Abstract

This article considers, for the first time, optical performance monitoring (OPM) in few mode fiber (FMF)-based optical networks. 1-D features vector, extracted by projecting a 2-D asynchronous in-phase quadrature histogram (IQH), and the 2D IQH are proposed to achieve OPM in FMF-based network. Three machine learning algorithms are employed for OPM and their performances are compared. These include support vector machine, random forest algorithm, and convolutional neural network. Extensive simulations are conducted to monitor optical to signal ratio (OSNR), chromatic dispersion (CD), and mode coupling (MC) for dual polarization-quadrature phase shift keying (DP-QPSK) at 10, 12, 16, 20, and 28 Gbaud transmission speeds. Besides, M-ary quadrature amplitude modulation (M = 8 and 16) is considered. Also, the OPM accuracy is investigated under different FMF channel conditions including phase noise and polarization mode dispersion. Simulation results show that the proposed 1D projection features vector provides better OPM results than those of the widely used asynchronous amplitude histogram (AAH) features. Furthermore, it has been found that the 2D IQH features outperform the 1D projection features but require larger number of features samples. Additionally, the effect of fiber nonlinearity on the OPM accuracy is investigated. Finally, OPM using the 2D IQH features has been verified experimentally for 10 Gbaud DP-QPSK signal. The obtained results show a good agreement between both simulation and experimental findings.

PDF Article

References

  • View by:
  • |
  • |
  • |

  1. W. Klaus, J. Sakaguchi, B. J. Puttnam, Y. Awaji, and N. Wada, “Optical technologies for space division multiplexing,” in Proc. 13th Workshop Inf. Opt., Neuchatel, 2014, pp. 1–3.
  2. G. M. Saridis, D. Alexandropoulos, G. Zervas, and D. Simeonidou, “Survey and evaluation of space division multiplexing: From technologies to optical networks,” IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2136–2156,  2015.
  3. Z. Dong, F. N. Khan, Q. Sui, K. Zhong, C. Lu, and A. P. T. Lau, “Optical performance monitoring: A review of current and future technologies,” J. Lightw. Technol., vol. 34, no. 2, pp. 525–543, 2015.
  4. A. E. Willner, Z. Pan, and C. Yu, “Optical performance monitoring,” in Proc. Opt. Fiber Telecommun. VB. Elsevier, 2008, pp. 233–292.
  5. C. Wang, “Joint OSNR and CD monitoring in digital coherent receiver using long short-term memory neural network,” Opt. Express, vol. 27, no. 5, pp. 6936–6945, 2019.
  6. F. N. Khan, A. P. T. Lau, T. B. Anderson, J. C. Li, C. Lu, and P. K. A. Wai, “Simultaneous and independent OSNR and chromatic dispersion monitoring using empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. J., vol. 4, no. 5, pp. 1340–1350,  2012.
  7. F. Wu, A. Yang, P. Guo, Y. Qiao, L. Zhuang, and S. Guo, “QPSK training sequence-based both OSNR and chromatic dispersion monitoring in DWDM systems,” IEEE Photon. J., vol. 10, no. 4, pp. 1–10,  2018.
  8. C. Courvoisier, J. Fatome, and C. Finot, “Measurement of residual chromatic dispersion or OSNR via nonlinear spectral evolution,” IEEE Photon. Technol. Lett., vol. 23, no. 9, pp. 537–539,  2011.
  9. S. J. Savory, “Digital filters for coherent optical receivers,” Opt. Express, vol. 16, no. 2, pp. 804–817, 2008.
  10. P. Sillard, M. Bigot-Astruc, and D. Molin, “Few-mode fibers for mode-division-multiplexed systems,” J. Lightw. Technol., vol. 32, no. 16, pp. 2824–2829, 2014.
  11. C. Do, A. V. Tran, C. Zhu, D. Hewitt, and E. Skafidas, “Data-aided OSNR estimation for QPSK and 16-QAM coherent optical system,” IEEE Photon. J., vol. 5, no. 5, pp. 6 601 609–6 601 609, 2013.
  12. C. C. Do, C. Zhu, and A. V. Tran, “Data-aided OSNR estimation using low-bandwidth coherent receivers,” IEEE Photon. Technol. Lett., vol. 26, no. 13, pp. 1291–1294,  2014.
  13. M. S. Faruk, Y. Mori, and K. Kikuchi, “Estimation of OSNR for Nyquist-WDM transmission systems using statistical moments of equalized signals in digital coherent receivers,” in Proc. Opt. Fiber Commun. Conf. Exhibition, San Francisco, CA, 2014, pp. 1–3.
  14. Y. Ma, M. Gao, L. Wang, Y. Sha, W. Shao, and G. Shen, “Accuracy enhancement of moments-based OSNR monitoring in QAM coherent optical communication,” IEEE Commun. Lett., vol. 24, no. 4, pp. 821–824,  2020.
  15. X. Lin, O. A. Dobre, T. M. Ngatched, and C. Li, “A non-data-aided OSNR estimation algorithm for coherent optical fiber communication systems employing multilevel constellations,” J. Lightw. Technol., vol. 37, no. 15, pp. 3815–3825, 2019.
  16. W. Moench and E. Loecklin, “Measurement of optical signal-to-noise-ratio in coherent systems using polarization multiplexed transmission,” in Proc. Opt. Fiber Commun. Conf. Exhib., Los Angeles, CA, 2017, pp. 1–3.
  17. D. Gariépy, S. Searcy, G. He, S. Tibuleac, M. Leclerc, and P. Gosselin-Badaroudine, “Novel OSNR measurement techniques based on optical spectrum analysis and their application to coherent-detection systems,” J. Lightw. Technol., vol. 37, no. 2, pp. 562–570, 2019.
  18. F. Wu, P. Guo, A. Yang, and Y. Qiao, “Chromatic dispersion estimation based on CAZAC sequence for optical fiber communication systems,” IEEE Access, vol. 7, pp. 139 388–139 393, 2019.
  19. Y. Ma, “Training sequence-based chromatic dispersion estimation with ultra-low sampling rate for optical fiber communication systems,” IEEE Photon. J., vol. 10, no. 6, pp. 1–9,  2018.
  20. D. Tang, X. Wang, L. Zhuang, P. Guo, A. Yang, and Y. Qiao, “Delay-tap-sampling-based chromatic dispersion estimation method with ultra-Low sampling rate for optical fiber communication systems,” IEEE Access, vol. 8, pp. 101 004–101 013, 2020.
  21. K. Horikoshi, A. Matsushita, S. Okamoto, and M. Nakamura, “Fast blind chromatic-dispersion esitimation for small-rolloff Nyquist pulse-shaped signal using spectral cyclostationarity,” in Proc. 45th Eur. Conf. Opt. Commun., Dublin, Ireland, 2019, pp. 1–3.
  22. W. G. Hatcher and W. Yu, “A survey of deep learning: Platforms, applications and emerging research trends,” IEEE Access, vol. 6, pp. 24 411–24 432, 2018.
  23. R. A. Eltaieb, “Efficient classification of optical modulation formats based on singular value decomposition and Radon transformation,” J. Lightw. Technol., vol. 38, no. 3, pp. 619–631, 2020.
  24. F. N. Khan, Y. Zhou, A. P. T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Opt. Express, vol. 20, no. 11, pp. 12 422–12 431, 2012.
  25. T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.
  26. J. Thrane, J. Wass, M. Piels, J. C. Diniz, R. Jones, and D. Zibar, “Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals,” J. Lightw. Technol., vol. 35, no. 4, pp. 868–875, 2016.
  27. W. Saif, M. A. Esmail, A. Ragheb, T. Alshawi, and S. Alshebeili, “Machine learning techniques for optical performance monitoring and modulation format identification: A Survey,” IEEE Commun. Surveys Tuts., pp. 1–1, 2020.
  28. F. N. Khan, C. Lu, and A. P. T. Lau, “Optical performance monitoring in fiber-optic networks enabled by machine learning techniques,” in Proc. Opt. Fiber Commun. Conf. Expo., San Diego, CA, 2018, pp. 1–3.
  29. Q. Xiang, Y. Yang, Q. Zhang, and Y. Yao, “Joint and accurate OSNR estimation and modulation format identification scheme using the feature-based ANN,” IEEE Photon. J., vol. 11, no. 4, pp. 1–11,  2019.
  30. J. Mata, “Artificial intelligence (AI) methods in optical networks: A comprehensive survey,” Opt. Switching Netw., vol. 28, pp. 43–57, 2018.
  31. F. N. Khan, Q. Fan, C. Lu, and A. P. T. Lau, “An optical communication's perspective on machine learning and its applications,” J. Lightw. Technol., vol. 37, no. 2, pp. 493–516, 2019.
  32. Y. Zhang, “Eye diagram measurement-based joint modulation format, OSNR, ROF, and skew monitoring of coherent channel using deep learning,” J. Lightw. Technol., vol. 37, no. 23, pp. 5907–5913, 2019.
  33. Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express, vol. 27, no. 8, pp. 11 281–11 291, 2019.
  34. D. Wang, “Cost-effective and data size–adaptive OPM at intermediated node using convolutional neural network-based image processor,” Opt. Express, vol. 27, no. 7, pp. 9403–9419, 2019.
  35. L. Xia, J. Zhang, S. Hu, M. Zhu, Y. Song, and K. Qiu, “Transfer learning assisted deep neural network for osnr estimation,” Opt. Express, vol. 27, no. 14, pp. 19 398–19 406, 2019.
  36. A. W. Snyder and J. Love, Optical Waveguide Theory, New York, NY, USA: Springer Science & Business Media, 2012.
  37. S. J. Garth, “Few-mode optical waveguides and their study by the four-photon mixing process,” Ph.D. dissertation, Dept. Appl. Math., The Australian National University, 1987.
  38. R. Paschotta, Field Guide to Optical Fiber Technology, Bellingham, WA, USA: SPIE, 2010.
  39. Y. Weng, X. He, and Z. Pan, “Space division multiplexing optical communication using few-mode fibers,” Opt. Fiber Technol., vol. 36, pp. 155–180, 2017.
  40. G. Rademacher, “159 Tbit/s C+ L band transmission over 1045 km 3-mode graded-index few-mode fiber,” in Proc. Opt. Fiber Commun. Conf., San Diego, CA, 2018, Paper Th4C–4.
  41. G. Rademacher, “93.34 Tbit/s/mode (280 Tbit/s) transmission in a 3-mode graded-index few-mode fiber,” in Proc. Opt. Fiber Commun. Conf., San Diego, CA, 2018, Paper. W4C–3.
  42. D. Soma, “2.05 Peta-bit/s super-Nyquist-WDM SDM transmission using 9.8-km 6-mode 19-core fiber in full C band,” in Proc. Eur. Conf. Opt. Commun., Valencia, 2015, pp. 1–3.
  43. D. Soma, T. Tsuritani, and I. Morita, “10 Pbit/s SDM/WDM transmission,” in Proc. IEEE Photon. Conf., Reston, 2018, pp. 1–2.
  44. T. Hu, “Demonstration of bidirectional PON based on mode division multiplexing,” IEEE Photon. Technol. Lett., vol. 28, no. 11, pp. 1201–1204,  2016.
  45. J. Vuong, “Mode coupling at connectors in mode-division multiplexed transmission over few-mode fiber,” Opt. Express, vol. 23, no. 2, pp. 1438–1455, 2015.
  46. W. S. Saif, A. M. Ragheb, H. E. Seleem, T. A. Alshawi, and S. A. Alshebeili, “Modulation format identification in mode division multiplexed optical networks,” IEEE Access, vol. 7, pp. 156 207–156 216, 2019.
  47. W. S. Saif, T. Alshawi, M. A. Esmail, A. Ragheb, and S. Alshebeili, “Separability of histogram based features for optical performance monitoring: An investigation using t-SNE technique,” IEEE Photon. J., vol. 11, no. 3, pp. 1–12,  2019.
  48. T. A. Almohamad, “Automatic modulation recognition in wireless communication systems using feature-based approach,” in Proc. 10th Int. Conf. Robot., Vis., Signal Process. Power Appl., Penang, Malaysia, 2018, pp. 403–409.
  49. V. Vapnik, The Nature Statistical Learning Theory, New York, NY, USA: Springer Science & Business Media, 2013.
  50. D. Basak, S. Pal, and D. C. Patranabis, “Support vector regression,” Neural Inf. Process.-Lett. Rev., vol. 11, no. 10, pp. 203–224, 2007.
  51. L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
  52. Y. Zhao, “Low-complexity and nonlinearity-tolerant modulation format identification using random forest,” IEEE Photon. Technol. Lett., vol. 31, no. 11, pp. 853–856,  2019.
  53. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Advances Neural Inf. Process. Syst., 2012, pp. 1097–1105.
  54. T. Tanimura, “Deep learning based OSNR monitoring independent of modulation format, symbol rate, and chromatic dispersion,” in Proc. 42nd Eur. Conf. Opt. Commun., Dusseldorf, Germany, 2016, pp. 1–3.
  55. L. Guesmi, A. M. Ragheb, H. Fathallah, and M. Menif, “Experimental demonstration of simultaneous modulation format/symbol rate identification and optical performance monitoring for coherent optical systems,” J. Lightw. Technol., vol. 36, no. 11, pp. 2230–2239, 2017.
  56. J. M. Chambers, Graphical Methods Data Analysis, New York, NY, USA: CRC Press, 2018.
  57. T. O. Kva, “Note on the $ {R^2}$ measure of goodness of fit for nonlinear models,” Bulletin Psychonomic Soc., vol. 21, no. 1, pp. 79–80, 1983.
  58. D. Wang, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express, vol. 25, no. 15, pp. 17 150–17 166, 2017.
  59. A. Mecozzi, C. Antonelli, and M. Shtaif, “Coupled Manakov equations in multimode fibers with strongly coupled groups of modes,” Opt. Express, vol. 20, no. 21, pp. 23 436–23 441, 2012.
  60. D. Marcuse, C. Manyuk, and P. Wai, “Application of the Manakov-PMD equation to studies of signal propagation in optical fibers with randomly varying birefringence,” J. Lightw. Technol., vol. 15, no. 9, pp. 1735–1746, 1997.

