Comparison of Wide Band Sensing and Narrow Band Sensing in Cognitive Radio Networks

Spectrum sensing is a crucial component of cognitive radio. A significant challenge in this area is the requirement for a high sample rate in the detection of a wideband signal. In this paper, a wideband spectrum sensing model using a sub-Nyquist sampling technique is suggested in order to obtain large sample rate reductions. In order to distinguish between the occupied and unoccupied channels of the spectrum, a subspace estimator computes the correlation matrix of a limited number of noisy samples. Contrary to existing methods, the suggested method can address the uncertainty issue without requiring knowledge of signal attributes. Spectrum sensing, or determining the presence of primary users in a licensed spectrum, is a significant obstacle in cognitive radio. Since they are normally different, it is possible to utilize the statistical co-variances of the received signal and noise to discriminate between circumstances in which the primary user's signal is present and circumstances in which there is simply noise. Based on the sample covariance matrix generated from a limited sample size of received signal samples, methods for spectrum sensing are suggested in this paper. From the sample covariance matrix, two test statistics are then extracted. If a signal exists, it can be identified by comparing the two test statistics. The suggested algorithms are examined theoretically. The relevant threshold and detection probability are calculated using statistical theory.

Keywords: Cognitive wireless powered communication networks; wide band sensing; narrow band sensing; sub-nyquist sampling; statistical co-variances.