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
Tamara Jiménez, Juan Carlos Aguado, Ignacio de Miguel, Ramón J. Durán, Marianna Angelou, Noemí Merayo, Patricia Fernández, Rubén M. Lorenzo, Ioannis Tomkos, and Evaristo J. Abril, "A Cognitive Quality of Transmission Estimator for Core Optical Networks," J. Lightwave Technol. 31, 942-951 (2013)