2020 (3)

D. Tang, X. Wang, L. Zhuang, P. Guo, A. Yang, and Y. Qiao, “Delay-tap-sampling-based chromatic dispersion estimation method with ultra-Low sampling rate for optical fiber communication systems,” IEEE Access, vol. 8, pp. 101 004–101 013, 2020.

R. A. Eltaieb, “Efficient classification of optical modulation formats based on singular value decomposition and Radon transformation,” J. Lightw. Technol., vol. 38, no. 3, pp. 619–631, 2020.

W. Saif, M. A. Esmail, A. Ragheb, T. Alshawi, and S. Alshebeili, “Machine learning techniques for optical performance monitoring and modulation format identification: A Survey,” IEEE Commun. Surveys Tuts., pp. 1–1, 2020.

2019 (11)

X. Lin, O. A. Dobre, T. M. Ngatched, and C. Li, “A non-data-aided OSNR estimation algorithm for coherent optical fiber communication systems employing multilevel constellations,” J. Lightw. Technol., vol. 37, no. 15, pp. 3815–3825, 2019.

F. Wu, P. Guo, A. Yang, and Y. Qiao, “Chromatic dispersion estimation based on CAZAC sequence for optical fiber communication systems,” IEEE Access, vol. 7, pp. 139 388–139 393, 2019.

C. Wang, “Joint OSNR and CD monitoring in digital coherent receiver using long short-term memory neural network,” Opt. Express, vol. 27, no. 5, pp. 6936–6945, 2019.

F. N. Khan, Q. Fan, C. Lu, and A. P. T. Lau, “An optical communication's perspective on machine learning and its applications,” J. Lightw. Technol., vol. 37, no. 2, pp. 493–516, 2019.

Y. Zhang, “Eye diagram measurement-based joint modulation format, OSNR, ROF, and skew monitoring of coherent channel using deep learning,” J. Lightw. Technol., vol. 37, no. 23, pp. 5907–5913, 2019.

Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express, vol. 27, no. 8, pp. 11 281–11 291, 2019.

D. Wang, “Cost-effective and data size–adaptive OPM at intermediated node using convolutional neural network-based image processor,” Opt. Express, vol. 27, no. 7, pp. 9403–9419, 2019.

L. Xia, J. Zhang, S. Hu, M. Zhu, Y. Song, and K. Qiu, “Transfer learning assisted deep neural network for osnr estimation,” Opt. Express, vol. 27, no. 14, pp. 19 398–19 406, 2019.

W. S. Saif, A. M. Ragheb, H. E. Seleem, T. A. Alshawi, and S. A. Alshebeili, “Modulation format identification in mode division multiplexed optical networks,” IEEE Access, vol. 7, pp. 156 207–156 216, 2019.

W. S. Saif, T. Alshawi, M. A. Esmail, A. Ragheb, and S. Alshebeili, “Separability of histogram based features for optical performance monitoring: An investigation using t-SNE technique,” IEEE Photon. J., vol. 11, no. 3, pp. 1–12,  2019.

Y. Zhao, “Low-complexity and nonlinearity-tolerant modulation format identification using random forest,” IEEE Photon. Technol. Lett., vol. 31, no. 11, pp. 853–856,  2019.

2018 (3)

F. Wu, A. Yang, P. Guo, Y. Qiao, L. Zhuang, and S. Guo, “QPSK training sequence-based both OSNR and chromatic dispersion monitoring in DWDM systems,” IEEE Photon. J., vol. 10, no. 4, pp. 1–10,  2018.

Y. Ma, “Training sequence-based chromatic dispersion estimation with ultra-low sampling rate for optical fiber communication systems,” IEEE Photon. J., vol. 10, no. 6, pp. 1–9,  2018.

J. Mata, “Artificial intelligence (AI) methods in optical networks: A comprehensive survey,” Opt. Switching Netw., vol. 28, pp. 43–57, 2018.

2017 (2)

Y. Weng, X. He, and Z. Pan, “Space division multiplexing optical communication using few-mode fibers,” Opt. Fiber Technol., vol. 36, pp. 155–180, 2017.

D. Wang, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express, vol. 25, no. 15, pp. 17 150–17 166, 2017.

2016 (1)

J. Thrane, J. Wass, M. Piels, J. C. Diniz, R. Jones, and D. Zibar, “Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals,” J. Lightw. Technol., vol. 35, no. 4, pp. 868–875, 2016.

2015 (2)

J. Vuong, “Mode coupling at connectors in mode-division multiplexed transmission over few-mode fiber,” Opt. Express, vol. 23, no. 2, pp. 1438–1455, 2015.

Z. Dong, F. N. Khan, Q. Sui, K. Zhong, C. Lu, and A. P. T. Lau, “Optical performance monitoring: A review of current and future technologies,” J. Lightw. Technol., vol. 34, no. 2, pp. 525–543, 2015.

2014 (2)

C. C. Do, C. Zhu, and A. V. Tran, “Data-aided OSNR estimation using low-bandwidth coherent receivers,” IEEE Photon. Technol. Lett., vol. 26, no. 13, pp. 1291–1294,  2014.

