AI-Driven Mobility Management for High-Speed Railway Communications: Compressed Measurements and Proactive Handover

TL;DR

This paper introduces AI-driven mobility management techniques for high-speed railway communications, utilizing compressed measurements and proactive handover strategies to improve prediction accuracy and reduce radio link failures.

Contribution

It proposes a compressed spatial multi-beam measurement scheme and an AI-based proactive handover method, enhancing efficiency and reliability in HSR wireless communications.

Findings

Improved spatial-temporal beam prediction accuracy with same measurement overhead.

Significantly reduced radio link failure rates.

50% reduction in beam measurement overhead compared to traditional methods.

Abstract

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.