## 3 Proposed Solution to Predict the AoA
### System Overview and Assumptions
#### 3.1.1 3 GPP Beamforming Limitations  
In traditional 3 GPP-compliant radio access networks (e.g., LTE and 5G NR), beamforming is typically executed through periodic channel measurements and feedback cycles. User Equipment (UE) receives reference signals, such as CSI-RS (Channel State Information Reference Signals), from the gNB, which then relies on feedback from the UE to estimate the preferred beam indices or channel quality indicators (CQI). The gNB uses this information to perform beam selection, adaptation, and scheduling. However, the latency between reference signal transmission, feedback reporting, and actual beam update creates a bottleneck, especially in high-speed or dense urban environments where the AoA may change faster than the feedback loop can track. Moreover, the 3 GPP beam management procedures, which include beam sweeping, measurement, reporting, and refinement, are optimised for semi-static or low-mobility scenarios. In high-mobility use cases, such as vehicular or UAV applications, these procedures may fail to respond quickly enough, resulting in outdated beam decisions and degraded link quality. Another limitation lies in the discrete nature of codebook-based beamforming: UE can only choose from a predefined set of beams, which may not align well with the instantaneous channel geometry, especially under multipath fading or non-line-of-sight (NLoS) conditions [34; 35]. Consequently, while the 3 GPP standard supports beamforming through explicit feedback mechanisms, its performance heavily depends on the feedback rate, channel coherence time, and codebook granularity. These limitations underscore the need for predictive and intelligent beamforming strategies. Our proposed DE-ACSW-R framework directly addresses these gaps by adaptively clustering prediction windows after AoA jumps, enforcing dominance-based fusion (dominance-enforced override) when one predictor outperforms the other, and continuously learning from mobility and AoA trajectory patterns. This results in adaptive, low-latency predictions that fully exploit the architectural flexibility of O-RAN Split 7-2x. Our solution enables proactive beam steering even in the presence of abrupt motion changes or non-linear trajectories, as confirmed by our simulation results and empirical model validation. In contrast to these 3 GPP-defined mechanisms, the architectural openness and functional disaggregation of O-RAN, particularly under the Split 7-2x configuration, provide a fundamentally more flexible and intelligent platform for predictive beamforming. Unlike legacy systems that rely on reactive feedback loops, the O-RAN architecture enables proactive and adaptive beam steering by separating real-time lower-layer signal processing (at the O-RU) from higher-level computation-intensive prediction and decision-making (at the O-DU) [36; 37; 38; 39]. In our model, the O-RU executes low-latency jump-aware clustered Weighted Linear Regression (WLR) predictions in real-time, dynamically splitting the prediction window when abrupt AoA changes are detected. Simultaneously, the O-DU leverages a broader history and additional context, including curvature-adaptive SVR, real-time confidence, and recent error metrics to refine, override, or fuse O-RU predictions using dynamic dominance-enforced error fusion logic. This dual-pipeline prediction architecture achieves two goals:  
* Immediate low-latency AoA adjustment at the RU for fast mobility or sudden AoA jumps;
* Continuous validation, correction, or fusion of predictions at the DU for robustness and overall performance. The O-RU can also autonomously trigger localised MUSIC scans based on detected AoA jumps or drops in prediction confidence, further refining AoA estimates in near-real-time without requiring DU intervention.