On Distributed Estimation in Hierarchical Power Constrained Wireless Sensor Networks

• Published in: IEEE transactions on signal and information processing over networks Vol. 6; pp. 442 - 459
• Main Authors: Shirazi, Mojtaba, Vosoughi, Azadeh
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayesian analysis; Bayesian Cramér-Rao bound; Channel estimation; Clusters; Complexity; Constraints; Data communication; Data transmission; Distributed estimation; Electric power distribution; Fading channels; hierarchical power constrained WSN; linear fusion; LMMSE estimator; Mean square errors; MSE-power tradeoff; Optimization; random source; Resource management; Sensors; Tradeoffs; Training; transmit power optimization; Wireless networks; Wireless sensor networks
• ISSN: 2373-776X; 2373-7778
• DOI: 10.1109/TSIPN.2020.2995046

We consider distributed estimation of a random source in a hierarchical power constrained wireless sensor network. Sensors within each cluster send their measurements to a cluster head (CH). CHs optimally fuse the received signals and transmit to the fusion center (FC) over orthogonal fading channels. To enable channel estimation at the FC, CHs send pilots, prior to data transmission. We derive the mean square error (MSE) corresponding to the linear minimum mean square error (LMMSE) estimator of the source at the FC, and obtain the Bayesian Cramér-Rao bound (CRB). Our goal is to find (i) the optimal training power, (ii) the optimal power that sensors in a cluster spend to transmit their amplified measurements to their CH, and (iii) the optimal weight vector employed by each CH for its linear signal fusion, such that the MSE is minimized, subject to a network power constraint. To untangle the performance gain that optimizing each set of these variables provide, we also analyze three special cases of the original problem, where in each special case, only two sets of variables are optimized across clusters. We define three factors that allow us to quantify the effectiveness of each power allocation scheme in achieving an MSE-power tradeoff that is close to that of the Bayesian CRB. Combining the information gained from the factors and Bayesian CRB with our computational complexity analysis provides the system designer with quantitative complexity-versus-MSE improvement tradeoffs offered by different power allocation schemes.