AI-Driven Mobility Management for High-Speed Railway Communications: Compressed Measurements and Proactive Handover
This work addresses critical mobility management challenges for high-speed railway systems, offering incremental improvements in efficiency and reliability for rail safety and passenger services.
The paper tackles the problem of rapidly time-varying wireless channels and high signaling overhead in high-speed railway communications by proposing an AI-driven mobility management approach, resulting in improved beam prediction accuracy and a 50% reduction in beam measurement overhead while significantly lowering radio link failure rates.
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. Particularly, we propose a compressed spatial multi-beam measurements scheme via compressive sensing for beam-level mobility management in HSR communications. In comparison to traditional down-sampling spatial beam measurements, this method leads to improved spatial-temporal beam prediction accuracy with the same measurement overhead. Moreover, we propose a novel AI-based proactive handover scheme to predict handover events and reduce radio link failure (RLF) rates in HSR communications. Compared with the traditional event A3-based handover mechanism, the proposed approach significantly reduces the RLF rates which saves 50% beam measurement overhead.