An Improved End-to-End Autoencoder Based on Reinforcement Learning by Using Decision Tree for Optical Transceivers.

In this paper, an improved end-to-end autoencoder based on reinforcement learning by using Decision Tree for optical transceivers is proposed and experimentally demonstrated. Transmitters and receivers are considered as an asymmetrical autoencoder combining a deep neural network and the Adaboost algorithm. Experimental results show that 48 Gb/s with 7% hard-decision forward error correction (HD-FEC) threshold under 65 km standard single mode fiber (SSMF) is achieved with proposed scheme.

Interconnecting data based on vortex beams by adjusting the ellipticity of a ring-core fiber.

In order to exchange data in a space-division multiplexing (SDM) system, a novel vortex-beam-based data interconnection concept, which is achieved by adjusting the ellipticity of a ring-core fiber, is proposed. A new ring-core fiber is also designed and fabricated for exchanging and propagating the data carried by first- or second-order vortex (orbital angular momentum) beams. The proposed scheme is not only analyzed and simulated in principle, but is also verified through experiments.