ANN-based LoRaWAN Channel Propagation Model

ANN-based LoRaWAN Channel Propagation Model

Artificial neural network, LoRAWAN channel, artificially intelligent, LoRa propagation loss models

LoRaWAN wireless communication channels are often impacted by noise and interference over long-range causing loss of a received signal. One of the main drawbacks of using existing propagation models is less accurate as these models in designing the communication link are tailored to simplify the estimation. In this paper, an artificial intelligent real time path loss model is proposed. It is capable of processing complex variables over a short period of time. Providing it with enough data, the model is able to learn channel behavior and predict the path loss accurately. Results of the model are benchmarked against classical statistical curve fitting models where RMSE values are also compared and indicating that the artificial intelligent model has better accurate prediction.

Mohamed Hadi Habaebi

Ahmad Shahmi Mod Rofi

Md Rafiqul Islam

Ahmed Basahel

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 11 (2022); pp. 91-106

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2022-06-07

Copyright (c) 2022 Mohamed Hadi Habaebi, Ahmad Shahmi Mod Rofi, Md Rafiqul Islam, Ahmed Basahel

https://creativecommons.org/licenses/by/4.0

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article