Sequential Learning of CSI for MmWave Initial Alignment

Abstract: 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.