Machine-Learning-Assisted Transmission Power Control for LoRaWAN Considering Environments With High Signal- to -Noise Variation

Machine-Learning-Assisted Transmission Power Control for LoRaWAN Considering Environments With High Signal- to -Noise Variation

To achieve an adequate tradeoff between range and energy efficiency, LoRaWAN End Nodes (ENs) choose their transmission parameters using an Adaptive Data Rate (ADR) scheme based on the maximum value of previous Signal-to-Noise (SNR) values. However, the ADR only performs well in favorable channel con...

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Bibliographic Details
Main Authors: Mauricio Gonzalez-Palacio, Diana Tobon-Vallejo, Lina M. Sepulveda-Cano, Mario MAURICIO, Christof Roehrig, Long Bao Le
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
LoRaWAN
transmission power control
adaptative data rate
machine learning
Online Access:https://ieeexplore.ieee.org/document/10497097/

Internet

https://ieeexplore.ieee.org/document/10497097/

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