Predictive modeling of RRC inactive transitions and latency impacts for energy optimization in live NR SA networks

With the rapid evolution of 5G and the anticipated advancements in future 6G networks, machine learning is unlocking unprecedented opportunities for network optimization. Among the most significant advancements in 5G Standalone (SA) networks is the Radio Resource Control (RRC) Inactive state, a feature that is critical for achieving low-latency performance. Building on this foundation, our study is categorized into two key contributions. First, we present a novel application of ensemble machine learning to predict transitions from the RRC Inactive state, specifically distinguishing between RRC Resume and RRC Fallback requests. This predictive capability, developed using real-world New Radio (NR) SA network data, offers insights into previously unexplored transition behavior. Second, we demonstrate how this predictive capability can be applied to optimize gNodeB (gNB) operations, proactively managing User Equipment (UE) contexts to minimize unnecessary paging and processing overhead. Our findings show that the proposed framework achieves considerable energy savings while maintaining latency requirements critical to RRC Inactive mechanisms. These results underscore the practicality and scalability of machine learning-driven approaches to enhance network resource allocation and operational efficiency in 5G SA networks, providing a pathway to sustainable and high-performing next-generation networks.