Cooperative Spectrum Sensing for Internet of Things Using Modeling of Power-Spectral-Density Estimation Errors

Abstract

The application of cognitive radio (CR) technology to the Internet of Things (IoT) network can effectively solve the bottleneck problem of spectrum scarcity. As one of the key steps in CR-IoT, the research over spectrum sensing is of great importance. The utilization of the spectrum resource can be described by the power spectral density (PSD) which can be estimated by the IoT cognitive radio (CR) nodes cooperatively. The PSD-estimation induced error (PEIE) at each node can decrease the performance of spectrum sensing seriously, but it often be treated as Gaussian distribution easily in the published works which is not the fact in real systems. This paper tries to propose a general modeling method of PEIE to address this problem. After that, an optimization function is constructed over the PSD. Then the prior model which can lead to sparsity-inducing penalization terms is built by introducing two auxiliary parameters to take full advantage of the sparse property. Also, the variational Bayesian inference algorithm using the factor graph is used to find the iterative solution. Finally, compared with the traditional approach, the simulation results demonstrate the superior performance of the proposed algorithm.