Beyond Codebook-Based Analog Beamforming at mmWave: Compressed Sensing and Machine Learning Methods

TL;DR

This paper introduces a novel approach combining compressed sensing and machine learning to improve analog beamforming in mmWave communication, aiming to narrow the spectral efficiency gap with digital methods.

Contribution

It presents a new refined beam selection method using channel estimation via compressed sensing and dictionary learning, enhancing performance over traditional codebook-based approaches.

Findings

Significant performance improvement over codebook-based methods

Effective joint dictionary learning and signal reconstruction

Applicable to realistic mmWave communication setups

Abstract

Analog beamforming is the predominant approach for millimeter wave (mmWave) communication given its favorable characteristics for limited-resource devices. In this work, we aim at reducing the spectral efficiency gap between analog and digital beamforming methods. We propose a method for refined beam selection based on the estimated raw channel. The channel estimation, an underdetermined problem, is solved using compressed sensing (CS) methods leveraging angular domain sparsity of the channel. To reduce the complexity of CS methods, we propose dictionary learning iterative soft-thresholding algorithm, which jointly learns the sparsifying dictionary and signal reconstruction. We evaluate the proposed method on a realistic mmWave setup and show considerable performance improvement with respect to code-book based analog beamforming approaches.