###### Abstract  
In high-mobility non-terrestrial networks (NTN), the reference signal received power (\(RSRP\))-based handover (RBH) mechanism is often unsuitable due to its limitations in handling dynamic satellite movements. \(RSRP\), a key metric in cellular networks, measures the received power of reference signals from a base station or satellite and is widely used for handover decision-making. However, in NTN environments, the high mobility of satellites causes frequent \(RSRP\) fluctuations, making RBH ineffective in managing handovers, often leading to excessive ping-pong handovers and a high handover failure rate. To address this challenge, we propose an innovative approach called location-based handover with particle filter and reinforcement learning (LBH-PRL). This approach integrates a particle filter to estimate the distance between user equipment (UE) and NTN satellites, combined with reinforcement learning (RL), to dynamically adjust hysteresis, time-to-trigger (\(TTT\)), and handover decisions to better adapt to the mobility characteristics of NTN. Unlike the location-based handover (LBH) approach, LBH-PRL introduces adaptive parameter tuning based on environmental dynamics, significantly improving handover decision-making robustness and adaptability, thereby reducing unnecessary handovers. Simulation results demonstrate that the proposed LBH-PRL approach significantly outperforms conventional LBH and RBH mechanisms in key performance metrics, including reducing the average number of handovers, lowering the ping-pong rate, and minimizing the handover failure rate. These improvements highlight the effectiveness of LBH-PRL in enhancing handover efficiency and service continuity in NTN environments, providing a robust solution for intelligent mobility management in high-mobility NTN scenarios. L 202514149414  
stand out due to their reduced development and launch costs, facilitated by satellite miniaturization and reusable launch vehicles. Additionally, LEO satellites offer significantly lower propagation delays compared to geostationary orbit (GEO) satellites, making them a viable alternative for communication systems [1; 2]. However, unlike GEO satellites, LEO satellites face frequent handover events due to their higher velocity and lower altitude. This presents unique challenges for mobility management within LEO satellite networks. NTN encompasses a broad range of communication scenarios, including satellite-to-terrestrial communication, where user equipment (UE) connects directly to satellites, as well as satellite-to-satellite communication, which facilitates inter-satellite data transfer. While NTN technologies include both communication paradigms, this study specifically focuses on satellite-to-terrestrial communication within NTN, where UE interacts with LEO satellites for mobility management and service continuity. Hybrid architectures integrating NTN with terrestrial networks (TN) represent a significant research area; however, they fall outside the scope of this work. By narrowing our focus to satellite-to-terrestrial communication, we aim to analyze and optimize handover mechanisms that directly impact NTN mobility performance. Despite these challenges, LEO satellites are gaining prominence for their shorter roundtrip time relative to both GEO and medium Earth orbit (MEO) satellites. Recent research has focused on improving handover mechanisms for LEO-based communication networks. For instance, in [3], a handover algorithm was proposed to optimize satellite selection and handover strategy. Additionally, ref. [4] analyzed the impact of time-to-trigger (\(TTT\)) and handover margin on LEO-based NTN handover performance. In [5], a method for integrating TN with NTN during handover was introduced. Furthermore, ref. [6] outlined a handover procedure between high altitude platforms (HAPs) and LEO satellites, proposing a strategy for performance optimization. Various algorithms and mechanisms for LEO satellite handover have been studied. For example, ref.