Power Control and Coding Formulation for State Estimation With Wireless Sensors

Technological advances made wireless sensors cheap and reliable enough to be brought into industrial use. A major challenge arises from the fact that wireless channels introduce random packet dropouts. Power control and coding are key enabling technologies in wireless communications to ensure efficient communication. In this paper, we examine the role of power control and coding for Kalman filtering over wireless correlated channels. Two estimation architectures are considered; initially, the sensors send their measurements directly to a single gateway (GW). Next, wireless relay nodes provide additional links. The GW decides on the coding scheme and the transmitter power levels of the wireless nodes. The decision process is carried out online and adapts to varying channel conditions to improve the tradeoff between state estimation accuracy and energy expenditure. In combination with predictive power control, we investigate the use of multiple-description coding (MDC), zero-error coding (ZEC), and network coding and provide sufficient conditions for the expectation of the estimation error covariance matrix to be bounded. Numerical results suggest that the proposed method may lead to energy savings of around 50%, when compared with an alternative scheme, wherein transmission power levels and bit-rates are governed by simple logic. In particular, ZEC is preferable at time instances with high channel gains, whereas MDC is superior for time instances with low gains. When channels between the sensors and the GW are in deep fades, network coding improves estimation accuracy significantly without sacrificing energy efficiency.