A Novel AI-Powered Approach for Narrow Beam Management in 5G and Beyond Networks

Future 5G and beyond networks will demand fast, energy-efficient, and secure narrow beam selection to maintain robust connectivity after the initial access phase. In this research, we leverage artificial intelligence (AI) and machine learning (ML) based methods for secure narrow beam selection that predicts the optimal narrow beam between the base station and user equipment (UE), maximizing connectivity while minimizing computational complexity. Our AI/ML models, evaluated through confusion matrices and top-k accuracy metrics, demonstrate outstanding performance, with a top-1 accuracy exceeding 90% for k-nearest neighbors (KNN), random forest (RF), and deep neural networks (DNN). A key novelty of this work is the incorporation of scatterer locations along with receiver locations as input features to the AI/ML model. While previous studies have discussed the importance of scatterer information, its implementation has not been explored extensively. By leveraging this innovative feature set, our model enhances prediction accuracy and provides a robust solution for beam management in 5G advanced networks.