## 1 Introduction  
Precise and timely beamforming has emerged as a cornerstone of network performance in recent wireless communication systems, particularly in the context of 5G and the upcoming 6G technology. Conventional beamforming in 3 GPP New Radio (NR) hinges on the systematic acquisition of Channel State Information (CSI) via CSI reference signal (CSI-RS) transmissions and their associated feedback mechanisms [1]. In high-mobility scenarios, the time lag between measurement, feedback, and beam adaptations can cause beam misalignment and a decreased signal-to-noise ratio (SNR), resulting in system throughput degradation [2]. Additionally, the complexities of real-world deployment are exacerbated in high-density urban areas, where user equipment (UE) mobility is rapid and channel conditions are highly variable. Moreover, conventional static-beam management approaches-lack sufficient adaptability to mitigate rapidly varying propagation conditions, multipaths, and non-linear user mobility patterns [3; 4; 5]. Angle of arrival (AoA) estimation in these conditions is inherently difficult, especially if the system assumes constant velocity or flat user trajectories. These issues are exacerbated by periodic signal oscillation, interference, and the limitations of traditional codebook-based beamforming systems [6; 7; 8]. Within this context, we propose a novel predictive beamforming framework, termed Dominance-Enforced Adaptive Clustered Sliding Window Regression (DE-ACSW-R). In this framework, the Radio Unit performs immediate low-latency AoA prediction using weighted Linear Regression (LR), while the Distributed Unit concurrently applies a curvature-adaptive Support Vector Regression (SVR) and executes the dynamic fusion and dominance logic. This approach ensures both fast slot-level responsiveness and robust, Mean Squared Error (MSE)-optimised prediction blending, enhancing overall performance in rapidly varying environments. The DE-ACSW-R system also incorporates an intelligent fallback mechanism to further enhance beam alignment precision and reliability under highly dynamic or unpredictable channel conditions. Specifically, when sudden AoA deviations or drops in prediction confidence are detected, the Radio Unit autonomously triggers a localised Multiple Signal Classification (MUSIC) scan, thereby promptly refining AoA estimation without incurring significant computational overhead or latency. Our solution is well embedded in the Open Radio Access Network (O-RAN) framework introduced by the O-RAN Alliance. O-RAN offers a virtualised and disaggregated architecture that incorporates flexibility, openness, and intelligence into the Radio Access Network (RAN). In particular, the O-RAN Category B functional split (Split 7-2x) enables the centralisation of beamforming decisions at the Open Distributed Unit (O-DU) while allowing for real-time signal processing in the Open Radio Unit (O-RU). This architecture facilitates improved flexibility in beam control by leveraging AI-based models, low-latency interfaces, and predictive algorithms running in near-real-time Radio Intelligent Controller (RIC) platforms [9; 10; 11; 12]. Despite O-RAN's architectural flexibility, predictive AoA modelling still presents several practical challenges:  
The first challenge arises from the assumptions inherent in the traditional Linear Regression assumptions, which assumes a constant-speed trajectory, treating all historical AoA samples equally. In reality, user motion is subject to dynamic behaviour acceleration, deceleration, and non-linear changes in direction. This leads to prediction errors, particularly during transitions and curved motion [13; 14; 15]. To address this, we implement a Weighted Linear Regression (WLR) model, assigning greater importance to more recent AoA samples, thus better capturing recent non-linear or abrupt mobility patterns. A second critical challenge is the sensitivity of Support Vector Regression (SVR) parameters to varying mobility dynamics.