  Neural networks (NNs) inspired by the forward-backward algorithm (FBA) are
used as equalizers for bandlimited channels with a memoryless nonlinearity. The
NN-equalizers are combined with successive interference cancellation (SIC) to
approach the information rates of joint detection and decoding (JDD) with
considerably less complexity than JDD and other existing equalizers.
Simulations for short-haul optical fiber links with square-law detection
illustrate the gains of NNs as compared to the complexity-limited FBA and Gibbs
sampling.
