ARTIFICIAL NEURAL NETWORKS IN THE SCOPE OF OPTICAL PERFORMANCE MONITORING

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1 1 th Portuguese Conference on Automatic Control July 212 CONTROLO 212 Funchal, Portugal ARTIFICIAL NEURAL NETWORKS IN THE SCOPE OF OPTICAL PERFORMANCE MONITORING Vítor Ribeiro,?? Mário Lima, António Teixeira,, Instituto de Telecomunicações, Campus Universitário de Santiago, P AVEIRO - PORTUGAL - Portugal University of Aveiro, Campus Universitario 381 City: Aveiro Postal Code: Nokia Siemens Networks, Rua Irmãos Siemens, Amadora, Portugal Abstract: Artificial Neural Networks (ANN) have been used in several scientific areas, such as economy, physics, mathematics, engineering, to mention some Recently ANN have been applied to the field of Optical Performance Monitoring (OPM) Several papers were published and interesting results were achieved We present Parametric Asynchronous Eye Diagram (PAED) used with ANN pattern recognition strategies, to extract features of PAED and deliver OPM parameters at the ANN output, such as Chromatic Dispersion (PMD), Polarization Mode Dispersion (PMD) and Optical Signal to Noise Ratio (OSNR), and monitor them simultaneously Keywords: Artificial Neural Networks, Optical Performance Monitoring, ANN, OPM 1 INTRODUCTION Artificial Neural Networks (ANN) have been applied to Optical Performance Monitoring(OPM) since 28 (Wu et al, 28), from the best of our knowledge ANN has the ability to distinguish between different parameters, with the aid of optical performance diagrams or histograms, which turns them suitable for multi-impairment optical performance monitoring Recently, techniques using machine learning and artificial neural networks have been proposed in conjunction with very well known optical performance diagrams These optical performance diagrams are based on asynchronous amplitude histograms (Shen et al, 21), delay-tap asynchronous diagrams (Anderson et al, 29a; Jargon et al, 29b) or synchronous eye diagrams (Jargon et al, 29a) Jargon et al (Jargon et al, 29b) have reported an optical performance technique using artificial neural networks, which monitors simultaneously CD, PMD and OSNR for an NRZ-OOK modulation format at 1 Gbit/s The monitoring window ranges from to ps/nm, to 3 ps and 18 to 3 db respectively The authors do not report the errors obtained in these computations Machine learning with delay-tap asynchronous sampling (DTAS) histograms has also been used to compute OSNR, CD and PMD (Anderson et al, 29b) The monitoring windows reported, range from to 16 ps/nm, to ps and 1 to 27 db, for CD, PMD and OSNR, respectively, using 1 Gbit/s NRZ-OOK modulation format The patent application (Anderson et al, 29b) uses several algorithms to predict CD, PMD and OSNR, including a linear and a nonlinear kernel algorithm, for pattern recognition It demonstrates good estimates for CD and PMD, although the monitoring accuracy for OSNR is reported as not so positive In (Wu et al, 211) delay tap asynchronous sampling is used to monitor a 1 Gbit/s QPSK modulation format signal Two different direct detection receivers have been tested (ie single ended detection and bal-

