Supervised Machine Learning for Signals Having RRC Shaped Pulses

Classification performances of the supervised machine learning techniques such as support vector machines, neural networks and logistic regression are compared for modulation recognition purposes. The simple and robust features are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals having root-raised-cosine shaped pulses are simulated in extreme noisy conditions having joint impurities of block fading, lack of symbol and sampling synchronization, carrier offset, and additive white Gaussian noise. The features are based on sample mean and sample variance of the imaginary part of the product of two consecutive complex signal values.Comment: 5 page

Deep learning for wireless communications : flexible architectures and multitask learning

Demand for wireless connectivity has never been higher and continues to grow rapidly. Connecting more devices requires mindfulness in managing the limited resources of energy and radio spectrum. The advent of Software Defined Radio (SDR) has enabled breathroughs in radio configurability, enabling dynamic spectrum access and physical layer optimizations at runtime. In recent years Machine Learning (ML) has been a key enabling technology of various innovations in the wireless communications domain, taking advantage of the newfound flexibility in SDR. The new ML-based signal processing models are no longer based entirely on Digital Signal Processing (DSP) expertise, but are developed in a data-driven approach. This paradigm shift in receiver design is recent, and appropriate architectures and best model training practices have yet to be established. This thesis explores multiple wireless communications tasks addressed with the toolbox of Deep Learning (DL), which is a subset of ML. Many existing DL solutions are hampered by the limitations of the chosen architectures, which limits their adoptability as drag-and-drop solutions by wireless system designers. Recurrent Neural Network (RNN) and Fully Convolutional Neural Network (FCN) architecture types are explored that enable the adaptability one would expect of classic DSP functions (like the filter). The field of wireless communications boasts a wealth of data, due to the mature and feature-rich simulation software ecosystem. In Radio Frequency Machine Learning (RFML) this is regularly leveraged to produce datasets for the new data-driven models. Techniques like Multitask Learning (MTL) can exploit this simulated data even further by allowing models to be trained on their primary task, like signal classification or demodulation, while simultaneously estimating the channel quality. The findings presented in this work show that fully convolutional architectures can be more appropriate for tasks like frame synchronization compared to commonly applied classification models. RNN-based autoencoders achieve good results as an end-to-end trainable receiver solution, however they can be challenging to apply to longer sequences. MTL is identified as an excellent technique not only for training unique models, capable of performing multiple tasks, but as a regularization technique in RFML.Demand for wireless connectivity has never been higher and continues to grow rapidly. Connecting more devices requires mindfulness in managing the limited resources of energy and radio spectrum. The advent of Software Defined Radio (SDR) has enabled breathroughs in radio configurability, enabling dynamic spectrum access and physical layer optimizations at runtime. In recent years Machine Learning (ML) has been a key enabling technology of various innovations in the wireless communications domain, taking advantage of the newfound flexibility in SDR. The new ML-based signal processing models are no longer based entirely on Digital Signal Processing (DSP) expertise, but are developed in a data-driven approach. This paradigm shift in receiver design is recent, and appropriate architectures and best model training practices have yet to be established. This thesis explores multiple wireless communications tasks addressed with the toolbox of Deep Learning (DL), which is a subset of ML. Many existing DL solutions are hampered by the limitations of the chosen architectures, which limits their adoptability as drag-and-drop solutions by wireless system designers. Recurrent Neural Network (RNN) and Fully Convolutional Neural Network (FCN) architecture types are explored that enable the adaptability one would expect of classic DSP functions (like the filter). The field of wireless communications boasts a wealth of data, due to the mature and feature-rich simulation software ecosystem. In Radio Frequency Machine Learning (RFML) this is regularly leveraged to produce datasets for the new data-driven models. Techniques like Multitask Learning (MTL) can exploit this simulated data even further by allowing models to be trained on their primary task, like signal classification or demodulation, while simultaneously estimating the channel quality. The findings presented in this work show that fully convolutional architectures can be more appropriate for tasks like frame synchronization compared to commonly applied classification models. RNN-based autoencoders achieve good results as an end-to-end trainable receiver solution, however they can be challenging to apply to longer sequences. MTL is identified as an excellent technique not only for training unique models, capable of performing multiple tasks, but as a regularization technique in RFML

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

Point-to-point advanced self-coherent 200 Gb/s multicore fiber links supported by neural networks

