http://iet.metastore.ingenta.com
1887

A novel support vector machine robust model based electrical equaliser for coherent optical orthogonal frequency division multiplexing systems

A novel support vector machine robust model based electrical equaliser for coherent optical orthogonal frequency division multiplexing systems

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Communications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Classifiers, such as artificial neural networks non-linear equaliser (ANN-NLE), Wiener–Hammerstein non-linear equaliser, Volterra non-linear equaliser (Volterra-NLE) and support vector machine non-linear equaliser (SVM-NLE), can play a significant role in compensating non-linear imperfections in the optical communications context. Using classifiers to mitigate the non-linear effects in coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems is an interesting idea to be investigated. In this study, a novel support vector machine robust version, specifically adapted to a 100 Gb/s CO-OFDM data structure for long haul distance, is proposed. Firstly, the authors demonstrate that SVM-NLE upgrades the system performance by about 10−1 in terms of bit-error rate compared to Volterra-NLE at optical signal-to-noise ratio equal to 14 dB. Then, they show that it can double the transmission distance up to 1600 km over single mode fibre channel. Furthermore, a performance comparison is performed using 16 quadrature amplitude modulation and 40 Gb/s bit rate for SVM-NLE, ANN-NLE and inverse Volterra series transfer function non-linear equaliser, respectively.