P. Sillard, M. Bigot-Astruc, and D. Molin, “Few-mode fibers for mode-division-multiplexed systems,” J. Lightw. Technol., vol. 32, no. 16, pp. 2824–2829, 2014.

2013 (1)

C. Do, A. V. Tran, C. Zhu, D. Hewitt, and E. Skafidas, “Data-aided OSNR estimation for QPSK and 16-QAM coherent optical system,” IEEE Photon. J., vol. 5, no. 5, pp. 6 601 609–6 601 609, 2013.

2012 (4)

F. N. Khan, A. P. T. Lau, T. B. Anderson, J. C. Li, C. Lu, and P. K. A. Wai, “Simultaneous and independent OSNR and chromatic dispersion monitoring using empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. J., vol. 4, no. 5, pp. 1340–1350,  2012.

F. N. Khan, Y. Zhou, A. P. T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Opt. Express, vol. 20, no. 11, pp. 12 422–12 431, 2012.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Advances Neural Inf. Process. Syst., 2012, pp. 1097–1105.

A. Mecozzi, C. Antonelli, and M. Shtaif, “Coupled Manakov equations in multimode fibers with strongly coupled groups of modes,” Opt. Express, vol. 20, no. 21, pp. 23 436–23 441, 2012.

2011 (1)

C. Courvoisier, J. Fatome, and C. Finot, “Measurement of residual chromatic dispersion or OSNR via nonlinear spectral evolution,” IEEE Photon. Technol. Lett., vol. 23, no. 9, pp. 537–539,  2011.

2010 (2)

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.

R. Paschotta, Field Guide to Optical Fiber Technology, Bellingham, WA, USA: SPIE, 2010.

2008 (1)

2007 (1)

D. Basak, S. Pal, and D. C. Patranabis, “Support vector regression,” Neural Inf. Process.-Lett. Rev., vol. 11, no. 10, pp. 203–224, 2007.

2001 (1)

L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.

1997 (1)

D. Marcuse, C. Manyuk, and P. Wai, “Application of the Manakov-PMD equation to studies of signal propagation in optical fibers with randomly varying birefringence,” J. Lightw. Technol., vol. 15, no. 9, pp. 1735–1746, 1997.

1983 (1)

T. O. Kva, “Note on the $ {R^2}$ measure of goodness of fit for nonlinear models,” Bulletin Psychonomic Soc., vol. 21, no. 1, pp. 79–80, 1983.

Alexandropoulos, D.

G. M. Saridis, D. Alexandropoulos, G. Zervas, and D. Simeonidou, “Survey and evaluation of space division multiplexing: From technologies to optical networks,” IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2136–2156,  2015.

Almohamad, T. A.

T. A. Almohamad, “Automatic modulation recognition in wireless communication systems using feature-based approach,” in Proc. 10th Int. Conf. Robot., Vis., Signal Process. Power Appl., Penang, Malaysia, 2018, pp. 403–409.

Alshawi, T.

W. Saif, M. A. Esmail, A. Ragheb, T. Alshawi, and S. Alshebeili, “Machine learning techniques for optical performance monitoring and modulation format identification: A Survey,” IEEE Commun. Surveys Tuts., pp. 1–1, 2020.

W. S. Saif, T. Alshawi, M. A. Esmail, A. Ragheb, and S. Alshebeili, “Separability of histogram based features for optical performance monitoring: An investigation using t-SNE technique,” IEEE Photon. J., vol. 11, no. 3, pp. 1–12,  2019.

Alshawi, T. A.

W. S. Saif, A. M. Ragheb, H. E. Seleem, T. A. Alshawi, and S. A. Alshebeili, “Modulation format identification in mode division multiplexed optical networks,” IEEE Access, vol. 7, pp. 156 207–156 216, 2019.

Alshebeili, S.

W. Saif, M. A. Esmail, A. Ragheb, T. Alshawi, and S. Alshebeili, “Machine learning techniques for optical performance monitoring and modulation format identification: A Survey,” IEEE Commun. Surveys Tuts., pp. 1–1, 2020.

W. S. Saif, T. Alshawi, M. A. Esmail, A. Ragheb, and S. Alshebeili, “Separability of histogram based features for optical performance monitoring: An investigation using t-SNE technique,” IEEE Photon. J., vol. 11, no. 3, pp. 1–12,  2019.

Alshebeili, S. A.

W. S. Saif, A. M. Ragheb, H. E. Seleem, T. A. Alshawi, and S. A. Alshebeili, “Modulation format identification in mode division multiplexed optical networks,” IEEE Access, vol. 7, pp. 156 207–156 216, 2019.

Anderson, T. B.

F. N. Khan, A. P. T. Lau, T. B. Anderson, J. C. Li, C. Lu, and P. K. A. Wai, “Simultaneous and independent OSNR and chromatic dispersion monitoring using empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. J., vol. 4, no. 5, pp. 1340–1350,  2012.

Antonelli, C.

A. Mecozzi, C. Antonelli, and M. Shtaif, “Coupled Manakov equations in multimode fibers with strongly coupled groups of modes,” Opt. Express, vol. 20, no. 21, pp. 23 436–23 441, 2012.

Awaji, Y.

W. Klaus, J. Sakaguchi, B. J. Puttnam, Y. Awaji, and N. Wada, “Optical technologies for space division multiplexing,” in Proc. 13th Workshop Inf. Opt., Neuchatel, 2014, pp. 1–3.

Basak, D.

D. Basak, S. Pal, and D. C. Patranabis, “Support vector regression,” Neural Inf. Process.-Lett. Rev., vol. 11, no. 10, pp. 203–224, 2007.

Bigot-Astruc, M.

P. Sillard, M. Bigot-Astruc, and D. Molin, “Few-mode fibers for mode-division-multiplexed systems,” J. Lightw. Technol., vol. 32, no. 16, pp. 2824–2829, 2014.

Breiman, L.

L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.

Chambers, J. M.

J. M. Chambers, Graphical Methods Data Analysis, New York, NY, USA: CRC Press, 2018.

Courvoisier, C.

C. Courvoisier, J. Fatome, and C. Finot, “Measurement of residual chromatic dispersion or OSNR via nonlinear spectral evolution,” IEEE Photon. Technol. Lett., vol. 23, no. 9, pp. 537–539,  2011.

Diniz, J. C.

J. Thrane, J. Wass, M. Piels, J. C. Diniz, R. Jones, and D. Zibar, “Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals,” J. Lightw. Technol., vol. 35, no. 4, pp. 868–875, 2016.

Do, C.

C. Do, A. V. Tran, C. Zhu, D. Hewitt, and E. Skafidas, “Data-aided OSNR estimation for QPSK and 16-QAM coherent optical system,” IEEE Photon. J., vol. 5, no. 5, pp. 6 601 609–6 601 609, 2013.

Do, C. C.

C. C. Do, C. Zhu, and A. V. Tran, “Data-aided OSNR estimation using low-bandwidth coherent receivers,” IEEE Photon. Technol. Lett., vol. 26, no. 13, pp. 1291–1294,  2014.

Dobre, O. A.

X. Lin, O. A. Dobre, T. M. Ngatched, and C. Li, “A non-data-aided OSNR estimation algorithm for coherent optical fiber communication systems employing multilevel constellations,” J. Lightw. Technol., vol. 37, no. 15, pp. 3815–3825, 2019.

Dong, Z.

Z. Dong, F. N. Khan, Q. Sui, K. Zhong, C. Lu, and A. P. T. Lau, “Optical performance monitoring: A review of current and future technologies,” J. Lightw. Technol., vol. 34, no. 2, pp. 525–543, 2015.

Dong, Z. Y.

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.

Eltaieb, R. A.

R. A. Eltaieb, “Efficient classification of optical modulation formats based on singular value decomposition and Radon transformation,” J. Lightw. Technol., vol. 38, no. 3, pp. 619–631, 2020.

Esmail, M. A.

W. Saif, M. A. Esmail, A. Ragheb, T. Alshawi, and S. Alshebeili, “Machine learning techniques for optical performance monitoring and modulation format identification: A Survey,” IEEE Commun. Surveys Tuts., pp. 1–1, 2020.

W. S. Saif, T. Alshawi, M. A. Esmail, A. Ragheb, and S. Alshebeili, “Separability of histogram based features for optical performance monitoring: An investigation using t-SNE technique,” IEEE Photon. J., vol. 11, no. 3, pp. 1–12,  2019.

Fan, Q.

F. N. Khan, Q. Fan, C. Lu, and A. P. T. Lau, “An optical communication's perspective on machine learning and its applications,” J. Lightw. Technol., vol. 37, no. 2, pp. 493–516, 2019.

Faruk, M. S.

M. S. Faruk, Y. Mori, and K. Kikuchi, “Estimation of OSNR for Nyquist-WDM transmission systems using statistical moments of equalized signals in digital coherent receivers,” in Proc. Opt. Fiber Commun. Conf. Exhibition, San Francisco, CA, 2014, pp. 1–3.

Fathallah, H.

L. Guesmi, A. M. Ragheb, H. Fathallah, and M. Menif, “Experimental demonstration of simultaneous modulation format/symbol rate identification and optical performance monitoring for coherent optical systems,” J. Lightw. Technol., vol. 36, no. 11, pp. 2230–2239, 2017.

Fatome, J.

C. Courvoisier, J. Fatome, and C. Finot, “Measurement of residual chromatic dispersion or OSNR via nonlinear spectral evolution,” IEEE Photon. Technol. Lett., vol. 23, no. 9, pp. 537–539,  2011.

Finot, C.

C. Courvoisier, J. Fatome, and C. Finot, “Measurement of residual chromatic dispersion or OSNR via nonlinear spectral evolution,” IEEE Photon. Technol. Lett., vol. 23, no. 9, pp. 537–539,  2011.

