Aplicação da função de densidade espectral de correntropia cíclica em uma arquitetura de sensoriamento espectral

Abstract

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.