Collaborative Compressive Sensing Based Dynamic Spectrum Sensing and Mobile Primary User Localization in Cognitive Radio Networks

In wideband cognitive radio (CR) networks, spectrum sensing is one of the key issues that enable the whole network functionality. Collaborative spectrum sensing among the cognitive radio nodes can greatly improve the sensing performance, and is also able to obtain the location information of primary radios (PRs). Most existing work merely studies the cognitive radio networks with static PRs, yet how to deal with the situations for mobile PRs remains less addressed. In this paper, we propose a collaborative compressive sensing based approach to estimate both the power spectrum and locations of the PRs by exploiting the sparsity facts: the relative narrow band nature of the transmitted signals compared with the broad bandwidth of available spectrum and the mobile PRs located sparsely in the operational space. To effectively track mobile PRs, we implement a Kalman filter using the current estimations to update the location information. To handle dynamics in spectrum usage, a dynamic compressive spectrum sensing algorithm is proposed. Joint consideration of the above two techniques is also investigated. Simulation results validate the effectiveness and robustness of the proposed approach.