Proactive Resource Management for Seamless Service: A Transition from 5G-Basic to 5G-Advanced Network Slicing

Network slicing, a key technology of next-generation wireless networks, has undergone significant evolution from its inception as Dedicated Core Network (DCN) in 4G-LTE to its current state in 5G-Advanced. This paper provides a comprehensive analysis of network slicing enhancements across 3GPP releases 13 to 17, categorized into three phases: 5G-Basic (Release 15), early 5G-Evolution (Release 16), and advanced 5G-Evolution (Release 17). Furthermore, our study identifies persistent challenges in network slicing implementation and proposes innovative enhancements for 5G-Advanced (Release 18), including a novel machine learning-based approach to minimize service interruptions within a Registration Area (RA). This approach combines predictive insights from a Long Short-Term Memory (LSTM) model with a Dynamic Proportional Resource Allocation (DPRA) method for resource reconfiguration. Evaluation of the LSTM-DPRA scheme demonstrates significant performance improvements and reduced service interruptions compared to benchmark schemes, contributing to the development of more efficient and reliable network slicing.