Distributed Detection With Vector Quantizer
Wenwen Zhao, Lifeng Lai · IEEE Transactions on Signal and Information Processing over Networks · 2016
Motivated by distributed inference over big datasets problems, we study multiterminal distributed inference problems when each terminal employs vector quantizer. The use of vector quantizer enables us to relax the conditional independence assumption normally used in the distributed detection with scalar quantizer scenarios. We first consider a case of practical interest in which each terminal is allowed to send zero-rate messages to a decision maker. Subject to a constraint that the error exponent of the type 1 error probability is larger than a certain level, we characterize the best error exponent of the type 2 error probability using basic properties of the r-divergent sequences. We then consider the scenario with positive rate constraints, for which we design schemes to benefit from the less strict rate constraints.