Simultaneous monitoring of chromatic dispersion and optical signal to noise ratio in optical links using convolutional neural network and asynchronous delay-tap sampling

• Autorzy: Mrozek Tomasz , Perlicki Krzysztof , Jakubiak Andrzej

Identyfikatory

DOI

10.37190/oa200301

• Języki publikacji: EN

Abstrakty

EN

The article presents a method for image analysis using asynchronous delay-tap sampling (ADTS) technique and convolutional neural networks (CNNs), allowing simultaneous monitoring of many phenomena occurring in the physical layer of the optical network. The ADTS method makes it possible to visualize the course of the optical signal in the form of characteristics (so-called phase portraits), which change their shape under the influence of phenomena (including chromatic dispersion, amplified spontaneous emission noise and other). Using the VPI photonics software,a simulation model of the ADTS technique was built. After the simulation tests, 10000 images were obtained, which after proper preparation were subjected to further analysis using CNN algorithms. The main goal of the study was to train a CNN to recognize the selected impairment (distortion); then to test its accuracy and estimate the impairment for the selected set of test images. The input data consisted of processed binary images in the form of two-dimensional matrices, with the position of the pixel. This article focuses on the analysis of images containing simultaneously the phenomena of chromatic dispersion and optical signal to noise ratio.

Słowa kluczowe

EN

deep learning; convolutional neural network; chromatic dispersion; OSNR; asynchronous delay-tap sampling

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2020
• Tom: Vol. 50, nr 3
• Strony: 331--341
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Mrozek Tomasz

• Institute of Telecommunications, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland

autor

Perlicki Krzysztof

• Institute of Telecommunications, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland

autor

Jakubiak Andrzej

• Institute of Telecommunications, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland

Bibliografia

• [1] DAHAN D., MAHLAB U., TEIXEIRA A., ZACHAROPOULOS I., TOMKOS I., Optical performance monitoring for translucent/transparent optical networks, IET Optoelectronics 5(1), 2011, pp. 1–18, DOI:10.1049/iet-opt.2010.0010.
• [2] PERLICKI K., Investigation of the state of polarization distribution generated by polarization scramblers on the Poincarè sphere, Optics Communications 252(1–3), 2005, pp. 58–63, DOI:10.1016/j.optcom.2005.04.002.
• [3] DODS S.D., ANDERSON T.B., Optical performance monitoring technique using delay tap asynchronous waveform sampling, 2006 Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference, Anaheim, CA, 2006, DOI:10.1109/OFC.2006.215890.
• [4] ANDERSON T.B., KOWALCZYK A., CLARKE K., DODS S.D., HEWIT D., LI J.C., Multi impairment monitoring for optical networks, Journal of Lightwave Technology 27(16) 2009, pp. 3729–3736, DOI:10.1109/JLT.2009.2025052.
• [5] ANDERSON T.B., DODS S.D., KOWALCZYK A., CLARKE K., HEWITT D., LI J.C., Chapter 7 - Optical performance monitoring based on asynchronous delay-tap sampling, [In] Chan C.C.K., Optical Performance Monitoring: Advanced Techniques for Next-Generation Photonic Networks, Academic Press, 2010, pp. 175–192, DOI:10.1016/B978-0-12-374950-5.00007-9.
• [6] CLARKE K., et al., Experimental demonstration of multi-impairment monitoring on a commercial 10 Gb/s NRZ channel, Proc. OFC/NFOEC, San Diego, California, 2009, paper OThH7.
• [7] BEAMAN D., et al., Demonstration of simultaneous OSNR and CD monitoring using asynchronous delay tap sampling on an 800 km WDM test bed, Proc., ECOC, Vienna, Austria, 2009, paper 9.3.4.
• [8] KOZICKI B., TAKUYA O., HIDEHIKO T., Optical performance monitoring of phase-modulated signals using asynchronous amplitude histogram analysis, Journal of Lightwave Technology 26(10), 2008,pp. 1353–1361, DOI:10.1109/JLT.2008.917374.
• [9] KOZICKI B., MARUTA A., KITAYAMA K., Experimental demonstration of optical performance monitoring for RZ-DPSK signals using delay-tap sampling method, Optics Express 16(6), 2008, pp. 3566–3576,DOI:10.1364/OE.16.003566.
• [10] KITAYAMA K., KOZICKI B., MARUTA A., Asynchronous optical performance monitoring of RZ DQPSK signals using delay-tap sampling, Proc. ECOC, Berlin, Germany, 2007, paper P060.
• [11] CHOI H.Y., TAKUSHIMA Y., CHUNG Y.C., Multiple impairment monitoring technique using optical field detection and asynchronous delay-tap sampling method, Proc. OFC/NFOEC, San Diego, California, 2009, paper 0ThJ5.
• [12] ZHAO J., LU C., LAM K.M., LI Z., TAM H.Y., WAI P.K.A., A novel optical signal monitoring method of DPSK signal based on delay tap sampling and Hausdorff distance measure, Proc. CLEO/QELS, San Jose, California, 2008, paper JWA108.
• [13] JARGON J.A., WU X., WILLNER A.E., Optical performance monitoring using artificial neural networks with features derived from asynchronous delay tap sampling, Proc. CLEO/QELS, San Jose, California, 2009, paper OThH1.
• [14] WILLNER A.E., PAN Z., YU C., 7 - Optical performance monitoring, [In], Kaminow I. P., Li T., Willner A.E. [Eds.], Optical Fiber Telecommunications V B, 5th Ed., Academic Press, 2008, pp. 233–292, DOI:10.1016/B978-0-12-374172-1.00007-2.
• [15] ZHAO J., LI Z., LIU D., CHENG L., LU C., TAM H.Y., NRZ-DPSK and RZ-DPSK signals signed chromatic dispersion monitoring using asynchronous delay-tap sampling, Journal of Lightwave Technology 27(23), 2009, pp. 5295–5301, DOI:10.1109/JLT.2009.2031610.
• [16] BRACEWELL R., The Fourier Transform and Its Applications, 3rd Ed., McGraw-Hill, New York, 2000, pp. 24–54 and 331–334.
• [17] SMITH S.W., The Scientist and Engineer’s Guide to Digital Signal Processing, 1st Ed., California Technical Publishing, 1997, pp. 246–252.
• [18] LECUN Y., BOTTOU L., BENGIO Y., HAFFNER P., Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (11), 1998, pp. 2278–2324, DOI:10.1109/5.726791.
• [19] ABADI M., AGARWAL A., BARHAM P., BREVDO E., CHEN Z., CITRO C., CORRADO G.S., DAVIS A., DEAN J.,DEVIN M., GHEMAWAT S., GOODFELLOW I., HARP A., IRVING G., ISARD M., JOZEFOWICZ R., JIA Y.,KAISER L., KUDLUR M., LEVENBERG J., MANÉ D., SCHUSTER M., MONGA R., MOORE S., MURRAY D., OLAH C., SHLENS J., STEINER B., SUTSKEVER I., TALWAR K., TUCKER P., VANHOUCKE V., VASUDEVAN V., VIÉGAS F., VINYALS O., WARDEN P., WATTENBERG M., WICKE M., YU Y., ZHENG X., TensorFlow: Large-scale machine learning on heterogeneous systems, 2015, Software available from tensorflow.org.
• [20] CAMERON A.C., WINDMEIJER F.A.G., An R-squared measure of goodness of fit for some common nonlinear regression models, Journal of Econometrics 77(2), 1997, pp. 329–342, DOI:10.1016/S0304-4076(96)01818-0.