Unlocking Cellular Antenna Capacity: Cell Splitting Enhanced By Machine Learning

Abstract

In the ever-evolving landscape of telecommunications, enhancing cellular antenna capacity has become paramount to meet the escalating demands for data services. This paper proposes a novel approach utilizing cell splitting augmented by machine learning (ML) algorithms to optimize antenna capacity. By leveraging ML techniques, the system intelligently analyzes network traffic patterns and user behavior to dynamically reconfigure cell boundaries, thereby redistributing the load across multiple smaller cells. This proactive cell splitting strategy aims to alleviate congestion and improve spectral efficiency, ultimately enhancing the overall network performance. Through simulations and real-world deployment scenarios, we demonstrate the efficacy of our proposed framework in significantly boosting cellular antenna capacity while maintaining quality of service metrics. This research presents a promising avenue for addressing the escalating demands on cellular networks and paving the way for more efficient and resilient telecommunications infrastructures.