmět: |
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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: |
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