Mobility Management for Network Slicing Based 5G Networks

Abstract

Network slicing has been viewed as one of the key architectural technologies for the next generation (5G) mobile network. The mobility pattern of User Equipments (UEs) in different network slices poses great challenge to the mobility management for the 5G systems, which is different from that for the 4G systems. In this paper we propose a multiple level tracking area (TA) and tracking area list (TAL) structure which considers the characteristics of UE mobility pattern and UE density of slices to analyze the location update frequency and the paging frequency of UE. Based on this structure, we employ an embedded Markov chain model to find a theoretically optimal TAL configuration for slice UEs by leveraging sequential and parallel paging schemes respectively. Numerical results demonstrate the effectiveness of our proposed analytical model and verify that the proposed multiple level TA architecture can significantly reduce the total signaling overhead in comparison with the legacy single level TA strategy.