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Uso de correntropia na generaliza??o de fun??es cicloestacion?rias e aplica??es para a extra??o de caracter?sticas de sinais moduladosFontes, Aluisio Igor R?go 11 September 2015 (has links)
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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.
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Aplica??o da fun??o de densidade espectral de correntropia c?clica em uma arquitetura de sensoriamento espectralC?mara, Tales Vin?cius Rodrigues de Oliveira 25 April 2016 (has links)
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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.
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Arquiteturas eficientes para sensoriamento espectral e classifica??o autom?tica de modula??es usando caracter?sticas cicloestacion?riasLima, Arthur Diego de Lira 28 June 2014 (has links)
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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
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