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Blind Equalization Using a Variational Autoencoder With Second Order Volterra Channel Model
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DOI：10.1109/TCCN.2025.3570452.png)
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Abstract
En 中文
Existing communication hardware is being exerted to its limits to accommodate for the ever increasing Internet usage globally. This leads to non-linear distortion in the communication link that requires non-linear equalization techniques to operate the link at a reasonable bit error rate. This paper addresses the challenge of blind non-linear equalization using a variational autoencoder (VAE) with a second-order Volterra channel model. The VAE framework’s costfunction, the evidence lower bound (ELBO), is derived for real-valued constellations and can be evaluated analytically without resorting to sampling techniques. We demonstrate the effectiveness of our approach through simulations on a synthetic Wiener-Hammerstein channel and a simulated intensity modulated direct detection (IM/DD) optical link. The results show significant improvements in equalization performance, compared to a VAE with linear channel assumptions, highlighting the importance of appropriate channel modeling in unsupervised VAE equalizer frameworks.
Keywords:
Blind equalizers
bayes methods
unsupervised learning
nonlinear distortion
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