Communication in high frequencies such as millimeter wave and terahertz suffer from high path-loss and intense shadowing which necessitates beamforming for reliable data transmission. On the other hand, at high frequencies the channels are sparse and consist of few spatial clusters. Therefore, beam alignment (BA) strategies are used to find the direction of these channel clusters and adjust the width of the beam used for data transmission. In this work, a single-user uplink scenario where the channel has one dominant cluster is considered. It is assumed that the user transmits a set of BA packets over a fixed duration. Meanwhile, the base-station (BS) uses different probing beams to scan different angular regions. Since the BS measurements are noisy, it is not possible to find a narrow beam that includes the angle of arrival (AoA) of the user with probability one. Therefore, the BS allocates a narrow beam to the user which includes the AoA of the user with a predetermined error probability while minimizing the expected beamwidth of the allocated beam. Due to intractability of this noisy BA problem, here this problem is posed as an end-to-end optimization of a deep neural network (DNN) and effects of different loss functions are discussed and investigated. It is observed that the proposed DNN based BA, at high SNRs, achieves a performance close to that of the optimal BA when there is no-noise and for all SNRs, outperforms state-of-the-art.

翻译：在高频通信中,如毫米波和梯度波和千兆赫兹等高频通信受到高路径失落和强烈阴影的困扰,因此需要为可靠的数据传输形成光束。另一方面,在高频中,频道是稀少的,由少数空间集群组成。因此,使用波束对齐(BA)战略来寻找这些频道集群的方向,并调整数据传输所使用的光束宽度。在这项工作中,一个单一用户的上链假设情景,即频道有一个占支配地位的集群。假定用户在固定期限内传输一套BA包。与此同时,基础站(BS)使用不同的探测波束扫描不同的角区域。由于BS测量很吵闹,因此不可能找到一个狭窄的波束,包括用户抵达角度(AoAA),并调整数据传输数据传输所使用的光束宽宽宽宽宽宽宽度。因此,BSAAA的概率是预先设定的概率,同时尽量减少所分配的BA包包。由于BA的精确度不近似性,因此,这里所观测到的SNR的深度BA和最佳网络的运行效果是其最后的。