Nonlinear distortion mitigation by machine learning of SVM classification for PAM-4 and PAM-8 modulated optical interconnection

Chen, Guoyao, Du, Jiangbing, Sun, Lin, Zhang, Wenjia, Xu, Ke, Chen, Xia, Reed, Graham T. and He, Zuyuan (2018) Nonlinear distortion mitigation by machine learning of SVM classification for PAM-4 and PAM-8 modulated optical interconnection. Journal of Lightwave Technology, 36 (3), 650-657. (doi:10.1109/JLT.2017.2763961).

Abstract

We demonstrated a support vector machine (SVM) based machine learning method to mitigate modulation nonlinearity distortion for PAM-4 and PAM-8 vertical cavity surface emitter laser multi-mode fiber (VCSEL-MMF) optical link. Simulations at 100 Gb/s data rate and experimental work at 60 Gb/s data rate were carried out. We achieved a significant improvement in bit error rate (BER) when complete binary tree SVMs (CBT-SVMs) are applied for both PAM-4 and PAM-8 signals. Quantitative analysis of the sensitivity gain versus modulation nonlinearity distortion is presented with experimentally verification. The results indicate that CBT-SVMs have better performance for PAM-8 compared to PAM-4. The sensitivity gain increases almost linearly with the increase of eye-linearity (increase of modulation nonlinearity distortion). Up to 2.5-dB sensitivity improvement is achieved by the proposed CBT-SVMs at eye-linearity of 1.72 for PAM-4.

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Contributors

Author: Xia Chen

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