Active Sensing for Two-Sided Beam Alignment Using Ping-Pong Pilots

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 to compensate for the significant path loss in very high-frequency bands. However, beam alignment is also a highly nontrivial task, because the hybrid beamforming architecture typical of large antenna arrays allows 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 shortterm memory (LSTM) based neural networks are employed to learn the adaptive sensing strategies and to produce the final aligned beamformers at both sides. In the proposed ping-pong protocol, the Tx and the Rx alternatively send pilots so that both sides can leverage local observations to sequentially design their respective sensing and data transmission beamformers. Numerical experiments demonstrate significant and interpretable performance improvement. The proposed strategy works well even for the challenging multipath channel environments.