## 3 Proposed Solution to Predict the AoA
### Curvature-Adaptive Kernel for Non-Linear Mobility
This approach enables the SVR model to generalise across a wide range of mobility profiles within a unified modelling framework, reducing the reliance on handcrafted heuristics or scenario-specific tuning. By continuously adapting to curvature dynamics, the model maintains robust prediction quality across diverse and time-varying propagation conditions. The Clustered and Curvature-Adaptive kernel is fully integrated into the SVR layer of the system, operating transparently to the O-RU. It enables the O-DU to perform the following:  
* Maintain prediction robustness across time-varying dynamics;
* Inform the confidence-weighted fusion module of trajectory nonlinearity, enhancing global prediction quality;
* Reduce fallback events by improving prediction precision in conditions where LR fails due to curvature;
* Operate asynchronously while allowing for the RU to continue local operations. **Adaptive Dynamic Fusion of LR and SVR Predictions (DE-ACSW-R Fusion)**  
In mobility-aware beamforming, no single model, whether linear or non-linear, consistently outperforms in all scenarios. While Weighted Linear Regression (WLR) enables low-latency real-time tracking on near-linear AoA segments, it can underperform in the presence of non-linear mobility or sudden trajectory jumps. Conversely, curvature-adaptive SVR (CA-SVR) is more robust to non-linearities and trajectory curvature, but relies on longer context and increased computation. To optimally leverage the strengths of both predictors, our system applies a DE-ACSW-R Fusion scheme. Here, both LR and SVR are computed on the current (possibly clustered) AoA window, and their outputs are dynamically fused. The fusion weights are not fixed. Instead, they are assigned to ensure that the predictor with the lower mean squared error (MSE) receives at least 65% of the total weight while the other predictor receives the remainder. If MSEs are similar, the weights default to a confidence-weighted scheme that accounts for prediction spread, residuals, and real-time SNR trends. Dominance-enforced weighting means that whenever one predictor outperforms the other, it is guaranteed to have the majority influence on the fused result while still allowing, the secondary predictor to contribute in ambiguous or transition cases. This dynamically rebalanced fusion approach provides the following:  
* Smooth, adaptive transitions between LR and SVR predictions without abrupt switching. * Robustness to abrupt mobility changes as the fusion rapidly increases the influence of the more reliable predictor. * Physical-layer awareness is achieved by optionally incorporating SNR or error statistics in the weighting logic. Outlier detection is incorporated; if the difference between LR and SVR predictions exceeds a threshold (e.g., 10\({}^{\circ}\)), the fusion automatically selects the prediction closer to the recent history as the final output. The resulting fused prediction \(\hat{\theta}_{fused}(t+\Delta)\) is then used both for beamforming weight calculation and to restrict the angular search space in MUSIC-based fallback procedures. This hybrid fusion guarantees robust and low-latency AoA prediction under a wide range of user mobility and channel conditions. Let \(\hat{\theta}_{LR}(t+\Delta)\) and \(\hat{\theta}_{SVR}(t+\Delta)\) denote the predicted AoA from the Linear Regression (LR) and Support Vector Regression (SVR) models, respectively, at time \(t+\Delta\). Let \(MSE_{LR}\) and \(MSE_{SVR}\) denote their respective mean squared prediction errors computed on the current window. Let \(\epsilon>0\) be a small regularisation constant to avoid division by zero.