Mobile distributed compressive sensing for spectrum sensing

This paper studies the effect of mobility on the sensing performance of a cognitive radio network with mobile nodes. The secondary nodes sense the spectrum using a distributed compressive sensing approach to detect the available channels. Distributed compressive sensing is suggested to reduce the number of samples by exploiting correlation between the samples. Channel occupancy at the two nodes will be jointly estimated and a channel available at the location of both nodes is chosen for communication. We show that mobility can be exploited to further decrease the number of samples by increasing the average level of correlation among the sensed samples over time.