DACNN-aided nonlinear equalizer for a probabilistic shaping coherent optical communication system

Yuzhe Li, Huan Chang*, Qi Zhang, Ran Gao, Feng Tian, Qinghua Tian, Yongjun Wang, Lan Rao, Dong Guo, Fu Wang, Sitong Zhou, Xiangjun Xin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The probabilistic shaping (PS) technique is a key technology for fiber optic communication systems to further approach the Shannon limit.To solve the problem that nonlinear equalizers are ineffective for probabilistic shaping optical communication systems with non-uniform distribution, a distribution alignment convolutional neural network (DACNN)-aided nonlinear equalizer is proposed. The approach calibrates the equalizer using the probabilistic shaping prior distribution, which reduces the training complexity and improves the performance of the equalizer simultaneously. Experimental results show nonlinear equalization of 120 Gb/s PS 64QAM signals in a 375 km transmission scenario. The proposed DACNN equalizer improves the receiver sensitivity by 2.6 dB and 1.1 dB over the Volterra equalizer and convolutional neural network (CNN) equalizer, respectively. Meanwhile, DACNN converges with fewer training epochs than CNN, which provides great potential for mitigating the nonlinear distortion of PS signals in fiber optic communication systems.

Original languageEnglish
Pages (from-to)1881-1887
Number of pages7
JournalApplied Optics
Volume63
Issue number7
DOIs
Publication statusPublished - 1 Mar 2024

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Li, Y., Chang, H., Zhang, Q., Gao, R., Tian, F., Tian, Q., Wang, Y., Rao, L., Guo, D., Wang, F., Zhou, S., & Xin, X. (2024). DACNN-aided nonlinear equalizer for a probabilistic shaping coherent optical communication system. Applied Optics, 63(7), 1881-1887. https://doi.org/10.1364/AO.517521