Advanced DSP Techniques for High-Capacity and Energy-Efficient Optical Fiber Communications

The rapid proliferation of the Internet has been driving communication networks closer and closer to their limits, while available bandwidth is disappearing due to an ever-increasing network load. Over the past decade, optical fiber communication technology has increased per fiber data rate from 10 Tb/s to exceeding 10 Pb/s. The major explosion came after the maturity of coherent detection and advanced digital signal processing (DSP). DSP has played a critical role in accommodating channel impairments mitigation, enabling advanced modulation formats for spectral efficiency transmission and realizing flexible bandwidth. This book aims to explore novel, advanced DSP techniques to enable multi-Tb/s/channel optical transmission to address pressing bandwidth and power-efficiency demands. It provides state-of-the-art advances and future perspectives of DSP as well

CNN-based eye pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by intercore crosstalk

To meet the required future challenge of providing enough bandwidth to achieve high data traffic rates in datacenter links, four-level pulse amplitude modulation (PAM4) signals transmission in short-haul intensity modulation-direct detection datacenters connections supported by homogeneous weakly-coupled multicore fibers has been proposed. However, in such fibers, a physical effect known as inter-core crosstalk (ICXT) limits significantly the performance of short-reach connections by causing large bit error rate (BER) fluctuations that can lead undesirable system outages. In this work, a convolutional neural network (CNN) is proposed for eye-pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by ICXT, with the aim of optical performance monitoring. The performance of the CNN is assessed using the root mean square error (RMSE). Considering PAM4 interdatacenter links with one interfering core and for different skew-symbol rate products, extinction ratios and crosstalk levels, the results show that the implemented CNN is able to predict the BER without surpassing the RMSE limit. The CNNs trained with different optical parameters obtained the best performance in terms of generalization comparing to CNNs trained with specific optical parameters. These results confirm that the CNN-based models can be able to extract features from received eye patterns to predict the BER without prior knowledge of the transmitted signals.De modo a colmatar a necessidade de fornecer largura de banda suficiente para atingir altas taxas de tráfego de dados em ligações entre centros de dados, foi proposta a transmissão de sinais com modulação de impulsos em amplitude com 4 níveis (PAM4) em ligações de curto alcance entre centro de dados com modulação de intensidade e deteção direta suportadas por fibras homogéneas multinúcleo fracamente acopladas. No entanto, neste tipo de fibras, a diafonia entre núcleos (ICXT) limita significativamente o desempenho das ligações, causando grandes flutuações da taxa de erros binários (BER), o que pode conduzir à indisponibilidade da ligação. Neste trabalho, através da análise de diagramas de olho usando uma rede neuronal convolucional (CNN) é estimada a BER em ligações ópticas entre centros de dados PAM4 degradadas por ICXT com o objetivo de monitorização do desempenho. Para avaliar o desempenho da CNN é usada como métrica a raiz do erro quadrático médio (RMSE). Para diferentes atrasos de propagação entre núcleos, razões de extinção e níveis de diafonia, a CNN é capaz de prever BERs sem ultrapassar o limite estabelecido para o RMSE. As CNNs treinadas com diferentes parâmetros ópticos obtiveram o melhor desempenho em termos de generalização em comparação com CNNs treinadas com parâmetros ópticos específicos. Estes resultados confirmam que os modelos baseados em CNN são capazes de extrair informação a partir de imagens de diagramas de olhos, prevendo a BER sem conhecimento prévio dos sinais transmitidos

Physical Layer Aware Optical Networks

This thesis describes novel contributions in the field of physical layer aware optical networks. IP traffic increase and revenue compression in the Telecom industry is putting a lot of pressure on the optical community to develop novel solutions that must both increase total capacity while being cost effective. This requirement is pushing operators towards network disaggregation, where optical network infrastructure is built by mix and match different physical layer technologies from different vendors. In such a novel context, every equipment and transmission technique at the physical layer impacts the overall network behavior. Hence, methods giving quantitative evaluations of individual merit of physical layer equipment at network level are a firm request during network design phases as well as during network lifetime. Therefore, physical layer awareness in network design and operation is fundamental to fairly assess the potentialities, and exploit the capabilities of different technologies. From this perspective, propagation impairments modeling is essential. In this work propagation impairments in transparent optical networks are summarized, with a special focus on nonlinear effects. The Gaussian Noise model is reviewed, then extended for wideband scenarios. To do so, the impact of polarization mode dispersion on nonlinear interference (NLI) generation is assessed for the first time through simulation, showing its negligible impact on NLI generation. Thanks to this result, the Gaussian Noise model is generalized to assess the impact of space and frequency amplitude variations along the fiber, mainly due to stimulated Raman scattering, on NLI generation. The proposed Generalized GN (GGN) model is experimentally validated on a setup with commercial linecards, compared with other modeling options, and an example of application is shown. Then, network-level power optimization strategies are discussed, and the Locally Optimization Global Optimization (LOGO) approach reviewed. After that, a novel framework of analysis for optical networks that leverages detailed propagation impairment modeling called the Statistical Network Assessment Process (SNAP) is presented. SNAP is motivated by the need of having a general framework to assess the impact of different physical layer technologies on network performance, without relying on rigid optimization approaches, that are not well-suited for technology comparison. Several examples of applications of SNAP are given, including comparisons of transceivers, amplifiers and node technologies. SNAP is also used to highlight topological bottlenecks in progressively loaded network scenarios and to derive possible solutions for them. The final work presented in this thesis is related to the implementation of a vendor agnostic quality of transmission estimator for multi-vendor optical networks developed in the context of the Physical Simulation Environment group of the Telecom Infra Project. The implementation of a module based on the GN model is briefly described, then results of a multi-vendor experimental validation performed in collaboration with Microsoft are shown