Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems

TL;DR

This paper proposes an early-scheduled handover preparation scheme in 5G NR millimeter-wave systems that uses machine learning to predict optimal trigger points, improving handover robustness and reducing latency in high-mobility scenarios.

Contribution

It introduces a novel proactive handover preparation method leveraging machine learning to optimize timing and improve performance in 5G MIMO networks.

Findings

Reduced handover preparation time.

Improved robustness in high-mobility scenarios.

Enhanced user-aware decision making.

Abstract

The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success rate of the entire HO procedure is significantly affected by the preparation stage. As massive Multiple-Input Multiple-Output (MIMO) systems with large antenna arrays allow resolving finer details of channel behavior, we investigate how machine learning can be applied to time series data of beam measurements in the Fifth Generation (5G) New Radio (NR) system to improve the HO procedure. This paper introduces the Early-Scheduled Handover Preparation scheme designed to enhance the robustness and efficiency of the HO procedure, particularly in scenarios involving high mobility and dense small cell deployments. Early-Scheduled Handover Preparation focuses on optimizing the timing of the HO preparation phase by leveraging machine learning techniques to predict the earliest possible trigger points for HO events. We identify a new early trigger for HO preparation and demonstrate how it can beneficially reduce the required time for HO execution reducing channel quality degradation. These insights enable a new HO preparation scheme that offers a novel, user-aware, and proactive HO decision making in MIMO scenarios incorporating mobility.