Distributed estimation in a phase-modulated wireless sensor network

In this paper, distributed estimation over an orthogonal multiple-access channel (MAC) wireless sensor network (WSN) is investigated where the sensors transmit with phase-modulated signals through independent and identically complex Gaussian channels. The maximum-likelihood (ML) estimator for ideal sensing environment and the corresponding modified Cramer-Rao lower bound are derived, and the estimation performance is shown to be asymptotically the same for both ideal and non-ideal sensing cases. Moreover, the approximated probability density function of the proposed ML estimator is obtained for ideal sensing environment. Under the same criterion, the orthogonal MAC WSN is shown to offer a better system performance than coherent MAC WSN.