Contribuição ao estudo do impacto das não linearidades nos sistemas de telecomunicações

Doutoramento em Engenharia ElectrotécnicaEsta tese insere-se na área de Electrónica de Rádio Frequência e Microondas e visa o desenvolvimento de ferramentas que permitam a melhor compreensão e análise do impacto da distorção não linear produzida em amplificadores de potência no desempenho de um sistema de telecomunicações sem fios. Devido à crescente complexidade dos amplificadores a simulação baseada em representações de circuito equivalente tornou-se extremamente pesada do ponto de vista computacional. Assim têm surgido várias técnicas de simulação de sistemas baseadas em modelos comportamentais, ou seja, que tentam aproximar a resposta do sistema a um sinal de entrada, independentemente dos elementos físicos que implementam o circuito. Neste trabalho foram estudadas as principais técnicas de modelação comportamental existentes assim como as principais características de um amplificador de potência que o modelo comportamental deve ser capaz de prever. Uma nova formulação de um modelo comportamental baseado na série de Volterra é apresentada em conjunto com o método de extracção ortogonal dos seus coeficientes. A principal vantagem deste novo método de extracção é permitir a determinação independente de cada valor coeficiente na série, garantindo-se deste modo um modelo com uma capacidade de aproximação óptima. A determinação dos coeficientes na série de modo independente é conseguida com base na reorganização dos termos da série e na identificação ortogonal de cada componente de saída. Adicionalmente, a identificação das componentes de saída de uma não linearidade é ainda utilizada na definição de uma métrica que permite avaliar de modo simples qual é a degradação imposta à qualidade do sinal ao ser passado num amplificador não linear. Esta métrica contabiliza simultaneamente a degradação imposta pelo ruído e pela distorção.This thesis is related to the RF and Microwave Electronics field and the main goal of this thesis is to develop tools that can contribute to understand and analyse the impact of nonlinear distortion generated by power amplifiers on wireless communication systems. Due to the growing complexity of amplifiers, equivalent circuit based simulations become a heavy computational task due to the large number of nonlinear elements to account for. So, several system simulation techniques have been proposed based on behavioural modelling, that is, models that can approximate the system’s response to a given input signal regardless of the physical circuit implementation description. In this thesis, the most important behavioural modelling techniques have been studied as well as the main power amplifier characteristics that the behavioural model should account for. A new formulation of a Volterra series based behavioural model is presented as well as the corresponding coefficient orthogonal extraction procedure. The main advantage of this new extraction method is to allow the independent determination of the exact value of each coefficient, guaranteeing this way an optimum approximation condition. The exact coefficient determination is achieved by reorganizing the series terms to reach independent subsets and by identifying separately each of systems’ output components. In addition, nonlinearity output component separation is also used to define a Figure of Merit that allows the simple evaluation of signal quality degradation when passed through a nonlinear amplifier. This Figure takes into account simultaneously the impact of noise and distortion.FCTFS

Approximately periodic time series and nonlinear structures

In this thesis a previously developed framework for modelling diversity of approximately periodic time series is considered. In this framework the diversity is modelled deterministically, exploiting the irregularity of chaos. This is an alternative to other well established frameworks which use probability distributions and other stochastic tools to describe diversity. The diversity which is to be modelled, on the other hand, is not assumed to be of chaotic nature, but can stem out from a stochastic process, though it has never been verified before, whether or not purely stochastic patterns can be modelled that way. The main application of such a modelling technique would be pattern recognition; once a model for a learning set of approximately periodic time series is found, synchronisation-like phenomena could be used to determine if a novel time series is similar to the members of the learning set. The most crucial step of the classification procedure outlined before is the automatic generation of a chaotic model from data, called identification. Originally, this was done using a simple low dimensional reference model. Here, on the other hand, a biologically inspired approach is taken. This has the advantage that the identification and classification procedure could be greatly simplified and the computational power involved significantly reduced. The biologically inspired model used for identification was announced several time in literature under the name "Echo State Network". The articles available on it consisted mainly of examples were it performed remarkably well, though a thorough analysis was still missing to the scientific community. Here the model is analysed using a measure that had appeared already in similar contexts and with help of this measure good settings of the models' parameter were determined. Finally, the model was used to assess if stochastic patterns can be modelled by chaotic signals. Indeed, it has been shown that, for the biologically inspired modelling technique considered, chaotic behaviour appears to implicitly model diversity and randomness of the learnt patterns whenever these are sufficiently structured; whilst chaos does not appear when the patterns are remarkably unstructured. In other words, deterministic chaos or strongly coloured noise lead to the chaos emergence as opposed to white-like noise which does not. With this result in mind, the classification of gait signals was attempted, as no signs of chaoticity could be found in them and the previously available modelling technique seemed to have difficulties to model their diversity. The identification and classification results with the biologically inspired model turned out to be very good

Blind channel identification/equalization with applications in wireless communications

Ph.DDOCTOR OF PHILOSOPH

Summary of Research 1994

The views expressed in this report are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government.This report contains 359 summaries of research projects which were carried out under funding of the Naval Postgraduate School Research Program. A list of recent publications is also included which consists of conference presentations and publications, books, contributions to books, published journal papers, and technical reports. The research was conducted in the areas of Aeronautics and Astronautics, Computer Science, Electrical and Computer Engineering, Mathematics, Mechanical Engineering, Meteorology, National Security Affairs, Oceanography, Operations Research, Physics, and Systems Management. This also includes research by the Command, Control and Communications (C3) Academic Group, Electronic Warfare Academic Group, Space Systems Academic Group, and the Undersea Warfare Academic Group