Centralized Spectrum Broker and Spectrum Sensing with Compressive Sensing techniques for resource allocation in Cognitive Radio Networks

In this paper we present a new approach of Spectrum Sensing using a Compressive Sensing technique named Finite Rate of Innovation in a Cognitive Radio Network with centralized Spectrum Management based Spectrum Broker in the next generation wireless communications networks. Through this document it will shown under simulations that the use of compressive sensing techniques improves the performance of the control channel in cognitive radio due the traffic control protocol requires smaller packet sizes. The performance of the cognitive network in function of the control packet size, was determinate by analysis of collisions when different secondary users trying to access spectrum resources and they make the request to the Spectrum Broker. We observed that there are fewer collisions between control packets and collision probability is smaller if compressive technique is used, thus improving the performance in a fair resource allocation for cognitive radio networks.