  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.