Gao, M.

Y. Ma, M. Gao, L. Wang, Y. Sha, W. Shao, and G. Shen, “Accuracy enhancement of moments-based OSNR monitoring in QAM coherent optical communication,” IEEE Commun. Lett., vol. 24, no. 4, pp. 821–824,  2020.

Gariépy, D.

D. Gariépy, S. Searcy, G. He, S. Tibuleac, M. Leclerc, and P. Gosselin-Badaroudine, “Novel OSNR measurement techniques based on optical spectrum analysis and their application to coherent-detection systems,” J. Lightw. Technol., vol. 37, no. 2, pp. 562–570, 2019.

Garth, S. J.

S. J. Garth, “Few-mode optical waveguides and their study by the four-photon mixing process,” Ph.D. dissertation, Dept. Appl. Math., The Australian National University, 1987.

Gosselin-Badaroudine, P.

D. Gariépy, S. Searcy, G. He, S. Tibuleac, M. Leclerc, and P. Gosselin-Badaroudine, “Novel OSNR measurement techniques based on optical spectrum analysis and their application to coherent-detection systems,” J. Lightw. Technol., vol. 37, no. 2, pp. 562–570, 2019.

Guesmi, L.

L. Guesmi, A. M. Ragheb, H. Fathallah, and M. Menif, “Experimental demonstration of simultaneous modulation format/symbol rate identification and optical performance monitoring for coherent optical systems,” J. Lightw. Technol., vol. 36, no. 11, pp. 2230–2239, 2017.

Guo, P.

D. Tang, X. Wang, L. Zhuang, P. Guo, A. Yang, and Y. Qiao, “Delay-tap-sampling-based chromatic dispersion estimation method with ultra-Low sampling rate for optical fiber communication systems,” IEEE Access, vol. 8, pp. 101 004–101 013, 2020.

F. Wu, P. Guo, A. Yang, and Y. Qiao, “Chromatic dispersion estimation based on CAZAC sequence for optical fiber communication systems,” IEEE Access, vol. 7, pp. 139 388–139 393, 2019.

F. Wu, A. Yang, P. Guo, Y. Qiao, L. Zhuang, and S. Guo, “QPSK training sequence-based both OSNR and chromatic dispersion monitoring in DWDM systems,” IEEE Photon. J., vol. 10, no. 4, pp. 1–10,  2018.

Guo, S.

F. Wu, A. Yang, P. Guo, Y. Qiao, L. Zhuang, and S. Guo, “QPSK training sequence-based both OSNR and chromatic dispersion monitoring in DWDM systems,” IEEE Photon. J., vol. 10, no. 4, pp. 1–10,  2018.

Hatcher, W. G.

W. G. Hatcher and W. Yu, “A survey of deep learning: Platforms, applications and emerging research trends,” IEEE Access, vol. 6, pp. 24 411–24 432, 2018.

He, G.

D. Gariépy, S. Searcy, G. He, S. Tibuleac, M. Leclerc, and P. Gosselin-Badaroudine, “Novel OSNR measurement techniques based on optical spectrum analysis and their application to coherent-detection systems,” J. Lightw. Technol., vol. 37, no. 2, pp. 562–570, 2019.

He, X.

Y. Weng, X. He, and Z. Pan, “Space division multiplexing optical communication using few-mode fibers,” Opt. Fiber Technol., vol. 36, pp. 155–180, 2017.

Hewitt, D.

C. Do, A. V. Tran, C. Zhu, D. Hewitt, and E. Skafidas, “Data-aided OSNR estimation for QPSK and 16-QAM coherent optical system,” IEEE Photon. J., vol. 5, no. 5, pp. 6 601 609–6 601 609, 2013.

Hinton, G. E.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Advances Neural Inf. Process. Syst., 2012, pp. 1097–1105.

Horikoshi, K.

K. Horikoshi, A. Matsushita, S. Okamoto, and M. Nakamura, “Fast blind chromatic-dispersion esitimation for small-rolloff Nyquist pulse-shaped signal using spectral cyclostationarity,” in Proc. 45th Eur. Conf. Opt. Commun., Dublin, Ireland, 2019, pp. 1–3.

Hu, S.

L. Xia, J. Zhang, S. Hu, M. Zhu, Y. Song, and K. Qiu, “Transfer learning assisted deep neural network for osnr estimation,” Opt. Express, vol. 27, no. 14, pp. 19 398–19 406, 2019.

Hu, T.

T. Hu, “Demonstration of bidirectional PON based on mode division multiplexing,” IEEE Photon. Technol. Lett., vol. 28, no. 11, pp. 1201–1204,  2016.

Jones, R.

J. Thrane, J. Wass, M. Piels, J. C. Diniz, R. Jones, and D. Zibar, “Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals,” J. Lightw. Technol., vol. 35, no. 4, pp. 868–875, 2016.

Khan, F. N.

F. N. Khan, Q. Fan, C. Lu, and A. P. T. Lau, “An optical communication's perspective on machine learning and its applications,” J. Lightw. Technol., vol. 37, no. 2, pp. 493–516, 2019.

Z. Dong, F. N. Khan, Q. Sui, K. Zhong, C. Lu, and A. P. T. Lau, “Optical performance monitoring: A review of current and future technologies,” J. Lightw. Technol., vol. 34, no. 2, pp. 525–543, 2015.

F. N. Khan, A. P. T. Lau, T. B. Anderson, J. C. Li, C. Lu, and P. K. A. Wai, “Simultaneous and independent OSNR and chromatic dispersion monitoring using empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. J., vol. 4, no. 5, pp. 1340–1350,  2012.

F. N. Khan, Y. Zhou, A. P. T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Opt. Express, vol. 20, no. 11, pp. 12 422–12 431, 2012.

F. N. Khan, C. Lu, and A. P. T. Lau, “Optical performance monitoring in fiber-optic networks enabled by machine learning techniques,” in Proc. Opt. Fiber Commun. Conf. Expo., San Diego, CA, 2018, pp. 1–3.

Kikuchi, K.

M. S. Faruk, Y. Mori, and K. Kikuchi, “Estimation of OSNR for Nyquist-WDM transmission systems using statistical moments of equalized signals in digital coherent receivers,” in Proc. Opt. Fiber Commun. Conf. Exhibition, San Francisco, CA, 2014, pp. 1–3.

Klaus, W.

W. Klaus, J. Sakaguchi, B. J. Puttnam, Y. Awaji, and N. Wada, “Optical technologies for space division multiplexing,” in Proc. 13th Workshop Inf. Opt., Neuchatel, 2014, pp. 1–3.

Krizhevsky, A.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Advances Neural Inf. Process. Syst., 2012, pp. 1097–1105.

Kva, T. O.

T. O. Kva, “Note on the $ {R^2}$ measure of goodness of fit for nonlinear models,” Bulletin Psychonomic Soc., vol. 21, no. 1, pp. 79–80, 1983.

Lau, A. P. T.

F. N. Khan, Q. Fan, C. Lu, and A. P. T. Lau, “An optical communication's perspective on machine learning and its applications,” J. Lightw. Technol., vol. 37, no. 2, pp. 493–516, 2019.

Z. Dong, F. N. Khan, Q. Sui, K. Zhong, C. Lu, and A. P. T. Lau, “Optical performance monitoring: A review of current and future technologies,” J. Lightw. Technol., vol. 34, no. 2, pp. 525–543, 2015.

F. N. Khan, A. P. T. Lau, T. B. Anderson, J. C. Li, C. Lu, and P. K. A. Wai, “Simultaneous and independent OSNR and chromatic dispersion monitoring using empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. J., vol. 4, no. 5, pp. 1340–1350,  2012.

F. N. Khan, Y. Zhou, A. P. T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Opt. Express, vol. 20, no. 11, pp. 12 422–12 431, 2012.

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.

F. N. Khan, C. Lu, and A. P. T. Lau, “Optical performance monitoring in fiber-optic networks enabled by machine learning techniques,” in Proc. Opt. Fiber Commun. Conf. Expo., San Diego, CA, 2018, pp. 1–3.

Leclerc, M.

D. Gariépy, S. Searcy, G. He, S. Tibuleac, M. Leclerc, and P. Gosselin-Badaroudine, “Novel OSNR measurement techniques based on optical spectrum analysis and their application to coherent-detection systems,” J. Lightw. Technol., vol. 37, no. 2, pp. 562–570, 2019.

Li, C.

X. Lin, O. A. Dobre, T. M. Ngatched, and C. Li, “A non-data-aided OSNR estimation algorithm for coherent optical fiber communication systems employing multilevel constellations,” J. Lightw. Technol., vol. 37, no. 15, pp. 3815–3825, 2019.

Li, J. C.

F. N. Khan, A. P. T. Lau, T. B. Anderson, J. C. Li, C. Lu, and P. K. A. Wai, “Simultaneous and independent OSNR and chromatic dispersion monitoring using empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. J., vol. 4, no. 5, pp. 1340–1350,  2012.

Lin, X.

X. Lin, O. A. Dobre, T. M. Ngatched, and C. Li, “A non-data-aided OSNR estimation algorithm for coherent optical fiber communication systems employing multilevel constellations,” J. Lightw. Technol., vol. 37, no. 15, pp. 3815–3825, 2019.

Loecklin, E.

W. Moench and E. Loecklin, “Measurement of optical signal-to-noise-ratio in coherent systems using polarization multiplexed transmission,” in Proc. Opt. Fiber Commun. Conf. Exhib., Los Angeles, CA, 2017, pp. 1–3.

Love, J.

A. W. Snyder and J. Love, Optical Waveguide Theory, New York, NY, USA: Springer Science & Business Media, 2012.

Lu, C.

