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

arXiv: https://arxiv.org/abs/2407.04336 · HTML: https://mlsanity.com/paper/2407.04336/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [LIDAR Data for Deep Learning-Based mmWave Beam-Selection](https://mlsanity.com/paper/1908.07488/) (2019)
- [Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility](https://mlsanity.com/paper/2102.09785/) (2021)
- [Deep learning-based beam alignment in mmWave vehicular networks](https://mlsanity.com/paper/1910.11402/) (2019)
- [Machine Learning for Beam Alignment in Millimeter Wave Massive MIMO](https://mlsanity.com/paper/2002.06362/) (2020)

## Abstract (arXiv)

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.