Cognitive Dynamic Optical Networks

Miguel, Ignacio de; Durán, Ramón J.; Lorenzo, Rubén M.; Caballero, Antonio; Monroy, Idelfonso Tafur; Ye, Yabin; Tymecki, Andrzej; Tomkos, Ioannis; Angelou, M.; Klonidis, Dimitrios; Francescon, Antonio; Siracusa, Domenico; Salvadori, Elio

doi:10.1364/ofc.2013.ow1h.1

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…We assume that the loss of each fiber span is exactly compensated for by an EDFA placed at the end of the span. 2 The received power is thus equal to the transmitted power P . Furthermore, since the link parameters Ln are assumed to be constant within a fiber span, the z dependence is dropped from now on.…”

Section: B Nli Accumulationmentioning

confidence: 99%

“…1. 2 While Raman amplification can be described within the GN model, it leads to the introduction of new system parameters and complicates the analytical expressions. It is outside the scope of the work presented here.…”

Section: Nli For a Single Fiber Spanmentioning

confidence: 99%

“…Optical networks are not as flexible as their electronic counterparts, but there are several efforts that aim at improving the situation by investigating, e.g., cognitive [2] and elastic [3] optical networks. An increased flexibility requires, e.g., hardware routing of channels in the optical domain and a monitoring system responsible for the scheduling of the data streams.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling of Nonlinear Signal Distortion in Fiber-Optic Networks

Johannisson

Agrell

2014

J. Lightwave Technol.

120

118

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A low-complexity model for signal quality prediction in a nonlinear fiber-optical network is developed. The model, which builds on the Gaussian noise model, takes into account the signal degradation caused by a combination of chromatic dispersion, nonlinear signal distortion, and amplifier noise. The center frequencies, bandwidths, and transmit powers can be chosen independently for each channel, which makes the model suitable for analysis and optimization of resource allocation, routing, and scheduling in large-scale optical networks applying flexible-grid wavelength-division multiplexing.

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Section: B Nli Accumulationmentioning

confidence: 99%

Section: Nli For a Single Fiber Spanmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of Nonlinear Signal Distortion in Fiber-Optic Networks

Johannisson

Agrell

2014

J. Lightwave Technol.

120

118

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“…The physical testbed consists of three basic elements: the Cognitive Decision System (CDS), the Control and Management Plane (CMP) and the Data Plane, as it is depicted in Figure 1. The control plane is distributed and it is hosted in 4 nodes (4 separate PCs that are emulated 4 real network nodes) [5]. One separate machine is used to host the CDS, which performs all the required actions (routing decisions, storing of database, etc.…”

Section: Cognitive Heterogeneous Reconfigurable Optical Network Testbedmentioning

confidence: 99%

Experimental Demonstration of a Cognitive Optical Network for Reduction of Restoration Time

Kachris

Klonidis

Francescon

et al. 2014

Optical Fiber Communication Conference

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This paper presents the implementation and performance evaluation of a cognitive heterogeneous optical network testbed. The testbed integrates the CMP, the data plane and the cognitive system and reduces by 48% the link restoration time. Introduction to Cognitive Reconfigurable Optical NetworksOptical networks are facing increased levels of heterogeneity, from types of services to transmission technologies. Hence, a key issue of highly heterogeneous networks is how to efficiently control and manage network resources while fulfilling user demands and complying with quality of service requirements. A solution for such a scenario may come from cognitive networks. A cognitive network is defined as "A network with a process that can perceive current network conditions, and then plan, decide, and act on those conditions. The network can learn from these adaptations and use them to make future decisions, all while taking into account end-to-end goals." [1]. Hence, there are three main ingredients in such a network:  Monitoring elements, which provide the network with the perception of the current conditions, and thus enable an aware network.  Software adaptable elements, which provide the network with the capacity of modifying its current configuration, thus enabling an adaptive network.  Cognitive processes, which learn or make use of past history, so that even when facing two equivalent scenarios, the network (or the element containing those cognitive processes) may act in a different way if its previous history is different. This third element is the main feature which enables a cognitive network. Therefore, a cognitive network is a network which is able to adapt itself to current or forecasted conditions by taking into account previous history, and which is able to act proactively, rather than reactively, in order to avoid problems before they arise. Moreover, those tasks should be performed autonomously, with little or no intervention of the network operator. Cognitive networks have already shown their advantages in wireless environments [2], but they are also applicable to wired communication architectures, and are especially appealing for optimizing performance in heterogeneous networks. The design of a cognitive optical network involves determining how the three aforementioned ingredients are implemented (and where), how they are glued together, as well as determining which tasks are going to be solved with the help of cognition.The CHRON project [3] has proposed a novel architecture for cognitive heterogeneous reconfigurable optical networks. The aim of the CHRON project is to develop and showcase network architecture and a control plane which efficiently use resources in a heterogeneous scenario while fulfilling QoS requirements of each type of services and applications supported by the network. This cognitive network observes acts, learns and optimizes its performance, taking into account its high degree of heterogeneity with respect to QoS, transmission and switching techniques. For that aim, ...

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