Caballero Antonio, Aguado Juan Carlos, Borkowski Robert, Saldaña Silvia, Jiménez Tamara, de Miguel Ignacio, Arlunno Valeria, Durán Ramón J, Zibar Darko, Jensen Jesper B, Lorenzo Rubén M, Abril Evaristo J, Monroy Idelfonso Tafur
DTU Fotonik, Tech Univ of Denmark, DK-2800 Kgs Lyngby, Denmark.
Opt Express. 2012 Dec 10;20(26):B64-70. doi: 10.1364/OE.20.000B64.
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).
近年来，物理层损伤对光网络设计和运行的影响受到了广泛关注，因此需要估算技术在光连接建立之前预测其传输质量（QoT）。在本文中，我们报告了基于案例推理（CBR）技术的实验演示，该技术用于预测光通道是否满足QoT要求，从而支持损伤感知网络。认知QoT估计器在WDM 80 Gb/s PDM-QPSK测试平台上进行了验证，我们证明，即使基础知识库非常小且未优化，基于误差矢量幅度（EVM）参数，它也能实现79%至98.7%的成功分类，而基于光信噪比（OSNR）进行分类时，成功率约为100%。
