Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language

Abstract

We present the first neural probabilistic amplitude shaping that outperforms existing methods while accounting for all implementation losses, using a block-less, easily implementable sequential autoregressive encoder compatible with arithmetic distribution matching, yielding reduced rate loss and higher achievable information rates.

4 pages, 2 figures, Submitted to the 52nd European Conference on Optical Communications