Abstract

The impact of physical layer impairments in optical network design and operation has received significant attention in the last years, thereby requiring estimation techniques to predict the quality of transmission (QoT) of optical connections before being established. In this paper, we report on the experimental demonstration of a case-based reasoning (CBR) technique to predict whether optical channels fulfill QoT requirements, thus supporting impairment-aware networking. The validation of the cognitive QoT estimator is performed in a WDM 80 Gb/s PDM-QPSK testbed, and we demonstrate that even with a very small and not optimized underlying knowledge base, it achieves between 79% and 98.7% successful classifications based on the error vector magnitude (EVM) parameter, and approximately 100% when the classification is based on the optical signal to noise ratio (OSNR).

© 2012 OSA

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  1. S. Azodolmolky, M. Klinkowski, E. Marin, D. Careglio, J. Solé Pareta, and I. Tomkos, “A survey on physical layer impairments aware routing and wavelength assignment algorithms in optical networks,” Comput. Netw.53(7), 926–944 (2009).
    [CrossRef]
  2. S. Azodolmolky, J. Perelló, M. Angelou, F. Agraz, L. Velasco, S. Spadaro, Y. Pointurier, A. Francescon, C. V. Saradhi, P. Kokkinos, E. Varvarigos, S. Al Zahr, M. Gagnaire, M. Gunkel, D. Klonidis, and I. Tomkos, “Experimental demonstration of an impairment aware network planning and operation tool for transparent/translucent optical networks,” J. Lightwave Technol.29(4), 439–448 (2011).
    [CrossRef]
  3. Y. Qin, K. Cheng, J. Triay, E. Escalona, G. S. Zervas, G. Zarris, N. Amaya-Gonzalez, C. Cervello-Pastor, R. Nejabati, and D. Simeonidou, “Demonstration of C/S based Hardware Accelerated QoT Estimation Tool in Dynamic Impairment-Aware Optical Network,” in European Conference in Optical Communications (ECOC 2010), Torino, IT, paper P5.17 (2010).
  4. P. Poggiolini, “The GN model of non-linear propagation in uncompensated coherent optical systems,” J. Lightwave Technol. (to be published).
  5. T. Jiménez, J. C. Aguado, I. de Miguel, R. J. Durán, N. Fernandez, M. Angelou, D. Sánchez, N. Merayo, P. Fernández, N. Atallah, R. M. Lorenzo, I. Tomkos, and E. J. Abril, “A cognitive system for fast quality of transmission estimation in core optical networks,” in Optical Fiber Communication Conference (OFC 2012), Los Angeles, CA, USA, paper OW3A.5 (2012).
  6. A. Aamodt and E. Plaza, “Case-based reasoning: Foundational issues, methodological variations, and system approaches,” Artificial Intelligence Communications7(1), 39–59 (1994).
  7. T. Jiménez, J. C. Aguado, I. de Miguel, R. J. Durán, D. Sánchez, M. Angelou, N. Merayo, P. Fernández, N. Fernández, R. M. Lorenzo, I. Tomkos, and E. J. Abril, “Optimization of the knowledge base of a cognitive quality of transmission estimator for core optical networks,” 16th Optical Network Design and Modeling Conference (ONDM 2012), Colchester, UK, (2012).
  8. D. W. Aha, “Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms,” Int. J. Man-Machine Studies36(2), 267–287 (1992).
    [CrossRef]
  9. I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed. (Morgan Kaufmann, 2011).

2011

2009

S. Azodolmolky, M. Klinkowski, E. Marin, D. Careglio, J. Solé Pareta, and I. Tomkos, “A survey on physical layer impairments aware routing and wavelength assignment algorithms in optical networks,” Comput. Netw.53(7), 926–944 (2009).
[CrossRef]

1994

A. Aamodt and E. Plaza, “Case-based reasoning: Foundational issues, methodological variations, and system approaches,” Artificial Intelligence Communications7(1), 39–59 (1994).

1992

D. W. Aha, “Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms,” Int. J. Man-Machine Studies36(2), 267–287 (1992).
[CrossRef]

Aamodt, A.

A. Aamodt and E. Plaza, “Case-based reasoning: Foundational issues, methodological variations, and system approaches,” Artificial Intelligence Communications7(1), 39–59 (1994).

Agraz, F.

Aha, D. W.

D. W. Aha, “Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms,” Int. J. Man-Machine Studies36(2), 267–287 (1992).
[CrossRef]

Al Zahr, S.

Angelou, M.

Azodolmolky, S.

Careglio, D.

