Deep Learning for Beam-Management: State-of-the-Art, Opportunities and Challenges

TL;DR

This paper reviews how deep learning can enhance beam-management in millimeter-wave wireless networks, addressing challenges like training overhead and blockages, and discusses future research directions.

Contribution

It provides a comprehensive review of deep learning applications in beam-management, highlighting design insights, mechanisms, and future opportunities in mmWave communications.

Findings

Deep learning improves beam-selection accuracy.

DL-based methods reduce training overhead.

Enhanced robustness against blockages.

Abstract

Benefiting from huge bandwidth resources, millimeter-wave (mmWave) communications provide one of the most promising technologies for next-generation wireless networks. To compensate for the high pathloss of mmWave signals, large-scale antenna arrays are required both at the base stations and user equipment to establish directional beamforming, where beam-management is adopted to acquire and track the optimal beam pair having the maximum received power. Naturally, narrow beams are required for achieving high beamforming gain, but they impose enormous training overhead and high sensitivity to blockages. As a remedy, deep learning (DL) may be harnessed for beam-management. First, the current state-of-the-art is reviewed, followed by the associated challenges and future research opportunities. We conclude by highlighting the associated DL design insights and novel beam-management mechanisms.