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

Authors

Shrutika Sinha, Kookmin University
G. Pradeep Reddy, Manipal Institute of Technology
Sea Moon Kim, Korea Research Institute of Ships & Ocean Engineering
Soo Hyun Park, Kookmin University

Document Type

Article

Publication Title

ICT Express

Abstract

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. The results demonstrate that ST-LSTM-CNN consistently surpasses other models for Native AI for Non-Terrestrial Networks (NTN).

DOI

10.1016/j.icte.2025.10.001

Publication Date

1-1-2025

Recommended Citation

Sinha, Shrutika; Reddy, G. Pradeep; Kim, Sea Moon; and Park, Soo Hyun, "Native AI-based hybrid deep learning for wireless link quality prediction in NTN waterside scenarios" (2025). Open Access archive. 14414.
https://impressions.manipal.edu/open-access-archive/14414