Mobility Management in 5G and Beyond: A Novel Smart Handover With Adaptive Time-to-Trigger and Hysteresis Margin

Abstract

The 5th Generation (5G) New Radio (NR) and beyond technologies will support enhanced mobile broadband, very low latency communications, and huge numbers of mobile devices. Therefore, for very high speed users, seamless mobility needs to be maintained during the migration from one cell to another in the handover. Due to the presence of a massive number of mobile devices, the management of the high mobility of a dense network becomes crucial. Moreover, a dynamic adaptation is required for the Time-to-Trigger (TTT) and hysteresis margin, which significantly impact the handover latency and overall throughput. Therefore, in this paper, we propose an online learning-based mechanism, known as <italic/><b>L</b><i>earning-based</i> <b>I</b><i>ntelligent</i> <b>M</b><i>obility</i> <b>M</b><i>anagement (LIM2)</i>, for mobility management in 5G and beyond, with an intelligent adaptation of the TTT and hysteresis values. LIM2 uses a Kalman filter to predict the future signal quality of the serving and neighbor cells, selects the target cell for the handover using <i>state-action-reward-state-action (SARSA)</i>-based reinforcement learning, and adapts the TTT and hysteresis using the <i><inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>-greedy</i> policy. We implement a prototype of the LIM2 in NS-3 and extensively analyze its performance, where it is observed that the LIM2 algorithm can significantly improve the handover operation in very high speed mobility scenarios.