On issues of equalization with the decorrelation algorithm : fast converging structures and finite-precision

To increase the rate of convergence of the blind, adaptive, decision feedback equalizer based on the decorrelation criterion, structures have been proposed which dramatically increase the complexity of the equalizer. The complexity of an algorithm has a direct bearing on the cost of implementing the algorithm in either hardware or software. In this thesis, more computationally efficient structures, based on the fast transversal filter and lattice algorithms, are proposed for the decorrelation algorithm which maintain the high rate of convergence of the more complex algorithms. Furthermore, the performance of the decorrelation algorithm in a finite-precision environment will be studied and compared to the widely used LMS algorithm

Digital processing of signals in the presence of inter-symbol interference and additive noise

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Sparse Nonlinear MIMO Filtering and Identification

In this chapter system identification algorithms for sparse nonlinear multi input multi output (MIMO) systems are developed. These algorithms are potentially useful in a variety of application areas including digital transmission systems incorporating power amplifier(s) along with multiple antennas, cognitive processing, adaptive control of nonlinear multivariable systems, and multivariable biological systems. Sparsity is a key constraint imposed on the model. The presence of sparsity is often dictated by physical considerations as in wireless fading channel-estimation. In other cases it appears as a pragmatic modelling approach that seeks to cope with the curse of dimensionality, particularly acute in nonlinear systems like Volterra type series. Three dentification approaches are discussed: conventional identification based on both input and output samples, semi–blind identification placing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulation

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Development Of Novel Neuro-Fuzzy Techniques For Adaptive Systems

Novel approaches for designing adaptive schemes based on neuro-fuzzy platform have been developed. Two kinds of adaptive schemes namely, adaptive equalization and system identification are implemented using the developed proposed techniques. The Radial basis function (RBF) equalizer is chosen as a case study for adaptive equalization of the digital communication channels. An efficient method for reducing the centers of a RBF equalizer based on eigenvalue analysis is presented. The efficiency of the method is further verified for RBF equalizers with decision feedback for tackling channels with overlapping channel states. A comparative study between the proposed center reduction technique and other center reduction techniques for the RBF equalizer is discussed. In another breakthrough a parallel interpretation of the ANFIS (adaptive network based fuzzy inference systems) architecture is proposed. This approach helps to investigate the role of the fuzzy inference part and the s..

Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise

We consider the problem of simultaneous reduction of acoustic echo, reverberation and noise. In real scenarios, these distortion sources may occur simultaneously and reducing them implies combining the corresponding distortion-specific filters. As these filters interact with each other, they must be jointly optimized. We propose to model the target and residual signals after linear echo cancellation and dereverberation using a multichannel Gaussian modeling framework and to jointly represent their spectra by means of a neural network. We develop an iterative block-coordinate ascent algorithm to update all the filters. We evaluate our system on real recordings of acoustic echo, reverberation and noise acquired with a smart speaker in various situations. The proposed approach outperforms in terms of overall distortion a cascade of the individual approaches and a joint reduction approach which does not rely on a spectral model of the target and residual signals

