Decentralized Estimation over Orthogonal Multiple-access Fading Channels in Wireless Sensor Networks - Optimal and Suboptimal Estimators

TL;DR

This paper develops optimal and suboptimal decentralized estimators for wireless sensor networks over fading channels, demonstrating improved performance with multiple-bit quantization and digital transmission schemes under bandwidth and energy constraints.

Contribution

It introduces maximum likelihood estimators for WSNs with known and unknown CSI, including suboptimal variants that leverage redundant information for better estimation accuracy.

Findings

Digital communication with multiple-bit quantization outperforms analog forwarding in fading channels.

MLE with multiple-bit quantization is superior at medium/high SNR under bandwidth and energy constraints.

Proposed estimators adapt to various transmission schemes through a general message function.

Abstract

Optimal and suboptimal decentralized estimators in wireless sensor networks (WSNs) over orthogonal multiple-access fading channels are studied in this paper. Considering multiple-bit quantization before digital transmission, we develop maximum likelihood estimators (MLEs) with both known and unknown channel state information (CSI). When training symbols are available, we derive a MLE that is a special case of the MLE with unknown CSI. It implicitly uses the training symbols to estimate the channel coefficients and exploits the estimated CSI in an optimal way. To reduce the computational complexity, we propose suboptimal estimators. These estimators exploit both signal and data level redundant information to improve the estimation performance. The proposed MLEs reduce to traditional fusion based or diversity based estimators when communications or observations are perfect. By introducing a general message function, the proposed estimators can be applied when various analog or digital transmission schemes are used. The simulations show that the estimators using digital communications with multiple-bit quantization outperform the estimator using analog-and-forwarding transmission in fading channels. When considering the total bandwidth and energy constraints, the MLE using multiple-bit quantization is superior to that using binary quantization at medium and high observation signal-to-noise ratio levels.