F. N. Khan, Q. Fan, C. Lu, and A. P. T. Lau, “An optical communication's perspective on machine learning and its applications,” J. Lightw. Technol., vol. 37, no. 2, pp. 493–516, 2019.

Z. Dong, F. N. Khan, Q. Sui, K. Zhong, C. Lu, and A. P. T. Lau, “Optical performance monitoring: A review of current and future technologies,” J. Lightw. Technol., vol. 34, no. 2, pp. 525–543, 2015.

F. N. Khan, A. P. T. Lau, T. B. Anderson, J. C. Li, C. Lu, and P. K. A. Wai, “Simultaneous and independent OSNR and chromatic dispersion monitoring using empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. J., vol. 4, no. 5, pp. 1340–1350,  2012.

F. N. Khan, Y. Zhou, A. P. T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Opt. Express, vol. 20, no. 11, pp. 12 422–12 431, 2012.

F. N. Khan, C. Lu, and A. P. T. Lau, “Optical performance monitoring in fiber-optic networks enabled by machine learning techniques,” in Proc. Opt. Fiber Commun. Conf. Expo., San Diego, CA, 2018, pp. 1–3.

Ma, Y.

Y. Ma, “Training sequence-based chromatic dispersion estimation with ultra-low sampling rate for optical fiber communication systems,” IEEE Photon. J., vol. 10, no. 6, pp. 1–9,  2018.

Y. Ma, M. Gao, L. Wang, Y. Sha, W. Shao, and G. Shen, “Accuracy enhancement of moments-based OSNR monitoring in QAM coherent optical communication,” IEEE Commun. Lett., vol. 24, no. 4, pp. 821–824,  2020.

Manyuk, C.

D. Marcuse, C. Manyuk, and P. Wai, “Application of the Manakov-PMD equation to studies of signal propagation in optical fibers with randomly varying birefringence,” J. Lightw. Technol., vol. 15, no. 9, pp. 1735–1746, 1997.

Marcuse, D.

D. Marcuse, C. Manyuk, and P. Wai, “Application of the Manakov-PMD equation to studies of signal propagation in optical fibers with randomly varying birefringence,” J. Lightw. Technol., vol. 15, no. 9, pp. 1735–1746, 1997.

Mata, J.

J. Mata, “Artificial intelligence (AI) methods in optical networks: A comprehensive survey,” Opt. Switching Netw., vol. 28, pp. 43–57, 2018.

Matsushita, A.

K. Horikoshi, A. Matsushita, S. Okamoto, and M. Nakamura, “Fast blind chromatic-dispersion esitimation for small-rolloff Nyquist pulse-shaped signal using spectral cyclostationarity,” in Proc. 45th Eur. Conf. Opt. Commun., Dublin, Ireland, 2019, pp. 1–3.

Mecozzi, A.

A. Mecozzi, C. Antonelli, and M. Shtaif, “Coupled Manakov equations in multimode fibers with strongly coupled groups of modes,” Opt. Express, vol. 20, no. 21, pp. 23 436–23 441, 2012.

Meng, K.

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.

Menif, M.

L. Guesmi, A. M. Ragheb, H. Fathallah, and M. Menif, “Experimental demonstration of simultaneous modulation format/symbol rate identification and optical performance monitoring for coherent optical systems,” J. Lightw. Technol., vol. 36, no. 11, pp. 2230–2239, 2017.

Moench, W.

W. Moench and E. Loecklin, “Measurement of optical signal-to-noise-ratio in coherent systems using polarization multiplexed transmission,” in Proc. Opt. Fiber Commun. Conf. Exhib., Los Angeles, CA, 2017, pp. 1–3.

Molin, D.

P. Sillard, M. Bigot-Astruc, and D. Molin, “Few-mode fibers for mode-division-multiplexed systems,” J. Lightw. Technol., vol. 32, no. 16, pp. 2824–2829, 2014.

Mori, Y.

M. S. Faruk, Y. Mori, and K. Kikuchi, “Estimation of OSNR for Nyquist-WDM transmission systems using statistical moments of equalized signals in digital coherent receivers,” in Proc. Opt. Fiber Commun. Conf. Exhibition, San Francisco, CA, 2014, pp. 1–3.

Morita, I.

D. Soma, T. Tsuritani, and I. Morita, “10 Pbit/s SDM/WDM transmission,” in Proc. IEEE Photon. Conf., Reston, 2018, pp. 1–2.

Nakamura, M.

K. Horikoshi, A. Matsushita, S. Okamoto, and M. Nakamura, “Fast blind chromatic-dispersion esitimation for small-rolloff Nyquist pulse-shaped signal using spectral cyclostationarity,” in Proc. 45th Eur. Conf. Opt. Commun., Dublin, Ireland, 2019, pp. 1–3.

Ngatched, T. M.

X. Lin, O. A. Dobre, T. M. Ngatched, and C. Li, “A non-data-aided OSNR estimation algorithm for coherent optical fiber communication systems employing multilevel constellations,” J. Lightw. Technol., vol. 37, no. 15, pp. 3815–3825, 2019.

Okamoto, S.

K. Horikoshi, A. Matsushita, S. Okamoto, and M. Nakamura, “Fast blind chromatic-dispersion esitimation for small-rolloff Nyquist pulse-shaped signal using spectral cyclostationarity,” in Proc. 45th Eur. Conf. Opt. Commun., Dublin, Ireland, 2019, pp. 1–3.

Pal, S.

D. Basak, S. Pal, and D. C. Patranabis, “Support vector regression,” Neural Inf. Process.-Lett. Rev., vol. 11, no. 10, pp. 203–224, 2007.

Pan, Z.

Y. Weng, X. He, and Z. Pan, “Space division multiplexing optical communication using few-mode fibers,” Opt. Fiber Technol., vol. 36, pp. 155–180, 2017.

A. E. Willner, Z. Pan, and C. Yu, “Optical performance monitoring,” in Proc. Opt. Fiber Telecommun. VB. Elsevier, 2008, pp. 233–292.

Paschotta, R.

R. Paschotta, Field Guide to Optical Fiber Technology, Bellingham, WA, USA: SPIE, 2010.

Patranabis, D. C.

D. Basak, S. Pal, and D. C. Patranabis, “Support vector regression,” Neural Inf. Process.-Lett. Rev., vol. 11, no. 10, pp. 203–224, 2007.

Piels, M.

J. Thrane, J. Wass, M. Piels, J. C. Diniz, R. Jones, and D. Zibar, “Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals,” J. Lightw. Technol., vol. 35, no. 4, pp. 868–875, 2016.

Puttnam, B. J.

W. Klaus, J. Sakaguchi, B. J. Puttnam, Y. Awaji, and N. Wada, “Optical technologies for space division multiplexing,” in Proc. 13th Workshop Inf. Opt., Neuchatel, 2014, pp. 1–3.

Qiao, Y.

D. Tang, X. Wang, L. Zhuang, P. Guo, A. Yang, and Y. Qiao, “Delay-tap-sampling-based chromatic dispersion estimation method with ultra-Low sampling rate for optical fiber communication systems,” IEEE Access, vol. 8, pp. 101 004–101 013, 2020.

F. Wu, P. Guo, A. Yang, and Y. Qiao, “Chromatic dispersion estimation based on CAZAC sequence for optical fiber communication systems,” IEEE Access, vol. 7, pp. 139 388–139 393, 2019.

F. Wu, A. Yang, P. Guo, Y. Qiao, L. Zhuang, and S. Guo, “QPSK training sequence-based both OSNR and chromatic dispersion monitoring in DWDM systems,” IEEE Photon. J., vol. 10, no. 4, pp. 1–10,  2018.

Qiu, K.

L. Xia, J. Zhang, S. Hu, M. Zhu, Y. Song, and K. Qiu, “Transfer learning assisted deep neural network for osnr estimation,” Opt. Express, vol. 27, no. 14, pp. 19 398–19 406, 2019.

Rademacher, G.

G. Rademacher, “159 Tbit/s C+ L band transmission over 1045 km 3-mode graded-index few-mode fiber,” in Proc. Opt. Fiber Commun. Conf., San Diego, CA, 2018, Paper Th4C–4.

G. Rademacher, “93.34 Tbit/s/mode (280 Tbit/s) transmission in a 3-mode graded-index few-mode fiber,” in Proc. Opt. Fiber Commun. Conf., San Diego, CA, 2018, Paper. W4C–3.

Ragheb, A.

W. Saif, M. A. Esmail, A. Ragheb, T. Alshawi, and S. Alshebeili, “Machine learning techniques for optical performance monitoring and modulation format identification: A Survey,” IEEE Commun. Surveys Tuts., pp. 1–1, 2020.

W. S. Saif, T. Alshawi, M. A. Esmail, A. Ragheb, and S. Alshebeili, “Separability of histogram based features for optical performance monitoring: An investigation using t-SNE technique,” IEEE Photon. J., vol. 11, no. 3, pp. 1–12,  2019.

Ragheb, A. M.

W. S. Saif, A. M. Ragheb, H. E. Seleem, T. A. Alshawi, and S. A. Alshebeili, “Modulation format identification in mode division multiplexed optical networks,” IEEE Access, vol. 7, pp. 156 207–156 216, 2019.

L. Guesmi, A. M. Ragheb, H. Fathallah, and M. Menif, “Experimental demonstration of simultaneous modulation format/symbol rate identification and optical performance monitoring for coherent optical systems,” J. Lightw. Technol., vol. 36, no. 11, pp. 2230–2239, 2017.

Saif, W.

W. Saif, M. A. Esmail, A. Ragheb, T. Alshawi, and S. Alshebeili, “Machine learning techniques for optical performance monitoring and modulation format identification: A Survey,” IEEE Commun. Surveys Tuts., pp. 1–1, 2020.

