## 5 Simulation Results and Discussion
### Prediction-Aided Fallback and Beam Alignment Behaviour  
To further illustrate the interplay between prediction, fallback logic, and beamforming, Figure 5 depicts the system's operation during an abrupt AoA change, highlighting the sequence of events as the system switches from enhanced MUSIC to a full MUSIC search. Here, the predicted AoA anticipates the sudden trajectory change, and the enhanced MUSIC estimator steps out of the tracking loop, as indicated by the activation of the fallback mode.  
At index 27, the system detects a significant discrepancy between the ongoing prediction and the estimated AoA. The enhanced MUSIC estimator, which typically tracks the predicted AoA with a narrow high-gain beam, is automatically disabled to prevent incorrect beam steering. The fallback control is triggered, causing the system to suspend enhanced MUSIC and revert to full MUSIC for direct angle estimation. During this phase, the predicted AoA and the full MUSIC AoA remain closely aligned with the estimated trajectory, while enhanced MUSIC is temporarily disabled to avoid misaligned beamforming.  
Supporting data shows that, at index 27, the AoA prediction abruptly drops from \(79.16^{\circ}\) to \(46.9^{\circ}\), corresponding to a jump in the estimated AoA. The system, recognising this jump, triggers a model re-clustering and disables enhanced MUSIC, switching to full MUSIC at index 28. This fallback mechanism prevents the narrow enhanced beam from steering toward an incorrect angle, thereby avoiding significant SNR loss and potential coverage gaps.  
Figure 6 further demonstrates the beam alignment behaviour in this regime. The \"Beam Angle\" closely follows the full MUSIC estimation during fallback and only resumes tracking the enhanced MUSIC output when confidence is restored. This mechanism prevents the risk of steering beams in the wrong direction during periods of high trajectory uncertainty or model disagreement.  
Subsequent indices show that, as the prediction stabilises and the clustered history realigns with the new trajectory, the system resumes enhanced MUSIC beamforming. This adaptive switching ensures that beam alignment, as shown in Figure 5, remains closely coupled to the ground-truth AoA, even in the presence of abrupt user movement or prediction failure. The beam angle output and source selection trace the full MUSIC estimator during periods of uncertainty (indices 28-30) and seamlessly return to enhanced MUSIC as prediction confidence is restored.  
Figure 5: Prediction-aided fallback mechanism: the system disables enhanced MUSIC and automatically switches to full MUSIC AoA estimation.  
This dynamic prediction-aided fallback process provides several key advantages for next-generation RAN systems. First, it ensures fault tolerance by immediately disabling the enhanced beam during prediction failures, thereby preventing the system from steering beams in incorrect directions, which minimises packet loss and maximises the received SNR. Second, the framework offers self-healing alignment capabilities by enabling rapid recovery: it leverages the full MUSIC estimator to quickly reacquire the proper user direction before re-enabling high-gain beams once model confidence is restored. Finally, this adaptive control logic is particularly valuable for O-RAN Category B deployments, where the ability to select beams and realign with low latency autonomously is crucial for supporting high-mobility users without centralised intervention.