Beam alignment is an important task for millimeter-wave (mmWave) communication, because constructing aligned narrow beams both at the transmitter (Tx) and the receiver (Rx) is crucial in terms of compensating the significant path loss in very high-frequency bands. However, beam alignment is also a highly nontrivial task because large antenna arrays typically have a limited number of radio-frequency chains, allowing only low-dimensional measurements of the high-dimensional channel. This paper considers a two-sided beam alignment problem based on an alternating ping-pong pilot scheme between Tx and Rx over multiple rounds without explicit feedback. We propose a deep active sensing framework in which two long short-term memory (LSTM) based neural networks are employed to learn the adaptive sensing strategies (i.e., measurement vectors) and to produce the final aligned beamformers at both sides. In the proposed ping-pong protocol, the Tx and the Rx alternately send pilots so that both sides can leverage local observations to sequentially design their respective sensing and data transmission beamformers. The proposed strategy can be extended to scenarios with a reconfigurable intelligent surface (RIS) for designing, in addition, the reflection coefficients at the RIS for both sensing and communications. Numerical experiments demonstrate significant and interpretable performance improvement. The proposed strategy works well even for the challenging multipath channel environments.

翻译：波束对准是毫米波通信中的一项重要任务，因为在发射端和接收端构建对准的窄波束对于补偿极高频段显著的路径损耗至关重要。然而，由于大型天线阵列通常配备有限数量的射频链路，只能对高维信道进行低维测量，这使得波束对准成为一项极具挑战性的任务。本文研究基于发射端与接收端之间多轮交替乒乓导频方案（无需显式反馈）的双侧波束对准问题。我们提出一种深度主动感知框架，采用两个基于长短期记忆网络的神经网络分别学习自适应感知策略（即测量向量），并在两侧生成最终的对准波束成形器。在所提出的乒乓协议中，发射端与接收端交替发送导频，使得双方能够利用本地观测结果依次设计各自的感知与数据传输波束成形器。该策略可扩展至可重构智能表面场景，除上述功能外，还能同时设计用于感知与通信的RIS反射系数。数值实验表明，该方法具有显著且可解释的性能提升，即使在具有挑战性的多径信道环境下也能良好工作。