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Machine learning adaptive receiver for PAM-4 modulated optical interconnection based on silicon microring modulator

Machine learning adaptive receiver for PAM-4 modulated optical interconnection based on silicon microring modulator
Machine learning adaptive receiver for PAM-4 modulated optical interconnection based on silicon microring modulator

Modulation nonlinearity can severely distort multi-level modulation, and signal processing to mitigate the distortion is highly desirable. In this work, we demonstrated a machine learning method for adaptive detection of 4-level pulse amplitude modulation (PAM-4) signals modulated by silicon micro-ring modulator (Si-MRM). The very limited linear modulation range of Si-MRM leads to serious modulation nonlinearity distortion for high-level modulations like PAM-4 with the consideration of wavelength drift. Our approach is based on the support vector machine (SVM) method which can learn the nonlinear distortion of Si-MRM during PAM-4 modulation. Thus, the detection can be made adaptive for PAM-4 signals with nonlinear levels and level dependent noise. The modulation nonlinearity distortion of PAM-4 has been characterized in terms of level deviation (LD) with respect to wavelength drift. Up to 2.7-dB receiver sensitivity gain is obtained at about 26% LD by using the proposed SVM machine learning method. The receiver sensitivity-float range can be squeezed to be within 0.3 dB even with up to 30% LD which indicates a stable detection of PAM-4 signals along with wavelength drift. Up to 3.63-dB receiver sensitivity improvement has been experimentally achieved at 50 Gbps for PAM-4 signals modulated by a Si-MRM and after 2-km standard single mode fiber (SSMF) transmission. The stable operation of Si-MRM is very difficult and very important. The proof-of-concept results indicate the very promising capability of machine learning method for stable detection of PAM-4 signals modulated by Si-MRM, which is of great significance for practical application of Si-MRM in optical interconnection.

Machine learning, machine learning, Modulation, Nonlinear distortion, Optical distortion, optical interconnection, Optical interconnections, pulse amplitude modulation, silicon micro-ring modulator, Support vector machines
0733-8724
4106-4113
Chen, Guoyao
77c3fcac-3128-411e-9dc4-d34880f76dc2
Du, Jiangbing
244dddeb-d4c5-47ac-ab5f-24821e990586
Sun, Lin
baf821b3-407b-4cb8-9262-14eb45f93ab7
Zheng, Lifang
b713a073-2f44-45d9-8e69-356e48753ff0
Xu, Ke
283b7469-7b53-403f-9583-2c95e7377969
Tsang, Hon Ki
4442683a-3892-4ea2-aaa7-ec5224481282
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
Zheng, Lifang
b713a073-2f44-45d9-8e69-356e48753ff0
Xu, Ke
283b7469-7b53-403f-9583-2c95e7377969
Tsang, Hon Ki
4442683a-3892-4ea2-aaa7-ec5224481282
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, Zheng, Lifang, Xu, Ke, Tsang, Hon Ki, Chen, Xia, Reed, Graham T. and He, Zuyuan (2018) Machine learning adaptive receiver for PAM-4 modulated optical interconnection based on silicon microring modulator. IEEE Journal of Lightwave Technology, 36 (18), 4106-4113. (doi:10.1109/JLT.2018.2861710).

Record type: Article

Abstract

Modulation nonlinearity can severely distort multi-level modulation, and signal processing to mitigate the distortion is highly desirable. In this work, we demonstrated a machine learning method for adaptive detection of 4-level pulse amplitude modulation (PAM-4) signals modulated by silicon micro-ring modulator (Si-MRM). The very limited linear modulation range of Si-MRM leads to serious modulation nonlinearity distortion for high-level modulations like PAM-4 with the consideration of wavelength drift. Our approach is based on the support vector machine (SVM) method which can learn the nonlinear distortion of Si-MRM during PAM-4 modulation. Thus, the detection can be made adaptive for PAM-4 signals with nonlinear levels and level dependent noise. The modulation nonlinearity distortion of PAM-4 has been characterized in terms of level deviation (LD) with respect to wavelength drift. Up to 2.7-dB receiver sensitivity gain is obtained at about 26% LD by using the proposed SVM machine learning method. The receiver sensitivity-float range can be squeezed to be within 0.3 dB even with up to 30% LD which indicates a stable detection of PAM-4 signals along with wavelength drift. Up to 3.63-dB receiver sensitivity improvement has been experimentally achieved at 50 Gbps for PAM-4 signals modulated by a Si-MRM and after 2-km standard single mode fiber (SSMF) transmission. The stable operation of Si-MRM is very difficult and very important. The proof-of-concept results indicate the very promising capability of machine learning method for stable detection of PAM-4 signals modulated by Si-MRM, which is of great significance for practical application of Si-MRM in optical interconnection.

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More information

Accepted/In Press date: 23 July 2018
e-pub ahead of print date: 31 July 2018
Published date: 15 September 2018
Keywords: Machine learning, machine learning, Modulation, Nonlinear distortion, Optical distortion, optical interconnection, Optical interconnections, pulse amplitude modulation, silicon micro-ring modulator, Support vector machines

Identifiers

Local EPrints ID: 425792
URI: https://eprints.soton.ac.uk/id/eprint/425792
ISSN: 0733-8724
PURE UUID: e7532e7b-a7d0-4498-9e90-2426ef07bbd1
ORCID for Xia Chen: ORCID iD orcid.org/0000-0002-0994-5401

Catalogue record

Date deposited: 02 Nov 2018 17:30
Last modified: 10 Dec 2019 01:36

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Contributors

Author: Guoyao Chen
Author: Jiangbing Du
Author: Lin Sun
Author: Lifang Zheng
Author: Ke Xu
Author: Hon Ki Tsang
Author: Xia Chen ORCID iD
Author: Graham T. Reed
Author: Zuyuan He

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