###### Abstract
[7] reviewed the latest trends in handover algorithms for LEO satellites, while [8] evaluated handover performance using the A4 event for measurement reporting. Notably, ref. [9] proposed a novel mobility management approach that eliminates traditional handover mechanisms by introducing a cell-free, on-demand coverage model, addressing the cost inefficiencies inherent in conventional cellular architectures. In [10], the authors presented an Area-Based Mobility Management (ABMM) scheme specifically tailored for LEO satellite networks. This scheme leverages the Global Positioning System (GPS) to mitigate the challenges posed by LEO satellites' high velocity, which often results in elevated mobility management costs and degraded Quality of Service (QoS) compared to terrestrial networks. Their algorithm effectively addressed these issues. Additionally, ref. [11] investigated LEO-based NTN deployments, focusing on innovative mobility solutions and evaluating performance under diverse scenarios, including urban macro environments and high-speed train use cases. To counteract frequent handovers induced by moving satellite beams, ref. [12] proposed a fixed-duration strategy tailored for LEO Earth-fixed configurations. Similarly, ref. [13] introduced a learning-based auction handover algorithm that optimizes handover decisions by considering factors such as received signal strength and service time. Meanwhile, ref. [14] proposed a reimagined core network architecture that embeds mobility management functionality directly into the satellite, enhancing the adaptability and efficiency of Satellite-Terrestrial Integrated Networks (STIN). Mobility management is critical for ensuring service continuity and optimizing handover performance. Despite the work in [15] addressing mobility management challenges in NTNs, most previous studies have not thoroughly considered multiple parameters for handover mobility management in these networks. Additionally, the A3 event for measurement reporting has been extensively studied, as NTN environments often do not experience significant drops in signal strength. In certain NTN scenarios, location-basedhandovers may outperform reference signal received power (\(RSRP\))-based handovers. Artificial intelligence (AI) techniques have gained significant attention in NTN due to their capability to handle dynamic and complex mobility scenarios. A comprehensive survey on AI-driven approaches for satellite communication and NTNs is provided in [16], where various machine learning (ML) and deep learning (DL) techniques are explored for enhancing network operations, including mobility management. The study highlights the growing role of AI-based solutions in optimizing satellite handover strategies, beamforming, and resource allocation. Unlike conventional rule-based methods, AI models can adapt to real-time environmental changes and improve handover decision-making. This underscores the potential of integrating AI-driven approaches, such as reinforcement learning (RL), with traditional mobility management techniques to enhance the adaptability and robustness of NTN systems. The NTN is designed to leverage LEO satellites operating at altitudes between 600 km and 1200 km, traveling at an impressive speed of approximately 7.5 km/s relative to the Earth's surface. The primary aim is to extend 5G New Radio (NR) services globally by utilizing multiple satellite beams. This introduces a unique and dynamic scenario that demands significant adaptations to conventional mobility management strategies, as discussed in [17]. A key concept in this context is Earth Moving Cells (EMC), where each satellite beam's coverage area shifts along the satellite's trajectory across the Earth's surface.