{"title":"基于时域和特征域神经网络的光特征值调制信号解调方法分析","authors":"Shingo Sato, K. Mishina, D. Hisano, A. Maruta","doi":"10.1109/OECC48412.2020.9273654","DOIUrl":null,"url":null,"abstract":"We investigate the reason why the artificial neural network-based demodulators for optical eigenvalue modulated signals show a BER improvement compared with conventional demodulators. Both nonlinear and high-dimensional classifications in eigenvalue-domain contribute to the BER improvement.","PeriodicalId":433309,"journal":{"name":"2020 Opto-Electronics and Communications Conference (OECC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of Time- and Eigenvalue-Domain Neural Network-Based Demodulators for Optical Eigenvalue Modulated Signals\",\"authors\":\"Shingo Sato, K. Mishina, D. Hisano, A. Maruta\",\"doi\":\"10.1109/OECC48412.2020.9273654\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We investigate the reason why the artificial neural network-based demodulators for optical eigenvalue modulated signals show a BER improvement compared with conventional demodulators. Both nonlinear and high-dimensional classifications in eigenvalue-domain contribute to the BER improvement.\",\"PeriodicalId\":433309,\"journal\":{\"name\":\"2020 Opto-Electronics and Communications Conference (OECC)\",\"volume\":\"87 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Opto-Electronics and Communications Conference (OECC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OECC48412.2020.9273654\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Opto-Electronics and Communications Conference (OECC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OECC48412.2020.9273654","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis of Time- and Eigenvalue-Domain Neural Network-Based Demodulators for Optical Eigenvalue Modulated Signals
We investigate the reason why the artificial neural network-based demodulators for optical eigenvalue modulated signals show a BER improvement compared with conventional demodulators. Both nonlinear and high-dimensional classifications in eigenvalue-domain contribute to the BER improvement.
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