Optical Power Control to Efficiently Handle Flex-Grid Spectrum Gain over Existing Fixed-Grid Network Infrastructures

International audienceThe exponential traffic growth in optical networks has triggered the evolution from Fixed-Grid to Flex-Grid technology. This evolution allows better spectral efficiency and spectrum usage over current networks in order to facilitate dynamic and huge traffic demands. The integration of Flex-Grid technology increases the number of optical channels established over optical links, leading, however, to an increase in amplification power and possibly saturating optical amplifiers. In this work, we propose a power adaptation process that takes advantage of link optical signal to noise ratio (OSNR) margins to allow network operators to support this power increase while maintaining the use of legacy amplifiers. Results show that controlling channel optical power benefits from the Flex-Grid in terms of spectrum and capacity gain using in-place amplifier infrastructure

Ultrafast Consensus in Small-World Networks

In this paper, we demonstrate a phase transition phenomenon in algebraic connectivity of small-world networks. Algebraic connectivity of a graph is the second smallest eigenvalue of its Laplacian matrix and a measure of speed of solving consensus problems in networks. We demonstrate that it is possible to dramatically increase the algebraic connectivity of a regular complex network by 1000 times or more without adding new links or nodes to the network. This implies that a consensus problem can be solved incredibly fast on certain small-world networks giving rise to a network design algorithm for ultra fast information networks. Our study relies on a procedure called "random rewiring" due to Watts & Strogatz (Nature, 1998). Extensive numerical results are provided to support our claims and conjectures. We prove that the mean of the bulk Laplacian spectrum of a complex network remains invariant under random rewiring. The same property only asymptotically holds for scale-free networks. A relationship between increasing the algebraic connectivity of complex networks and robustness to link and node failures is also shown. This is an alternative approach to the use of percolation theory for analysis of network robustness. We also show some connections between our conjectures and certain open problems in the theory of random matrices

System Identification of multi-rotor UAVs using echo state networks

Controller design for aircraft with unusual configurations presents unique challenges, particularly in extracting valid mathematical models of the MRUAVs behaviour. System Identification is a collection of techniques for extracting an accurate mathematical model of a dynamic system from experimental input-output data. This can entail parameter identification only (known as grey-box modelling) or more generally full parameter/structural identification of the nonlinear mapping (known as black-box). In this paper we propose a new method for black-box identification of the non-linear dynamic model of a small MRUAV using Echo State Networks (ESN), a novel approach to train Recurrent Neural Networks (RNN)