Ultra-wideband signal acquisition by use of channelinterleaved photonic analog-to-digital converter under the assistance of dilated fully convolutional network  

作　　者：Rui Wang Shaofu Xu Jianping Chen Weiwen Zou 汪锐;徐绍夫;陈建平;邹卫文(State Key Laboratory of Advanced Optical Communication Systems and Networks,Intelligent Microwave Lightwave Integration Innovation Center(iMLic),Department of Electronic Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]State Key Laboratory of Advanced Optical Communication Systems and Networks,Intelligent Microwave Lightwave Integration Innovation Center(iMLic),Department of Electronic Engineering,Shanghai Jiao Tong University,Shanghai 200240,China

出　　处：《Chinese Optics Letters》2020年第12期88-93,共6页中国光学快报（英文版）

基　　金：the National Key R&D Program of China(No.2019YFB2203700);the National Natu ral Science Foundation of China(Nos.61822508 and 61571292).

摘　　要：We demonstrate a photonic architecture to enable the separation of ultra-wideband signals.The architecture consists of a channel-interleaved photonic analog-to-digital converter(PADC)and a dilated fully convolutional network(DFCN).The aim of the PADC is to perform ultra-wideband signal acquisition,which introduces the mixing of signals between different frequency bands.To alleviate the interference among wideband signals,the DFCN is applied to reconstruct the waveform of the target signal from the ultra-wideband mixed signals in the time domain.The channel-interleaved PADC provides a wide spectrum reception capability.Relying on the DFCN reconstruction algorithm,the ultra-wideband signals,which are originally mixed up,are effectively separated.Additionally,experimental results show that the DFCN reconstruction algorithm improves the average bit error rate by nearly three orders of magnitude compared with that without the algorithm.

关 键 词：ultra-wideband signal acquisition photonic analog-to-digitai converter deep learning 

分 类 号：TN792[电子电信—电路与系统] TP183[自动化与计算机技术—控制理论与控制工程]