ANN-based LoRaWAN channel propagation model - Habaebi, Mohamed Hadi; Mod Rofi, Ahmad Shahmi; Islam, Md Rafiqul; Basahel, Ahmed (International Association of Online Engineering, 2022)
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.