References

    1. 1)
      • 1. Mhatli, S., Ghanbarisabagh, M., Tawade, L., et al: ‘Long-reach OFDM WDM–PON delivering 100 Gb/s of data downstream and 2 Gb/s of data upstream using a continuous-wave laser and a reflective semiconductor optical amplifier’, Opt. Lett., 2014, 39, (23), pp. 67116714.
    2. 2)
      • 2. Shieh, W., Djordjevic, I.: ‘OFDM for optical communications’ (Elsevier, Academic Press, 2010).
    3. 3)
      • 3. Kaminov, I., Li, T., Willner, A.: ‘Optical fiber telecommunications, volume VIB: systems and network’ (Academic Press, 2013, 6th edn.).
    4. 4)
      • 4. Shieh, W., Bao, H., Tang, Y.: ‘Coherent optical OFDM: theory and design’, Opt. Express, 2008, 16, (2), pp. 841859.
    5. 5)
      • 5. Du, L.B., Lowery, A.J.: ‘Improved single channel backpropagation for intra-channel fiber nonlinearity compensation in long-haul optical communication systems’, Opt. Express, 2010, 18, (16), pp. 1707517088.
    6. 6)
      • 6. Zhao, C., Chen, Y., Zhang, S., et al: ‘Experimental demonstration of 1.08 Tb/s PDM CO-SCFDM transmission over 3170 km SSMF’, Opt. Express, 2012, 20, (2), pp. 787793.
    7. 7)
      • 7. Uncini, A., Vecci, L., Campolucci, P., et al: ‘Complex-valued neural networks with adaptive spline activation function for digital radio links nonlinear equalization’’, IEEE Trans. Signal Process., 1999, 47, (2), pp. 505514.
    8. 8)
      • 8. Sebald, D.J., Bucklew, J.A.: ‘Support vector machine techniques for nonlinear equalization’, IEEE Trans. Signal Process., 2000, 48, (11), pp. 32173226.
    9. 9)
      • 9. Shulkind, G., Azarathy, M.: ‘Nonlinear digital back propagation compensator for coherent optical OFDM based on factorizing the Volterra series transfer function’, Opt. Express, 2013, 21, (11), pp. 1314513161.
    10. 10)
      • 10. Nowakand, R.D., VanVeen, B.D.: ‘Tensor product basis approximations for Volterra filters’, IEEE Trans. Signal Process., 1996, 44, pp. 3650.
    11. 11)
      • 11. Benedetto, S., Biglieri, E., Astellani, V.: ‘Digital transmission theory’ (Prentice-Hall, Englewood Cliffs, NJ, 1987).
    12. 12)
      • 12. Jarajreh, M.A., Rajbhandari, S., Gacoumidis, E., et al: ‘Fibre impairment compensation using artificial neural network equalizer for high-capacity coherent optical OFDM signals’. CSNDSP, UK, 2014.
    13. 13)
      • 13. Jarajreh, M.A., Giacoumidis, E., Aldaya, I., et al: ‘Artificial neural network nonlinear equalizer for coherent optical OFDM’, IEEE PTL, 2014, 27, (4).
    14. 14)
      • 14. Jarajreh, M.A.: ‘Coherent optical OFDM modem employing artificial neural networks for dispersion and nonlinearity compensation in a long-haul transmission system’ (Northumbria University, 2012).
    15. 15)
      • 15. Chen, S., Gibson, G.J., N Cowan, C.F., Grant, P.M., ‘Adaptive equalization of finite non-linear channels using multilayer perceptrons’, Signal Processing, Volume 20, Elsevier Science Publishers B.V., pp. 107119. 1990..
    16. 16)
      • 16. Li, X., Zhong, W.-D., Alphones, A., et al: ‘Channel equalization in optical OFDM systems using independent component analysis’, J. Lightwave Technol., 2014, 32, (18), pp. 32063214.
    17. 17)
      • 17. Mousa-Pasandi, M.E., Plant, D.V.: ‘Zero-overhead phase noise compensation via decision-direct phase equalizer for coherent optical OFDM’, Opt. Express, 2010, 18, (20), pp. 2065120660.
    18. 18)
      • 18. Pan, J., Cheng, C.: ‘Nonlinear electrical compensation for the coherent optical OFDM system’, J. Lightwave Technol., 2011, 29, (2), pp. 215221.
    19. 19)
      • 19. Pan, J., Cheng, C.: ‘Wiener–Hammerstein model based electrical equalizer for optical communication systems’, J. Lightwave Technol., 2011, 29, (16), pp. 24542459.
    20. 20)
      • 20. Abdulkader, H., Benammar, B., Poulliat, C., et al: ‘Neural networks-based turbo equalization of a satellite communication channel’. Proc. of IEEE 15th Int. Workshop on Signal Processing Advances in Wireless Communications, 2014.
    21. 21)
      • 21. Pavel, T., Senderáková, D., Páta, P., et al: ‘Analysis of wiener Hammerstein equalizer for optical OFDM modem’. Proc. of Photonics Devices and Systems Conf., Prague, 2015.
    22. 22)
      • 22. Tawade, L., Pinjarkar, U., Awade, K., et al: ‘An optical OFDM modem with adaptive Volterra equalizer’, J. Opt. Commun., 2015, 30, (1), pp. 716.
    23. 23)
      • 23. Kharbech, S., Dayoub, I., wingelstein-Colin, M.Z., et al: ‘On classifiers for blind feature-based automatic modulation classification over multiple-input–multiple-output channels’, IET Commun., 2016, 10, (7), pp. 790795.
    24. 24)
      • 24. Agrawal, G.P.: ‘Fiber-optic communication systems’ (Wiley, 2012, 4th edn.).
    25. 25)
      • 25. Recommendation ITU-T G.652, Characteristics of a single-mode optical fibre and cable, 2009.
    26. 26)
      • 26. Nguyen, T., Mhatli, S., Giacoumidis, E., et al: ‘Fiber nonlinearity equalizer based on support vector classification for coherent optical OFDM’, IEEE Photon. J., 2016, 8, (2)..
    27. 27)
      • 27. Giacoumidis, E., Aldaya, I., Jarajreh, M.A., et al: ‘Volterra-based reconfigurable nonlinear equalizer for coherent OFDM’, IEEE Photon. Technol. Lett., 2014, 26, (14), pp. 13831386.
    28. 28)
      • 28. Jarajreh, M., Giacoumidis, E., Aldaya, I., et al: ‘Artificial neural network nonlinear equalizer for coherent optical OFDM’, IEEE Photon. Technol. Lett., 2015, 27, (4), pp. 387390.
    29. 29)
      • 29. Giacoumidis, E., Mhatli, S., Le, S.T., et al: ‘Nonlinearities blind equalization for 16-QAM coherent optical OFDM using support vector machines’. ECOC 2016, Dusseldorf, September 2016.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2016.1115
Loading

Related content

content/journals/10.1049/iet-com.2016.1115
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address