## 4 Numerical Results
### Performance Discussion  
To assess the performance of the proposed ARTHF, we compared it with the following methods: (1) a classic rainbow DQN algorithm incorporating traffic prediction, denoted as 'rainbow DQN + traffic prediction'; (2) an attention\(-\)enhanced rainbow DQN algorithm operating under fixed traffic conditions (i.e., without traffic prediction), denoted as 'attention\(-\)enhanced rainbow DQN'; and (3) a classic rainbow DQN algorithm under fixed traffic conditions, denoted as 'rainbow DQN'. These methods were selected to isolate and evaluate the individual contributions of the traffic prediction and attention mechanisms, while also providing fair comparisons against both traditional and advanced DRL\(-\)based handover strategies. For the algorithms without traffic prediction, the lack of insight into TTG traffic variations leads to the adoption of a greedy approach for load\(-\)balancing rewards. Specifically, these algorithms encourage all TTGs to select the satellite with the currently lowest load, without considering the real traffic demands of the TTGs in the reward design. Figure 5 shows the average number of handovers per slot for each algorithm. The different algorithms evaluated and presented in the figure's legend are the 'attention\(-\)enhanced rainbow DQN'; the 'rainbow DQN'; the 'ARTHF (attention\(-\)enhanced rainbow DQN + traffic prediction)', which is our proposed framework; and the 'rainbow DQN + traffic prediction'. The attention\(-\)enhanced rainbow DQN significantly reduces the handover frequency compared with the classic rainbow DQN, which struggles with varying numbers of candidate satellites due to fixed input structures. This leads to positional bias and unbalanced training, where satellites later in the input are undervalued. By introducing a self\(-\)attention layer, the improved architecture captures inter\(-\)satellite relationships and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sub\(-\)constellation** & **Orbital Altitude** & **Number of Satellites** & **Inclination** & **Number of Orbital Planes** \\ \hline
1 & 540 km & 1584 & 53\({}^{\circ}\) & 72 \\
2 & 550 km & 1584 & 53\({}^{\circ}\) & 72 \\
3 & 560 km & 348 & 97.6\({}^{\circ}\) & 6 \\
4 & 560 km & 172 & 97.6\({}^{\circ}\) & 4 \\
5 & 570 km & 720 & 70\({}^{\circ}\) & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Constellation structure parameters. eliminates positional bias, enabling more accurate and stable handover decisions. Notably, the ARTHF performs similarly to its non\(-\)predictive counterpart in terms of handover frequency, indicating that traffic prediction does not compromise handover stability and instead improves other performance aspects. Thanks to the Milan dataset's inclusion of both peak and off\(-\)peak traffic scenarios, the framework's robustness across fluctuating demand levels is well validated, consistently achieving lower handover frequency compared with the baseline methods and demonstrating its efficiency and stability in diverse network loads. To evaluate the load\(-\)balancing performance, we used [PERSON]'s index [23] and the satellite peak load as key metrics. [PERSON]'s index is a widely used measure of fairness, ranging from 0 (worst) to 1 (perfect fairness), indicating how evenly traffic is distributed across satellites. As shown in Figure 6, the attention\(-\)enhanced algorithms consistently achieved significantly higher Jain's fairness indices compared with the classic DQN architecture, highlighting the limitations of the traditional approach in managing constrained candidate satellites. By incorporating the traffic prediction, the improved architecture further increased the Jain's fairness index, though the improvement was modest, demonstrating that anticipating future demand promotes a more equitable distribution of network load. Figure 7 illustrates the peak load experienced by each satellite at each time slot, providing a clear visualization of the congestion risk.