On Distributed Estimation in Hierarchical Power Constrained Wireless Sensor Networks
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 channel...
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Published in | IEEE transactions on signal and information processing over networks Vol. 6; pp. 442 - 459 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
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IEEE
2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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Online Access | Get full text |
ISSN | 2373-776X 2373-7778 |
DOI | 10.1109/TSIPN.2020.2995046 |
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Abstract | 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. |
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AbstractList | 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. |
Author | Vosoughi, Azadeh Shirazi, Mojtaba |
Author_xml | – sequence: 1 givenname: Mojtaba orcidid: 0000-0003-2947-2370 surname: Shirazi fullname: Shirazi, Mojtaba email: mojsh@knights.ucf.edu organization: Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL, USA – sequence: 2 givenname: Azadeh orcidid: 0000-0002-1937-2838 surname: Vosoughi fullname: Vosoughi, Azadeh email: azadeh@ucf.edu organization: Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL, USA |
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SubjectTerms | 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 |
Title | On Distributed Estimation in Hierarchical Power Constrained Wireless Sensor Networks |
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