Abstract

Four-wave mixing and in-band crosstalk noise can limit the performance of wavelength division multiplexing optical networks. The statistics of these noises depend on the nature of the traffic. In this paper, the performance of an IP over MPλS-based network is analyzed using the Multi-Canonical Monte Carlo (MCMC) method and it is shown that the Gaussian approximation does not yield accurate results. The importance of the traffic load and the traffic distribution in the optical channels is also investigated. It is shown that a proper traffic distribution in the optical channels, can improve the performance of the network.

© 2005 Optical Society of America

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References

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  1. D. Awduche, and Y. Rekhter, “Multiprotocol lambda switching: combining MPLS traffic engineering control with optical crossconnects,” IEEE Com. Magazine 39, 111 – 116 (2001).
    [CrossRef]
  2. I. Neokosmidis, Th. Kamalakis, A. Chipouras and Th. Sphicopoulos, “Estimation of the FWM noise probability density function using the MultiCanonical Monte-Carlo Method,” Opt. Lett. 30, 11-13 (2005).
    [CrossRef] [PubMed]
  3. Th. Kamalakis, Th. Sphicopoulos and M. Sagriotis, “Accurate error probability estimation in the presence of in-band crosstalk noise in WDM networks,” J. Lightwave Technol. 21, 2172-2181 (2003).
    [CrossRef]
  4. J. Tang, C. K. Siew, and L. Zhang, “Optical nonlinear effects on the performance of IP traffic over GMPLS based DWDM networks,” Computer Commun. 26, 1330-1340 (2003).
    [CrossRef]
  5. K. Hyunchin, L. Tancevski,.G. An and G. Castanon, “FWM noise reduction in bursty metro transmission,” Optical Fiber Communication Conference and Exhibit, (OFC 2001), 3, WN3-1-WN3-3 (2001).
  6. D. Yevick, “Multicanonical communication system modeling – application to PMD statistics,” IEEE Photonic Technol. Lett. 14, 1512-1514 (2002).
    [CrossRef]
  7. R. Hozhonner et al., “Use of multicanonical Monte Carlo simulations to obtain accurate bit error rates in optical communication systems,” Opt. Lett. 28, 1894 - 1896 (2003).
    [CrossRef]
  8. David P. Landau and K. Binder, A Guide to Monte Carlo Simulations in Statistical Physics (Cambridge: Cambridge University Press, 2002).
  9. J. Roberts, “Traffic Theory and the Internet,” IEEE Communications Magazine 39, 94 - 99 (2001).
    [CrossRef]
  10. R. Cooper, Introduction to Queueing Theory (New York: North Holland 1981).
  11. Th. Kamalakis, D. Varoutas, Th. Sphicopoulos, “Statistical study of in-band crosstalk noise using the multicanonical Monte Carlo method,” IEEE Photonic Technol. Lett. 16, 2242 – 2244 (2004).
    [CrossRef]

Computer Commun.

J. Tang, C. K. Siew, and L. Zhang, “Optical nonlinear effects on the performance of IP traffic over GMPLS based DWDM networks,” Computer Commun. 26, 1330-1340 (2003).
[CrossRef]

IEEE Com. Magazine

D. Awduche, and Y. Rekhter, “Multiprotocol lambda switching: combining MPLS traffic engineering control with optical crossconnects,” IEEE Com. Magazine 39, 111 – 116 (2001).
[CrossRef]

IEEE Communications Magazine

J. Roberts, “Traffic Theory and the Internet,” IEEE Communications Magazine 39, 94 - 99 (2001).
[CrossRef]

IEEE Photonic Technol. Lett.

D. Yevick, “Multicanonical communication system modeling – application to PMD statistics,” IEEE Photonic Technol. Lett. 14, 1512-1514 (2002).
[CrossRef]

Th. Kamalakis, D. Varoutas, Th. Sphicopoulos, “Statistical study of in-band crosstalk noise using the multicanonical Monte Carlo method,” IEEE Photonic Technol. Lett. 16, 2242 – 2244 (2004).
[CrossRef]

J. Lightwave Technol.

OFC 2001

K. Hyunchin, L. Tancevski,.G. An and G. Castanon, “FWM noise reduction in bursty metro transmission,” Optical Fiber Communication Conference and Exhibit, (OFC 2001), 3, WN3-1-WN3-3 (2001).

Opt. Lett.

Other

R. Cooper, Introduction to Queueing Theory (New York: North Holland 1981).

David P. Landau and K. Binder, A Guide to Monte Carlo Simulations in Statistical Physics (Cambridge: Cambridge University Press, 2002).

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Figures (5)

Fig. 1.
Fig. 1.

(a) The Label Edge Router of an IP over MPXS-based DWDM network and (b) “1” and “0” generated under bursty traffic.

Fig. 2.
Fig. 2.

Comparison between the MCMC method and the Gaussian approximation. a) BER vs traffic load ρ for Pin =8dBm and SXR=12dB and b) Pin and SXR for obtaining BER=10-9.

Fig. 3.
Fig. 3.

BER as a function of Pin (or SXR), for a system limited by a) FWM-induced distortion, b) in-band crosstalk noise.

Fig. 4.
Fig. 4.

Packet Error Rate (PER) as a function of the traffic load ρ for two types of packets in the case of a system limited by a) FWM noise with Pin =8dBm and b) In-band crosstalk noise with SXR=12dB.

Fig. 5.
Fig. 5.

(a) Traffic load distribution along the channels, (b) PER as a function of the input peak power Pin when all the channels are equally loaded with ρ=0.6 (solid line) and unequally loaded with mean ρ equal to 0.6 (dashed line) and (c) BER values for all channels at Pin =7.6dBm.

Tables (1)

Tables Icon

Table 1. Parameters used in the MCMC simulations

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