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

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

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.

Machine learning, optical interconnection, pulse amplitude modulation
0733-8724
650-657
Chen, Guoyao
77c3fcac-3128-411e-9dc4-d34880f76dc2
Du, Jiangbing
244dddeb-d4c5-47ac-ab5f-24821e990586
Sun, Lin
baf821b3-407b-4cb8-9262-14eb45f93ab7
Zhang, Wenjia
ea592842-abe9-47d8-928d-23b321b7c2f1
Xu, Ke
283b7469-7b53-403f-9583-2c95e7377969
Chen, Xia
64f6ab92-ca11-4489-8c03-52bc986209ae
Reed, Graham T.
ca08dd60-c072-4d7d-b254-75714d570139
He, Zuyuan
150ad775-7969-49d6-8ecb-8159ada54631
Chen, Guoyao
77c3fcac-3128-411e-9dc4-d34880f76dc2
Du, Jiangbing
244dddeb-d4c5-47ac-ab5f-24821e990586
Sun, Lin
baf821b3-407b-4cb8-9262-14eb45f93ab7
Zhang, Wenjia
ea592842-abe9-47d8-928d-23b321b7c2f1
Xu, Ke
283b7469-7b53-403f-9583-2c95e7377969
Chen, Xia
64f6ab92-ca11-4489-8c03-52bc986209ae
Reed, Graham T.
ca08dd60-c072-4d7d-b254-75714d570139
He, Zuyuan
150ad775-7969-49d6-8ecb-8159ada54631

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).

Record type: Article

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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More information

Accepted/In Press date: 13 October 2017
e-pub ahead of print date: 16 October 2017
Published date: 1 February 2018
Keywords: Machine learning, optical interconnection, pulse amplitude modulation

Identifiers

Local EPrints ID: 418891
URI: https://eprints.soton.ac.uk/id/eprint/418891
ISSN: 0733-8724
PURE UUID: 60ac0f25-78da-4c22-9682-cad4cdc5f237
ORCID for Xia Chen: ORCID iD orcid.org/0000-0002-0994-5401

Catalogue record

Date deposited: 23 Mar 2018 17:30
Last modified: 13 Sep 2018 00:30

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Contributors

Author: Guoyao Chen
Author: Jiangbing Du
Author: Lin Sun
Author: Wenjia Zhang
Author: Ke Xu
Author: Xia Chen ORCID iD
Author: Graham T. Reed
Author: Zuyuan He

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