## 6 Conclusions  
This work presents a novel, distributed prediction-enhanced beamforming framework called Dominance-Enforced Adaptive Clustered Sliding Window Regression (DE-ACSW-R), designed explicitly for O-RAN Split 7-2x architectures in high-mobility 5G environments. By combining clustered, jump-aware Weighted Linear Regression (WLR) at the O-RU with curvature-adaptive SVR and dynamic dominance-based fusion at the O-DU, the proposed framework overcomes the core limitations of conventional LR/SVR switching and static fusion approaches.  
Simulation results demonstrate that DE-ACSW-R provides a decisive improvement in AoA prediction accuracy and beamforming robustness under challenging user mobility scenarios. The introduction of a window clustering mechanism allows for the system to adapt immediately to abrupt trajectory changes, avoiding lag and minimising prediction errors that would otherwise degrade beam alignment. The dynamic fusion logic ensures that the most statistically reliable predictor, whether linear or non-linear, always guides the system. At the same time, the autonomous fallback to full MUSIC estimation enables rapid self-recovery in the presence of prediction outliers or confidence drops.  
Crucially, these innovations translate into a significant reduction in SNR collapse events, the rapid restoration of link quality after mobility-induced anomalies, and sustained high SNR across the entire user trajectory. The DE-ACSW-R framework consistently maintains optimal beam alignment, minimises the risk of misdirected beams, and adapts beam width and selection in real-time without the need for centralised intervention or excessive computational overhead.  
These advances are fully aligned with the architectural principles of O-RAN Category B and are compatible with 3 GPP New Radio requirements for ultra-reliable low-latency communication (URLLC). The proposed approach offers a practical, AI-native solution for future O-RAN deployments, supporting scalable, modular, and resilient beamforming in dense urban and high-mobility environments. By integrating predictive intelligence at both the edge (O-RU) and higher layers (O-DU), DE-ACSW-R paves the way for robust self-optimising RANs, meeting the evolving demands of 5G and emerging 6G networks.  
Future work will extend the model to multi-user coordination, further optimise computational resource allocation between the O-RU and O-DU, and explore its integration with real-time AI/ML pipelines, as defined by the latest O-RAN specifications.  
Conceptualisation, J.M.B.; methodology, J.Z.; software and validation, M.M.; formal analysis, M.M. and J.M.B.; investigation, M.M. and P.K.; data curation, M.M.; writing--review and editing, all; funding acquisition, J.M.B. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Centre for Research and Development under the \"Next Gen Cloud\" project (grant No. KPOD.05.10-IW.10-0003/24) funded in the \"Important Project of Common European Interest--Cloud Infrastructure and Services (IPCEI-CIS)\" call.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.