\section{Conclusion}\label{sec:conclusion}

Surprisingly, the learned pulse-shaping filter of the AE trained on a CD-only channel $\mathcal{C}_\mathrm{AD}$ matches the analytical solution for a CD-compensating filter almost perfectly, while the constellation is plausible. Hence, the AE is indeed able to learn a proper compensation of physical impairments while it can still be interpreted by choosing a classic communication structure, consisting of well-known but trainable blocks like a mapper or pulse-shaper. This gives rise to the hypothesis that an AE is also able of learning a compensation for a fiber model $\mathcal{C}_\mathrm{ADN}$ including also \ac{KNL} for which no closed form solution is known today.
In this work, we were able to achieve a gain of up to $4.46\,\mathrm{\sfrac{bits}{s\cdot Hz}}$ in terms of spectral efficiency by the AE's design for \ac{AWGN}, \ac{CD}, and \ac{KNL}. Nevertheless, it was not possible to compensate for the impairments over all input powers. To our believe, this is due to our purely linear design template of the AE's TX-DSP. Ongoing work focuses on the search for a nonlinear structure that is flexible enough to compensate better for nonlinear effects, while still being interpretable in terms of classic communcation signal processing.

\section*{Acknowledgement}

We want to thank Laurent Schmalen for all the exciting discussions and the chance for benefiting from his long experience in the field of machine learning for optical communications, especially in the beginning of this project.
{"config": "arxiv", "file": "2006.15027/content/conclusion.tex"}