Electrical Engineering and Systems Science > Signal Processing
[Submitted on 27 Oct 2022 (v1), last revised 28 Jun 2023 (this version, v3)]
Title:Model Order Selection with Variational Autoencoding
View PDFAbstract:Classical methods for model order selection often fail in scenarios with low SNR or few snapshots. Deep learning-based methods are promising alternatives for such challenging situations as they compensate lack of information in the available observations with training on large datasets. This manuscript proposes an approach that uses a variational autoencoder (VAE) for model order selection. The idea is to learn a parameterized conditional covariance matrix at the VAE decoder that approximates the true signal covariance matrix. The method is unsupervised and only requires a small representative dataset for calibration after training the VAE. Numerical simulations show that the proposed method outperforms classical methods and even reaches or beats a supervised approach depending on the considered snapshots.
Submission history
From: Michael Baur [view email][v1] Thu, 27 Oct 2022 13:13:08 UTC (243 KB)
[v2] Thu, 6 Apr 2023 13:16:47 UTC (226 KB)
[v3] Wed, 28 Jun 2023 14:26:06 UTC (226 KB)
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