Electrical Engineering and Systems Science > Signal Processing
[Submitted on 15 Sep 2023 (v1), last revised 5 Jan 2024 (this version, v2)]
Title:Channel Estimation in Underdetermined Systems Utilizing Variational Autoencoders
View PDF HTML (experimental)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.
Submission history
From: Michael Baur [view email][v1] Fri, 15 Sep 2023 14:13:52 UTC (55 KB)
[v2] Fri, 5 Jan 2024 15:03:52 UTC (55 KB)
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