Experimental demonstration of a cognitive quality of transmission estimator for optical communication systems | Zendy

Antonio Caballero | Zendy; Juan Carlos Aguado | Zendy; Robert Borkowski | Zendy; Silvia Saldaña | Zendy; Tamara Jiménez | Zendy; Ignacio de Miguel | Zendy; Valeria Arlunno | Zendy; Ramón J. Durán Barroso | Zendy; Darko Zibar | Zendy; Jesper Bevensee Jensen | Zendy; Rubén M. Lorenzo | Zendy; Evaristo J. Abril | Zendy; Idelfonso Tafur Monroy | Zendy

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Experimental demonstration of a cognitive quality of transmission estimator for optical communication systems

Author(s) -

Antonio Caballero,

Juan Carlos Aguado,

Robert Borkowski,

Silvia Saldaña,

Tamara Jiménez,

Ignacio de Miguel,

Valeria Arlunno,

Ramón J. Durán Barroso,

Darko Zibar,

Jesper Bevensee Jensen,

Rubén M. Lorenzo,

Evaristo J. Abril,

Idelfonso Tafur Monroy

Publication year - 2012

Publication title -

optics express

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.394

H-Index - 271

ISSN - 1094-4087

DOI - 10.1364/oe.20.000b64

Subject(s) - testbed , computer science , transmission (telecommunications) , estimator , optical communication , physical layer , signal to noise ratio (imaging) , wavelength division multiplexing , electronic engineering , optics , telecommunications , computer network , physics , mathematics , statistics , engineering , wavelength , wireless

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).

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