Abstract

We propose a simple and cost-effective technique for modulation format identification (MFI) in next-generation heterogeneous fiber-optic networks using an artificial neural network (ANN) trained with the features extracted from the asynchronous amplitude histograms (AAHs). Results of numerical simulations conducted for six different widely-used modulation formats at various data rates demonstrate that the proposed technique can effectively classify all these modulation formats with an overall estimation accuracy of 99.6% and also in the presence of various link impairments. The proposed technique employs extremely simple hardware and digital signal processing (DSP) to enable MFI and can also be applied for the identification of other modulation formats at different data rates without necessitating hardware changes.

© 2012 OSA

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References

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  1. I. T. Monroy, D. Zibar, N. G. Gonzalez, and R. Borkowski, “Cognitive heterogeneous reconfigurable optical networks (CHRON): enabling technologies and techniques,” in 2011 13th International Conference on Transparent Optical Networks (ICTON)(2011), paper Th.A1.2.
  2. A. Nag, M. Tornatore, and B. Mukherjee, “Optical network design with mixed line rates and multiple modulation formats,” J. Lightwave Technol. 28(4), 466–475 (2010).
    [CrossRef]
  3. D. C. Kilper, R. Bach, D. J. Blumenthal, D. Einstein, T. Landolsi, L. Ostar, M. Preiss, and A. E. Willner, “Optical performance monitoring,” J. Lightwave Technol. 22(1), 294–304 (2004).
    [CrossRef]
  4. Z. Pan, C. Yu, and A. E. Willner, “Optical performance monitoring for the next generation optical communication networks,” Opt. Fiber Technol. 16(1), 20–45 (2010).
    [CrossRef]
  5. C. K. Chan, Optical Performance Monitoring (Academic, 2010).
  6. N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of amplitude histograms to monitor performance of optical channels,” Electron. Lett. 35(5), 403–404 (1999).
    [CrossRef]
  7. B. Kozicki, O. Takuya, and T. Hidehiko, “Optical performance monitoring of phase-modulated signals using asynchronous amplitude histogram analysis,” J. Lightwave Technol. 26(10), 1353–1361 (2008).
    [CrossRef]
  8. S. D. Dods and T. B. Anderson, “Optical performance monitoring technique using delay tap asynchronous waveform sampling,” in Optical Fiber Communication Conference and Exposition and The National Fiber Optic Engineers Conference, Technical Digest (CD) (Optical Society of America, 2006), paper OThP5.
  9. T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightwave Technol. 27(16), 3729–3736 (2009).
    [CrossRef]
  10. X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightwave Technol. 27(16), 3580–3589 (2009).
    [CrossRef]
  11. F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett. 24(12), 982–984 (2012).
  12. F. N. Khan, A. P. T. Lau, C. Lu, and P. K. A. Wai, “Chromatic dispersion monitoring for multiple modulation formats and data rates using sideband optical filtering and asynchronous amplitude sampling technique,” Opt. Express 19(2), 1007–1015 (2011).
    [CrossRef] [PubMed]
  13. B. Kozicki, A. Maruta, and K. Kitayama, “Transparent performance monitoring of RZ-DQPSK systems employing delay-tap sampling,” J. Opt. Netw. 6(11), 1257–1269 (2007).
    [CrossRef]
  14. A. P. T. Lau, Z. Li, F. N. Khan, C. Lu, and P. K. A. Wai, “Analysis of signed chromatic dispersion monitoring by waveform asymmetry for differentially-coherent phase-modulated systems,” Opt. Express 19(5), 4147–4156 (2011).
    [CrossRef] [PubMed]
  15. F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “OSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique,” IEEE Photon. Technol. Lett. 22(11), 823–825 (2010).
    [CrossRef]
  16. F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “Statistical analysis of optical signal-to-noise ratio monitoring using delay-tap sampling,” IEEE Photon. Technol. Lett. 22(3), 149–151 (2010).
    [CrossRef]
  17. Y. Zhou, T. B. Anderson, K. Clarke, A. Nirmalathas, and K. L. Lee, “Bit-rate identification using asynchronous delayed sampling,” IEEE Photon. Technol. Lett. 21(13), 893–895 (2009).
    [CrossRef]
  18. N. G. Gonzalez, D. Zibar, and I. T. Monroy, “Cognitive digital receiver for burst mode phase modulated radio over fiber links,” in 2010 36th European Conference and Exhibition on Optical Communication (ECOC) (2010.), paper P6.11.
  19. E. E. Azzouz and A. K. Nandi, Automatic Modulation Recognition of Communication Signals (Kluwer Academic Publishers, Boston, 1996).
  20. O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, “A survey of automatic modulation classification techniques: classical approaches and new trends,” IET Commun. 1(2), 137–156 (2007).
    [CrossRef]
  21. A. K. Nandi and E. E. Azzouz, “Algorithms for automatic modulation recognition of communication signals,” IEEE Trans. Commun. 46(4), 431–436 (1998).
    [CrossRef]
  22. W. Wei and J. M. Mendel, “Maximum-likelihood classification for digital amplitude-phase modulations,” IEEE Trans. Commun. 48(2), 189–193 (2000).
    [CrossRef]
  23. W. Su, J. L. Xu, and M. Zhou, “Real-time modulation classification based on maximum-likelihood,” IEEE Commun. Lett. 12(11), 801–803 (2008).
    [CrossRef]
  24. A. K. Nandi and E. E. Azzouz, “Modulation recognition using artificial neural networks,” Signal Process. 56(2), 165–175 (1997).
    [CrossRef]
  25. Z. Yaqin, R. Guanghui, W. Xuexia, W. Zhilu, and G. Xuemai, “Automatic digital modulation recognition using artificial neural networks,” in Proceedings of the 2003 International Conference on Neural Networks and Signal Processing (2003), Vol.1, pp. 257–260.
  26. M. L. D. Wong and A. K. Nandi, “Automatic digital modulation recognition using spectral and statistical features with multi-layer perceptrons,” in Sixth International, Symposium on Signal Processing and its Applications (2001.), Vol. 2, pp. 390–393.
  27. I. Kaastra and M. Boyd, “Designing a neural network for forecasting financial and economic time series,” Neurocomputing 10(3), 215–236 (1996).
    [CrossRef]
  28. H. Yu and B. M. Wilamowski, “Levenberg-Marquardt Training,” in The Industrial Electronics Handbook, Vol. 5−Intelligent Systems, 2nd ed. (CRC Press, Boca Raton, 2011).
  29. VPIsystemsTM, “VPltransmissionMakerTM.”

