[TuP-B] Poster Session 1

[TuP-B-9] Low-Complexity and Rapid-Adaptive Neural Network Equalizer based on SkipNet for High-Speed Optical IM/DD Communication System

○Chengxi Wang¹, Zhongya Li¹, An Yan¹, Junhao Zhao¹, Yingjun Zhou¹, Jianyang Shi¹, Nan Chi¹, Zhixue He², Junwen Zhang¹ (1. Key Laboratory of Information Science of Electromagnetic Waves(MoE), Fudan University (China), 2. Pengcheng Laboratory(PCL) (China))

Keywords:

Artificial intelligence and machine learning for optical transmission systems and subsystems,Digital signal processing algorithms for optical communications

We propose and demonstrate a low-complexity and rapid-adaptive neural network equalizer based on SkipNet for high-speed optical IM/DD system, achieving 300- and 270-Gbps data transmission under B2B and 1-km fiber with improved sensitivity and data-rates.