Enhancing Channel Estimation in Quantized Systems with a Generative Prior

TL;DR

This paper introduces a novel channel estimation method for quantized systems that uses a Gaussian mixture model as a generative prior, significantly improving accuracy over existing methods.

Contribution

It proposes a new approach combining GMM priors with EM algorithm for enhanced channel estimation in low-resolution quantized systems.

Findings

Significant performance improvement over Gaussian prior methods

Outperforms current state-of-the-art estimators

Adaptable to higher resolution systems and other priors

Abstract

Channel estimation in quantized systems is challenging, particularly in low-resolution systems. In this work, we propose to leverage a Gaussian mixture model (GMM) as generative prior, capturing the channel distribution of the propagation environment, to enhance a classical estimation technique based on the expectation-maximization (EM) algorithm for one-bit quantization. Thereby, a maximum a posteriori (MAP) estimate of the most responsible mixture component is inferred for a quantized received signal, which is subsequently utilized in the EM algorithm as side information. Numerical results demonstrate the significant performance improvement of our proposed approach over both a simplistic Gaussian prior and current state-of-the-art channel estimators. Furthermore, the proposed estimation framework exhibits adaptability to higher resolution systems and alternative generative priors.