Distributed spectrum sensing in cognitive radios via graphical models

There is a recent interest in developing algorithms for the reconstruction of jointly sparse signals, which arises in a large number of applications. In this work, we study the problem of wide-band spectrum sensing for cognitive radio networks using compressed sensing to exploit the underlying joint sparsity structure in a distributed setting. In particular, we use the recently proposed Approximate Message Passing (AMP) framework and exploit the spatial correlation that exists locally between different CRs in a non-centralized fashion. We will show that with the suggested scheme, the nodes iteratively exploit the local spatial information and achieve the consensus on the spectrum in a distributed fashion.