A Low-Complexity Map Detector For Distributed Networks

This work describes a generalization of our previous maximum likelihood (ML) detector to a maximum a posteriori (MAP) detector in distributed networks using the diffusion LMS algorithm. Nodes in the network must decide between two concurrent hypotheses concerning their environment, using local measurements and shared estimates from neighbors. The generalization is provided by a new approximation concerning the network connectivity, whose accuracy is shown by simulations. The new MAP detector inherits from our ML formulation} an exponential decay rate in probability of error independent of the LMS step size, if it is sufficiently small.