Exceeding the Nonlinear Shannon-Limit in Coherent Optical Communications by MIMO Machine Learning
Abstract
The nonlinear Shannon capacity limit has been identified as the fundamental barrier to the maximum rate of transmitted information in optical communications. In long-haul high-bandwidth optical networks, this limit is mainly attributed to deterministic Kerr-induced fiber nonlinearities and from the interaction of amplified spontaneous emission noise from cascaded optical amplifiers with fiber nonlinearity: the stochastic parametric noise amplification. Unlike earlier impractical approaches that compensate solely deterministic nonlinearities, here we demonstrate a novel electronic-based deep neural network with multiple-inputs and outputs (MIMO) that tackles the interplay of deterministic and stochastic nonlinearity manifestation in coherent optical signals. Our demonstration shows that MIMO deep learning can compensate nonlinear inter-carrier crosstalk effects even in the presence of frequency stochastic variations, which has hitherto been considered impossible. Our solution significantly outperforms conventional machine learning and gold-standard nonlinear equalizers without sacrificing computational complexity, leading to record-breaking transmission performance for up to 40 Gbit/sec high-spectral-efficient optical signals.
Cite
@article{arxiv.1802.09120,
title = {Exceeding the Nonlinear Shannon-Limit in Coherent Optical Communications by MIMO Machine Learning},
author = {Elias Giacoumidis and Jinlong Wei and Ivan Aldaya and Liam P. Barry},
journal= {arXiv preprint arXiv:1802.09120},
year = {2019}
}