## 3 Proposed Solution to Predict the AoA
### Curvature-Adaptive Kernel for Non-Linear Mobility
#### 3.3.6 Confidence-Driven MUSIC Fallback Mechanism  
While predictive AoA estimation using weighted LR, curvature-adaptive SVR, and enhanced MUSIC windowing significantly reduce latency and computational overhead, these methods may still degrade in performance under rapid user trajectory changes, multipath anomalies, or temporary signal loss. In such cases, relying solely on predicted AoAs risks beam misalignment and reduced signal quality. To address this, we integrate a confidence-driven fallback mechanism that autonomously reverts to classical full-spectrum MUSIC scanning whenever predictive estimates become unreliable. This mechanism acts as a fail-safe within the DE-ACSW-R framework, ensuring estimation accuracy and robust beamforming, even when the prediction models (LR, SVR, and fusion) encounter outliers, trajectory discontinuities, or low-SNR conditions. Fallback Triggering Conditions  
The fallback logic is minimally invasive, activating only when prediction reliability is clearly compromised. The switch to full MUSIC is triggered under the following conditions:  
**Low Prediction Confidence**  
If the confidence score \(\gamma(t)\in[0,1]\), derived from recent AoA history (such as standard deviation and model residuals), falls below a system-defined threshold, \(\tau_{conf}\),  
\[\gamma(t)<\tau_{conf}\]  
This threshold is typically chosen based on the system tuning and environmental noise characteristics. **Abrupt AoA Discontinuity (Jump Detection)**  
If the difference between consecutive AoA estimates exceeds a preset jump threshold \(\delta_{jump}\),  
\[\mid\theta(t)-\theta(t-1)\mid>\delta_{jump} \tag{26}\]  
The jump detection threshold, \(\delta_{jump}\), used in this system was determined using simulation experiments covering a wide range of user mobility scenarios, including linear, non-linear, and abrupt directional changes under varying speeds and multipath intensities. The threshold was evaluated by comparing the current AoA with the previous AoA in each time slot, with candidate thresholds ranging from \(5^{\circ}\) to \(25^{\circ}\). For each case, user speed was derived from AoA evolution over time and distance, allowing us to correlate angular variation with mobility dynamics. This approach enabled us to select an optimal threshold that consistently balanced between the target AoA and false AoA caused by noise or minor fluctuations. **Prediction Residual Thresholding**  
If the absolute difference between the predicted AoA and the current observed AoA exceeds a residual tolerance, \(\epsilon_{r}\),  
\[\mid\hat{\theta}_{est}(t)-\theta_{est}(t)\mid>\epsilon_{r} \tag{27}\]  
Any of these conditions, low confidence, an AoA jump, or a significant prediction residual will bypass the enhanced (windowed) MUSIC scan in favour of a full-spectrum search. When fallback is triggered, the system performs a comprehensive MUSIC scan over the entire angular domain. This resets and resynchronizes the AoA estimation logic with the real channel environment, discarding potentially outdated predictions. The new AoA estimate is then added back to the O-RU's local AoA buffer, enabling the DE-ACSW-R models to resume predictions for subsequent slots unless fallback is invoked again. Some important implementation details are as follows:  
* All fallback logic, confidence calculations, and jump detections operate at the O-RU level, and no O-DU or RIC interventions are required. * Fallback decisions are updated slot-by-slot, ensuring rapid adaptation to dynamic propagation conditions. * When the fallback AoA \(\theta_{MUSIC-full}\) is obtained, it is immediately used to update steering vectors and realism the beam. * The updated AoA is reinserted into the O-RU's history window, enabling the seamless recovery and transition back to predictive operation.