Channel Estimation based on Gaussian Mixture Models with Structured Covariances

TL;DR

This paper introduces a low-complexity Gaussian mixture model (GMM) based channel estimator with structured covariances, optimized for practical wideband systems, outperforming traditional methods with similar computational costs.

Contribution

It proposes modifications to the EM algorithm for structured covariances and a cascaded 1D estimation approach, reducing complexity and memory while maintaining high performance.

Findings

GMM estimators trained on realistic data outperform PDP and DS methods.

Structured covariance constraints reduce computational complexity.

Cascaded 1D estimation significantly lowers memory requirements.

Abstract

In this work, we propose variations of a Gaussian mixture model (GMM) based channel estimator that was recently proven to be asymptotically optimal in the minimum mean square error (MMSE) sense. We account for the need of low computational complexity in the online estimation and low cost for training and storage in practical applications. To this end, we discuss modifications of the underlying expectation-maximization (EM) algorithm, which is needed to fit the parameters of the GMM, to allow for structurally constrained covariances. Further, we investigate splitting the 2D time and frequency estimation problem in wideband systems into cascaded 1D estimations with the help of the GMM. The proposed cascaded GMM approach drastically reduces the complexity and memory requirements. We observe that due to the training on realistic channel data, the proposed GMM estimators seem to inherently perform a trade-off between saving complexity/parameters and estimation performance. We compare these low-complexity approaches to a practical and low cost method that relies on the power delay profile (PDP) and the Doppler spectrum (DS). We argue that, with the training on scenario-specific data from the environment, these practical baselines are outperformed by far with equal estimation complexity.