Spectrum sensing is an important function to enable cognitive radios to detect the underutilized ... more Spectrum sensing is an important function to enable cognitive radios to detect the underutilized spectrum licensed to the primary systems and improve the overall spectrum efficiency. Some well-known spectrum sensing techniques are energy detection, matched filter and cyclostationary feature detection that have been proposed for narrowband sensing. In these methods, based on the signal properties, a decision is made to detect presence or absence of a primary user in the considered band. The proposed model for wideband spectrum sensing is illustrated in Figure 2. The analog received signal at the sensing cognitive radio is sampled by the multicoset sampler at a sample rate lower than the Nyquist rate. The sampling reduction ratio is affected by the channel occupancy and multicoset sampling parameters. The outputs of the multicoset sampler are partially shifted using a multirate system, which contains the interpolation, delaying and down sampling stages. Next, the sample correlation matrix is computed from the finite number of obtained data. Finally, the correlation matrix is investigated to discover the position of the active channels by subspace methods. We evaluate this method by computing the probability of detecting signal occupancy in terms of the number of samples and signal to noise ratio (SNR).
Spectrum sensing is an important function to enable cognitive radios to detect the underutilized ... more Spectrum sensing is an important function to enable cognitive radios to detect the underutilized spectrum licensed to the primary systems and improve the overall spectrum efficiency. Some well-known spectrum sensing techniques are energy detection, matched filter and cyclostationary feature detection that have been proposed for narrowband sensing. In these methods, based on the signal properties, a decision is made to detect presence or absence of a primary user in the considered band. The proposed model for wideband spectrum sensing is illustrated in Figure 2. The analog received signal at the sensing cognitive radio is sampled by the multicoset sampler at a sample rate lower than the Nyquist rate. The sampling reduction ratio is affected by the channel occupancy and multicoset sampling parameters. The outputs of the multicoset sampler are partially shifted using a multirate system, which contains the interpolation, delaying and down sampling stages. Next, the sample correlation matrix is computed from the finite number of obtained data. Finally, the correlation matrix is investigated to discover the position of the active channels by subspace methods. We evaluate this method by computing the probability of detecting signal occupancy in terms of the number of samples and signal to noise ratio (SNR).
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