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.