Saif, W. S.

W. S. Saif, A. M. Ragheb, H. E. Seleem, T. A. Alshawi, and S. A. Alshebeili, “Modulation format identification in mode division multiplexed optical networks,” IEEE Access, vol. 7, pp. 156 207–156 216, 2019.

W. S. Saif, T. Alshawi, M. A. Esmail, A. Ragheb, and S. Alshebeili, “Separability of histogram based features for optical performance monitoring: An investigation using t-SNE technique,” IEEE Photon. J., vol. 11, no. 3, pp. 1–12,  2019.

Sakaguchi, J.

W. Klaus, J. Sakaguchi, B. J. Puttnam, Y. Awaji, and N. Wada, “Optical technologies for space division multiplexing,” in Proc. 13th Workshop Inf. Opt., Neuchatel, 2014, pp. 1–3.

Saridis, G. M.

G. M. Saridis, D. Alexandropoulos, G. Zervas, and D. Simeonidou, “Survey and evaluation of space division multiplexing: From technologies to optical networks,” IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2136–2156,  2015.

Savory, S. J.

Searcy, S.

D. Gariépy, S. Searcy, G. He, S. Tibuleac, M. Leclerc, and P. Gosselin-Badaroudine, “Novel OSNR measurement techniques based on optical spectrum analysis and their application to coherent-detection systems,” J. Lightw. Technol., vol. 37, no. 2, pp. 562–570, 2019.

Seleem, H. E.

W. S. Saif, A. M. Ragheb, H. E. Seleem, T. A. Alshawi, and S. A. Alshebeili, “Modulation format identification in mode division multiplexed optical networks,” IEEE Access, vol. 7, pp. 156 207–156 216, 2019.

Sha, Y.

Y. Ma, M. Gao, L. Wang, Y. Sha, W. Shao, and G. Shen, “Accuracy enhancement of moments-based OSNR monitoring in QAM coherent optical communication,” IEEE Commun. Lett., vol. 24, no. 4, pp. 821–824,  2020.

Shao, W.

Y. Ma, M. Gao, L. Wang, Y. Sha, W. Shao, and G. Shen, “Accuracy enhancement of moments-based OSNR monitoring in QAM coherent optical communication,” IEEE Commun. Lett., vol. 24, no. 4, pp. 821–824,  2020.

Shen, G.

Y. Ma, M. Gao, L. Wang, Y. Sha, W. Shao, and G. Shen, “Accuracy enhancement of moments-based OSNR monitoring in QAM coherent optical communication,” IEEE Commun. Lett., vol. 24, no. 4, pp. 821–824,  2020.

Shen, T. S. R.

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.

Shtaif, M.

A. Mecozzi, C. Antonelli, and M. Shtaif, “Coupled Manakov equations in multimode fibers with strongly coupled groups of modes,” Opt. Express, vol. 20, no. 21, pp. 23 436–23 441, 2012.

Shu, L.

Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express, vol. 27, no. 8, pp. 11 281–11 291, 2019.

Sillard, P.

P. Sillard, M. Bigot-Astruc, and D. Molin, “Few-mode fibers for mode-division-multiplexed systems,” J. Lightw. Technol., vol. 32, no. 16, pp. 2824–2829, 2014.

Simeonidou, D.

G. M. Saridis, D. Alexandropoulos, G. Zervas, and D. Simeonidou, “Survey and evaluation of space division multiplexing: From technologies to optical networks,” IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2136–2156,  2015.

Skafidas, E.

C. Do, A. V. Tran, C. Zhu, D. Hewitt, and E. Skafidas, “Data-aided OSNR estimation for QPSK and 16-QAM coherent optical system,” IEEE Photon. J., vol. 5, no. 5, pp. 6 601 609–6 601 609, 2013.

Snyder, A. W.

A. W. Snyder and J. Love, Optical Waveguide Theory, New York, NY, USA: Springer Science & Business Media, 2012.

Soma, D.

D. Soma, “2.05 Peta-bit/s super-Nyquist-WDM SDM transmission using 9.8-km 6-mode 19-core fiber in full C band,” in Proc. Eur. Conf. Opt. Commun., Valencia, 2015, pp. 1–3.

D. Soma, T. Tsuritani, and I. Morita, “10 Pbit/s SDM/WDM transmission,” in Proc. IEEE Photon. Conf., Reston, 2018, pp. 1–2.

Song, Y.

L. Xia, J. Zhang, S. Hu, M. Zhu, Y. Song, and K. Qiu, “Transfer learning assisted deep neural network for osnr estimation,” Opt. Express, vol. 27, no. 14, pp. 19 398–19 406, 2019.

Sui, Q.

Z. Dong, F. N. Khan, Q. Sui, K. Zhong, C. Lu, and A. P. T. Lau, “Optical performance monitoring: A review of current and future technologies,” J. Lightw. Technol., vol. 34, no. 2, pp. 525–543, 2015.

Sutskever, I.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Advances Neural Inf. Process. Syst., 2012, pp. 1097–1105.

Tang, D.

D. Tang, X. Wang, L. Zhuang, P. Guo, A. Yang, and Y. Qiao, “Delay-tap-sampling-based chromatic dispersion estimation method with ultra-Low sampling rate for optical fiber communication systems,” IEEE Access, vol. 8, pp. 101 004–101 013, 2020.

Tanimura, T.

T. Tanimura, “Deep learning based OSNR monitoring independent of modulation format, symbol rate, and chromatic dispersion,” in Proc. 42nd Eur. Conf. Opt. Commun., Dusseldorf, Germany, 2016, pp. 1–3.

Thrane, J.

J. Thrane, J. Wass, M. Piels, J. C. Diniz, R. Jones, and D. Zibar, “Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals,” J. Lightw. Technol., vol. 35, no. 4, pp. 868–875, 2016.

Tibuleac, S.

D. Gariépy, S. Searcy, G. He, S. Tibuleac, M. Leclerc, and P. Gosselin-Badaroudine, “Novel OSNR measurement techniques based on optical spectrum analysis and their application to coherent-detection systems,” J. Lightw. Technol., vol. 37, no. 2, pp. 562–570, 2019.

Tran, A. V.

C. C. Do, C. Zhu, and A. V. Tran, “Data-aided OSNR estimation using low-bandwidth coherent receivers,” IEEE Photon. Technol. Lett., vol. 26, no. 13, pp. 1291–1294,  2014.

C. Do, A. V. Tran, C. Zhu, D. Hewitt, and E. Skafidas, “Data-aided OSNR estimation for QPSK and 16-QAM coherent optical system,” IEEE Photon. J., vol. 5, no. 5, pp. 6 601 609–6 601 609, 2013.

Tsuritani, T.

D. Soma, T. Tsuritani, and I. Morita, “10 Pbit/s SDM/WDM transmission,” in Proc. IEEE Photon. Conf., Reston, 2018, pp. 1–2.

Vapnik, V.

V. Vapnik, The Nature Statistical Learning Theory, New York, NY, USA: Springer Science & Business Media, 2013.

Vuong, J.

Wada, N.

W. Klaus, J. Sakaguchi, B. J. Puttnam, Y. Awaji, and N. Wada, “Optical technologies for space division multiplexing,” in Proc. 13th Workshop Inf. Opt., Neuchatel, 2014, pp. 1–3.

Wai, P.

D. Marcuse, C. Manyuk, and P. Wai, “Application of the Manakov-PMD equation to studies of signal propagation in optical fibers with randomly varying birefringence,” J. Lightw. Technol., vol. 15, no. 9, pp. 1735–1746, 1997.

Wai, P. K. A.

F. N. Khan, A. P. T. Lau, T. B. Anderson, J. C. Li, C. Lu, and P. K. A. Wai, “Simultaneous and independent OSNR and chromatic dispersion monitoring using empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. J., vol. 4, no. 5, pp. 1340–1350,  2012.

Wan, Z.

Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express, vol. 27, no. 8, pp. 11 281–11 291, 2019.

Wang, C.

Wang, D.

D. Wang, “Cost-effective and data size–adaptive OPM at intermediated node using convolutional neural network-based image processor,” Opt. Express, vol. 27, no. 7, pp. 9403–9419, 2019.

D. Wang, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express, vol. 25, no. 15, pp. 17 150–17 166, 2017.

Wang, L.

Y. Ma, M. Gao, L. Wang, Y. Sha, W. Shao, and G. Shen, “Accuracy enhancement of moments-based OSNR monitoring in QAM coherent optical communication,” IEEE Commun. Lett., vol. 24, no. 4, pp. 821–824,  2020.

Wang, X.

D. Tang, X. Wang, L. Zhuang, P. Guo, A. Yang, and Y. Qiao, “Delay-tap-sampling-based chromatic dispersion estimation method with ultra-Low sampling rate for optical fiber communication systems,” IEEE Access, vol. 8, pp. 101 004–101 013, 2020.

Wass, J.

J. Thrane, J. Wass, M. Piels, J. C. Diniz, R. Jones, and D. Zibar, “Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals,” J. Lightw. Technol., vol. 35, no. 4, pp. 868–875, 2016.

Weng, Y.

Y. Weng, X. He, and Z. Pan, “Space division multiplexing optical communication using few-mode fibers,” Opt. Fiber Technol., vol. 36, pp. 155–180, 2017.

Willner, A. E.

A. E. Willner, Z. Pan, and C. Yu, “Optical performance monitoring,” in Proc. Opt. Fiber Telecommun. VB. Elsevier, 2008, pp. 233–292.

Wu, F.

