## 4 Performance Evaluation
These results demonstrate the effectiveness of combining advanced estimation and adaptive learning techniques for robust handover management in NTN environments. (4) The computational complexity  
In this study, we analyze the computational cost of different handover approaches and evaluate their time complexity. The RBH approach relies solely on received signal strength measurements for handover decisions, where the primary computation involves comparing the _RSRP_ values of multiple candidate satellites. As a result, its time complexity is \(O(N)\), where \(N\) is the number of candidate satellites. In contrast, the LBH approach determines handovers based on the distance between the UE and the satellites. While the distance calculation itself is relatively simple, LBH requires historical position information to predict future mobility trajectories, leading to a slightly higher computational cost, though it remains at the \(O(N)\) level. In the proposed LBH-PRL approach, a particle filter is utilized to estimate the distance between the UE and satellites. Assuming \(M\) particles are used, the weight update and resampling operations for each particle contribute to a time complexity of \(O(M)\), making the overall particle filter estimation cost \(O(NM)\). Additionally, LBH-PRL incorporates RL to dynamically adjust handover parameters such as hysteresis and _TTT_, where the computational complexity is determined by Q-learning or Actor-Critic algorithms. In each time step, the RL update complexity is \(O(A)\), where \(A\) represents the size of the action space. Overall, the total time complexity of LBH-PRL is  
Figure 5: Handover failure rate. \(O(NM+A)\), making it computationally more demanding than RBH and LBH. However, since particle filter computations can be optimized using parallel processing and RL-based decision-making can be executed at a lower cost after training, LBH-PRL effectively balances computational efficiency and decision accuracy. Despite its higher computational cost, LBH-PRL significantly improves handover decision robustness, effectively reducing unnecessary handovers while minimizing the handover failure rate, demonstrating its practicality and advantages in high-mobility NTN environments. While the proposed LBH-PRL approach achieves superior performance in mobility management, its practical implementation overhead must also be considered, especially in user terminals with constrained computational resources. The integration of the particle filter and reinforcement learning involves iterative processing and parameter tuning that may pose a burden on devices with limited memory and processing power, such as IoT terminals or lightweight mobile equipment. To address this, lightweight versions of the RL model can be developed using model compression or quantization techniques. Additionally, the inference stage of the RL model can be offloaded to edge servers in real-world deployments, allowing user equipment UE to benefit from adaptive handover strategies without handling complex computations locally. These considerations suggest that the LBH-PRL approach remains feasible for practical NTN applications with proper system-level optimization. The performance comparison is summarized in Table 2, along with relevant discussions to illustrate the effectiveness of our proposed LBH-PRL approach compared to conventional handover approaches. Our proposed LBH-PRL approach significantly reduces the average number of handovers, ping-pong rate, and handover failure rate, demonstrating improved stability and efficiency. RBH relies solely on signal strength, making it susceptible to frequent handovers and high ping-pong effects due to satellite mobility. The LBH mitigates unnecessary handovers by using UE location but lacks adaptive optimization. LBH-PRL extends LBH by integrating a particle filter to estimate the UE-to-satellite distance and reinforcement learning to dynamically adjust handover parameters (hysteresis, _TTT_). This results in a more efficient and adaptive handover mechanism in NTN environments.