Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-06-21T19:35:53Z No. of bitstreams: 1 AluisioIgorRegoFontes_TESE.pdf: 3702181 bytes, checksum: ebee4d30550394d35c706823dc2169ae (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-06-22T21:45:56Z (GMT) No. of bitstreams: 1 AluisioIgorRegoFontes_TESE.pdf: 3702181 bytes, checksum: ebee4d30550394d35c706823dc2169ae (MD5) / Made available in DSpace on 2016-06-22T21:45:56Z (GMT). No. of bitstreams: 1 AluisioIgorRegoFontes_TESE.pdf: 3702181 bytes, checksum: ebee4d30550394d35c706823dc2169ae (MD5) Previous issue date: 2015-09-11 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / A extra??o de informa??es de sinais aleat?rios ? um problema frequente e relevante em muitas aplica??es de processamento digital de sinais. Nos ?ltimos anos, diferentes m?todos t?m sido utilizados para a parametriza??o de sinais ou obten??o de descritores eficientes de suas caracter?sticas. Quando os sinais aleat?rios possuem propriedades es- tat?sticas cicloestacion?rias, as Fun??es de Autocorrela??o C?clica (CAF) e a Densidade Espectral C?clica (SCD) podem ser utilizadas na obten??o de informa??es cicloestacion?- rias de segunda ordem. Entretanto, em sinais n?o-gaussianos, as informa??es cicloestaci- on?rias de segunda ordem s?o fracas e, neste caso a an?lise cicloestacion?ria deve ocorrer sobre informa??es estat?sticas de ordem superior. Este trabalho prop?e uma nova ferra- menta matem?tica para a an?lise cicloestacion?ria de ordem superior baseada na fun??o de correntropia. Especificamente, a teoria de an?lise cicloestacion?ria ? revisitada sob um enfoque de teoria da informa??o, e as Fun??es de Correntropia C?clica (CCF) e Densidade Espectral de Correntropia C?clica (CCSD) s?o definidas. ? comprovado analiticamente que a CCF cont?m informa??es de momentos cicloestacion?rios de segunda ordem e de ordem superior, sendo uma generaliza??o da CAF. O desempenho dessas novas fun??es, na extra??o de caracter?sticas cicloestacion?rias de ordem superior, ? analisado em um cen?rio de comunica??o sem fio com ru?do n?o-gaussiano. / Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and the Spectral Cyclic Density (SCD) can be used to extract second-order cyclostationary information. However, second-order cyclostationary information is poor in nongaussian signals, as the cyclostationary analysis in this case should comprise higher-order statistical information. This paper proposes a new mathematical tool for the higher-order cyclostationary analysis based on the correntropy function. Specifically, the cyclostationary analysis is revisited focusing on the information theory, while the Cyclic Correntropy Function (CCF) and Cyclic Correntropy Spectral Density (CCSD) are also defined. Besides, it is analytically proven that the CCF contains information regarding second- and higher-order cyclostationary moments, being a generalization of the CAF. The performance of the aforementioned new functions in the extraction of higher-order cyclostationary characteristics is analyzed in a wireless communication system where nongaussian noise exists.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-12-15T18:09:01Z No. of bitstreams: 1 TalesViniciusRodriguesDeOliveiraCamara_DISSERT.pdf: 3587261 bytes, checksum: ddffd176ad921074a281b50e904134f5 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-12-28T19:00:56Z (GMT) No. of bitstreams: 1 TalesViniciusRodriguesDeOliveiraCamara_DISSERT.pdf: 3587261 bytes, checksum: ddffd176ad921074a281b50e904134f5 (MD5) / Made available in DSpace on 2016-12-28T19:00:57Z (GMT). No. of bitstreams: 1 TalesViniciusRodriguesDeOliveiraCamara_DISSERT.pdf: 3587261 bytes, checksum: ddffd176ad921074a281b50e904134f5 (MD5) Previous issue date: 2016-04-25 / T?cnicas de Classifica??o Autom?tica de Modula??o (AMC) t?m sido utilizadas por sistemas modernos de comunica??o para otimizar o uso do espectro e com isso aumen- tar as taxas de transmiss?o de dados. No processo de AMC, v?rias arquiteturas podem ser utilizadas para retirar informa??o e avaliar caracter?sticas do sinal modulado em um canal. Uma grande parte dessas arquiteturas s?o constru?das utilizando como base a ci- cloestacionariedade. A an?lise cicloestacion?ria ? realizada por meio das ferramentas: Fun??o de Autocorrela??o C?clica (CAF) e Fun??o Densidade Espectral C?clica (SCD). Esta ultima particularmente, ? utilizada para observar as caracter?sticas cicloestacion?rias de diferentes sinais, as quais s?o chamadas de assinaturas. Embora tenha v?rias aplica- ??es bem sucedidas no ?mbito de AMC, a cicloestacionariedade possui restri??es pois a CAF e SCD s?o limitadas ? an?lise estat?stica de segunda ordem, devido ao uso da correla??o com cerne de sua express?o. Com o objetivo de generalizar a avalia??o da cicloestacionariedade sobre infinitos momentos estat?sticos de um sinal, surgem Fun??o de Autocorrentropia C?clica (CCAF) e a Fun??o Densidade Espectral de Correntropia C?clica (CCSD). Tais fun??es s?o fundamentadas no c?lculo da correntropia. Neste tra- balho a CCSD ser? investigada quanto capacidade de gerar assinaturas para diferentes modula??es e seu potencial de uso em AMC ser? avaliado. / The steady growth in the use of wireless communication systems has contributed to finding new ways to exploit the maximum capacity of use spectrum. In this context, cognitive radios appear as an appropriate option able to offer an efficient use of the channel, ensuring greater bandwidth to users. In the scenario of cognitive radios, cyclostationary analysis techniques have shown to be quite effective in extracting features that can be used in the spectrum sensing. Such features called cyclostationary signatures are generated by the spectral correlation density function (SCD) and can be directly associated with the type of modulation used on the channel. Architectures for spectrum sensing using SCD has good performed when used in AWGN channels. However, recent studies show that the tool doesn?t have a good performance in the extraction of signal characteristics contaminated with impulsive noise (Outlier), because it is limited to second order statistical analysis. In order to generalize the SCD cyclostationary analysis for endless statistical moments, arise the function correntropy cyclic spectral density (CCSD) This work proposes a spectrum sensing architecture using CCSD, which is applied to the extraction cyclostationary features from digital modulations: ASK, FSK, BPSK, QPSK and MSK. The sensing architecture proposed is evaluated in various parameters: different sensing thresholds, change of SNR levels of a AWGN channel, different kernel sizes (s) from CCSD and extraction of cyclostationary features from modulations contaminated with noise impulsive. The results of this study demonstrate the effectiveness of the proposed architecture.

