  MmWave communications aim to meet the demand for higher data rates by using
highly directional beams with access to larger bandwidth. An inherent challenge
is acquiring channel state information (CSI) necessary for mmWave transmission.
We consider the problem of adaptive and sequential learning of the CSI during
the mmWave initial alignment phase of communication. We focus on the
single-user with a single dominant path scenario where the problem is
equivalent to acquiring an optimal beamforming vector, where ideally, the
resulting beams point in the direction of the angle of arrival with the desired
resolution. We extend our prior by proposing two algorithms for adaptively and
sequentially selecting beamforming vectors for learning of the CSI, and that
formulate a Bayesian update to account for the time-varying fading model.
Numerically, we analyze the outage probability and expected spectral efficiency
of our proposed algorithms and demonstrate improvements over strategies that
utilize a practical hierarchical codebook.
