Search

This paper proposes a metaheuristic optimization method based on Particle Swarm Optimization (PSO) that can be conveniently used to determine launch power profiles of elastic optical channels subject to nonlinear physical impairments. The method focuses on optimizing the overall OSNR margin of heterogeneous connections in a single-link network. The evolutionary method is contrasted to an analytical process widely used for WDM channels and shows a disruptive behavior to what is expected as well as a relevant gain in terms of additional propagated spans when applied to flexible-grid links.

In Elastic Optical Networks (EON), the problem of routing and spectrum Allocation (RSA) has been solved along the years through the use of optimization techniques or heuristics, with the aim of minimizing the use of network resources or maximizing the capacity for future requests. The effects of the physical layer in the RSA problem are of fundamental importance given that there is a distance limitation with the required bit rate and modulation format used to transmit the signal. On considering the linear and non-linear noise effects, it is possible to include quality of transmission (QoT) in the optimization process. In this paper, we propose a modification to a traditional heuristic for the RSA problem in Elastic Optical networks to consider both effects of the physical layer. The proposed modification takes into account the channel input power and SNR Margin to choose all modulation formats and to assign resources. The main goal is to increase the spectral efficiency and throughput of the channels by mitigating blockages in the physical layer. Simulations were performed for various topologies, and the results suggest advantages in terms of number of blocked channels and spectrum usage of the new proposal in relation to the traditional BSR heuristics.

This paper proposes a novel joint resource allocation technique for flexible-grid systems by utilizing non-dominant sort genetic algorithm (NSGA-II) in a multi-objective optimization framework. It pioneers the implementation of an evolutionary mechanism to optimize resources as means of mitigation of physical layer impairments. This investigation initially introduces a proposal in which bandwidth reduction, maximization of the minimum signal-to-noise ratio (SNR) margin, and minimization/maximization of the sum of SNR margins are studied under dual-objective Pareto analysis in the link-level scenario. Later, the technique extends existing provisioning strategies for network planning by targeting the reduction of blocking and spectral utilization of optical connections.