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A Cognitive Quality of Transmission Estimator for Core Optical Networks

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10 Author(s)
Jimenez, T. ; Univ. of Valladolid, Valladolid, Spain ; Aguado, J.C. ; de Miguel, I. ; Duran, R.J.
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We propose a cognitive Quality of Transmission (QoT) estimator for classifying lightpaths into high or low quality categories in impairment-aware wavelength-routed optical networks. The technique is based on Case-Based Reasoning (CBR), an artificial intelligence technique which solves new problems by exploiting previous experiences, which are stored on a knowledge base. We also show that by including learning and forgetting techniques, the underlying knowledge base can be optimized, thus leading to a significant reduction on the computing time for on-line operation. The performance of the cognitive estimator is evaluated in a long haul and in an ultra-long haul network, and we demonstrate that it achieves more than 98% successful classifications, and that it is up to four orders of magnitude faster when compared with a non-cognitive QoT estimator, the Q-Tool.

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Lightwave Technology, Journal of  (Volume:31 ,  Issue: 6 )