Optimum decision fusion in cognitive wireless sensor networks with unknown users location

We consider a cooperative cognitive wireless network scenario where a primary wireless network is co-located with a cognitive (or secondary) network. In the considered scenario, the nodes of the secondary network make local binary decisions about the presence of a signal emitted by a primary node. Then, they transmit their decisions to a fusion center (FC). The final decision about the channel state is up to the FC by means of a proper fusion rule. In this scenario, we derive the optimum decision strategy for the FC and the optimum local decision thresholds of the secondary nodes in a Neyman-Pearson setup. In particular, the overall system performance are derived by making the realistic assumption that the position of the primary user is completely unknown to the FC.