2012

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett. 24(12), 982–984 (2012).

2011

2010

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “OSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique,” IEEE Photon. Technol. Lett. 22(11), 823–825 (2010).
[CrossRef]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “Statistical analysis of optical signal-to-noise ratio monitoring using delay-tap sampling,” IEEE Photon. Technol. Lett. 22(3), 149–151 (2010).
[CrossRef]

Z. Pan, C. Yu, and A. E. Willner, “Optical performance monitoring for the next generation optical communication networks,” Opt. Fiber Technol. 16(1), 20–45 (2010).
[CrossRef]

A. Nag, M. Tornatore, and B. Mukherjee, “Optical network design with mixed line rates and multiple modulation formats,” J. Lightwave Technol. 28(4), 466–475 (2010).
[CrossRef]

2009

2008

2007

O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, “A survey of automatic modulation classification techniques: classical approaches and new trends,” IET Commun. 1(2), 137–156 (2007).
[CrossRef]

B. Kozicki, A. Maruta, and K. Kitayama, “Transparent performance monitoring of RZ-DQPSK systems employing delay-tap sampling,” J. Opt. Netw. 6(11), 1257–1269 (2007).
[CrossRef]

2004

2000

W. Wei and J. M. Mendel, “Maximum-likelihood classification for digital amplitude-phase modulations,” IEEE Trans. Commun. 48(2), 189–193 (2000).
[CrossRef]

1999

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of amplitude histograms to monitor performance of optical channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

1998

A. K. Nandi and E. E. Azzouz, “Algorithms for automatic modulation recognition of communication signals,” IEEE Trans. Commun. 46(4), 431–436 (1998).
[CrossRef]

1997

A. K. Nandi and E. E. Azzouz, “Modulation recognition using artificial neural networks,” Signal Process. 56(2), 165–175 (1997).
[CrossRef]

1996

I. Kaastra and M. Boyd, “Designing a neural network for forecasting financial and economic time series,” Neurocomputing 10(3), 215–236 (1996).
[CrossRef]

Abdi, A.

