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dc.contributor.authorChuang, Chun-Yenen_US
dc.contributor.authorLiu, Li-Chunen_US
dc.contributor.authorWei, Chia-Chienen_US
dc.contributor.authorLiu, Jun-Jieen_US
dc.contributor.authorHenrickson, Lindoren_US
dc.contributor.authorHuang, Wan-Jouen_US
dc.contributor.authorWang, Chih-Linen_US
dc.contributor.authorChen, Young-Kaien_US
dc.contributor.authorChen, Jyehongen_US
dc.date.accessioned2018-08-21T05:56:28Z-
dc.date.available2018-08-21T05:56:28Z-
dc.date.issued2018-01-01en_US
dc.identifier.urihttp://hdl.handle.net/11536/146228-
dc.description.abstractWe have designed a novel convolutional neural network based nonlinear classifier that outperforms traditional Volterra nonlinear equalizers. A BER of 3.50 x 10(-6) is obtained for a 112-Gbps PAM4 EML-based optical link over 40-km SMF transmission.en_US
dc.language.isoen_USen_US
dc.titleConvolutional Neural Network based Nonlinear Classifier for 112-Gbps High Speed Optical Linken_US
dc.typeProceedings Paperen_US
dc.identifier.journal2018 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXPOSITION (OFC)en_US
dc.contributor.department光電工程學系zh_TW
dc.contributor.departmentDepartment of Photonicsen_US
dc.identifier.wosnumberWOS:000437286300442en_US
Appears in Collections:Conferences Paper


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