Cognitive Dynamic Optical Networks

Cognitive networks are a promising solution for the control of heterogeneous optical networks. We review their fundamentals as well as a number of applications developed in the framework of the EU FP7 CHRON project

Self-Configuration and Self-Healing for Cognitive Optical Networks

In this article we propose a fuzzy controller, as an\ud inference engine for cognitive optical networks, to take decisions\ud about routing of new demands of lightpaths, considering physical\ud layer impairments (Fuzzy Controlled-PLIARWA algorithm), selfconfiguration,\ud self-healing and cross-layer optimization\ud functionalities. The proposed algorithm has been tested in a\ud metropolitan-scaled network. The preliminary results obtained are\ud promising in terms of modularity, flexibility, and high processing\ud speed, independency of underlying technology and scalability of the\ud solution

Next Generation Flexible and Cognitive Heterogeneous Optical Networks:Supporting the Evolution to the Future Internet

Optical networking is the cornerstone of the Future Internet as it provides the physical infrastructure of the core backbone networks. Recent developments have enabled much better quality of service/experience for the end users, enabled through the much higher capacities that can be supported. Furthermore, optical networking developments facilitate the reduction of complexity of operations at the IP layer and therefore reduce the latency of the connections and the expenditures to deploy and operate the networks. New research directions in optical networking promise to further advance the capabilities of the Future Internet. In this book chapter, we highlight the latest activities of the optical networking community and in particular what has been the focus of EU funded research. The concepts of flexible and cognitive optical networks are introduced and their key expected benefits are highlighted. The overall framework envisioned for the future cognitive flexible optical networks are introduced and recent developments are presented

Autonomous Physical Layer Characterization in Cognitive Optical Line Systems

We develop a procedure to autonomously characterize the optical line system physical layer, span-by-span, using in-line OTDRs and OCMs. This procedure has been experimentally validated, showing a clear correlation between the experimental outcomes and emulations

Cognitive and Autonomous Software-Defined Open Optical Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Experimental demonstration of machine-learning-aided QoT estimation in multi-domain elastic optical networks with alien wavelengths

In multi-domain elastic optical networks with alien wavelengths, each domain needs to consider intradomain and interdomain alien traffic to estimate and guarantee the required quality of transmission (QoT) for each lightpath and perform provisioning operations. This paper experimentally demonstrates an alien wavelength performance monitoring technique and machine-learning-aided QoT estimation for lightpath provisioning of intradomain/interdomain traffic. Testbed experiments demonstrate modulation format recognition, QoT monitoring, and cognitive routing for a 160 Gbaud alien multi-wavelength lightpath. By using experimental training datasets from the testbed and an artificial neural network, we demonstrated an accurate optical-signal-to-noise ratio prediction with an accuracy of ∼95% when using 1200 data points