Development of QOS Aware Spectrum Access Algorithm for Different Radio Access Techniques (RATS)

The paper introduces a novel approach to spectrum sensing within cognitive radio systems, proposing the utilization of sub-Nyquist sampling for wideband spectrum sensing to achieve substantial reductions in sample rates without compromising detection accuracy. Employing a subspace estimator, the method discerns occupied and unoccupied channels by analyzing the correlation matrix derived from limited noisy samples. Spectrum sensing, a critical task in cognitive radio, faces challenges due to varying conditions between major user signals and background noise. Leveraging statistical covariances of received signals and noise enables the differentiation between the two. Through comparative analysis utilizing the sample covariance matrix, the paper evaluates spectrum sensing techniques, offering insights into improving detection capabilities amidst dynamic and noisy environments. This research addresses a significant challenge in cognitive radio, providing valuable contributions to the field.