  In cognitive radio (CR) technology, the trend of sensing is no longer to only
detect the presence of active primary users. A large number of applications
demand for more comprehensive knowledge on primary user behaviors in spatial,
temporal, and frequency domains. To satisfy such requirements, we study the
statistical relationship among primary users by introducing a Bayesian network
(BN) based framework. How to learn such a BN structure is a long standing
issue, not fully understood even in the statistical learning community.
Besides, another key problem in this learning scenario is that the CR has to
identify how many variables are in the BN, which is usually considered as prior
knowledge in statistical learning applications. To solve such two issues
simultaneously, this paper proposes a BN structure learning scheme consisting
of an efficient structure learning algorithm and a blind variable
identification scheme. The proposed approach incurs significantly lower
computational complexity compared with previous ones, and is capable of
determining the structure without assuming much prior knowledge about
variables. With this result, cognitive users could efficiently understand the
statistical pattern of primary networks, such that more efficient cognitive
protocols could be designed across different network layers.
