Adaptive Compressive Sampling for Wideband Signals

In traditional compressive sampling approach, it´s always assumed the sparsity of a signal is known. However, we always can´t get this in Cognitive Radio (CR) network. This makes a great barrier to the practical usage of compressive sampling. This paper develops an Adaptive Compressive Sampling (ACS) approach for wideband signals. It doesn´t need the sparsity as a priori knowledge. Also, once the sparsity changes, it can adaptively change the proper sampling rate through comparing the different results recovered by different sampling rates. When there is no difference between the results recovered by two different sampling rates, the smaller sampling rate of these two is the proper sampling rate to recover the original information of the signal exactly. Moreover, ACS can provide the unoccupied spectrum holes to Secondary Users (SU) in CR for dynamic spectrum access.