O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, “A survey of automatic modulation classification techniques: classical approaches and new trends,” IET Commun. 1(2), 137–156 (2007).
[CrossRef]

Anderson, T. B.

T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightwave Technol. 27(16), 3729–3736 (2009).
[CrossRef]

Y. Zhou, T. B. Anderson, K. Clarke, A. Nirmalathas, and K. L. Lee, “Bit-rate identification using asynchronous delayed sampling,” IEEE Photon. Technol. Lett. 21(13), 893–895 (2009).
[CrossRef]

Azzouz, E. E.

A. K. Nandi and E. E. Azzouz, “Algorithms for automatic modulation recognition of communication signals,” IEEE Trans. Commun. 46(4), 431–436 (1998).
[CrossRef]

A. K. Nandi and E. E. Azzouz, “Modulation recognition using artificial neural networks,” Signal Process. 56(2), 165–175 (1997).
[CrossRef]

Bach, R.

Bar-Ness, Y.

O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, “A survey of automatic modulation classification techniques: classical approaches and new trends,” IET Commun. 1(2), 137–156 (2007).
[CrossRef]

Blumenthal, D. J.

Boyd, M.

I. Kaastra and M. Boyd, “Designing a neural network for forecasting financial and economic time series,” Neurocomputing 10(3), 215–236 (1996).
[CrossRef]

Caspar, C.

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of amplitude histograms to monitor performance of optical channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

Clarke, K.

Y. Zhou, T. B. Anderson, K. Clarke, A. Nirmalathas, and K. L. Lee, “Bit-rate identification using asynchronous delayed sampling,” IEEE Photon. Technol. Lett. 21(13), 893–895 (2009).
[CrossRef]

T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightwave Technol. 27(16), 3729–3736 (2009).
[CrossRef]

Dobre, O. A.

O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, “A survey of automatic modulation classification techniques: classical approaches and new trends,” IET Commun. 1(2), 137–156 (2007).
[CrossRef]

Dods, S. D.

Einstein, D.

Gladisch, A.

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of amplitude histograms to monitor performance of optical channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

Hanik, N.

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of amplitude histograms to monitor performance of optical channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

Hewitt, D.

Hidehiko, T.

Jargon, J. A.

Kaastra, I.

I. Kaastra and M. Boyd, “Designing a neural network for forecasting financial and economic time series,” Neurocomputing 10(3), 215–236 (1996).
[CrossRef]

Khan, F. N.

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett. 24(12), 982–984 (2012).

A. P. T. Lau, Z. Li, F. N. Khan, C. Lu, and P. K. A. Wai, “Analysis of signed chromatic dispersion monitoring by waveform asymmetry for differentially-coherent phase-modulated systems,” Opt. Express 19(5), 4147–4156 (2011).
[CrossRef] [PubMed]

F. N. Khan, A. P. T. Lau, C. Lu, and P. K. A. Wai, “Chromatic dispersion monitoring for multiple modulation formats and data rates using sideband optical filtering and asynchronous amplitude sampling technique,” Opt. Express 19(2), 1007–1015 (2011).
[CrossRef] [PubMed]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “Statistical analysis of optical signal-to-noise ratio monitoring using delay-tap sampling,” IEEE Photon. Technol. Lett. 22(3), 149–151 (2010).
[CrossRef]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “OSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique,” IEEE Photon. Technol. Lett. 22(11), 823–825 (2010).
[CrossRef]

Kilper, D. C.

Kitayama, K.

Kowalczyk, A.

Kozicki, B.

Landolsi, T.

Lau, A. P. T.

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett. 24(12), 982–984 (2012).

A. P. T. Lau, Z. Li, F. N. Khan, C. Lu, and P. K. A. Wai, “Analysis of signed chromatic dispersion monitoring by waveform asymmetry for differentially-coherent phase-modulated systems,” Opt. Express 19(5), 4147–4156 (2011).
[CrossRef] [PubMed]

F. N. Khan, A. P. T. Lau, C. Lu, and P. K. A. Wai, “Chromatic dispersion monitoring for multiple modulation formats and data rates using sideband optical filtering and asynchronous amplitude sampling technique,” Opt. Express 19(2), 1007–1015 (2011).
[CrossRef] [PubMed]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “Statistical analysis of optical signal-to-noise ratio monitoring using delay-tap sampling,” IEEE Photon. Technol. Lett. 22(3), 149–151 (2010).
[CrossRef]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “OSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique,” IEEE Photon. Technol. Lett. 22(11), 823–825 (2010).
[CrossRef]

Lee, K. L.

