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

We study optimal and suboptimal decentralized estimators in wireless sensor networks over orthogonal multipleaccess fading channels in this paper. Considering multiple-bit quantization for 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 CSI and exploits channel estimation in an optimal way and performs the best in realistic scenarios where CSI needs to be estimated and transmission energy is constrained. To reduce the computational complexity of the MLE with unknown CSI, we propose a suboptimal estimator. These optimal and suboptimal estimators exploit both signaland data-level redundant information to combat the observation noise and the communication errors. Simulation results show that the proposed estimators are superior to the existing approaches, and the suboptimal estimator performs closely to the optimal MLE.