Variational Autoencoder for Channel Estimation: Real-World Measurement Insights

TL;DR

This paper presents a variational autoencoder-based channel estimator trained on noisy real-world data, demonstrating superior performance and robustness, especially when pre-trained on synthetic data, reducing the need for extensive measurements.

Contribution

It introduces a novel VAE-based channel estimator trained solely on noisy observations and shows its effectiveness on real-world data, including benefits of synthetic pre-training.

Findings

Outperforms state-of-the-art estimators on real measurements

Pre-training on synthetic data yields comparable results to real data training

Synthetic pre-training reduces the amount of real measurement data needed

Abstract

This work utilizes a variational autoencoder for channel estimation and evaluates it on real-world measurements. The estimator is trained solely on noisy channel observations and parameterizes an approximation to the mean squared error-optimal estimator by learning observation-dependent conditional first and second moments. The proposed estimator significantly outperforms related state-of-the-art estimators on real-world measurements. We investigate the effect of pre-training with synthetic data and find that the proposed estimator exhibits comparable results to the related estimators if trained on synthetic data and evaluated on the measurement data. Furthermore, pre-training on synthetic data also helps to reduce the required measurement training dataset size.