A conceptual model and prototype of Cognitive Radio Cloud Networks in TV White Spaces

A Cognitive Radio Cloud Network (CRCN) model is proposed for wireless communications in TV White Spaces (TVWS). Making use of the flexible and vast computing capacity of the Cloud, a database and a sparse Bayesian learning (SBL) algorithm are developed for cooperative spectrum sensing (CSS) and implemented on Microsoft´s Windows Azure Cloud platform. A medium access control (MAC) scheme is also prototyped for this CRCN model to collect sensing reports and access channels with Rice University´s wireless access research platform (WARP). Through this CRCN prototype, important network parameters such as the mean squared errors in CSS, the time to detect the presence and/or the absence of primary users, and the channel vacating delay are measured and analyzed for the design and deployment of the future CRCN.