摘要
In future optical transport networks,lightpath performance analysis is of great practical significance for fully automated management.In general,the quality of transmission(QoT)of lightpaths,measured by optical quality factor or optical signal-to-noise ratio,has a complex time-varying process,along with the interactions of the other lightpath state parameters(LSPs),such as transmit power,chromatic dispersion,polarization mode dispersion.Current studies are mostly focused on lightpath QoT estimation,but ignoring lightpath-level data analytics.In this case,our article proposes a novel lightpath performance analysis method considering recurrence plot(RP)and cross recurrence plot(CRP).Firstly,we give a detailed interpretation on the recurrence patterns of LSPs via a qualitative 2-D RP representation and its quantitative measure.It can potentially enable the accurate and fast lightpath failure detection with a low computational burden.On the other hand,CRP is devoted to modeling the relationships between lightpath QoT and LSPs,and the correlation degree is determined by a geometric mean of multiple indexes of cross recurrence quantification analysis.From the view of application,such CRP analysis can provide the effective knowledge sharing to guarantee more credible QoT prediction.Extensive experiments on a real-world optical network dataset have clearly demonstrated the effectiveness of our proposal.
基金
supported in part by the Science and Technology Project of Hebei Education Department,Grant ZD2021088
in part by the S&T Major Project of the Science and Technology Ministry of China,Grant 2017YFE0135700。
作者简介
Sun Xiaochuan received the M.S.degree from the Guilin University of Electronic Technology,Guilin,China,in 2010,and the Ph.D degree from the Beijing University of Posts and Telecommunications,Beijing,China,2013.He is currently with the College of Artificial Intelligence,North China University of Science and Technology,Tangshan,China.He is also a Post-Doctoral Fellow with the School of Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing,China.His current research interests include reservoir computing,neural networks,and deep learning architectures with applications in the domains of time series prediction,classification,and nonlinear system identification;Cao Difei received the B.E.degree from Hengshui University,Hengshui,China,in 2019.Now he is studying for a post-graduate degree at North China University of Science and Technology,Tangshan,China.His current research interests include neural computing and reservoir computing theory;Wei Biao received the B.E.degree from Qinggong College,North China University of Science and Technology,Tangshan,China,in 2020.Now he is studying for the postgraduate degree at the college of Artificial Intelligence,North China University of Science and Technology,Tangshan,China.His current research interests include deep learning and target identification;Li Zhigang received the M.S.degree from the Shanxi Agricultural University,Shanxi,China,in 1993,and the Ph.D degree from the China University of Mining and Technology,Beijing,China,2007.From 2010 to 2012,he joined the Research Station of Beijing Jiaotong University,as a Post-Doctoral Research Fellow.He has been a Professor in North China University of Science and Technology,Tangshan,China,since 2003,where,he is a dean of the College of Information Engineering.He is currently vice President of the Hebei Computer Society and Hebei Electronics Society.His current research content focuses on the directions of data mining and intelligent control theory;The corresponding author:Li Yingqi received the B.S.and M.S.degrees in Guilin University of Electronic Technology,Guilin,China,in 2007 and 2010,respectively.She has been with College of Information Engineering,North China University of Science and Technology,Tangshan,since 2010,and is currently a lecturer.Her current research interests include pattern recognition,evolutionary computation,neural networks.email:liyingqi@ncst.edu.cn。
