典型文献
Blind Nonlinearity Equalization by Machine-Learning-Based Clustering for QAM-Based Quantum Noise Stream Cipher Transmission
文献摘要：
In quantum noise stream cipher(QNSC)systems,it is difficult to compensate fiber nonlinear-ity by digital signal processing(DSP)due to inter-actions between chromatic dispersion(CD),ampli-fied spontaneous emission(ASE)noise from erbium-doped fiber amplifier(EDFA)and Kerr nonlinearity.Nonlinearity equalizer(NLE)based on machine learn-ing(ML)algorithms have been extensively studied.However,most NLE based on supervised ML algo-rithms have high training overhead and computation complexity.In addition,the performance of these algorithms have a lot of randomness.This paper proposes two clustering algorithms based on Fuzzy-logic C-Means Clustering(FLC)to compensate the fiber nonlinearity in quadrature amplitude modulation(QAM)-based QNSC system,including FLC based on subtractive clustering(SC)and annealing evolu-tion(AE)algorithm.The performance of FLC-SC and FLC-AE are evaluated through simulation and ex-periment.The proposed algorithms can promptly ob-tain suitable initial centroids and choose optimal initial centroids of the clusters to achieve the global optimal initial centroids especially for high order modulation scheme.In the simulation,different parameter config-urations are considered,including fiber length,optical signal-to-noise ratio(OSNR),clipping ratio and reso-lution of digital to analog converter(DAC).Further-more,we measure the Q-factor of transmission sig-nal with different launched powers,DAC resolution and laser linewidth in the optical back-to-back(BTB)experiment with 80-km single mode fiber.Both sim-ulation and experimental results show that the pro-posed techniques can greatly mitigate the signal im-pairments.
文献关键词：
中图分类号：
作者姓名：
Yajie Li;Shoudong Liu;Yongli Zhao;Chao Lei;Jie Zhang
作者机构：
State Key Laboratory of Information Photonics and Optical Communications,Beijing University of Posts and Telecommunications,Beijing 100876,China
文献出处：
引用格式：
[1]Yajie Li;Shoudong Liu;Yongli Zhao;Chao Lei;Jie Zhang-.Blind Nonlinearity Equalization by Machine-Learning-Based Clustering for QAM-Based Quantum Noise Stream Cipher Transmission)[J].中国通信（英文版）,2022(08):127-137
A类：
Cipher,QNSC,NLE,subtractive,urations,pairments
B类：
Blind,Nonlinearity,Equalization,by,Machine,Learning,Based,Clustering,QAM,Quantum,Noise,Stream,Transmission,In,quantum,noise,stream,cipher,systems,difficult,compensate,fiber,digital,signal,processing,DSP,due,inter,actions,between,chromatic,dispersion,CD,fied,spontaneous,emission,ASE,from,erbium,doped,amplifier,EDFA,Kerr,nonlinearity,equalizer,machine,learn,ML,algorithms,have,been,extensively,studied,However,most,supervised,high,training,overhead,computation,complexity,addition,performance,these,lot,randomness,This,paper,proposes,two,clustering,Fuzzy,logic,Means,FLC,quadrature,amplitude,modulation,including,annealing,evolu,AE,are,evaluated,through,simulation,proposed,can,promptly,tain,suitable,initial,centroids,choose,optimal,clusters,achieve,global,especially,order,scheme,different,parameter,config,considered,length,optical,OSNR,clipping,analog,converter,DAC,Further,more,measure,transmission,launched,powers,resolution,laser,linewidth,back,BTB,single,mode,Both,experimental,results,show,that,techniques,greatly,mitigate
AB值：
0.550833
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