5G NR毫米波系统中的早期调度切换准备
Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems
AI总结：
本文提出早期调度切换准备方案，利用机器学习预测5G NR毫米波MIMO系统中的最早切换触发点，优化准备时机以减少执行时间并降低信道质量退化。
摘要：
切换（HO）过程是蜂窝网络中最关键的功能之一，由服务小区和相邻小区的用户信道测量驱动。整个HO过程的成功率显著受准备阶段的影响。由于配备大型天线阵列的大规模多输入多输出（MIMO）系统能够解析信道行为的更精细细节，我们研究了如何将机器学习应用于第五代（5G）新空口（NR）系统中波束测量的时间序列数据，以改进HO过程。本文提出了早期调度切换准备方案，旨在增强HO过程的鲁棒性和效率，特别是在涉及高移动性和密集小基站部署的场景中。早期调度切换准备侧重于利用机器学习技术预测HO事件最早可能的触发点，从而优化HO准备阶段的时机。我们确定了一种新的HO准备早期触发器，并展示了它如何有益地减少HO执行所需时间，从而降低信道质量退化。这些见解促成了一种新的HO准备方案，在结合移动性的MIMO场景中提供了一种新颖的、用户感知的且主动的HO决策制定。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
