Abstract
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.
© 2013 IEEE
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