Neural Network Equalizers and Successive Interference Cancellation for Bandlimited Channels with a Nonlinearity

Abstract

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.

Accepted at IEEE Intern. Symp. on Inf. Theory 2024 in Athens. arXiv admin note: substantial text overlap with arXiv:2401.09217