The needs for ultra-high speed short- to medium-reach optical fiber links beyond 100-Gbit/s is becoming larger and larger especially for intra and inter-data center applications. In recent intensity-modulated/direct-detection (IM/DD) high-speed optical transceivers with the channel bit rate of 50 and/or 100 Gbit/s, multilevel pulse amplitude modulation (PAM) is finally adopted to lower the signaling speed. To further increase the transmission capacity for the next-generation optical transceivers, various signaling techniques have been studied, especially thanks to advanced digital signal processing (DSP). In this paper, we review various signaling technologies proposed so far for short-to-medium reach applications.

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.