I'm about to finish my PhD in the field of optical communications and applied machine learning to increase the data rate. More specifically, I used an autoencoder with an embedded fiber channel model, to learn something called a geometric constellation shape or modulation format. (if you ever heard of QAM then you're on the right track, you can think of it as a modern Morse code for transmitting bits) Thereafter, we took the learned constellations to the lab and conducted an experiment, transmitting the learned constellations through actual fiber. I've learned so much during the whole process

Although, the gains are marginal, I like the method a lot since it combines physics (nonlinear schroedinger equation derived fiber channel model), information theory (optimizing for mutual information) and machine learning. It wouldn't have been possible without the people who published the fiber channel model, my colleagues and in particular the colleagues who could help me in the lab.

The debugging was hell, there are so many dimensions where stuff can go wrong (besides the usual bugs): physical parameters with the wrong unit, the implementation of the fiber model in tensorflow, the machine learning parts with its training process. Plus the things that can go wrong in the lab.

There are still pieces where I'm not 100% sure, and would love to speak to someone with some background in autoencoders.

I've open sourced the autoencoder and the fiber channel model, checkout my github with the same username as here!