Power Control and Coding Formulation for State Estimation with Wireless Sensors

TL;DR

This paper investigates adaptive power control and coding strategies for wireless sensor networks to enhance Kalman filtering-based state estimation, balancing energy efficiency and estimation accuracy amid channel variability.

Contribution

It introduces an adaptive decision framework for coding and power control in wireless sensors, optimizing Kalman filter performance under correlated channel conditions.

Findings

Energy savings of around 50% compared to simple logic schemes.

Zero-error coding is effective during high channel gains.

Network coding improves accuracy during deep fades.

Abstract

Technological advances have 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 communications. In the present work, we examine the role of power control and coding for Kalman filtering over wireless correlated channels. Two estimation architectures are considered: In the first, the sensors send their measurements directly to a single gateway. In the second scheme, wireless relay nodes provide additional links. The gateway decides on the coding scheme and the transmitter power levels of the wireless nodes. The decision process is carried out on-line and adapts to varying channel conditions in order to improve the trade-off between state estimation accuracy and energy expenditure. In combination with predictive power control, we investigate the use of multiple-description coding, zero-error coding 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 to an alternative scheme, wherein transmission power levels and bit-rates are governed by simple logic. In particular, zero-error coding is preferable at time instances with high channel gains, whereas multiple-description coding is superior for time instances with low gains. When channels between the sensors and the gateway are in deep fades, network coding improves estimation accuracy significantly without sacrificing energy efficiency.