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.
Proactive Resource Management for Seamless Service: A Transition from 5G-Basic to 5G-Advanced Network Slicing
07.10.2024
629375 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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