High-speed railway (HSR) communications are pivotal for ensuring rail safety, operations, maintenance, and delivering passenger information services. The high speed of trains creates rapidly time-varying wireless channels, increases the signaling overhead, and reduces the system throughput, making it difficult to meet the growing and stringent needs of HSR applications. In this article, we explore artificial intelligence (AI)-based beam-level and cell-level mobility management suitable for HSR communications, including the use cases, inputs, outputs, and key performance indicators (KPI)s of AI models. Particularly, in comparison to traditional down-sampling spatial beam measurements, we show that the compressed spatial multi-beam measurements via compressive sensing lead to improved spatial-temporal beam prediction. Moreover, we demonstrate the performance gains of AI-assisted cell handover over traditional mobile handover mechanisms. In addition, we observe that the proposed approaches to reduce the measurement overhead achieve comparable radio link failure performance with the traditional approach that requires all the beam measurements of all cells, while the former methods can save 50% beam measurement overhead.

翻译：高速铁路通信对于保障铁路安全、运营维护以及提供旅客信息服务至关重要。列车的高速运行导致无线信道快速时变，增加了信令开销并降低了系统吞吐量，难以满足日益增长且严苛的高速铁路应用需求。本文探讨适用于高速铁路通信的基于人工智能的波束级与小区级移动性管理方案，包括AI模型的应用场景、输入输出参数及关键性能指标。特别地，相较于传统的降采样空间波束测量方法，我们证明通过压缩感知获得的压缩空间多波束测量能够提升时空波束预测性能。此外，我们展示了AI辅助小区切换相较于传统移动切换机制的性能增益。同时，我们观察到所提出的降低测量开销的方法在无线链路失效性能方面与需要测量所有小区全部波束的传统方法相当，而前者可节省50%的波束测量开销。