2 Transmission Channel Receiver Power Splitter Filter (Electrical differentiator) PAED drawing and processing Fig 1 Schematic of a Parametric Asynchronous Eye Diagram generator anced detection) Superior accuracy is achieved with balanced detection A novel technique has been developed in (Jargon et al, 29a) using parameters extracted from synchronous eye diagrams, presenting a monitoring range of 18 to 3 db, to 7 ps/nm and to 3 ps for OSNR, CD and PMD, respectively This results are shown for a 1 Gbit/s NRZ-OOK modulation format In (Wu et al, 29) synchronous eye diagrams with ANN, have been used to compute several impairments using 4 Gbit/s RZ-OOK and 4 Gbit/s RZ- Differential Phase Shift Keying (DPSK) The errors reported are 23 ps/nm for CD and 8 db for OSNR, when using 4 Gbit/s RZ-OOK signal The OSNR is 18 db and CD is 318 ps/nm for 4 Gbit/s RZ-DPSK The errors reported for CD using RZ-OOK modulation format are about % of the maximum range of the monitoring window used ( ps/nm) In this paper we present some results using PAED and ANN, to monitor several impairments, such as CD, PMD and OSNR We present the diagram obtained from PAED technique, to monitor a signal modulated at 1 Gbit/s NRZ The results are compared between two pattern recognition techniques using ANNs One using 6 subsets of the diagram and other using 4 subsets (quadrants) of the diagram PAED is completely independent of the bit rate and modulation format, which gives it significant advantage over competing technologies Amplitude of the modulated signal (W) Amplitude of the electrical x 1 3 signal derivative (W) Fig 2 PAED divided in 6 subsets of the diagram, for a NRZ 4 Gbit/s modulated signal Amplitude of the modulated signal (W) Amplitude of the electrical x 1 3 signal derivative (W) Fig 3 PAED degraded with CD=7 ps/nm, PMD=4 ps and OSNR=21 db As can be seen from the figure the amplitude of the modulated signal is plotted against the amplitude of the derivative of the signal, obtaining a diagram that is similar (but not equal), to the synchronous eye diagram Due to the fact that is not required to use synchronous sampling the cost and flexibility is increased, allowing that it can be applied to optical performance monitoring applications at reduced cost 2 SIMULATION SETUP 3 METHODS PAED was first presented in (Ribeiro et al, 212b) and further developed in (Ribeiro et al, 212a) We In Fig 2, PAED is already, splitted in 6 subsets of the have used a experimental setup that comprises an diagram, for training with an ANN The ANN uses 4 optical differentiator, which provides the first order neurons in the hidden layer, and takes the mean and derivative of the signal and in parallel, we get samples standard deviation for each subset of the diagram and of the modulated signal and put it X-Y mode The for each signal (amplitude of the modulated signal and optical differentiator uses the effect of cross phase amplitude of the electrical signal derivative), as inputs, modulation (XPM), to compute the derivative of the leading to 24 inputs, for the case where we choose electrical signal, which is a nonlinear effect in optical 6 subsets of the diagram The outputs are the CD, mediums, such as semiconductor optical amplifiers PMD and OSNR Four quadrants can also be used, or dispersion shifted fibers The PAED obtained with which lead to 16 inputs The structure of the ANN is our technique is like the one shown in Fig2, with shown in Fig 4 In the hidden layer sigmoidal function an electrical differentiator, which also can be applied neurons are used and linear function neurons are used to draw the diagram as is shown in the schematic in the output layer The training was performed used of Fig1 The diagram is obtained from simulation a toolbox developed by (?), Neuromodeler, suitable 229

3 for feedforward ANNs This toolbox has been used for Radio Frequency applications, but is also suitable for other applications It is available through the internet We have used an implementation of the Quasi- Neuwton (Quasi-Newton MLP) algorithm, available in this toolbox The training is done using 2 epochs Using the previously mentioned algorithm the result is computed very fast Inputs Hidden Layer ANN Output Layer Outputs CD RMSE (ps/nm) CD CD (ps/nm) (a) CD PMD OSNR Fig 4 Artificial Neural Network Q n represents the n th subset of the diagram of PAED (see 2), σ the standard deviation, Q n and dqn dt, represents the mean of the amplitude and derivative of the subset of the diagram, respectively OSNR RMSE (db) OSNR (db) (b) PMD 4 SIMULATION RESULTS AND DISCUSSION In Fig 3, a PAED is drawn for a signal modulated at 4 Gbit/s NRZ, impaired with CD=7 ps/nm, PMD=4 ps and OSNR = 21 DB As can be seen PAED tends to close, as a normal synchronous eye diagram tends to do The impact of CD is evident The amplitude signal derivative tends to decrease due to symmetrical pulse broadening caused by CD The OSNR in this case was not decreased relatively to PAED of Fig 2 The impact of decreasing OSNR is to spread the samples along the contour of PAED PMD acts differently than CD It also causes pulse broadening, but it s antisymetrical We can see that PAED is not now completely centered at Watt amplitude signal derivative This differences between impairments are clearly described by PAED, allowing the independent distinguishing between them, with the aid of ANN The benefit of ANN is that it makes correlations between inputs and outputs, without the user have to care how the network is modeled inside its structure Also its nonlinear CD, PMD and OSNR are linear, but the interconnections between them, turns out the system nonlinear So ANN is suitable in this case In Fig we present a comparison between the accuracy of the technique obtained using 4 and 6 subsets of the diagram for a 1 Gbit/s NRZ modulated signal, PMD RMSE (ps/nm) PMD (ps) (c) OSNR Fig Comparison between the accuracy of our technique using 4 and 6 subsets of the diagram where the Root Mean Squared Error (RMSE) is plotted against the value of each impairment Significantly better results are achieved for 6 subsets of the diagram, due to better accuracy in matching the impact of the impairments on PAED, with the increased number of subsets In Fig 6 the results for a 4 Gbit/s QPSK modulated signal are shown We have used single ended detection in our simulations Comparing our results with the results (Wu et 23

