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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Rafique, Danish; Ellis, Andrew (2012)
Languages: English
Types: Unknown
Subjects:
We report that, contrary to common perception, intra-channel nonlinearity compensation offers significant improvements of up to 4dB, in nonlinear tolerance (Q-factor), in a flexible traffic scenario, and further improvements with increasing local link dispersion, for an optical transport network employing flexible 28Gbaud PM-mQAM transponders.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • We have demonstrated that advantages of intrachannel nonlinear compensation are significantly beyond conventionally thought bound of ~1-2dB. In particular, we have shown that in a flexible network scenario, intra-channel nonlinearity compensation may improve the nonlinear tolerance by 4dB, irrespective of modulation order of the co-propagating traffic.
    • Furthermore, we have shown that increasing the local link dispersion enables significant performance improvements. We have reported up to 2dB and 3.5dB improvements with linear compensation, and nonlinear compensation, respectively, compared to the typical dispersion coefficient of 17ps/nm/km, and a relative improvement between linear and nonlinear compensation of about 5.5dB.
    • This work was supported by Science Foundation Ireland: Grant numbers 06/IN/I969 and 10/CE/I1853.
    • References [1] A.D. Ellis, et al., JLT, 28, 423 (2010) [2] D.Rafique, et al., Optics Express, 19, 5219 (2011) [3] X.Li, et al., Optics Express, 16, 880 (2008) [4] E.Ip, JLT, 28, 939 (2010) [5] S.J Savory, et al., PTL, 22, 673 (2010) [6] X. Zhou, et al., OFC, NthB4 (2011) [7] T. Wuth, et al., OFC, NTuB3 (2008) [8] D.Rafique, et al., PTL, 23, 1213 (2011) [9] A.Nag, et al., JLT, 28, 466 (2010) [10] R.-J.Essiambre, et al., Optical Fibre Telecommunications IV B. Academic (2002). [11] L. Lei, et al., OFC, OWW3 (2011) [12] D.Rafique, et al., OFC, OM3A.1 (2012)
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

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