Channel aware sensor selection in distributed detection systems

We propose a novel censoring scheme for the distributed detection problem in a wireless sensor network (WSNs) with N sensors, where the channels between the sensors and the fusion center (FC) is subject to fading and noise. To achieve the best tradeoff between energy efficiency and detection reliability, the FC forms the maximum ratio combing (MRC) fusion rule by integrating the partial knowledge of fading channel state information (CSI) and the local sensor performance indices, finds the best set of K (K < N) sensors that maximizes the total detection probability in the Neyman-Pearson (NP) sense, and informs the selected sensors via one bit feedback. The FC learns the Rayleigh flat fading channels, utilizing training symbols sent by the sensors, via applying minimum mean square error (MMSE) channel estimator. Assuming the sensors employ BPSK to modulate their binary local decisions, we derive the MRC fusion rule that depends on the channel estimates and the sensors' performance indices, and incorporates the effect of channel estimation error. Simulation results are provided to support the analytical derivations.