## 1 Introduction
While SVR provides powerful modelling capabilities, its performance heavily depends on kernel hyperparameters like Gaussian width (\(\sigma\)) and regularisation strength (\(\lambda\)). Static settings of these parameters can lead to underfitting in environments with minimal mobility or overfitting in scenarios involving high speeds or multipath-rich conditions [16; 17]. To mitigate this, we introduce a Curvature-Adaptive SVR kernel, dynamically adjusting the Gaussian width (\(\sigma\)) based on real-time estimated AoA trajectory curvature. This adaptation allows for the SVR model to respond to rapid and non-linear changes in user motion more effectively. A third challenge involves the overreliance on confidence without the sufficient consideration of physical insights. Current switching mechanisms between LR and SVR often rely solely on statistical metrics, such as the confidence derived from AoA spread. However,this alone may misrepresent actual reliability, particularly in cases of high SNRs with erratic movement or low SNRs with steady motion [18,19]. Our system employs a Dominance-Enforced Adaptive Fusion approach. This method first uses a clustered sliding window: when a sharp AoA jump is detected, only the most recent post-jump samples are used for both LR and SVR. Then, both predictions are blended using a dynamic fusion rule that assigns a higher weight to the method with a lower instantaneous Mean Squared Error (MSE) over the window. If one predictor is clearly more accurate, a \"dominance-enforced override\" forces the fused prediction to select that value, ensuring optimal reliability under both linear and non-linear mobility. This system leverages adaptive predictions combined with the real-time signal processing capabilities that are uniquely enabled by O-RAN's Split 7-2x architecture. The key novel contributions of this work are as follows:  
* Parallel distributed prediction architecture: We uniquely leverage the O-RAN Split 7-2x architecture to distribute prediction tasks. Lightweight LR inference is executed at the O-RU for immediate slot-level responsiveness, while adaptive, curvature-aware SVR is performed at the O-DU. This parallel dual-pipeline eliminates the latency overhead and instability associated with switching between reactive or sequential models. * Dominance-Enforced Adaptive Clustered Sliding Window Fusion (DE-ACSW-R): Our core innovation is a mathematically novel fusion framework that (1) detects sharp AoA jumps within the sliding prediction window and clusters (selects) only post-jump samples for subsequent regression and SVR inference, maximising reliability after rapid trajectory changes, and also (2) applies a dynamic, dominance-enforced fusion rule: the fused AoA prediction is weighted toward the model (LR or SVR) that exhibits the lowest MSE against the most recent ground truth, with a \"dominance-enforced\" override when there is a clear performance gap. * Curvature-adaptive SVR kernel: We introduce a dynamic SVR kernel-width adaptation strategy, where the Gaussian width (\(\sigma\)) is modulated by real-time AoA trajectory curvature. This enables highly responsive and robust prediction under non-linear or unpredictable mobility. * Autonomous MUSIC-based fallback: When a jump is detected or when the confidence in prediction drops, the O-RU autonomously triggers a localised high-speed MUSIC scan, refining AoA estimation within a reduced angular search space. This approach enables a rapid and robust recovery without the cost of a full-spectrum search. Simulation results show that our DE-ACSW-R approach consistently achieves lower AoA-predicted Root Mean Squared Error (RMSE) than classical LR/SVR switching or static-fusion approaches and significantly sharpens MUSIC spectrum peak localisation. These improvements result in more accurate beam selection, a higher average signal-to-noise ratio (SNR), and faster, more robust beam alignment in O-RAN-based 5G and 6G systems.