## 3 Proposed Solution to Predict the AoA
### System Overview and Assumptions  
The Open Radio Access Network (O-RAN) Alliance promotes openness, intelligence, and interoperability in next-generation RAN deployments [26]. Among its key architectural innovations is the functional split framework, particularly Split 7-2x, which is associated with Category B O-RAN deployments. This split delineates the distribution of physical (PHY) layer processing between the O-RU (Radio Unit) and the O-DU (Distributed Unit), enabling flexible deployment and more efficient beamforming control [27]. In Split 7-2x, the lower PHY functions (such as cyclic prefix insertion/removal and digital front-end operations) are handled by the O-RU. In contrast, the upper PHY functions (e.g., modulation/demodulation, coding, scrambling, and precoding) are centralised at the O-DU. This allows for the O-DU to manage beamforming and precoding operations across multiple RUs in a coordinated fashion, leveraging centralised CSI feedback and scheduling decisions [28; 29]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Methodology** & **Key Contributions** & **Limitations** &
\begin{tabular}{c} **Comparison with** \\ **DE-ACSW-R** \\ \end{tabular} \\ \hline
[20] [PERSON] et al. & MUSIC (SRS-based) & \begin{tabular}{c} High-resolution \\ AoA estimation \\ \end{tabular} & \
\begin{tabular}{c} Lacks predictive and \\ latency-sensitive real-time \\ beam steering \\ \end{tabular} &
\begin{tabular}{c} DE-ACSW-R incorporates \\ predictive modelling for \\ proactive beamforming \\ \end{tabular} \\ \hline
[21] [PERSON] et al. & \begin{tabular}{c} Computationally \\ Efficient 2D MUSIC \\ \end{tabular} & \begin{tabular}{c} Reduced complexity \\ for static scenarios \\ \end{tabular} & \
\begin{tabular}{c} No predictive mechanism, \\ limited mobility handling \\ \end{tabular} &
\begin{tabular}{c} DE-ACSW-R provides \\ dynamic clustering and \\ adaptive prediction, \\ suitable for non- \\ linear mobility \\ \end{tabular} \\ \hline
[22] [PERSON] et al. & \begin{tabular}{c} SVR with \\ Chaos Mapping \\ \end{tabular} & \begin{tabular}{c} Improved SVR \\ predictive capability \\ \end{tabular} & \
\begin{tabular}{c} Lacks AoA predictive \\ modelling in \\ mobile scenarios \\ \end{tabular} &
\begin{tabular}{c} DE-ACSW-R leverages \\ SVR with \\ curvature-adaptive \\ kernels for wireless AoA \\ \end{tabular} \\ \hline
[23] [PERSON] et al. & \begin{tabular}{c} ML-based \\ beam selection \\ \end{tabular} & \begin{tabular}{c} Optimises beam \\ selection and timing \\ \end{tabular} & \
\begin{tabular}{c} Lacks distributed \\ integration, no direct \\ AoA modelling \\ \end{tabular} &
\begin{tabular}{c} DE-ACSW-R integrates \\ directly into O-RAN with \\ distributed inference \\ \end{tabular} \\ \hline
[24] [PERSON] et al. & \begin{tabular}{c} Low-complexity ML \\ beam prediction \\ \end{tabular} & \begin{tabular}{c} Effective low- \\ latency prediction \\ \end{tabular} & \
\begin{tabular}{c} No fallback mechanism, \\ static kernel \\ \end{tabular} &
\begin{tabular}{c} DE-ACSW-R provides \\ dynamic adaptation and \\ MUSIC-based \\ fallback mechanisms \\ \end{tabular} \\ \hline
[25] [PERSON] et al. & \begin{tabular}{c} Neural network-driven \\ AoA estimation \\ \end{tabular} & \begin{tabular}{c} Enhances accuracy \\ under multipath \\ \end{tabular} & \
\begin{tabular}{c} Deep learning \\ overhead, lacks low- \\ latency deployment \\ \end{tabular} &
\begin{tabular}{c} DE-ACSW-R avoids \\ overhead through \\ adaptive regression and \\ real-time prediction \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works and key advancements in DE-ACSW-R. The advantage of Split 7-2x lies in its ability to support high-bandwidth low-latency fronthaul (e.g., eCPRI) while enabling real-time signal processing closer to the antenna. This is particularly beneficial for beamforming in high-mobility environments, where quick adaptation to AoA variations is required.