Sequential Learning of CSI for MmWave Initial Alignment

TL;DR

This paper introduces two adaptive algorithms for sequentially learning channel state information during mmWave initial alignment, improving beamforming accuracy and spectral efficiency over traditional methods.

Contribution

The paper proposes novel adaptive, sequential algorithms for CSI learning in mmWave initial alignment, incorporating Bayesian updates for time-varying channels.

Findings

Improved outage probability performance

Enhanced spectral efficiency compared to hierarchical codebook strategies

Effective beamforming vector selection for single dominant path scenarios

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.