Consistent-estimated eigenvalues based cooperative spectrum sensing for dense cognitive Small Cell Network

In this paper, we consider the spectrum sensing problem of detecting a primary signal of a macro cell in a cognitive radio network by employing multiple dense small cell base stations. In consideration of the number of cooperative small cells (sample dimension) is comparable to the number of sample (sample size) due to the dense deployment of small cells, sample covariance matrix is no more a good estimator of statistical covariance matrix. A consistent-estimated eigenvalues based cooperative spectrum sensing (CEE-CSS) algorithm is proposed by utilizing consistent estimators of eigenvalues which are proven to be consistent when the sample dimension goes to infinity at the same rate as sample size. Effect of the eigenvalue splitting condition on sensing performance of the CEE-CSS is analyzed through simulations. Further simulation results present that the proposed CEE-CSS enables better sensing performance than a maximum-minimum eigenvalue detection based on oracle approximating shrinkage estimator (OAS-MME).