Sparsity-aware cooperative cognitive radio sensing using channel gain maps

The cooperative cognitive radio (CR) sensing problem is considered, where a number of CRs collaboratively detect the presence of an unknown number of primary users (PUs) in the geographical area where the CR network is operating. A novel concept of channel gain maps is introduced to model and dynamically track the spatio-temporal evolution of the propagation environment. The channel gain map estimates are then exploited to perform dynamic cooperative sensing of time-varying PU activities based on a sparse regression formulation. A distributed algorithm is proposed to reduce the message-passing overhead of the centralized counterpart for solving the ¿₁-penalized weighted least-squares problem.