## 2 Handover Issues in TN and NTN Networks
A handover is usually triggered when the difference in \(RSRP\) values between the target and serving cells exceeds a handover margin (hysteresis) and remains stable for the \(TTT\) period. However, in NTN environments, \(RSRP\)-based handover encounters significant limitations. The rapid movement of satellites, particularly in LEO systems, causes frequent changes in signal strength. As satellites pass over a given location, the \(RSRP\) of the serving satellite may decline rapidly while that of a neighboring satellite increases. These rapid transitions often result in frequent handovers, sometimes occurring within seconds, leading to excessive signaling overhead and increased network congestion. Additionally, the inherent propagation delay in NTN networks exacerbates the issue, as handover decisions based on outdated \(RSRP\) measurements may no longer be optimal by the time they are executed. The ping-pong effect, where a UE rapidly switches back and forth between satellites due to fluctuating \(RSRP\) levels, further degrades network efficiency and increases the likelihood of service interruptions. Another major concern with \(RSRP\)-based handover in NTN is the impact of the Doppler shift, which alters the perceived frequency of received signals as satellites move relative to the UE. This frequency shift affects the accuracy of \(RSRP\) measurements, potentially leading to incorrect handover decisions. As demonstrated in Figure 1, the reliance on signal-based triggers alone results in a reactive mobility management approach, which is inefficient in the presence of frequent satellite transitions. As a result, while \(RSRP\)-based handover remains a viable solution in TN networks, its application in NTN is challenged by satellite mobility, frequent signal variations, and long propagation delays, necessitating alternative handover strategies. To address these challenges, LBH offers an alternative approach by utilizing geographical positioning and satellite trajectory information to determine the optimal handover timing. Unlike \(RSRP\)-based handover, which reacts to signal strength changes, LBH proactively schedules handovers based on predicted UE movement and satellite coverage areas. By leveraging GNSS (Global Navigation Satellite System) positioning and predefined coverage maps, handovers can be performed before signal degradation occurs, reducing the reliance on real-time \(RSRP\) fluctuations. In NTN networks, LBH is particularly advantageous due to the predictable orbital paths of satellites. Since the movement of satellites follows well-defined trajectories, network operators can pre-calculate handover events based on UE location and satellite visibility. This predictive capability reduces unnecessary handovers and minimizes the ping-pong effect observed in \(RSRP\)-based approaches. While \(RSRP\)-based handover is widely used in terrestrial networks, its dependence on real-time signal strength makes it less effective in NTN due to frequent satellite movement, long propagation delays, and Doppler shift effects. In contrast, location-based handover offers a more predictive mechanism, leveraging satellite trajectory data to optimize transition timing. These findings underscore the need for hybrid handover strategies that integrate signal-based metrics, predictive mobility models, and adaptive learning techniques to optimize NTN mobility management. In the following sections, we describe the more advanced handover optimization approach we have proposed, including reinforcement learning-based adaptive handover strategies to enhance NTN network efficiency and service reliability.