4 CD out (ps/nm) We made a comparison between two pattern recognition schemes, one using six subsets of the diagram and other using 4 subsets of the diagram Better results were achieved in the former case We also present our results for QPSK modulated signals, using single ended photodetection Better accuracy was achieved relatively to the results in the state of the art ANN proves to be a valid solution to be used with optical performance diagrams, in optical performance monitoring applications CD in (ps/nm) PMD out (ps) OSNR out (db) (a) CD PMD in (ps) (b) PMD OSNR in (db) (c) OSNR Fig 6 Error bars for the error obtained between the values expected and the values obtained for (a)- CD, (b)-pmd, (c)-osnr al, 211), we can see that our results present better accuracy, for single ended detection In (Wu et al, 211) the authors propose balanced detection to increase the accuraccy for QPSK signals, although it increases complexity and cost at the receiver CONCLUSIONS We present PAED as an optical performance diagram, able to monitor simultaneously multiple impairments 6 ACKNOWLEDGMENTS The grant SFRH/BD/6977/21 from the Portuguese Foundation for Science and Technology is acknowledged We acknowledge the criticism of the anonymous reviewers of this paper which greatly helped us improving it 7 REFERENCES Anderson, TB, A Kowalczyk, K Clarke, SD Dods, D Hewitt and JC Li (29a) Multi impairment monitoring for optical networks J Lightw Technol 27(16), Anderson, Trevor, Sarah Dods, Adam Kowalczyk, Justin Bedo and Kenneth Paul Clarke (29b) Method and apparatus for sampled signal monitoring Patent Application Jargon, JA, Xiaoxia Wu and AE Willner (29a) Optical performance monitoring using artificial neural networks trained with eye-diagram parameters IEEE Photonic Tech L 21(1), 4 6 Jargon, Jeffrey A, Xiaoxia Wu and Alan E Willner (29b) Optical performance monitoring by use of artificial neural networks trained with parameters derived from delay-tap asynchronous sampling In: Optical Fiber Communication Conference Optical Society of America p OThH1 Ribeiro, Vìtor, Liliana Costa, Mário Lima and António L J Teixeira (212a) Optical performance monitoring using the novel parametric asynchronous eye diagram Opt Express 2(9), Ribeiro, Vítor M, Mário Lima and António Teixeira (212b) Parametric asynchronous eye diagram for optical performance monitoring In: Optical Fiber Communication Conference Optical Society of America p JW2A33 Shen, TSR, Ke Meng, APT Lau and Zhao Yang Dong (21) Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms IEEE Photonic Tech L 22(22), Wu, Xiaoxia, J Jargon, L Christen and A Willner (28) Training of neural networks to perform optical performance monitoring of a combination of accumulated signal nonlinearity, cd, pmd, and osnr In: IEEE Lasers and Electro-Optics Society, 231

5 28 LEOS 28 21st Annual Meeting of the pp Wu, Xiaoxia, JA Jargon, L Paraschis and AE Willner (211) Ann-based optical performance monitoring of qpsk signals using parameters derived from balanced-detected asynchronous diagrams IEEE Photonic Tech L 23(4), Wu, Xiaoxia, JA Jargon, RA Skoog, L Paraschis and AE Willner (29) Applications of artificial neural networks in optical performance monitoring J Lightw Technol 27(16),