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

Ke Ma, Zhaocheng Wang, Wenqiang Tian, Sheng Chen, Lajos Hanzo
2021 arXiv   pre-print
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
more » ... hieving 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.
arXiv:2111.11177v2 fatcat:u2quxlc67vfmxfxefvd5lkynku