  Cooperative cognitive radio networks are investigated by using an
information-theoretic approach. This approach consists of interpreting the
decision process carried out at the fusion center as a binary (asymmetric)
channel, whose input is the presence of a primary signal and output is the
fusion center decision itself. The error probabilities of this channel are the
false-alarm and missed-detection probabilities. After calculating the mutual
information between the binary random variable representing the primary signal
presence and the set of sensor (or secondary user) output samples, we apply the
data-processing inequality to derive a lower bound to the receiver operating
characteristic. This basic idea is developed through the paper in order to
consider the cases of full channel and signal knowledge and of knowledge in
probability distribution. The advantage of this approach is that the ROC lower
bound derived is independent of the particular type of spectrum detection
algorithm and fusion rule considered. Then, it can be used as a benchmark for
existing practical systems.
