@article{MF26D0C71,
title = "Terminal Mobility Prediction for Deep Reinforcement Learning-Based Handover Optimization in Non-Terrestrial Networks",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.5.728",
author = "Junyoung Kim, Huiyeon Jang, In-Sop Cho, Minsu Shin, Soyi Jung",
keywords = "Non-terrestrial networks, LEO satellites communication, Handover, Deep reinforcement learning",
abstract = "Low Earth orbit (LEO) satellites are crucial for global coverage and real-time communication services.
However, their rapid mobility and unique channel characteristics pose challenges for conventional handover techniques, leading to frequent disruptions and limited seamless connectivity. Optimized methods are needed to address the satellites' movement and the stochastic mobility of user terminals. This paper proposes a novel approach combining deep learning and reinforcement learning to optimize handovers. Time-series data of satellite and terminal movements are analyzed to predict the received signal strength (RSSI) using deep learning. Based on the predicted RSSI, a reinforcement learning-based framework determines the optimal handover timing. This integration achieves faster convergence and precise handover decisions, enhancing RSSI and overall system performance."
}