Machine-learning detector based on support vector machine for 122-Gbps multi-CAP optical communication system
Machine-learning detector based on support vector machine for 122-Gbps multi-CAP optical communication system
In this work, we firstly apply support vector machine (SVM) detector in 122-Gbps Multi-CAP system. It can de-map the rotated constellations directly without any correction. The concrete simulations indicate such a machine-learning based detector provides considerable BER reduction for high-density CAPs in low-frequency band, compared with hard decision.
Sun, Lin
baf821b3-407b-4cb8-9262-14eb45f93ab7
Du, Jiangbing
244dddeb-d4c5-47ac-ab5f-24821e990586
Chen, Guoyao
77c3fcac-3128-411e-9dc4-d34880f76dc2
He, Zuyuan
150ad775-7969-49d6-8ecb-8159ada54631
Chen, Xia
64f6ab92-ca11-4489-8c03-52bc986209ae
Reed, Graham T.
ca08dd60-c072-4d7d-b254-75714d570139
2017
Sun, Lin
baf821b3-407b-4cb8-9262-14eb45f93ab7
Du, Jiangbing
244dddeb-d4c5-47ac-ab5f-24821e990586
Chen, Guoyao
77c3fcac-3128-411e-9dc4-d34880f76dc2
He, Zuyuan
150ad775-7969-49d6-8ecb-8159ada54631
Chen, Xia
64f6ab92-ca11-4489-8c03-52bc986209ae
Reed, Graham T.
ca08dd60-c072-4d7d-b254-75714d570139
Sun, Lin, Du, Jiangbing, Chen, Guoyao, He, Zuyuan, Chen, Xia and Reed, Graham T.
(2017)
Machine-learning detector based on support vector machine for 122-Gbps multi-CAP optical communication system.
In 2017 Opto-Electronics and Communications Conference (OECC) and Photonics Global Conference (PGC).
IEEE..
(doi:10.1109/OECC.2017.8114893).
Record type:
Conference or Workshop Item
(Paper)
Abstract
In this work, we firstly apply support vector machine (SVM) detector in 122-Gbps Multi-CAP system. It can de-map the rotated constellations directly without any correction. The concrete simulations indicate such a machine-learning based detector provides considerable BER reduction for high-density CAPs in low-frequency band, compared with hard decision.
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Published date: 2017
Venue - Dates:
CLEO-PR OECC & PGC 2017, Singapore, 2017-07-31 - 2017-08-04
Identifiers
Local EPrints ID: 442734
URI: http://eprints.soton.ac.uk/id/eprint/442734
PURE UUID: c4eb6203-bf71-413d-bcda-d20a8a793989
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Date deposited: 24 Jul 2020 16:30
Last modified: 16 Mar 2024 08:45
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Contributors
Author:
Lin Sun
Author:
Jiangbing Du
Author:
Guoyao Chen
Author:
Zuyuan He
Author:
Xia Chen
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