Cognitive Spectrum Sensing with Multiple Primary Users in Rayleigh Fading Channels

Accurate detection of white spaces is crucial in cognitive radio networks. Initial investigations show that the accurate detection in a multiple primary users environment is challenging, especially under severe multipath conditions. Among many techniques, recently proposed eigenvalue-based detectors that use random matrix theories to eliminate the need of prior knowledge of the signals proved to be a solid approach. In this work, we study the effect of Rayleigh multipath fading channels on spectrum sensing in a multiple primary user environment for a pre-proposed detector called the spherical detector using the eigenvalue approach. Simulation results show interesting outcomes.

Cognitive Spectrum Sensing with Multiple Primary Users in Rayleigh Fading Channels

Accurate detection of white spaces is crucial in cognitive radio networks. Initial investigations show that the accurate detection in a multiple primary users environment is challenging, especially under severe multipath conditions. Among many techniques, recently proposed eigenvalue-based detectors that use random matrix theories to eliminate the need of prior knowledge of the signals proved to be a solid approach. In this work, we study the effect of Rayleigh multipath fading channels on spectrum sensing in a multiple primary user environment for a pre-proposed detector called the spherical detector using the eigenvalue approach. Simulation results show interesting outcomes.

Machine Learning Techniques Applied to Multiband Spectrum Sensing in Cognitive Radios

In this work, three specific machine learning techniques (neural networks, expectation maximization and k-means) are applied to a multiband spectrum sensing technique for cognitive radios. All of them have been used as a classifier using the approximation coefficients from a Multiresolution Analysis in order to detect presence of one or multiple primary users in a wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results presented of these three methods are effective options for detecting primary user transmission on the multiband spectrum. These methodologies work for 99% of cases under simulated signals of SNR higher than 0 dB and are feasible in the case of real signals.

A Novel Multiband Spectrum Sensing Method Based on Wavelets and the Higuchi Fractal Dimension

In this work, two novel methodologies for the multiband spectrum sensing in cognitive radios are implemented. Methods are based on the continuous wavelet transform (CWT) and the multiresolution analysis (MRA) to detect the edges of available holes in the considered wideband spectrum. Besides, MRA is also combined with the Higuchi fractal dimension (a non-linear measure) to establish the decision rule permitting the detection of the absence or presence of one or multiple primary users in the studied wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results present these two methods as effective options for detecting primary user activity on the multiband spectrum. The first methodology works for 95% of cases, while the second one presents 98% of effectivity under simulated signals of signal-to-noise ratios (SNR) higher than 0 dB.