ERROR EXPONENTS FOR DECENTRALIZED DETECTION IN FEEDBACK ARCHITECTURES
Presented by: Wee Peng Tay, Author(s): Wee Peng Tay, Nanyang Technological University, Singapore; John Tsitsiklis, Massachusetts Institute of Technology, United States
We consider the decentralized Bayesian binary hypothesis testing problem in feedback architectures, in which the fusion center broadcasts information based on the messages of some sensors to some or all sensors in the network. We show that the asymptotically optimal detection performance (as quantified by error exponents) does not benefit from the feedback messages. In addition, we determine the corresponding optimal error exponents.