## 2 Related Work  
The increasing complexity of wireless communication environments in 5G and emerging 6G networks has accelerated the development of intelligent beamforming and AoA estimation techniques. These efforts aim to address core challenges, including user mobility, multipath propagation, and latency-sensitive processing. Traditional subspace methods like MUSIC offer high resolution in AoA estimation, but suffer from computational limitations under dynamic conditions. Meanwhile, machine learning (ML)-based approaches have shown promise in enhancing prediction accuracy and adapting to fast-changing propagation characteristics. In parallel, the O-RAN architecture has emerged as a modular and flexible framework for enabling AI-driven signal processing, distributed inference, and real-time beam management. In this context, a range of research works have explored techniques spanning from classical signal estimation to predictive modelling and AI-native RAN architectures. The following studies provide a representative overview of prior work and serve as a basis for comparison with our DE-ACSW-R framework. [PERSON] et al. present an enhanced MUSIC-based AoA estimation approach for 5G NR uplink by leveraging Sounding Reference Signals (SRS) [20]. Their method improves resolution under mobility using a full subspace decomposition, but does not address the latency and does not consider the predictive mechanisms for proactive real-time beam steering. In contrast, our work introduces lightweight prediction pipelines to proactively steer beams, thereby reducing reliance on full-spectrum scanning in real-time. [PERSON] et al. propose a computationally efficient 2D-MUSIC method tailored for joint range and angle estimation in 5G and 6G sensing applications [21]. While their focus is on reducing complexity for static and linear tracking, their solution lacks a predictive mechanism for AoA evolution. Their approach is limited to static scenarios, making it less suitable for high-mobility or non-linear environments. Our approach extends these by introducing (1) clustered sliding window regression to adaptively re-centre the prediction window after AoA jumps, (2) dominance-enforced fusion to privilege the most accurate predictor (LR or SVR) per window based on instantaneous error, and (3) a dominance-enforced override for sharply non-stationary scenarios. [PERSON] et al. explore the use of SVR combined with AoA and chaos mapping to forecast tunnel crown displacement under dynamic excavation [22]. Although not wireless-focused, the paper demonstrates how SVR performance improves when paired with dynamic trajectory modelling, supporting our adoption of curvature-adaptive SVR for non-linear AoA prediction in mobile environments. We selected this study as it illustrates the effectiveness of SVR in capturing non-linear dynamics, reinforcing our approach's validity in complex AoA trajectory prediction. [PERSON] et al. introduce a machine learning-aided approach to optimise beam selection and update timing in mmWave 5G systems [23]. Their solution uses historical measurements to reduce handover delays, but does not explicitly model AoA prediction or support distributed O-RAN integration. Their lack of direct AoA predictive modelling highlights a key gap addressed by our method, which integrates predictive AoA modelling within distributed O-RAN architectures. [PERSON] et al. propose a low-complexity machine-learning design for mmWave beam prediction using time-series inputs [24]. While effective for low-latency inference, the method lacks a fallback mechanism or kernel adaptation for trajectory curvature. Their static kernel limits adaptability in dynamic scenarios. Our framework addresses both issues by combining clustered LR and curvature-sensitive SVR with a dominance-enforced and error-weighted fusion, along with an autonomous, localised MUSIC fallback, which uniquely enables both rapid adaptation and robust recovery without relying solely on confidence metrics or static thresholds. [PERSON] et al.