Abstract

High-symbol-rate coherentoptical transceivers suffer more from the critical responses of transceiver components at high frequency, especially when applying a higher order modulation format. We recently proposed a neural network (NN)-based digital pre-distortion (DPD) technique trained to mitigate the transceiver response of a 128 GBaud optical coherent transmission system. In this paper, we further detail this work and assess the NN-based DPD by training it using either a direct learning architecture (DLA) or an indirect learning architecture (ILA), and compare performance against a Volterra series-based ILA DPD and a linear DPD. Furthermore, we deliberately increase the transmitter nonlinearity and compare the performance of the three DPDs schemes. The proposed NN-based DPD trained using DLA performs the best among the three contenders. In comparison to a linear DPD, it provides more than 1 dB signal-to-noise ratio (SNR) gains at the output of a conventional coherent receiver DSP for uniform 64-quadrature amplitude modulation (QAM) and PCS-256-QAM signals. Finally, the NN-based DPD enables achieving a record 1.61 Tb/s net rate transmission on a single channel after 80 km of standard single mode fiber (SSMF).

Digital pre-distortion using a Gauss–Newton-based direct learning architecture for coherent optical transmitters

Hexun Jiang, Mengfan Fu, Yixiao Zhu, Lilin Yi, Weisheng Hu, and Qunbi Zhuge
Opt. Lett. 48(7) 1706-1709 (2023)

Performance vs. complexity in NN pre-distortion for a nonlinear channel

Hamza Imtiaz, Zibo Zheng, Rizan Homayoun Nejad, Leslie A. Rusch, and Ming Zeng
Opt. Express 31(23) 38513-38528 (2023)

Nonlinearity mitigation for high-speed optical OFDM transmitters using digital pre-distortion

Yuan Bao, Zhaohui Li, Jianping Li, Xinhuan Feng, Bai-ou Guan, and Guifang Li
Opt. Express 21(6) 7354-7361 (2013)

Wiener-Hammerstein model and its learning for nonlinear digital pre-distortion of optical transmitters

Takeo Sasai, Masanori Nakamura, Etsushi Yamazaki, Asuka Matsushita, Seiji Okamoto, Kengo Horikoshi, and Yoshiaki Kisaka
Opt. Express 28(21) 30952-30963 (2020)

Compensation of nonlinear distortion in coherent optical OFDM systems using a MIMO deep neural network-based equalizer

Ivan Aldaya, Elias Giacoumidis, Athanasios Tsokanos, Mutsam Jarajreh, Yannuo Wen, Jinlong Wei, Gabriel Campuzano, Marcelo L. F. Abbade, and Liam P. Barry
Opt. Lett. 45(20) 5820-5823 (2020)