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Fiber communication receiver models based on the multi-head attention mechanism

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【Author】 Yubin Zang;Zhenming Yu;Kun Xu;Minghua Chen;Sigang Yang;Hongwei Chen;Beijing National Research Center for Information Science and Technology [BNRist] and Department of Electronic Engineering, Tsinghua University;State Key Laboratory of Information Phonetics and Optical Communications, Beijing University of Post and Telecommunications;

【通讯作者】于振明;陈宏伟;

【机构】 Beijing National Research Center for Information Science and Technology [BNRist] and Department of Electronic Engineering, Tsinghua University； State Key Laboratory of Information Phonetics and Optical Communications, Beijing University of Post and Telecommunications；

【摘要】 In this paper, an artificial-intelligence-based fiber communication receiver model is put forward. With the multi-head attention mechanism it contains, this model can extract crucial patterns and map the transmitted signals into the bit stream.Once appropriately trained, it can obtain the ability to restore the information from the signals whose transmission distances range from 0 to 100 km, signal-to-noise ratios range from 0 to 20 dB, modulation formats range from OOK to PAM4,and symbol rates range from 10 to 40 GBaud. The validity of the model is numerically demonstrated via MATLAB and Pytorch scenarios and compared with traditional communication receivers.更多还原

【Abstract】 In this paper, an artificial-intelligence-based fiber communication receiver model is put forward. With the multi-head attention mechanism it contains, this model can extract crucial patterns and map the transmitted signals into the bit stream.Once appropriately trained, it can obtain the ability to restore the information from the signals whose transmission distances range from 0 to 100 km, signal-to-noise ratios range from 0 to 20 dB, modulation formats range from OOK to PAM4,and symbol rates range from 10 to 40 GBaud. The validity of the model is numerically demonstrated via MATLAB and Pytorch scenarios and compared with traditional communication receivers.更多还原

【关键词】 fiber receiver model； neural networks； multi-head attention mechanism；
【Key words】 fiber receiver model； neural networks； multi-head attention mechanism；

【基金】 supported by the National Key Research and Development Program of China (No. 2019YFB1803501);the National Natural Science Foundation of China(No. 62135009)

【文献出处】 Chinese Optics Letters ,中国光学快报(英文版) , 编辑部邮箱 ,2023年03期

【分类号】TN929.11
【下载频次】6

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