Grassmann Manifold-Based Spectrum Sensing for TV White Spaces

Abstract

In this paper, a new method of sensing primary user’s signal for cognitive radios in Grassmann manifold is proposed. The Grassmann covariance matrix (GCM) is formed with the help of covariance matrix of the transmitted and the received symbols. By using GCM, a new test statistic is defined, which is the modification of Binet–Cauchy metric. On the basis of this new test statistic, primary user’s signal is detected. We also show that the new test statistic is a valid detector since it follows the concentration phenomena. We derive the distribution of new test statistic under null hypothesis and alternative hypothesis. Lower bound for the probability of detection of signal is also derived using separating function and distribution of new test statistic. Simulations using the real-world measurements of digital television (DTV) signal show performance gain of the proposed method in terms of signal detection over existing methods and their agreement with the derived distribution. Additionally, we extend the proposed method for the cooperative spectrum sensing and derive the distribution under both hypotheses. Experimental verification on the software defined radio is also performed and it is found that the proposed method fulfills the requirement of maximum protection of the DTV signal.