EH-Enabled Distributed Detection Over Temporally Correlated Markovian MIMO Channels

Abstract

We address distributed detection problem in a wireless sensor network, where each sensor harvests and stores randomly arriving energy units in a finite-size battery. Sensors transmit their symbols simultaneously to a fusion center (FC) with M >1 antennas, over temporally correlated fading channels. To characterize the channel time variation we adopt a Markovian model and assume that the channel time-correlation is defined by Jakes-Clark’s correlation function. We consider limited feedback of channel gain, defined as the Frobenius norm of MIMO channel matrix, at a fixed feedback frequency (e.g., every T time slots) from the FC to sensors. Modeling the randomly arriving energy units as a Poisson process and the quantized channel gain and the battery dynamics as homogeneous finite-state Markov chains, we propose an adaptive transmit power control strategy such that the J-divergence based detection metric is maximized at the FC, subject to an average transmit power per-sensor constraint.