Y. Zhou, T. B. Anderson, K. Clarke, A. Nirmalathas, and K. L. Lee, “Bit-rate identification using asynchronous delayed sampling,” IEEE Photon. Technol. Lett. 21(13), 893–895 (2009).
[CrossRef]

Li, J. C.

Li, Z.

A. P. T. Lau, Z. Li, F. N. Khan, C. Lu, and P. K. A. Wai, “Analysis of signed chromatic dispersion monitoring by waveform asymmetry for differentially-coherent phase-modulated systems,” Opt. Express 19(5), 4147–4156 (2011).
[CrossRef] [PubMed]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “Statistical analysis of optical signal-to-noise ratio monitoring using delay-tap sampling,” IEEE Photon. Technol. Lett. 22(3), 149–151 (2010).
[CrossRef]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “OSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique,” IEEE Photon. Technol. Lett. 22(11), 823–825 (2010).
[CrossRef]

Lu, C.

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett. 24(12), 982–984 (2012).

A. P. T. Lau, Z. Li, F. N. Khan, C. Lu, and P. K. A. Wai, “Analysis of signed chromatic dispersion monitoring by waveform asymmetry for differentially-coherent phase-modulated systems,” Opt. Express 19(5), 4147–4156 (2011).
[CrossRef] [PubMed]

F. N. Khan, A. P. T. Lau, C. Lu, and P. K. A. Wai, “Chromatic dispersion monitoring for multiple modulation formats and data rates using sideband optical filtering and asynchronous amplitude sampling technique,” Opt. Express 19(2), 1007–1015 (2011).
[CrossRef] [PubMed]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “Statistical analysis of optical signal-to-noise ratio monitoring using delay-tap sampling,” IEEE Photon. Technol. Lett. 22(3), 149–151 (2010).
[CrossRef]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “OSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique,” IEEE Photon. Technol. Lett. 22(11), 823–825 (2010).
[CrossRef]

Maruta, A.

Mendel, J. M.

W. Wei and J. M. Mendel, “Maximum-likelihood classification for digital amplitude-phase modulations,” IEEE Trans. Commun. 48(2), 189–193 (2000).
[CrossRef]

Mukherjee, B.

Nag, A.

Nandi, A. K.

A. K. Nandi and E. E. Azzouz, “Algorithms for automatic modulation recognition of communication signals,” IEEE Trans. Commun. 46(4), 431–436 (1998).
[CrossRef]

A. K. Nandi and E. E. Azzouz, “Modulation recognition using artificial neural networks,” Signal Process. 56(2), 165–175 (1997).
[CrossRef]

Nirmalathas, A.

Y. Zhou, T. B. Anderson, K. Clarke, A. Nirmalathas, and K. L. Lee, “Bit-rate identification using asynchronous delayed sampling,” IEEE Photon. Technol. Lett. 21(13), 893–895 (2009).
[CrossRef]

Ostar, L.

Pan, Z.

Z. Pan, C. Yu, and A. E. Willner, “Optical performance monitoring for the next generation optical communication networks,” Opt. Fiber Technol. 16(1), 20–45 (2010).
[CrossRef]

Paraschis, L.

Preiss, M.

Shen, T. S. R.

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett. 24(12), 982–984 (2012).

Skoog, R. A.

Strebel, B.

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of amplitude histograms to monitor performance of optical channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

Su, W.

W. Su, J. L. Xu, and M. Zhou, “Real-time modulation classification based on maximum-likelihood,” IEEE Commun. Lett. 12(11), 801–803 (2008).
[CrossRef]

O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, “A survey of automatic modulation classification techniques: classical approaches and new trends,” IET Commun. 1(2), 137–156 (2007).
[CrossRef]

Takuya, O.

Tornatore, M.

Wai, P. K. A.

