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.

翻译：利用巨大的带宽资源,毫米波通信为下一代无线网络提供了最有希望的技术之一;为补偿毫米波信号的高度路况损失,基地台和用户设备都需要大型天线阵列,以建立定向波束成型,采用波束管理方法获取和跟踪拥有最大接收力的最佳光束对子。自然,实现高波束增益需要窄波束,但需要大量培训,对阻隔装置的敏感度很高。作为一种补救措施,可以利用深度学习(DL)来管理波束。首先,对当前的最新技术进行审查,然后研究相关的挑战和未来研究机会。我们最后强调相关的DL设计洞察力和新颖的波束管理机制。