Erick de A. Barboza

A performance comparison of multi-objective optimization evolutionary algorithms for all-optical networks design

In this paper we investigate the performance of well known multi-objective optimization evolutionary algorithms (MOEA) applied to the design of all-optical networks. We focused on the simultaneous optimization of the network topology and the device specifications in order to both minimize the total cost to build the network, i.e. the capital expenditure, and to maximize the overall network performance. We used the network blocking probability to assess the quality of the network service. We have considered the following five different MOEA: NSGAII, SPEA2, PESAII, PAES and MODE. In order to suggest a suitable algorithm to solve the problem, we performed a set of simulations aiming to analyze the convergence ability and the diversity of the generated solutions. We used four well known metrics to compare the achieved Pareto Fronts: hypervolume, spacing, maximum spread and coverage. From our results, we believe that the NSGAII and the SPEA2 algorithms are more suitable to solve this specific problem.

A multi-objective approach to design all-optical and translucent optical networks considering CapEx and QoT

In this paper we propose a methodology based on an evolutionary multi-objective optimization algorithm, called NSGA-II to design the topology and define the devices for both all-optical and translucent optical network. We aim to define the topology layout and the specification of the optical devices that should be deployed in the network in order to minimize simultaneously the total installation cost of a communication network (CapEx) and the total network blocking probability (performance criterion). To accomplish that, we propose a capital cost model for the network. We considered the following physical layer impairments: losses in optical devices, amplified spontaneous emission in optical amplifier and homodyne crosstalk in optical cross connect, polarization mode dispersion and residual dispersion. Our proposed methodology can solve the network topology design problem taking into account the physical layer impairments and the capital costs simultaneously. We also present a case study to show the effectiveness of our methodology to define the degree of transparency of the network.

Design of transparent optical networks considering physical impairments, CAPEX and energy consumption

We propose a methodology which applies a multi-objective Evolutionary Algorithm, the NSGAII, in order to design transparent optical networks, aiming to minimize simultaneously the total cost to build the network, the blocking probability and the energy consumption during operation. The optimizer provides a set of non-dominated solutions and, after that, the designer can decide which solution is more suitable for a specific case. We believe these three different important aspects must be taken into account during the design process. However, just some few papers tackle energy consumption from a physical network design perspective. Besides, to the best of our knowledge, none of the previous presented proposals consider all these issues simultaneously.

An efficient multi-objective evolutionary optimizer to design all-optical networks considering physical impairments and CAPEX

In this paper we propose efficient operators for a well known multi-objective evolutionary optimizer, called NSGA II, applied to design all-optical networks regarding the network topology and the device specifications in order to both minimize the capital expenditure to build the network and to maximize the overall network performance. From the experiments, we perceived that it is better to use an uniform crossover, to include preferences a priori and to initialize the individuals emphasizing the network topology.

Self-adaptive erbium-doped fiber amplifiers using machine learning

This paper presents a method to autonomously adjust the operating point of amplifiers in a cascade using an approach based on machine learning. The goal is to smoothly adjust the gain of each amplifier in the cascade in order to reach predefined input and output power levels for the entire link, aiming to minimize both the noise figure and the gain flatness of the transmission system. The proposal uses an iterative method and performs feedforward and backward error adjustments based on local information. The experimental results indicate that our proposal can optimize the performance of the link ensuring predefined input and output power levels, which is important in a network scenario. As an example, our proposal was capable to define the gain of 6 amplifiers returning a link with a noise figure equal to 30.06 dB and a gain flatness equal to 5.26 dB, while maintaing the input and output powers around 3 dBm with an error lower than 0.1 dB.

Simple design of Raman fiber amplifiers using a multi-objective optimizer

This paper presents a method to design Raman amplifiers using a multi-objective particle swarm optimizer. The goal is to define the number of pump lasers and their wavelengths and powers in order to maximize both the average on-off gain and the flatness of the gain. The multi-objective optimizer aims to generate non-dominated solutions considering the average on-off gain, the number of used pump lasers and the gain ripple over the transmission bandwidth. The experimental results showed that our proposal is robust and can generate Pareto Fronts which are useful to design real Raman amplifiers. From these results, we observed that the designer can easily determine the minimum number of pump lasers to build the amplifier properly. As an example, we designed an amplifier with ripple and average on-off gain equal to 0.2 dB and 20 dB, respectively, for the case where 40 signal channels are amplified with total pump power equal to 1.5 W.

Estimating the spectral gain and the noise figure of EDFA using artificial neural networks

In this paper, we propose a new approach to estimate the gain and the noise figure of EDFAs. This is an important tool for solving the adaptive control of operating point (ACOP) problem in optical amplifiers. The proposal uses an artificial neural network to enable a quick estimation of both amplifiers features requiring a small amount of memory. Results show that the neural network estimator is 80 times faster and uses 804 times fewer data than state of the art estimator, without losing significant accuracy. These advantages will help the use of iterative optimization techniques, like computational intelligence, to tackle the ACOP problem. Besides, it can assist in the implementation of ACOP approaches in simple devices like microcontrollers.

Comparação do Desempenho de Algoritmos Bioinspirados de Otimização Multiobjetivos para Projeto de Amplificadores Raman

Erick de A. Barboza e Carmelo J. A. Bastos-Filho Escola Politécnica de Pernambuco Universidade de Pernambuco Recife, Brasil Email: carmelofilho@ieee.org Joaquim F. Martins-Filho Departamento de Eletrônica e Sistemas Universidade Federal de Pernambuco Recife, Brasil Email: jfmf@ufpe.br Marcelo E. V. Segatto e Maria J. Pontes Departamento de Engenharia Elétrica Universidade Federal do Espírito Santo Vitória, Brasil Email: segatto@ele.ufes.br

Impact of nonlinear effects on the performance of 120 Gb/s 64 QAM optical system using adaptive control of cascade of amplifiers

Several approaches have been proposed in order to solve the adaptive control of optical amplifier operating point problem (ACOP). These approaches aim to adjust the gain of each amplifier in a cascade of amplifiers. Some previous results show that it is possible to decrease the noise added by the amplifiers by using these strategies. However, the configurations found by these approaches may degrade the quality of the transmission due to nonlinear effect, because the amplifiers may operate with a high output power. Therefore, the goal of this work is to analyze the impact of the nonlinear effects in the solutions found by these approaches by evaluating the BER of a system that uses a very sensitive modulation in terms of OSNR (64QAM). We tested LossComp, WAdGC and AsHB approaches in a system with 40 channels, using 50GHz fixed grid and considering high and low system input powers. The results showed that for all analyzed approaches the system quality of transmission is affected by the nonlinear effects, and the effect is worse in those with higher amplifier output powers.