Channel Estimation in Underdetermined Systems Utilizing Variational Autoencoders

TL;DR

This paper introduces a variational autoencoder-based approach for channel estimation in underdetermined systems, extending previous methods to more practical scenarios without requiring perfect CSI during training.

Contribution

It extends VAE-based channel estimation from fully-determined to underdetermined systems, enabling training without perfect CSI, which enhances practical applicability.

Findings

VAE-based estimator outperforms traditional methods in simulations.

Extension to underdetermined systems broadens practical use cases.

Method performs well in hybrid and wideband systems.

Abstract

In this work, we propose to utilize a variational autoencoder (VAE) for channel estimation (CE) in underdetermined (UD) systems. The basis of the method forms a recently proposed concept in which a VAE is trained on channel state information (CSI) data and used to parameterize an approximation to the mean squared error (MSE)-optimal estimator. The contributions in this work extend the existing framework from fully-determined (FD) to UD systems, which are of high practical relevance. Particularly noteworthy is the extension of the estimator variant, which does not require perfect CSI during its offline training phase. This is a significant advantage compared to most other deep learning (DL)-based CE methods, where perfect CSI during the training phase is a crucial prerequisite. Numerical simulations for hybrid and wideband systems demonstrate the excellent performance of the proposed methods compared to related estimators.