## 4 Performance Evaluation  
In this section, we used LBH and RBH as comparison benchmarks for our proposed approach, which integrates particle filters and reinforcement learning. The evaluation focuses on three key performance metrics: (1) average number of handovers, (2) average ping-pong rate, (3) handover failure rate, and (4) computational complexity. By comparing these metrics, we aim to comprehensively assess the effectiveness of our proposed approach in optimizing handover efficiency, stability, and reliability in NTN scenarios. The parameters used for the simulation experiments are summarized in Table 1.  
(1) The average number of handovers  
As shown in Figure 3, the LBH approach significantly reduces the number of handovers compared to the RBH in NTN applications. By utilizing the geographical location of the UE and satellite trajectory data, LBH can proactively predict and trigger handovers, avoiding unnecessary transitions. In contrast, RBH depends solely on signal strength comparisons, which are vulnerable to frequent fluctuations in the dynamic NTN environment, leading to more handovers and reduced efficiency. Our proposed approach, which combines particle filters for accurate distance estimation and reinforcement learning for dynamic optimization of hysteresis and _TTT_, further reduces handover counts compared to LBH. This integration of precise predictions and adaptive decision-making ensures even fewer unnecessary handovers and improved overall efficiency. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Parameter** & **Assumption** \\ \hline Satellite & Altitude of satellite & 650 km \\ Configuration & Orbital inclination angle & 53\({}^{\circ}\) \\  & Satellite velocity & 7.5 km/s \\  & Tx Gain of NTN Satellite & 30 dBi \\  & Number of beams & 13 \\  & Satellite beam diameter & 60 km \\  & Tx power of UE & 30 dBm \\  & & 3 km/h (pedestrian), \\ UE Configuration & UE velocity profile & 120 km/h (vehicle), 300 km/h \\  & Mobility model & Random Walk, Highway Mobility Model \\  & Carrier frequency & 2 GHz (S-Band FDD) \\ Radio and Network & Frequency reuse & FR1 \\ Configuration & System Bandwidth & 10 MHz \\  & Load of Traffic & 30\% PRBs \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. (2) The average ping-pong rate  
Figure 4 illustrates that LBH achieves a much lower ping-pong rate compared to RBH in NTN scenarios. The predictive nature of LBH, which utilizes UE location and satellite trajectory data, prevents frequent handover reversals caused by short-term signal variations. In contrast, RBH's reliance on real-time signal strength makes it more susceptible to the rapid fluctuations in NTN, resulting in a higher ping-pong rate. Our proposed approach surpasses LBH by further reducing ping-pong occurrences. Through the use of particle filters for precise distance estimation and RL for adaptive parameter tuning, our approach not only inherits the predictive strengths of LBH but also intelligently adjusts to network dynamics, enabling more stable and efficient handover decisions. Figure 4: Average ping-pong rate. Figure 3: Average number of handovers. The experimental results, as depicted in Figure 5, highlight that LBH effectively lowers the handover failure rate compared to RBH when applied to NTN. By leveraging UE location data and satellite trajectories, LBH predicts and executes handovers proactively, mitigating failures caused by signal degradation or delays. On the other hand, RBH's dependence on real-time signal comparisons makes it prone to disruptions caused by signal fluctuations and latency, leading to a higher failure rate. Our proposed approach further enhances handover reliability by integrating particle filters for precise distance estimation with RL to optimize hysteresis and _TTT_ parameters. This approach ensures more accurate and adaptive handover decisions, achieving a significant reduction in failure rates compared to both LBH and RBH.