Mitigation of Nonlinear Impairments by Using Support Vector Machine and Nonlinear Volterra Equalizer

A support vector machine (SVM) based detection is applied to different equalization schemes for a data center interconnect link using coherent 64 GBd 64-QAM over 100 km standard single mode fiber (SSMF). Without any prior knowledge or heuristic assumptions, the SVM is able to learn and capture the transmission characteristics from only a short training data set. We show that, with the use of suitable kernel functions, the SVM can create nonlinear decision thresholds and reduce the errors caused by nonlinear phase noise (NLPN), laser phase noise, I/Q imbalances and so forth. In order to apply the SVM to 64-QAM we introduce a binary coding SVM, which provides a binary multiclass classification with reduced complexity. We investigate the performance of this SVM and show how it can improve the bit-error rate (BER) of the entire system. After 100 km the fiber-induced nonlinear penalty is reduced by 2 dB at a BER of 3.7 × 10 −3 . Furthermore, we apply a nonlinear Volterra equalizer (NLVE), which is based on the nonlinear Volterra theory, as another method for mitigating nonlinear effects. The combination of SVM and NLVE reduces the large computational complexity of the NLVE and allows more accurate compensation of nonlinear transmission impairments

Anwendung von maschinellem Lernen in der optischen Nachrichtenübertragungstechnik

Aufgrund des zunehmenden Datenverkehrs wird erwartet, dass die optischen Netze zukünftig mit höheren Systemkapazitäten betrieben werden. Dazu wird bspw. die kohärente Übertragung eingesetzt, bei der das Modulationsformat erhöht werden kann, erforder jedoch ein größeres SNR. Um dies zu erreichen, wird die optische Signalleistung erhöht, wodurch die Datenübertragung durch die nichtlinearen Beeinträchtigungen gestört wird. Der Schwerpunkt dieser Arbeit liegt auf der Entwicklung von Modellen des maschinellen Lernens, die auf diese nichtlineare Signalverschlechterung reagieren. Es wird die Support-Vector-Machine (SVM) implementiert und als klassifizierende Entscheidungsmaschine verwendet. Die Ergebnisse zeigen, dass die SVM eine verbesserte Kompensation sowohl der nichtlinearen Fasereffekte als auch der Verzerrungen der optischen Systemkomponenten ermöglicht. Das Prinzip von EONs bietet eine Technologie zur effizienten Nutzung der verfügbaren Ressourcen, die von der optischen Faser bereitgestellt werden. Ein Schlüsselelement der Technologie ist der bandbreitenvariable Transponder, der bspw. die Anpassung des Modulationsformats oder des Codierungsschemas an die aktuellen Verbindungsbedingungen ermöglicht. Um eine optimale Ressourcenauslastung zu gewährleisten wird der Einsatz von Algorithmen des Reinforcement Learnings untersucht. Die Ergebnisse zeigen, dass der RL-Algorithmus in der Lage ist, sich an unbekannte Link-Bedingungen anzupassen, während vergleichbare heuristische Ansätze wie der genetische Algorithmus für jedes Szenario neu trainiert werden müssen

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

Feature binding of MPEG-7 Visual Descriptors Using Chaotic Series

Due to advanced segmentation and tracking algorithms, a video can be divided into numerous objects. Segmentation and tracking algorithms output different low-level object features, resulting in a high-dimensional feature vector per object. The challenge is to generate feature vector of objects which can be mapped to human understandable description, such as object labels, e.g., person, car. MPEG-7 provides visual descriptors to describe video contents. However, generally the MPEG-7 visual descriptors are highly redundant, and the feature coefficients in these descriptors need to be pre-processed for domain specific application. Ideal case would be if MPEG-7 visual descriptor based feature vector, can be processed similar to some functional simulations of human brain activity. There has been a established link between the analysis of temporal human brain oscillatory signals and chaotic dynamics from the electroencephalography (EEG) of the brain neurons. Neural signals in limited brain activities are found to be behaviorally relevant (previously appeared to be noise) and can be simulated using chaotic series. Chaotic series is referred to as either a finite-difference or an ordinary differential equation, which presents non-random, irregular fluctuations of parameter values over time in a dynamical system. The dynamics in a chaotic series can be high - or low -dimensional, and the dimensionality can be deduced from the topological dimension of the attractor of the chaotic series. An attractor is manifested by the tendency of a non-linear finite difference equation or an ordinary differential equation, under various but delimited conditions, to go to a reproducible active state, and stay there. We propose a feature binding method, using chaotic series, to generate a new feature vector, C-MP7 , to describe video objects. The proposed method considers MPEG-7 visual descriptor coefficients as dynamical systems. Dynamical systems are excited (similar to neuronal excitation) with either high- or low-dimensional chaotic series, and then histogram-based clustering is applied on the simulated chaotic series coefficients to generate C-MP7 . The proposed feature binding offers better feature vector with high-dimensional chaotic series simulation than with low-dimensional chaotic series, over MPEG-7 visual descriptor based feature vector. Diverse video objects are grouped in four generic classes (e.g., has [barbelow]person, has [barbelow]group [barbelow]of [barbelow]persons, has [barbelow]vehicle, and has [barbelow]unknown ) to observe how well C-MP7 describes different video objects compared to MPEG-7 feature vector. In C-MP7 , with high dimensional chaotic series simulation, 1). descriptor coefficients are reduced dynamically up to 37.05% compared to 10% in MPEG-7 , 2) higher variance is achieved than MPEG-7 , 3) multi-class discriminant analysis of C-MP7 with Fisher-criteria shows increased binary class separation for clustered video objects than that of MPEG-7 , and 4) C-MP7 , specifically provides good clustering of video objects for has [barbelow]vehicle class against other classes. To test C-MP7 in an application, we deploy a combination of multiple binary classifiers for video object classification. Related work on video object classification use non-MPEG-7 features. We specifically observe classification of challenging surveillance video objects, e.g., incomplete objects, partial occlusion, background over lapping, scale and resolution variant objects, indoor / outdoor lighting variations. C-MP7 is used to train different classes of video objects. Object classification accuracy is verified with both low-dimensional and high-dimensional chaotic series based feature binding for C-MP7 . Testing of diverse video objects with high-dimensional chaotic series simulation shows, 1) classification accuracy significantly improves on average, 83% compared to the 62% with MPEG-7 , 2) excellent clustering of vehicle objects leads to above 99% accuracy for only vehicles against all other objects, and 3) with diverse video objects, including objects from poor segmentation. C-MP7 is more robust as a feature vector in classification than MPEG-7 . Initial results on sub-group classification for male and female video objects in has [barbelow]person class are also presentated as subjective observations. Earlier, chaos series properties have been used in video processing applications for compression and digital watermarking. To our best knowledge, this work is the first to use chaotic series for video object description and apply it for object classificatio