Distributed estimation over fading channels using one-bit quantization

The problem of distributed estimation of an unknown parameter corrupted by noise is studied in this paper. In consideration of the stringent bandwidth constraint in practical wireless sensor network (WSN) applications, a one-bit quantization scheme is adopted to compress local sensor observations. Imperfect data transmission between local sensors and a fusion center is considered and modeled as a Rayleigh fading channel. The conventional maximum likelihood estimation (MLE) usually involves high computational complexity. In this paper, we propose a simple mean estimator which requires only the mean of the channel gain. It is further modified by utilizing the properties of the intermediate parameter under estimation. Theoretical analysis and simulation results show that the proposed estimators are not only more computationally efficient than MLE, but also achieve near MLE performance over a wide range of the channel SNR and the number of sensors, which makes them suitable for practical resource-constraint WSN applications.