A. P. T. Lau, Z. Li, F. N. Khan, C. Lu, and P. K. A. Wai, “Analysis of signed chromatic dispersion monitoring by waveform asymmetry for differentially-coherent phase-modulated systems,” Opt. Express 19(5), 4147–4156 (2011).
[CrossRef] [PubMed]

F. N. Khan, A. P. T. Lau, C. Lu, and P. K. A. Wai, “Chromatic dispersion monitoring for multiple modulation formats and data rates using sideband optical filtering and asynchronous amplitude sampling technique,” Opt. Express 19(2), 1007–1015 (2011).
[CrossRef] [PubMed]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “Statistical analysis of optical signal-to-noise ratio monitoring using delay-tap sampling,” IEEE Photon. Technol. Lett. 22(3), 149–151 (2010).
[CrossRef]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “OSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique,” IEEE Photon. Technol. Lett. 22(11), 823–825 (2010).
[CrossRef]

Wei, W.

W. Wei and J. M. Mendel, “Maximum-likelihood classification for digital amplitude-phase modulations,” IEEE Trans. Commun. 48(2), 189–193 (2000).
[CrossRef]

Willner, A. E.

Wu, X.

Xu, J. L.

W. Su, J. L. Xu, and M. Zhou, “Real-time modulation classification based on maximum-likelihood,” IEEE Commun. Lett. 12(11), 801–803 (2008).
[CrossRef]

Yu, C.

Z. Pan, C. Yu, and A. E. Willner, “Optical performance monitoring for the next generation optical communication networks,” Opt. Fiber Technol. 16(1), 20–45 (2010).
[CrossRef]

Zhou, M.

W. Su, J. L. Xu, and M. Zhou, “Real-time modulation classification based on maximum-likelihood,” IEEE Commun. Lett. 12(11), 801–803 (2008).
[CrossRef]

Zhou, Y.

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett. 24(12), 982–984 (2012).

Y. Zhou, T. B. Anderson, K. Clarke, A. Nirmalathas, and K. L. Lee, “Bit-rate identification using asynchronous delayed sampling,” IEEE Photon. Technol. Lett. 21(13), 893–895 (2009).
[CrossRef]

Electron. Lett.

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of amplitude histograms to monitor performance of optical channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

IEEE Commun. Lett.

W. Su, J. L. Xu, and M. Zhou, “Real-time modulation classification based on maximum-likelihood,” IEEE Commun. Lett. 12(11), 801–803 (2008).
[CrossRef]

IEEE Photon. Technol. Lett.

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “OSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique,” IEEE Photon. Technol. Lett. 22(11), 823–825 (2010).
[CrossRef]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “Statistical analysis of optical signal-to-noise ratio monitoring using delay-tap sampling,” IEEE Photon. Technol. Lett. 22(3), 149–151 (2010).
[CrossRef]

Y. Zhou, T. B. Anderson, K. Clarke, A. Nirmalathas, and K. L. Lee, “Bit-rate identification using asynchronous delayed sampling,” IEEE Photon. Technol. Lett. 21(13), 893–895 (2009).
[CrossRef]

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett. 24(12), 982–984 (2012).

IEEE Trans. Commun.

A. K. Nandi and E. E. Azzouz, “Algorithms for automatic modulation recognition of communication signals,” IEEE Trans. Commun. 46(4), 431–436 (1998).
[CrossRef]

W. Wei and J. M. Mendel, “Maximum-likelihood classification for digital amplitude-phase modulations,” IEEE Trans. Commun. 48(2), 189–193 (2000).
[CrossRef]

IET Commun.

O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, “A survey of automatic modulation classification techniques: classical approaches and new trends,” IET Commun. 1(2), 137–156 (2007).
[CrossRef]

J. Lightwave Technol.

J. Opt. Netw.

Neurocomputing

I. Kaastra and M. Boyd, “Designing a neural network for forecasting financial and economic time series,” Neurocomputing 10(3), 215–236 (1996).
[CrossRef]

Opt. Express

Opt. Fiber Technol.

Z. Pan, C. Yu, and A. E. Willner, “Optical performance monitoring for the next generation optical communication networks,” Opt. Fiber Technol. 16(1), 20–45 (2010).
[CrossRef]

Signal Process.

A. K. Nandi and E. E. Azzouz, “Modulation recognition using artificial neural networks,” Signal Process. 56(2), 165–175 (1997).
[CrossRef]

Other

Z. Yaqin, R. Guanghui, W. Xuexia, W. Zhilu, and G. Xuemai, “Automatic digital modulation recognition using artificial neural networks,” in Proceedings of the 2003 International Conference on Neural Networks and Signal Processing (2003), Vol.1, pp. 257–260.

