We present our data-collection and deep neural network (DNN)-training scheme for extracting the optical status from signals received by digital coherent optical receivers in fiber-optic networks. The DNN is trained with unlabeled datasets across multiple administrative network domains by combining federated learning and unsupervised learning. The scheme allows network administrators to train a common DNN-based encoder that extracts optical status in their networks without revealing their private datasets. An early-stage proof of concept was numerically demonstrated by simulation by estimating the optical signal-to-noise ratio and modulation format with 64-GBd 16QAM and quadrature phase-shift keying signals.

Nonlinearity compensation algorithm and soft-decision forward error correction (FEC) are considered as key technologies for future high-capacity and long-haul optical transmission system. In this report, we experimentally demonstrate the following three benefits brought by low complexity perturbation back-propagation nonlinear compensation algorithm in 224Gb/s DP-16QAM transmission over large-Aeff pure silica core fiber; (1) improvement of pre-FEC bit error ratio, (2) reshaping noise distribution to more Gaussian, and (3) reduction of cycle slip probability.