F. Wu, P. Guo, A. Yang, and Y. Qiao, “Chromatic dispersion estimation based on CAZAC sequence for optical fiber communication systems,” IEEE Access, vol. 7, pp. 139 388–139 393, 2019.

F. Wu, A. Yang, P. Guo, Y. Qiao, L. Zhuang, and S. Guo, “QPSK training sequence-based both OSNR and chromatic dispersion monitoring in DWDM systems,” IEEE Photon. J., vol. 10, no. 4, pp. 1–10,  2018.

Xia, L.

L. Xia, J. Zhang, S. Hu, M. Zhu, Y. Song, and K. Qiu, “Transfer learning assisted deep neural network for osnr estimation,” Opt. Express, vol. 27, no. 14, pp. 19 398–19 406, 2019.

Xiang, Q.

Q. Xiang, Y. Yang, Q. Zhang, and Y. Yao, “Joint and accurate OSNR estimation and modulation format identification scheme using the feature-based ANN,” IEEE Photon. J., vol. 11, no. 4, pp. 1–11,  2019.

Xu, K.

Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express, vol. 27, no. 8, pp. 11 281–11 291, 2019.

Yang, A.

D. Tang, X. Wang, L. Zhuang, P. Guo, A. Yang, and Y. Qiao, “Delay-tap-sampling-based chromatic dispersion estimation method with ultra-Low sampling rate for optical fiber communication systems,” IEEE Access, vol. 8, pp. 101 004–101 013, 2020.

F. Wu, P. Guo, A. Yang, and Y. Qiao, “Chromatic dispersion estimation based on CAZAC sequence for optical fiber communication systems,” IEEE Access, vol. 7, pp. 139 388–139 393, 2019.

F. Wu, A. Yang, P. Guo, Y. Qiao, L. Zhuang, and S. Guo, “QPSK training sequence-based both OSNR and chromatic dispersion monitoring in DWDM systems,” IEEE Photon. J., vol. 10, no. 4, pp. 1–10,  2018.

Yang, Y.

Q. Xiang, Y. Yang, Q. Zhang, and Y. Yao, “Joint and accurate OSNR estimation and modulation format identification scheme using the feature-based ANN,” IEEE Photon. J., vol. 11, no. 4, pp. 1–11,  2019.

Yao, Y.

Q. Xiang, Y. Yang, Q. Zhang, and Y. Yao, “Joint and accurate OSNR estimation and modulation format identification scheme using the feature-based ANN,” IEEE Photon. J., vol. 11, no. 4, pp. 1–11,  2019.

Yu, C.

A. E. Willner, Z. Pan, and C. Yu, “Optical performance monitoring,” in Proc. Opt. Fiber Telecommun. VB. Elsevier, 2008, pp. 233–292.

Yu, W.

W. G. Hatcher and W. Yu, “A survey of deep learning: Platforms, applications and emerging research trends,” IEEE Access, vol. 6, pp. 24 411–24 432, 2018.

Yu, Z.

Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express, vol. 27, no. 8, pp. 11 281–11 291, 2019.

Zervas, G.

G. M. Saridis, D. Alexandropoulos, G. Zervas, and D. Simeonidou, “Survey and evaluation of space division multiplexing: From technologies to optical networks,” IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2136–2156,  2015.

Zhang, H.

Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express, vol. 27, no. 8, pp. 11 281–11 291, 2019.

Zhang, J.

L. Xia, J. Zhang, S. Hu, M. Zhu, Y. Song, and K. Qiu, “Transfer learning assisted deep neural network for osnr estimation,” Opt. Express, vol. 27, no. 14, pp. 19 398–19 406, 2019.

Zhang, Q.

Q. Xiang, Y. Yang, Q. Zhang, and Y. Yao, “Joint and accurate OSNR estimation and modulation format identification scheme using the feature-based ANN,” IEEE Photon. J., vol. 11, no. 4, pp. 1–11,  2019.

Zhang, Y.

Y. Zhang, “Eye diagram measurement-based joint modulation format, OSNR, ROF, and skew monitoring of coherent channel using deep learning,” J. Lightw. Technol., vol. 37, no. 23, pp. 5907–5913, 2019.

Zhao, Y.

Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express, vol. 27, no. 8, pp. 11 281–11 291, 2019.

Y. Zhao, “Low-complexity and nonlinearity-tolerant modulation format identification using random forest,” IEEE Photon. Technol. Lett., vol. 31, no. 11, pp. 853–856,  2019.

Zhong, K.

Z. Dong, F. N. Khan, Q. Sui, K. Zhong, C. Lu, and A. P. T. Lau, “Optical performance monitoring: A review of current and future technologies,” J. Lightw. Technol., vol. 34, no. 2, pp. 525–543, 2015.

Zhou, Y.

F. N. Khan, Y. Zhou, A. P. T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Opt. Express, vol. 20, no. 11, pp. 12 422–12 431, 2012.

Zhu, C.

C. C. Do, C. Zhu, and A. V. Tran, “Data-aided OSNR estimation using low-bandwidth coherent receivers,” IEEE Photon. Technol. Lett., vol. 26, no. 13, pp. 1291–1294,  2014.

C. Do, A. V. Tran, C. Zhu, D. Hewitt, and E. Skafidas, “Data-aided OSNR estimation for QPSK and 16-QAM coherent optical system,” IEEE Photon. J., vol. 5, no. 5, pp. 6 601 609–6 601 609, 2013.

Zhu, M.

L. Xia, J. Zhang, S. Hu, M. Zhu, Y. Song, and K. Qiu, “Transfer learning assisted deep neural network for osnr estimation,” Opt. Express, vol. 27, no. 14, pp. 19 398–19 406, 2019.

Zhuang, L.

D. Tang, X. Wang, L. Zhuang, P. Guo, A. Yang, and Y. Qiao, “Delay-tap-sampling-based chromatic dispersion estimation method with ultra-Low sampling rate for optical fiber communication systems,” IEEE Access, vol. 8, pp. 101 004–101 013, 2020.

F. Wu, A. Yang, P. Guo, Y. Qiao, L. Zhuang, and S. Guo, “QPSK training sequence-based both OSNR and chromatic dispersion monitoring in DWDM systems,” IEEE Photon. J., vol. 10, no. 4, pp. 1–10,  2018.

Zibar, D.

J. Thrane, J. Wass, M. Piels, J. C. Diniz, R. Jones, and D. Zibar, “Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals,” J. Lightw. Technol., vol. 35, no. 4, pp. 868–875, 2016.

Bulletin Psychonomic Soc. (1)

T. O. Kva, “Note on the $ {R^2}$ measure of goodness of fit for nonlinear models,” Bulletin Psychonomic Soc., vol. 21, no. 1, pp. 79–80, 1983.

Field Guide to Optical Fiber Technology (1)

R. Paschotta, Field Guide to Optical Fiber Technology, Bellingham, WA, USA: SPIE, 2010.

IEEE Access (3)

F. Wu, P. Guo, A. Yang, and Y. Qiao, “Chromatic dispersion estimation based on CAZAC sequence for optical fiber communication systems,” IEEE Access, vol. 7, pp. 139 388–139 393, 2019.

D. Tang, X. Wang, L. Zhuang, P. Guo, A. Yang, and Y. Qiao, “Delay-tap-sampling-based chromatic dispersion estimation method with ultra-Low sampling rate for optical fiber communication systems,” IEEE Access, vol. 8, pp. 101 004–101 013, 2020.

W. S. Saif, A. M. Ragheb, H. E. Seleem, T. A. Alshawi, and S. A. Alshebeili, “Modulation format identification in mode division multiplexed optical networks,” IEEE Access, vol. 7, pp. 156 207–156 216, 2019.

IEEE Commun. Surveys Tuts. (1)

W. Saif, M. A. Esmail, A. Ragheb, T. Alshawi, and S. Alshebeili, “Machine learning techniques for optical performance monitoring and modulation format identification: A Survey,” IEEE Commun. Surveys Tuts., pp. 1–1, 2020.

IEEE Photon. J. (5)

W. S. Saif, T. Alshawi, M. A. Esmail, A. Ragheb, and S. Alshebeili, “Separability of histogram based features for optical performance monitoring: An investigation using t-SNE technique,” IEEE Photon. J., vol. 11, no. 3, pp. 1–12,  2019.

Y. Ma, “Training sequence-based chromatic dispersion estimation with ultra-low sampling rate for optical fiber communication systems,” IEEE Photon. J., vol. 10, no. 6, pp. 1–9,  2018.

F. N. Khan, A. P. T. Lau, T. B. Anderson, J. C. Li, C. Lu, and P. K. A. Wai, “Simultaneous and independent OSNR and chromatic dispersion monitoring using empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. J., vol. 4, no. 5, pp. 1340–1350,  2012.

F. Wu, A. Yang, P. Guo, Y. Qiao, L. Zhuang, and S. Guo, “QPSK training sequence-based both OSNR and chromatic dispersion monitoring in DWDM systems,” IEEE Photon. J., vol. 10, no. 4, pp. 1–10,  2018.

C. Do, A. V. Tran, C. Zhu, D. Hewitt, and E. Skafidas, “Data-aided OSNR estimation for QPSK and 16-QAM coherent optical system,” IEEE Photon. J., vol. 5, no. 5, pp. 6 601 609–6 601 609, 2013.

IEEE Photon. Technol. Lett. (4)

C. C. Do, C. Zhu, and A. V. Tran, “Data-aided OSNR estimation using low-bandwidth coherent receivers,” IEEE Photon. Technol. Lett., vol. 26, no. 13, pp. 1291–1294,  2014.

C. Courvoisier, J. Fatome, and C. Finot, “Measurement of residual chromatic dispersion or OSNR via nonlinear spectral evolution,” IEEE Photon. Technol. Lett., vol. 23, no. 9, pp. 537–539,  2011.

