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Nonlinearities can be introduced into communication systems by the physicalcomponents such as the power amplifier, or during signal propagation through anonlinear channel. These nonlinearities can be compensated by a nonlinearequalizer at the receiver side. The nonlinear equalizer also operates on theadditive noise, which can lead to noise enhancement. In this work we evaluatethis trade-off between distortion reduction and noise-enhancement via nonlinearequalization techniques. We first, evaluate the trade-off between nonlinearitycompensation and noise enhancement for the Volterra equalizer, and propose amethod to determine the training SNR that optimizes this performance trade-off.We then propose a new approach for nonlinear equalization that alternatesbetween neural networks (NNs) for nonlinearity compensation, and beliefpropagation (BP) for noise removal. This new approach achieves a 0.6 dB gaincompared to the Volterra equalizer with the optimal training SNR, and a 1.7 dBgain compared to a system with no nonlinearity compensation.

Low Noise Non-Linear Equalization Using Neural Networks and Belief Propagation