## 3 Proposed Solution to Predict the AoA
### Predictive AoA Framework and Decision Logic  
In high-mobility environments, conventional AoA estimation approaches that rely solely on real-time MUSIC scanning are often limited by computational overhead and scanning latency. To address these challenges, our proposed DE-ACSW-R framework integrates a jump-aware predictive pipeline that exploits the clustered structure of recent AoA history to improve prediction robustness and adaptivity. The predicted AoA is used proactively to update beamforming weights, allowing for beams to be dynamically steered for the next transmission interval with minimal latency and improved resilience to abrupt mobility changes. As depicted in Figure 1, DE-ACSW-R operates in a dual-layer distributed architecture conforming to O-RAN Split 7-2x: the O-RU runs a lightweight, clustered, WLR predictor using a sliding window of recent AoA estimates, dynamically detecting and isolating abrupt trajectory jumps. This ensures that sudden mobility events are rapidly captured and that predictions are focused on the most relevant, recent samples. In parallel, the O-DU receives updated AoA histories and performs curvature-adaptive SVR with windowed error and curvature-based kernel adaptation. A dominance-enforced logic fuses the LR and SVR predictions, always selecting or favouring the predictor with the lower observed windowed MSE for each prediction step [40]. Additionally, an enhanced fallback mechanism is triggered at the O-RU whenever the prediction confidence drops or a jump is detected. In such cases, the O-RU autonomously initiates a localised MUSIC scan over a reduced angular sector, refining the AoA estimate without the computational penalty of a full-spectrum search. The choice of LR and SVR is motivated by their complementary strengths: LR provides fast, interpretable, and low-complexity predictions, ideal for real-time execution at the RU, while SVR, enhanced by curvature adaptation, runs at the DU and provides robustness in  
Figure 1: Distributed AoA prediction and fallback: The O-RU uses clustered window regression for fast local prediction, the O-DU refines with adaptive SVR and error-based fusion, and the MUSIC fallback handles uncertainty for robust beamforming. the presence of non-linear mobility and multipath effects. The clustering and weighting in the sliding window ensure its responsiveness to dynamic scenarios, while the dominance-enforced fusion strategy guarantees that only the most reliable prediction influences the final beam update at each time slot. In contrast, SVR provides enhanced flexibility for modelling non-linear AoA paths, especially under NLoS and dynamic mobility conditions. Its accuracy, however, depends critically on the selection of kernel parameters. To address this, our O-DU implementation utilises a curvature-adaptive SVR, dynamically adjusting the Gaussian kernel width based on the estimated AoA curvature using second-order differences within the sliding window. This adaptation ensures stable operation and steady-state mobility while allowing for SVR to respond rapidly to sharp non-linear trajectory changes. Given the higher computational requirements, SVR is executed at the O-DU, where an extended AoA history and additional physical layer information, such as SNR and ToA, are available. Unlike conventional approaches that switch between LR and SVR based on confidence or error metrics, our framework applies a dominance-enforced fusion logic at the O-DU. The fusion process first checks for in-window jumps and then enforces a clustered window for regression when a jump is detected. LR and SVR predictions are both computed over the current (or clustered) window, and their mean squared errors (MSE) against recent AoA ground truth are compared. The predictor with the lower MSE is assigned a dominant weight in the final fused output. Only if the two predictors are within a small error margin is a weighted average used; otherwise, the more accurate model is selected for that time slot.