Exploiting optical signal analysis for autonomous communications

(English) Optical communications have been extensively investigated and enhanced in the last decades. Nowadays, they are responsible to transport all the data traffic generated around the world, from access to the core network segments. As the data traffic is increasing and changing in both type and patterns, the optical communications networks and systems need to readapt and continuous advances to face the future data traffic demands in an efficient and cost-effective way. This PhD thesis focuses on investigate and analyze the optical signals in order to extract useful knowledge from them to support the autonomous lightpath operation, as well as to lightpath characterization. The first objective of this PhD thesis is to investigate the optical transmission feasibility of optical signals based on high-order modulation formats (MF) and high symbol rates (SR) in hybrid filterless, filtered and flexible optical networks. It is expected a higher physical layer impairments impact on these kinds of optical signals that can lead to degradation of the quality of transmission. In particular, the impact of the optical filter narrowing arising from the node cascade is evaluated. The obtained simulation results for the required optical-signal-to-noise ratio in a cascade up to 10 optical nodes foresee the applicability of these kinds of optical signals in such scenarios. By using high-order MF and high SR, the number of the optical transponders cab be reduced, as well as the spectral efficiency is enhanced. The second objective focuses on MF and SR identification at the optical receiver side to support the autonomous lightpath operation. Nowadays, optical transmitters can generate several optical signal configurations in terms of MF and SR. To increase the autonomous operation of the optical receiver, it is desired it can autonomously recognize the MF and SR of the incoming optical signals. In this PhD thesis, we propose an accurate and low complex MF and SR identification algorithm based on optical signal analysis and minimum Euclidean distance to the expected points when the received signals are decoded with several available MF and SR. The extensive simulation results show remarkable accuracy under several realistic lightpath scenarios, based on different fiber types, including linear and nonlinear noise interference, as well as in single and multicarrier optical systems. The final objective of this PhD thesis is the deployment of a machine learning-based digital twin for optical constellations analysis and modeling. An optical signal along its lightpath in the optical network is impaired by several effects. These effects can be linear, e.g., the noise coming from the optical amplification, or nonlinear ones, e.g., the Kerr effects from the fiber propagation. The optical constellations are a good source of information regarding these effects, both linear and nonlinear. Thus, by an accurate and deep analysis of the received optical signals, visualized in optical constellations, we can extract useful information from them to better understand the several impacts along the crossed lightpath. Furthermore, by learning the different impacts from different optical network elements on the optical signal, we can accurately model it in order to create a partial digital twin of the optical physical layer. The proposed digital twin shows accurate results in modeled lightpaths including both linear and nonlinear interference noise, in several lightpaths configuration, i.e., based on different kind of optical links, optical powers and optical fiber parameters. In addition, the proposed digital twin can be useful to predict quality of transmission metrics, such as bit error rate, in typical lightpath scenarios, as well as to detect possible misconfigurations in optical network elements by cooperation with the software-defined networking controller and monitoring and data analytics agents.(Español) Las comunicaciones ópticas han sido ampliamente investigadas y mejoradas en las últimas décadas. En la actualidad, son las encargadas de transportar la mayoría del tráfico de datos que se genera en todo el mundo, desde el acceso hasta los segmentos de la red troncal. A medida que el tráfico de datos aumenta y cambia tanto en tipo como en patrones, las redes y los sistemas de comunicaciones ópticas necesitan readaptarse y avanzar continuamente para, de una manera eficiente y rentable, hacer frente a las futuras demandas de tráfico de datos. Esta tesis doctoral se centra en investigar y analizar las señales ópticas con el fin de extraer de ellas conocimiento útil para apoyar el funcionamiento autónomo de las conexiones ópticas, así como para su caracterización. El primer objetivo de esta tesis doctoral es investigar la viabilidad de transmisión de señales ópticas basadas en formatos de modulación de alto orden y altas tasas de símbolos en redes ópticas híbridas con y sin filtros. Se espera un mayor impacto de las degradaciones de la capa física en este tipo de señales ópticas que pueden conducir a la degradación de la calidad de transmisión. En particular, se evalúa el impacto de la reducción del ancho de banda del filtro óptico que surge tras atravesar una cascada de nodos. Los resultados de simulación obtenidos para la relación señal óptica/ruido requerida en una cascada de hasta 10 nodos ópticos prevén la aplicabilidad de este tipo de señales ópticas en tales escenarios. Mediante el uso de modulación de alto orden y altas tasas de símbolos, se reduce el número de transpondedores ópticos y se mejora la eficiencia espectral. El segundo objetivo se centra en la identificación de formatos de modulación y tasas de símbolos en el lado del receptor óptico para respaldar la operación autónoma de la conexión óptica. Para aumentar el funcionamiento autónomo del receptor óptico, se desea que pueda reconocer de forma autónoma la configuración de las señales ópticas entrantes. En esta tesis doctoral, proponemos un algoritmo de identificación de formatos de modulación y tasas de símbolos preciso y de baja complejidad basado en el análisis de señales ópticas cuando las señales recibidas se decodifican con varios formatos de modulación y tasas de símbolos disponibles. Los extensos resultados de la simulación muestran una precisión notable en varios escenarios realistas, basados en diferentes tipos de fibra, incluida la interferencia de ruido lineal y no lineal, así como en sistemas ópticos de portadora única y múltiple. El objetivo final de esta tesis doctoral es el despliegue de un gemelo digital basado en aprendizaje automático para el análisis y modelado de constelaciones ópticas. Una señal óptica a lo largo de su trayectoria en la red óptica se ve afectada por varios efectos, pueden ser lineales o no lineales. Las constelaciones ópticas son una buena fuente de información sobre estos efectos, tanto lineales como no lineales. Por lo tanto, mediante un análisis preciso y profundo de las señales ópticas recibidas, visualizadas en constelaciones ópticas, podemos extraer información útil de ellas para comprender mejor los diversos impactos a lo largo del camino propagado. Además, al aprender los diferentes impactos de los diferentes elementos de la red óptica en la señal óptica, podemos modelarla con precisión para crear un gemelo digital parcial de la camada física óptica. El gemelo digital propuesto muestra resultados precisos en conexiones que incluyen ruido de interferencia tanto lineal como no lineal, en varias configuraciones basados en diferentes tipos de enlaces ópticos, potencias ópticas y parámetros de fibra óptica. Además, el gemelo digital propuesto puede ser útil para predecir la calidad de las métricas de transmisión así como para detectar posibles errores de configuración en los elementos de la red óptica mediante la cooperación con el controlador de red, el monitoreo y agentes de análisis de datosPostprint (published version

Algorithms for Blind Equalization Based on Relative Gradient and Toeplitz Constraints

Blind Equalization (BE) refers to the problem of recovering the source symbol sequence from a signal received through a channel in the presence of additive noise and channel distortion, when the channel response is unknown and a training sequence is not accessible. To achieve BE, statistical or constellation properties of the source symbols are exploited. In BE algorithms, two main concerns are convergence speed and computational complexity. In this dissertation, we explore the application of relative gradient for equalizer adaptation with a structure constraint on the equalizer matrix, for fast convergence without excessive computational complexity. We model blind equalization with symbol-rate sampling as a blind source separation (BSS) problem and study two single-carrier transmission schemes, specifically block transmission with guard intervals and continuous transmission. Under either scheme, blind equalization can be achieved using independent component analysis (ICA) algorithms with a Toeplitz or circulant constraint on the structure of the separating matrix. We also develop relative gradient versions of the widely used Bussgang-type algorithms. Processing the equalizer outputs in sliding blocks, we are able to use the relative gradient for adaptation of the Toeplitz constrained equalizer matrix. The use of relative gradient makes the Bussgang condition appear explicitly in the matrix adaptation and speeds up convergence. For the ICA-based and Bussgang-type algorithms with relative gradient and matrix structure constraints, we simplify the matrix adaptations to obtain equivalent equalizer vector adaptations for reduced computational cost. Efficient implementations with fast Fourier transform, and approximation schemes for the cross-correlation terms used in the adaptation, are shown to further reduce computational cost. We also consider the use of a relative gradient algorithm for channel shortening in orthogonal frequency division multiplexing (OFDM) systems. The redundancy of the cyclic prefix symbols is used to shorten a channel with a long impulse response. We show interesting preliminary results for a shortening algorithm based on relative gradient

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes