用机器学习提前预测5G毫米波切换时机，提升高速移动下的连接稳定性。
Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems
- 基于毫米波波束测量时序数据，用机器学习预测最优切换触发点。
- 提前触发可减少切换耗时，降低信道质量下降风险。
- 适合高移动性、密集小站场景，适用于5G MIMO系统优化。
切换（HO）是蜂窝网络中由服务小区和邻近小区用户信道测量驱动的关键功能。整个切换过程的成功率显著受准备阶段影响。由于大规模多输入多输出（MIMO）系统配备大天线阵列，能够解析更精细的信道行为，本文研究如何在第五代（5G）新无线（NR）系统中对波束测量时序数据应用机器学习以改进切换流程。本文提出早期调度切换准备方案（Early-Scheduled Handover Preparation），旨在增强切换过程在高移动性和密集小站部署场景下的鲁棒性与效率。该方案通过机器学习技术优化切换准备阶段的时机，预测最早的切换触发点。我们识别出一种新的早期触发机制，并证明其可有效缩短切换执行所需时间，减少信道质量退化。这些发现促成了一种新型、用户感知且主动的切换决策机制，适用于包含移动性的MIMO场景。
原文摘要 · Abstract (English)
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.
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