M. L. D. Wong and A. K. Nandi, “Automatic digital modulation recognition using spectral and statistical features with multi-layer perceptrons,” in Sixth International, Symposium on Signal Processing and its Applications (2001.), Vol. 2, pp. 390–393.

H. Yu and B. M. Wilamowski, “Levenberg-Marquardt Training,” in The Industrial Electronics Handbook, Vol. 5−Intelligent Systems, 2nd ed. (CRC Press, Boca Raton, 2011).

VPIsystemsTM, “VPltransmissionMakerTM.”

C. K. Chan, Optical Performance Monitoring (Academic, 2010).

I. T. Monroy, D. Zibar, N. G. Gonzalez, and R. Borkowski, “Cognitive heterogeneous reconfigurable optical networks (CHRON): enabling technologies and techniques,” in 2011 13th International Conference on Transparent Optical Networks (ICTON)(2011), paper Th.A1.2.

S. D. Dods and T. B. Anderson, “Optical performance monitoring technique using delay tap asynchronous waveform sampling,” in Optical Fiber Communication Conference and Exposition and The National Fiber Optic Engineers Conference, Technical Digest (CD) (Optical Society of America, 2006), paper OThP5.

N. G. Gonzalez, D. Zibar, and I. T. Monroy, “Cognitive digital receiver for burst mode phase modulated radio over fiber links,” in 2010 36th European Conference and Exhibition on Optical Communication (ECOC) (2010.), paper P6.11.

E. E. Azzouz and A. K. Nandi, Automatic Modulation Recognition of Communication Signals (Kluwer Academic Publishers, Boston, 1996).

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Fig. 1
Fig. 1

Eye-diagrams and corresponding AAHs for (a) 10 Gbps RZ-OOK, (b) 40 Gbps NRZ-DPSK, (c) 40 Gbps ODB, (d) 40 Gbps RZ-DQPSK, (e) 100 Gbps PM-RZ-QPSK and (f) 200 Gbps PM-NRZ-16QAM formats after direct detection. The second column shows the AAHs for OSNR = 18 dB, neither CD nor PMD while the third column shows the AAHs for OSNR = 18 dB, CD = 100 ps/nm and DGD = 5 ps when the signal’s state-of-polarization (SOP) is 45° with respect to the principal states-of-polarization (PSP) of the PMD emulator.

Fig. 2
Fig. 2

Structure of an MLP3-ANN with AAH bin-count vector x as input and estimated modulation format type vector y as output.

Fig. 3
Fig. 3

An example illustrating the relationship between x, v, y and the signal modulation format type. In the training phase, for each input x, the corresponding binary vector y contains a ‘1’ and all zeros. The location of ‘1’ in y, or argmax{y}, indicates the signal modulation format. The ANN attempts to minimize the MSE between y and the analogue ANN output v. In the testing phase, argmax{v} is used as an identifier of the signal modulation format.

Fig. 4
Fig. 4

Dependence of MSE on the number of epochs for the training and validation data sets. Best validation performance at MSE = 4.97 x 10−3 is achieved for 53 epochs and the training process is then terminated.

Fig. 5
Fig. 5

(a) System configuration for MFI using ANN trained with AAHs. (b) Modified MFI configuration for distinguishing between the RZ-DQPSK and PM-RZ-QPSK formats for small CD and DGD values.

Fig. 6
Fig. 6

The six elements of the ANN outputs vi corresponding to (a) 10 Gbps RZ-OOK, (b) 40 Gbps NRZ-DPSK, (c) 40 Gbps ODB, (d) 40 Gbps RZ-DQPSK, (e) 100 Gbps PM-RZ-QPSK and (f) 200 Gbps PM-NRZ-16QAM modulation formats for the testing data set containing 6552 test cases randomly drawn from the overall data set and corresponding to different OSNR, CD, DGD and polarization angle values.

Tables (2)

Tables Icon

Table 1 Estimation accuracies of the proposed MFI technique using ANN trained with AAHs and using the setup shown in Fig. 5(a). The overall MFI accuracy is 99.06%.

Tables Icon

Table 2 Estimation accuracies of the proposed MFI technique using ANN trained with AAHs and exploiting the polarization characteristics of the input signal using the modified MFI configuration shown in Fig. 5(b). The overall MFI accuracy is 99.6%.

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