S. Azodolmolky, M. Klinkowski, E. Marin, D. Careglio, J. Solé Pareta, and I. Tomkos, “A survey on physical layer impairments aware routing and wavelength assignment algorithms in optical networks,” Comput. Netw.53(7), 926–944 (2009).
[CrossRef]

Francescon, A.

Gagnaire, M.

Gunkel, M.

Klinkowski, M.

S. Azodolmolky, M. Klinkowski, E. Marin, D. Careglio, J. Solé Pareta, and I. Tomkos, “A survey on physical layer impairments aware routing and wavelength assignment algorithms in optical networks,” Comput. Netw.53(7), 926–944 (2009).
[CrossRef]

Klonidis, D.

Kokkinos, P.

Marin, E.

S. Azodolmolky, M. Klinkowski, E. Marin, D. Careglio, J. Solé Pareta, and I. Tomkos, “A survey on physical layer impairments aware routing and wavelength assignment algorithms in optical networks,” Comput. Netw.53(7), 926–944 (2009).
[CrossRef]

Perelló, J.

Plaza, E.

A. Aamodt and E. Plaza, “Case-based reasoning: Foundational issues, methodological variations, and system approaches,” Artificial Intelligence Communications7(1), 39–59 (1994).

Poggiolini, P.

P. Poggiolini, “The GN model of non-linear propagation in uncompensated coherent optical systems,” J. Lightwave Technol. (to be published).

Pointurier, Y.

Saradhi, C. V.

Solé Pareta, J.

S. Azodolmolky, M. Klinkowski, E. Marin, D. Careglio, J. Solé Pareta, and I. Tomkos, “A survey on physical layer impairments aware routing and wavelength assignment algorithms in optical networks,” Comput. Netw.53(7), 926–944 (2009).
[CrossRef]

Spadaro, S.

Tomkos, I.

Varvarigos, E.

Velasco, L.

Artificial Intelligence Communications

A. Aamodt and E. Plaza, “Case-based reasoning: Foundational issues, methodological variations, and system approaches,” Artificial Intelligence Communications7(1), 39–59 (1994).

Comput. Netw.

S. Azodolmolky, M. Klinkowski, E. Marin, D. Careglio, J. Solé Pareta, and I. Tomkos, “A survey on physical layer impairments aware routing and wavelength assignment algorithms in optical networks,” Comput. Netw.53(7), 926–944 (2009).
[CrossRef]

Int. J. Man-Machine Studies

D. W. Aha, “Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms,” Int. J. Man-Machine Studies36(2), 267–287 (1992).
[CrossRef]

J. Lightwave Technol.

Other

T. Jiménez, J. C. Aguado, I. de Miguel, R. J. Durán, N. Fernandez, M. Angelou, D. Sánchez, N. Merayo, P. Fernández, N. Atallah, R. M. Lorenzo, I. Tomkos, and E. J. Abril, “A cognitive system for fast quality of transmission estimation in core optical networks,” in Optical Fiber Communication Conference (OFC 2012), Los Angeles, CA, USA, paper OW3A.5 (2012).

T. Jiménez, J. C. Aguado, I. de Miguel, R. J. Durán, D. Sánchez, M. Angelou, N. Merayo, P. Fernández, N. Fernández, R. M. Lorenzo, I. Tomkos, and E. J. Abril, “Optimization of the knowledge base of a cognitive quality of transmission estimator for core optical networks,” 16th Optical Network Design and Modeling Conference (ONDM 2012), Colchester, UK, (2012).

I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed. (Morgan Kaufmann, 2011).

Y. Qin, K. Cheng, J. Triay, E. Escalona, G. S. Zervas, G. Zarris, N. Amaya-Gonzalez, C. Cervello-Pastor, R. Nejabati, and D. Simeonidou, “Demonstration of C/S based Hardware Accelerated QoT Estimation Tool in Dynamic Impairment-Aware Optical Network,” in European Conference in Optical Communications (ECOC 2010), Torino, IT, paper P5.17 (2010).

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Figures (3)

Fig. 1
Fig. 1

Experimental testbed.

Fig. 2
Fig. 2

(a) Percentage of successful classifications of lightpaths into high/low QoT categories according to an EVM threshold. (b) Impact of the size of the knowledge base on the percentage of successful classifications for the case of 19.5% EVM threshold.

Fig. 3
Fig. 3

(a). Percentage of successful classifications of lightpaths into high/low QoT categories according to an OSNR threshold. (b) Impact of the size of the knowledge base on the percentage of successful classifications for the case of 26 dB OSNR threshold.

Tables (1)

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Table 1 Example of experimental measurements used to populate the KB. Only one of the QoT parameters (OSNR or EVM) is included in the KB.

Equations (1)

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Similarity (x,y)= a=1 n W a 2 ( x a y a ) 2

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