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.

Y. Zhao, “Low-complexity and nonlinearity-tolerant modulation format identification using random forest,” IEEE Photon. Technol. Lett., vol. 31, no. 11, pp. 853–856,  2019.

J. Lightw. Technol. (8)

D. Marcuse, C. Manyuk, and P. Wai, “Application of the Manakov-PMD equation to studies of signal propagation in optical fibers with randomly varying birefringence,” J. Lightw. Technol., vol. 15, no. 9, pp. 1735–1746, 1997.

J. Thrane, J. Wass, M. Piels, J. C. Diniz, R. Jones, and D. Zibar, “Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals,” J. Lightw. Technol., vol. 35, no. 4, pp. 868–875, 2016.

R. A. Eltaieb, “Efficient classification of optical modulation formats based on singular value decomposition and Radon transformation,” J. Lightw. Technol., vol. 38, no. 3, pp. 619–631, 2020.

F. N. Khan, Q. Fan, C. Lu, and A. P. T. Lau, “An optical communication's perspective on machine learning and its applications,” J. Lightw. Technol., vol. 37, no. 2, pp. 493–516, 2019.

Y. Zhang, “Eye diagram measurement-based joint modulation format, OSNR, ROF, and skew monitoring of coherent channel using deep learning,” J. Lightw. Technol., vol. 37, no. 23, pp. 5907–5913, 2019.

Z. Dong, F. N. Khan, Q. Sui, K. Zhong, C. Lu, and A. P. T. Lau, “Optical performance monitoring: A review of current and future technologies,” J. Lightw. Technol., vol. 34, no. 2, pp. 525–543, 2015.

P. Sillard, M. Bigot-Astruc, and D. Molin, “Few-mode fibers for mode-division-multiplexed systems,” J. Lightw. Technol., vol. 32, no. 16, pp. 2824–2829, 2014.

X. Lin, O. A. Dobre, T. M. Ngatched, and C. Li, “A non-data-aided OSNR estimation algorithm for coherent optical fiber communication systems employing multilevel constellations,” J. Lightw. Technol., vol. 37, no. 15, pp. 3815–3825, 2019.

Mach. Learn. (1)

L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.

Neural Inf. Process.-Lett. Rev. (1)

D. Basak, S. Pal, and D. C. Patranabis, “Support vector regression,” Neural Inf. Process.-Lett. Rev., vol. 11, no. 10, pp. 203–224, 2007.

Opt. Express (9)

D. Wang, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express, vol. 25, no. 15, pp. 17 150–17 166, 2017.

A. Mecozzi, C. Antonelli, and M. Shtaif, “Coupled Manakov equations in multimode fibers with strongly coupled groups of modes,” Opt. Express, vol. 20, no. 21, pp. 23 436–23 441, 2012.

J. Vuong, “Mode coupling at connectors in mode-division multiplexed transmission over few-mode fiber,” Opt. Express, vol. 23, no. 2, pp. 1438–1455, 2015.

C. Wang, “Joint OSNR and CD monitoring in digital coherent receiver using long short-term memory neural network,” Opt. Express, vol. 27, no. 5, pp. 6936–6945, 2019.

S. J. Savory, “Digital filters for coherent optical receivers,” Opt. Express, vol. 16, no. 2, pp. 804–817, 2008.

Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express, vol. 27, no. 8, pp. 11 281–11 291, 2019.

D. Wang, “Cost-effective and data size–adaptive OPM at intermediated node using convolutional neural network-based image processor,” Opt. Express, vol. 27, no. 7, pp. 9403–9419, 2019.

L. Xia, J. Zhang, S. Hu, M. Zhu, Y. Song, and K. Qiu, “Transfer learning assisted deep neural network for osnr estimation,” Opt. Express, vol. 27, no. 14, pp. 19 398–19 406, 2019.

F. N. Khan, Y. Zhou, A. P. T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Opt. Express, vol. 20, no. 11, pp. 12 422–12 431, 2012.

Opt. Fiber Technol. (1)

Y. Weng, X. He, and Z. Pan, “Space division multiplexing optical communication using few-mode fibers,” Opt. Fiber Technol., vol. 36, pp. 155–180, 2017.

Opt. Switching Netw. (1)

J. Mata, “Artificial intelligence (AI) methods in optical networks: A comprehensive survey,” Opt. Switching Netw., vol. 28, pp. 43–57, 2018.

Proc. Advances Neural Inf. Process. Syst. (1)

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Advances Neural Inf. Process. Syst., 2012, pp. 1097–1105.

Other (23)

T. Tanimura, “Deep learning based OSNR monitoring independent of modulation format, symbol rate, and chromatic dispersion,” in Proc. 42nd Eur. Conf. Opt. Commun., Dusseldorf, Germany, 2016, pp. 1–3.

L. Guesmi, A. M. Ragheb, H. Fathallah, and M. Menif, “Experimental demonstration of simultaneous modulation format/symbol rate identification and optical performance monitoring for coherent optical systems,” J. Lightw. Technol., vol. 36, no. 11, pp. 2230–2239, 2017.

J. M. Chambers, Graphical Methods Data Analysis, New York, NY, USA: CRC Press, 2018.

F. N. Khan, C. Lu, and A. P. T. Lau, “Optical performance monitoring in fiber-optic networks enabled by machine learning techniques,” in Proc. Opt. Fiber Commun. Conf. Expo., San Diego, CA, 2018, pp. 1–3.

Q. Xiang, Y. Yang, Q. Zhang, and Y. Yao, “Joint and accurate OSNR estimation and modulation format identification scheme using the feature-based ANN,” IEEE Photon. J., vol. 11, no. 4, pp. 1–11,  2019.

T. A. Almohamad, “Automatic modulation recognition in wireless communication systems using feature-based approach,” in Proc. 10th Int. Conf. Robot., Vis., Signal Process. Power Appl., Penang, Malaysia, 2018, pp. 403–409.

V. Vapnik, The Nature Statistical Learning Theory, New York, NY, USA: Springer Science & Business Media, 2013.

G. Rademacher, “159 Tbit/s C+ L band transmission over 1045 km 3-mode graded-index few-mode fiber,” in Proc. Opt. Fiber Commun. Conf., San Diego, CA, 2018, Paper Th4C–4.

G. Rademacher, “93.34 Tbit/s/mode (280 Tbit/s) transmission in a 3-mode graded-index few-mode fiber,” in Proc. Opt. Fiber Commun. Conf., San Diego, CA, 2018, Paper. W4C–3.

D. Soma, “2.05 Peta-bit/s super-Nyquist-WDM SDM transmission using 9.8-km 6-mode 19-core fiber in full C band,” in Proc. Eur. Conf. Opt. Commun., Valencia, 2015, pp. 1–3.

D. Soma, T. Tsuritani, and I. Morita, “10 Pbit/s SDM/WDM transmission,” in Proc. IEEE Photon. Conf., Reston, 2018, pp. 1–2.

T. Hu, “Demonstration of bidirectional PON based on mode division multiplexing,” IEEE Photon. Technol. Lett., vol. 28, no. 11, pp. 1201–1204,  2016.

A. W. Snyder and J. Love, Optical Waveguide Theory, New York, NY, USA: Springer Science & Business Media, 2012.

S. J. Garth, “Few-mode optical waveguides and their study by the four-photon mixing process,” Ph.D. dissertation, Dept. Appl. Math., The Australian National University, 1987.

K. Horikoshi, A. Matsushita, S. Okamoto, and M. Nakamura, “Fast blind chromatic-dispersion esitimation for small-rolloff Nyquist pulse-shaped signal using spectral cyclostationarity,” in Proc. 45th Eur. Conf. Opt. Commun., Dublin, Ireland, 2019, pp. 1–3.

W. G. Hatcher and W. Yu, “A survey of deep learning: Platforms, applications and emerging research trends,” IEEE Access, vol. 6, pp. 24 411–24 432, 2018.

A. E. Willner, Z. Pan, and C. Yu, “Optical performance monitoring,” in Proc. Opt. Fiber Telecommun. VB. Elsevier, 2008, pp. 233–292.

W. Klaus, J. Sakaguchi, B. J. Puttnam, Y. Awaji, and N. Wada, “Optical technologies for space division multiplexing,” in Proc. 13th Workshop Inf. Opt., Neuchatel, 2014, pp. 1–3.

G. M. Saridis, D. Alexandropoulos, G. Zervas, and D. Simeonidou, “Survey and evaluation of space division multiplexing: From technologies to optical networks,” IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2136–2156,  2015.

W. Moench and E. Loecklin, “Measurement of optical signal-to-noise-ratio in coherent systems using polarization multiplexed transmission,” in Proc. Opt. Fiber Commun. Conf. Exhib., Los Angeles, CA, 2017, pp. 1–3.

D. Gariépy, S. Searcy, G. He, S. Tibuleac, M. Leclerc, and P. Gosselin-Badaroudine, “Novel OSNR measurement techniques based on optical spectrum analysis and their application to coherent-detection systems,” J. Lightw. Technol., vol. 37, no. 2, pp. 562–570, 2019.

M. S. Faruk, Y. Mori, and K. Kikuchi, “Estimation of OSNR for Nyquist-WDM transmission systems using statistical moments of equalized signals in digital coherent receivers,” in Proc. Opt. Fiber Commun. Conf. Exhibition, San Francisco, CA, 2014, pp. 1–3.

Y. Ma, M. Gao, L. Wang, Y. Sha, W. Shao, and G. Shen, “Accuracy enhancement of moments-based OSNR monitoring in QAM coherent optical communication,” IEEE Commun. Lett., vol. 24, no. 4, pp. 821–824,  2020.

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.