Efficient Modeling and Simulation of Wavelength Division Multiplexing Dual Polarization QPSK Optical Fiber Transmission

Due to enormous growth in communications, wavelength division multiplexing (WDM) systems are popular because these systems allow us to expand the capacity of the networks without laying more optical fiber cables. In this thesis, we have systematically derived the coupled nonlinear Schrödinger (CNLS) equations, including a consistent definition of the complex envelope, Fourier transform, the state of polarization, and derivation under the engineering notation. After a discussion of coarse step based second order symmetrized split-step Fourier (SSSF) simulation method, which is applicable to the numerical solution of the CNLS equations, an analytical step-size selection based local error method is applied to the WDM optical fiber communication systems. With systematical simulation study of both standard single mode fiber (SSMF) fiber links and true-wave reduced slope (TWRS) fiber links. It is found that similar to the single channel systems, the global simulation accuracy for the vector propagation can be satisfied using the local error bound (LEB) obtained from a scalar propagation model for the same global error over a large range of simulation accuracy and differential group delay (DGD). Furthermore, carefully designed numerical simulations are used to show that the proposed local error method leads to higher computational efficiency compared to other prevalent step-size selection schemes in vector WDM simulations. The scaling of the global simulation error with respect to the number of optical fiber spans is demonstrated, and global error control for multi-span WDM simulations is proposed

Efficient Modeling and Simulation of Wavelength Division Multiplexing Dual Polarization QPSK Optical Fiber Transmission

Due to enormous growth in communications, wavelength division multiplexing (WDM) systems are popular because these systems allow us to expand the capacity of the networks without laying more optical fiber cables. In this thesis, we have systematically derived the coupled nonlinear Schrödinger (CNLS) equations, including a consistent definition of the complex envelope, Fourier transform, the state of polarization, and derivation under the engineering notation. After a discussion of coarse step based second order symmetrized split-step Fourier (SSSF) simulation method, which is applicable to the numerical solution of the CNLS equations, an analytical step-size selection based local error method is applied to the WDM optical fiber communication systems. With systematical simulation study of both standard single mode fiber (SSMF) fiber links and true-wave reduced slope (TWRS) fiber links. It is found that similar to the single channel systems, the global simulation accuracy for the vector propagation can be satisfied using the local error bound (LEB) obtained from a scalar propagation model for the same global error over a large range of simulation accuracy and differential group delay (DGD). Furthermore, carefully designed numerical simulations are used to show that the proposed local error method leads to higher computational efficiency compared to other prevalent step-size selection schemes in vector WDM simulations. The scaling of the global simulation error with respect to the number of optical fiber spans is demonstrated, and global error control for multi-span WDM simulations is proposed

Stochastic Digital Backpropagation with Residual Memory Compensation

Stochastic digital backpropagation (SDBP) is an extension of digital backpropagation (DBP) and is based on the maximum a posteriori principle. SDBP takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments. The decisions in SDBP are taken on a symbol-by-symbol (SBS) basis, ignoring any residual memory, which may be present due to non-optimal processing in SDBP. In this paper, we extend SDBP to account for memory between symbols. In particular, two different methods are proposed: a Viterbi algorithm (VA) and a decision directed approach. Symbol error rate (SER) for memory-based SDBP is significantly lower than the previously proposed SBS-SDBP. For inline dispersion-managed links, the VA-SDBP has up to 10 and 14 times lower SER than DBP for QPSK and 16-QAM, respectively.Comment: 7 pages, accepted to publication in 'Journal of Lightwave Technology (JLT)

Observing and Modeling the Physical Layer Phenomena in Open Optical Systems for Network planning and management

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

Improved Lower Bounds on Mutual Information Accounting for Nonlinear Signal-Noise Interaction

In fiber-optic communications, evaluation of mutual information (MI) is still an open issue due to the unavailability of an exact and mathematically tractable channel model. Traditionally, lower bounds on MI are computed by approximating the (original) channel with an auxiliary forward channel. In this paper, lower bounds are computed using an auxiliary backward channel, which has not been previously considered in the context of fiber-optic communications. Distributions obtained through two variations of the stochastic digital backpropagation (SDBP) algorithm are used as auxiliary backward channels and these bounds are compared with bounds obtained through the conventional digital backpropagation (DBP). Through simulations, higher information rates were achieved with SDBP, {which can be explained by the ability of SDBP to account for nonlinear signal--noise interactionsComment: 8 pages, 5 figures, accepted for publication in Journal of Lightwave Technolog

Compensação digital de distorções da fibra em sistemas de comunicação óticos de longa distância

The continuous increase of traffic demand in long-haul communications motivated the network operators to look for receiver side techniques to mitigate the nonlinear effects, resulting from signal-signal and signal-noise interaction, thus pushing the current Capacity boundaries. Machine learning techniques are a very hot-topic with given proofs in the most diverse applications. This dissertation aims to study nonlinear impairments in long-haul coherent optical links and the current state of the art in DSP techniques for impairment mitigation as well as the integration of machine learning strategies in optical networks. Starting with a simplified fiber model only impaired by ASE noise, we studied how to integrate an ANN-based symbol estimator into the signal pipeline, enabling to validate the implementation by matching the theoretical performance. We then moved to nonlinear proof of concept with the incorporation of NLPN in the fiber link. Finally, we evaluated the performance of the estimator under realistic simulations of Single and Multi- Channel links in both SSFM and NZDSF fibers. The obtained results indicate that even though it may be hard to find the best architecture, Nonlinear Symbol Estimator networks have the potential to surpass more conventional DSP strategies.O aumento contínuo de tráfego nas comunicações de longo-alcance motivou os operadores de rede a procurar técnicas do lado do receptor para atenuar os efeitos não lineares resultantes da interacção sinal-sinal e sinal-ruído, alargando assim os limites da capacidade do sistema. As técnicas de aprendizagem-máquina são um tópico em ascenção com provas dadas nas mais diversas aplicações e setores. Esta dissertação visa estudar as principais deficiências nas ligações de longo curso e o actual estado da arte em técnicas de DSP para mitigação das mesmas, bem como a integração de estratégias de aprendizagem-máquina em redes ópticas. Começando com um modelo simplificado de fibra apenas perturbado pelo ruído ASE, estudámos como integrar um estimador de símbolos baseado em ANN na cadeia do prodessamento de sinal, conseguindo igualar o desempenho teórico. Procedemos com uma prova de conceito perante não linearidades com a incorporação do ruído de fase não linear na propagação. Finalmente, avaliamos o desempenho do estimador com simulações realistas de links Single e Multi canal tanto em fibras SSFM como NZDSF. Os resultados obtidos indicam que apesar da dificuldade de encontrar a melhor arquitectura, a estimação não linear baseada em redes neuronais têm o potencial para ultrapassar estratégias DSP mais convencionais.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields

Nonlinear Interference Generation in Wideband and Disaggregated Optical Network Architectures

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