  Spectrum sensing receives much attention recently in the cognitive radio (CR)
network research, i.e., secondary users (SUs) constantly monitor channel
condition to detect the presence of the primary users (PUs). In this paper, we
go beyond spectrum sensing and introduce the PU separation problem, which
concerns with the issues of distinguishing and characterizing PUs in the
context of collaborative spectrum sensing and monitor selection. The
observations of monitors are modeled as boolean OR mixtures of underlying
binary sources for PUs. We first justify the use of the binary OR mixture model
as opposed to the traditional linear mixture model through simulation studies.
Then we devise a novel binary inference algorithm for PU separation. Not only
PU-SU relationship are revealed, but PUs' transmission statistics and
activities at each time slot can also be inferred. Simulation results show that
without any prior knowledge regarding PUs' activities, the algorithm achieves
high inference accuracy even in the presence of noisy measurements.
