Hierarchical AI for Optimized 5G RAN Performance: A Distributed Intelligence Approach

The rapid expansion of 5G networks necessitates advanced optimization techniques to manage the increased complexity and diverse service demands. Hierarchical Artificial Intelligence (AI) has emerged as a promising approach to enhance Radio Access Network (RAN) performance, offering distributed intelligence across cloud, edge, and device levels. This paper explores the integration of hierarchical AI in 5G RAN, emphasizing its potential to optimize resource allocation, reduce latency, and improve Quality of Service (QoS). We propose a framework that leverages AI-driven decision-making at multiple network layers to address the challenges of dynamic traffic management and personalized service delivery. Through comprehensive simulations and case studies, we demonstrate the efficacy of hierarchical AI in achieving real-time optimization, thereby paving the way for more responsive and efficient 5G networks. The findings suggest that hierarchical AI is not only feasible but also essential for the future scalability and adaptability of 5G infrastructure.