Este trabalho propõe um sistema de fibra multi-núcleo (MCF) de curto alcance de 200 Gb/s considerando lasers reais com ruído de fase (LPN) e recetores Kramers Kronig. Os lasers usados neste sistema consideram larguras de linha típicas de lasers de cavidade externa (ECL) e lasers de feedback distribuído (DFB). Uma rede neural feed-forward (FFNN) é implementada para mitigar os efeitos do LPN e da diafonia entre núcleos (ICXT). O objetivo principal deste trabalho é avaliar o impacto do LPN na melhoria de desempenho proporcionada pela FFNN proposta. Em primeiro lugar, um sistema sem LPN é estudado como referência. Posteriormente, o LPN é introduzido no sistema, com o sinal ótico injetado no núcleo interferente a considerar um laser com largura de linha típica de ECLs e lasers DFB. O sinal ótico injetado no núcleo interferido considera um laser ideal sem LPN. Concluiu-se que, aplicando a FFNN, o BER médio obtido com o ECL e o laser DFB, em comparação com o caso de referência, aumentou mais de uma ordem de grandeza. O BER médio obtido com ambos os lasers manteve-se abaixo do limite de BER quando o intervalo de tempo entre a fase de treino e uso da FFNN (ΔT) não excedeu 20% do tempo de coerência (Tc). Considerando ΔT/Tc=0.1, a FFNN proporcionou uma melhoria de 25% e 22% na probabilidade de indisponibilidade em comparação com a probabilidade de indisponibilidade antes da FFNN, ao considerar um ECL e um laser DFB, respetivamente. Essa melhoria diminui com o aumento de ΔT/Tc.This work proposes a 200 Gb/s short-reach multi-core fiber (MCF) system considering real lasers with laser phase noise (LPN) and Kramers Kronig receivers. The lasers employed in this system consider linewidths typical of external cavity lasers (ECL) and distributed feedback lasers (DFB). A feed-forward neural network (FFNN) is implemented to mitigate the effects of the LPN and inter-core crosstalk (ICXT). The main objective of this work is to assess the impact of the LPN on the performance improvement provided by the proposed FFNN. Firstly, a system without LPN is studied as reference. Afterwards, the LPN is introduced in the system as the optical signal injected in the interfering core considered a laser with linewidth typical of ECLs and DFB lasers. The optical signal injected in the interfered core considered an ideal laser without LPN. It was concluded that, when employing the FFNN, the mean BER obtained with the ECL and DFB laser, compared to the reference case, increased more than one order of magnitude. Furthermore, the mean BER obtained with both lasers was kept below the BER threshold when the time interval between the training phase and the use of the FFNN (ΔT) did not exceed 20% of the coherence time (Tc). Considering ΔT/Tc=0.1, the FFNN provided a 25% and a 22% improvement on the outage probability when compared with the outage probability before the FFNN, while considering an ECL and a DFB laser, respectively. This improvement will decrease with the increase of the ΔT/Tc

Mitigating Fiber Nonlinearity with Machine Learning

Nowadays, optical communication transmission is based mainly on optical fiber networks. Increasing demands for higher-capacity systems are hampered by signal distortions due to nonlinear effects of the commercial optic fibers. Different techniques have been proposed to reverse and mitigate this noise effect on the transmitted signal such as the digital backpropagation (DBP), the Volterra nonlinear compensation, the advanced modulation transmission, and perturbation pre-compensation techniques. While these techniques achieve good results they are too complicated for practical industrial implementation and add more complexity overhead on the system. This thesis is focused on investigating the merits of optical fiber mitigation using Artificial Intelligence (AI) techniques instead of analytical methods. Different AI techniques combined with perturbation-based nonlinear compensation method are used to predict the added nonlinear noise to a 16-Quadrature Amplitude Modulation (QAM) propagating signal. A MATLAB simulation program has been used to model the propagation of the signal and generate the transmitted data. The AI simulations have been employed using Python on dual-polarization single channel systems using single-stage AI techniques such as Neural Network (NN) at receiver or transmitter side and Siamese neural network (SNN), or two-stage AI techniques. In the two-stage method, different supervised classifiers have been used at the receiver side such as multi-layer perceptrons (MLP), decision tree, AdaBoosting, GBoosting, random forest, and extra trees while NN is placed at the transmitter. Additionally, different complexity reduction techniques have been applied to the proposed systems to achieve more practical performance in industrial environment applications. For the first time, a nonlinear-compensation robustness study is applied to the proposed AI techniques by detecting the performance of each technique while changing the single-mode fiber’s nonlinear coefficient value. Moreover, empirical equations are developed to represent the system’s Q-factor enhancement achieved using each of the proposed techniques as a function of the fiber nonlinear coefficient and the data features

Design of neural network-based nonlinear equalisers for coherent optical communication systems

Recent advancements in beyond 5G (B5G) networks and future communications demand ultra-high capacity and, therefore, impose stringent requirements on fibre-optic transmission infrastructures. Optical fibre channel impairments such as nonlinearities induced by the Kerr effect and their interactions with chromatic dispersion pose a significant challenge in achieving desirable transmission capacity in current coherent optical communication systems. Digital signal processing techniques, such as electronic dispersion compensation and digital backpropagation, may achieve suboptimal performance or require impractical high computing resources. Machine learning (ML) techniques offer a promising solution to the optical fibre nonlinearity equalisation problem due to their ability to exploit underlying features among the vast volume of digital information available in modern society. This thesis focuses on the design of nonlinear equalisers using machine learning, specifically neural networks (NNs), for coherent optical communication systems. A comprehensive study of machine learning algorithms, including unsupervised and supervised learning algorithms, applied in nonlinear equalisation is conducted, demonstrating that the bit error rate performance of these algorithms is superior to conventional symbol detection using mean square error. The results demonstrate the potential of machine learning algorithms, particularly NNs, for nonlinearity equalisation in coherent optical systems and provide a motivation for further exploration. The ML- and recurrent neural network (RNN)-based nonlinear equalisers with sequential symbol input are investigated. The results suggest the input sequences can provide relevant residual channel memory information for these equalisers to enhance the system performance after training, offering confidence in the design of low-complexity NN-based equalisers. Furthermore, an attention-aided partial bidirectional recurrent neural network (BRNN)-based nonlinear equaliser is proposed, successfully reducing complexity of ∼56.2% with the assistance of the attention mechanism, which also provides evidence of symbol-wise nonlinear memory. The contributions presented in this thesis demonstrate the potential of machine learning algorithms and NN-based equalisers, investigate and validate the feasibility of sequential input for them, and provide an effective evidence-based pruning process for the design of NN-based equalisers for optical transmission systems. xv

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

Cognitive and Autonomous Software-Defined Open Optical Networks

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