2024/10/21 by Søren F. V. Nielsen, Nielsen, Søren Føns, Darko Zibar +3
Computer Science · #Blind Source Separation Techniques #FOS: Electrical engineering #Image and Signal Denoising Methods #Neural Networks and Applications #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.16125
openalex publication_date 2024/10/21 · openalex created_date 2024/11/12 · openalex updated_date 2026/07/28
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.