Sparsity-aware cooperative cognitive radio sensing using channel gain maps

Authors:
Seung-Jun Kim;Emiliano Dall'Anese;Georgios B. Giannakis
Affiliations:
Dept. of Electr. & Computer Engr., University of Minnesota, Minneapolis, MN;Dept. of Information Engineering, University of Padova, Padova, Italy;Dept. of Electr. & Computer Engr., University of Minnesota, Minneapolis, MN
Venue:
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Year:
2009

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Abstract

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 timevarying 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 l1- penalized weighted least-squares problem.