Data-driven Optical Fiber Channel Modeling: A Deep Learning Approach

• Published in: Journal of lightwave technology Vol. 38; no. 17; pp. 4730 - 4743
• Main Authors: Wang, Danshi, Song, Yuchen, Li, Jin, Qin, Jun, Yang, Tao, Zhang, Min, Chen, Xue, Boucouvalas, Anthony C.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; bidirectional long short-term memory (BiLSTM); Communications systems; Computer networks; Computer simulation; Computing time; data driven; Deep learning; fiber channel modeling; Iterative methods; Keying; Machine learning; Mathematical model; Modelling; Optical communication; Optical fiber communication; Optical fiber dispersion; Optical fiber theory; Optical fibers; Optical transmitters; Pulse amplitude modulation; Transfer functions; Waveforms

A data-driven fiber channel modeling method based on deep learning (DL) is introduced in an optical communication system. In this study, bidirectional long short-term memory (BiLSTM) is selected from a diverse range of DL algorithms to perform fiber channel modeling for on-off keying and pulse ampli...