Cooperative spectrum sensing technique based on sub space analysis for cognitive radio networks

Recently, a new blind spectrum sensing technique based on signal space dimension estimation was developed for sensing the spectrum holes in the primary user’s bands. The mean idea of this technique is that the number of significant eigenvalues of the covariance matrix of the received signal is directly related to the presence/absence of data in the signal. In this paper, we study the collaborative sensing as a means to improve the performance of the proposed spectrum sensing technique and show their effect on cooperative cognitive radio network. Specifically, we will present the performance evaluation and advantages of this method and propose an optimization method that compute only the first dominates eigenvalues in order to reduce the complexity of the proposed cooperative spectrum sensing algorithm. Simulations results and performances evaluation presented in this paper are based on experimental measurements captured by Eurecom RF Agile Platform. Keywords—Cognitive radio, cooperative spectrum sensing, sub space analysis, eigenvalues.