Native AI-based hybrid deep learning for wireless link quality prediction in NTN waterside scenarios

Predicting link quality before establishing communication between transmitter and receiver enhances channel selection. With the advancements in artificial intelligence, prediction is now possible for complex environments such as riverside, maritime and polar regions. This paper evaluates Wi-Fi and LoRa radios, utilizing Received Signal Strength Indicator (RSSI) to understand link quality in riverside environments. The proposed approach compares traditional regression techniques with advanced deep learning models. Error metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE), assess performance. This study aligns with Native AI for Non-Terrestrial Networks (NTN). ST-LSTM-CNN consistently surpasses other models.