  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.
