Model Order Selection with Variational Autoencoding

Abstract

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.

arXiv commentAccepted for publication at IEEE SPAWC 2023

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• Auto-Encoding Variational Bayes
• An Introduction to Variational Autoencoders
• Tune: A Research Platform for Distributed Model Selection and Training
• Model Selection Techniques -- An Overview
• A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling
• Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders
• Variational Autoencoder Leveraged MMSE Channel Estimation
• Model Order Selection in DoA Scenarios via Cross-Entropy based Machine Learning Techniques