JOINT LINK LEARNING AND COGNITIVE RADIO SENSING A THESIS SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Nitin Jain IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

In this thesis, novel cooperative spectrum sensing algorithms for cognitive radios (CRs) are developed, which can blindly learn the channel gains between CRs and licensed primary users (PUs), while jointly detecting active PU transmitters at each time instant. A dictionary learning approach is taken to decompose the received signal energy samples per CR into linear combinations of channel gains and PU transmit-powers, up to scaling ambiguity. In addition to a iterative batch baseline algorithm, an efficient online implementation that can track slow variation of channel gains with reduced computational complexity is developed, as well as a distributed alternative, which requires only local message passing among neighbors in CR networks. Two approaches for selecting the sparsity parameter in the batch, online and distributed learning cases are also developed. In order to remove scaling ambiguity from the columns of the channel gain matrix, an assumption that the PU transmit-powers take values from the known set of finite levels is made. Again, dictionary learning approach is used and batch and online algorithms are developed. We have shown through numerical results that recovery of channel gains and PU transmit-powers is possible.