Evaluation of a Gaussian Mixture Model-based Channel Estimator using Measurement Data

TL;DR

This paper evaluates a Gaussian mixture model-based channel estimator using real measurement data, demonstrating its ability to learn environmental characteristics and outperform traditional methods in uplink channel estimation.

Contribution

It introduces a GMM-based channel estimation method trained on real data, showing how ambient environment information improves estimation accuracy.

Findings

GMM estimator learns environmental characteristics effectively.

Significant performance gains over non-ambient-aware approaches.

Training on real data enhances estimation performance on synthetic and real channels.

Abstract

In this work, we use real-world data in order to evaluate and validate a machine learning (ML)-based algorithm for physical layer functionalities. Specifically, we apply a recently introduced Gaussian mixture model (GMM)-based algorithm in order to estimate uplink channels stemming from a measurement campaign. For this estimator, there is an initial (offline) training phase, where a GMM is fitted onto given channel (training) data. Thereafter, the fitted GMM is used for (online) channel estimation. Our experiments suggest that the GMM estimator learns the intrinsic characteristics of a given base station's whole radio propagation environment. Essentially, this ambient information is captured due to universal approximation properties of the initially fitted GMM. For a large enough number of GMM components, the GMM estimator was shown to approximate the (unknown) mean squared error (MSE)-optimal channel estimator arbitrarily well. In our experiments, the GMM estimator shows significant performance gains compared to approaches that are not able to capture the ambient information. To validate the claim that ambient information is learnt, we generate synthetic channel data using a state-of-the-art channel simulator and train the GMM estimator once on these and once on the real data, and we apply the estimator once to the synthetic and once to the real data. We then observe how providing suitable ambient information in the training phase beneficially impacts the later channel estimation performance.