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

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