AI-Based Handover Decision Algorithm for Conditional Handover in Non-Terrestrial Networks (NTNs)

Non-Terrestrial Networks (NTNs) have emerged as a pivotal technology to extend the reach of modern communication systems. Their integration with 5G networks enables unprecedented global coverage, especially in remote regions. However, the inherent challenges of NTNs, such as high latency, significant Doppler shifts, and the mobility of Low-Earth-Orbit (LEO) satellites, complicate efficient handover mechanisms. Traditional rule-based algorithms often fail to provide seamless connectivity under such dynamic conditions. To address these issues, this paper proposes a Deep Q-Network (DQN) based handover decision algorithm tailored to NTNs. Unlike conventional methods relying on fixed thresholds, our AI-driven approach dynamically adapts to network conditions, enhancing decision accuracy and reducing handover latency. The contributions of this work include a novel implementation of DQN for NTNs and an in-depth comparison with traditional and AI-based benchmarks.