Blind Channel Equalization using Variational Autoencoders

TL;DR

This paper introduces a novel blind channel equalization method using variational autoencoders, achieving near-optimal performance with lower latency and reasonable complexity compared to traditional algorithms.

Contribution

The paper presents a new VAE-based maximum likelihood approach for blind channel equalization, improving error rates and reducing latency over existing methods.

Findings

Significant error rate improvements over constant modulus equalizers

Performance close to nonblind MMSE equalizers

Lower latency channel acquisition

Abstract

A new maximum likelihood estimation approach for blind channel equalization, using variational autoencoders (VAEs), is introduced. Significant and consistent improvements in the error rate of the reconstructed symbols, compared to constant modulus equalizers, are demonstrated. In fact, for the channels that were examined, the performance of the new VAE blind channel equalizer was close to the performance of a nonblind adaptive linear minimum mean square error equalizer. The new equalization method enables a significantly lower latency channel acquisition compared to the constant modulus algorithm (CMA). The VAE uses a convolutional neural network with two layers and a very small number of free parameters. Although the computational complexity of the new equalizer is higher compared to CMA, it is still reasonable, and the number of free parameters to estimate is small.