Made available in DSpace on 2014-12-17T14:56:19Z (GMT). No. of bitstreams: 1 ArthurDLL_DISSERT.pdf: 2517302 bytes, checksum: c3d693c770dc1c58bad5f378aba6d268 (MD5) Previous issue date: 2014-06-28 / The increasing demand for high performance wireless communication systems has shown the inefficiency of the current model of fixed allocation of the radio spectrum. In this context, cognitive radio appears as a more efficient alternative, by providing opportunistic spectrum access, with the maximum bandwidth possible. To ensure these requirements, it is necessary that the transmitter identify opportunities for transmission and the receiver recognizes the parameters defined for the communication signal. The techniques that use cyclostationary analysis can be applied to problems in either spectrum sensing and modulation classification, even in low signal-to-noise ratio (SNR) environments. However, despite the robustness, one of the main disadvantages of cyclostationarity is the high computational cost for calculating its functions. This work proposes efficient architectures for obtaining cyclostationary features to be employed in either spectrum sensing and automatic modulation classification (AMC). In the context of spectrum sensing, a parallelized algorithm for extracting cyclostationary features of communication signals is presented. The performance of this features extractor parallelization is evaluated by speedup and parallel eficiency metrics. The architecture for spectrum sensing is analyzed for several configuration of false alarm probability, SNR levels and observation time for BPSK and QPSK modulations. In the context of AMC, the reduced alpha-profile is proposed as as a cyclostationary signature calculated for a reduced cyclic frequencies set. This signature is validated by a modulation classification architecture based on pattern matching. The architecture for AMC is investigated for correct classification rates of AM, BPSK, QPSK, MSK and FSK modulations, considering several scenarios of observation length and SNR levels. The numerical results of performance obtained in this work show the eficiency of the proposed architectures / O aumento da demanda por sistemas de comunica??o sem fio de alto desempenho tem evidenciado a inefici?ncia do atual modelo de aloca??o fixa do espectro de r?dio. Nesse contexto, o r?dio cognitivo surge como uma alternativa mais eficiente, ao proporcionar o acesso oportunista ao espectro, com a maior largura de banda poss?vel. Para garantir esses requisitos, ? necess?rio que o transmissor identifique as oportunidades de transmiss?o e que o receptor reconhe?a os par?metros definidos para o sinal de comunica??o. As t?cnicas que utilizam a an?lise cicloestacion?ria podem ser aplicadas tanto em problemas de sensoriamento espectral, quanto na classifica??o de modula??es, mesmo em ambientes de baixa rela??o sinal-ru?do (SNR). Entretanto, apesar da robustez, uma das principais desvantagens da cicloestacionariedade est? no elevado custo computacional para o c?lculo das suas fun??es. Este trabalho prop?e arquiteturas eficientes de obten??o de caracter?sticas cicloestacion?rias para serem empregadas no sensoriamento espectral e na classifica??o autom?tica de modula??es (AMC). No contexto do sensoriamento espectral, um algoritmo paralelizado para extrair as caracter?sticas cicloestacion?rias de sinais de comunica??o ? apresentado. O desempenho da paraleliza??o desse extrator de caracter?sticas ? avaliado atrav?s das m?tricas de speedup e efici?ncia paralela. A arquitetura de sensoriamento espectral ? analisada para diversas configura??es de probabilidades de falso alarme, n?veis de SNR e tempo de observa??o das modula??es BPSK e QPSK. No contexto da AMC, o perfil-alfa reduzido ? proposto como uma assinatura cicloestacion?ria calculada para um conjunto reduzido de frequ?ncia c?clicas. Essa assinatura ? validada por meio de uma arquitetura de classifica??o baseada no casamento de padr?es. A arquitetura para AMC ? investigada para as taxas de acerto obtidas para as modula??es AM, BPSK, QPSK, MSK e FSK, considerando diversos cen?rios de tempo de observa??o e n?veis de SNR. Os resultados num?ricos de desempenho obtidos neste trabalho demonstram a efici?ncia das arquiteturas propostas