Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications
arXiv:2507.16012
Abstract
We present an autoregressive end-to-end learning approach for probabilistic shaping on nonlinear fiber channels. Our proposed scheme learns the joint symbol distribution and provides a 0.3-bits/2D achievable information rate gain over an optimized marginal distribution for dual-polarized 64-QAM transmission over a single-span 205 km link.
4 pages, 3 figures, Submitted to the 51st European Conference on Optical Communications