Estimation of the conditional distribution of an optical nonlinear channel with memory using deep neural networks

We demonstrate that deep neural networks can be used to approximate the conditional distribution of a non-linear channel with memory. This distribution is then used in the BCJR algorithm to infer the unknown transmitted data. This procedure is validated experimentally in a IM/DD 3.2 km long transmission of 64 GBd PAM4 signal.