Abstract
This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency-based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplify-and-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the nonconvex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for collocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC.
| Original language | English |
|---|---|
| Article number | 7870580 |
| Pages (from-to) | 549-562 |
| Number of pages | 14 |
| Journal | IEEE Journal on Selected Topics in Signal Processing |
| Volume | 11 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2017 Apr 1 |
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering
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Wireless Power Transfer for Distributed Estimation in Sensor Networks. / Mai, Vien V.; Shin, Won-Yong; Ishibashi, Koji.
In: IEEE Journal on Selected Topics in Signal Processing, Vol. 11, No. 3, 7870580, 01.04.2017, p. 549-562.Research output: Contribution to journal › Article
TY - JOUR
T1 - Wireless Power Transfer for Distributed Estimation in Sensor Networks
AU - Mai, Vien V.
AU - Shin, Won-Yong
AU - Ishibashi, Koji
PY - 2017/4/1
Y1 - 2017/4/1
N2 - This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency-based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplify-and-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the nonconvex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for collocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC.
AB - This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency-based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplify-and-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the nonconvex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for collocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC.
UR - http://www.scopus.com/inward/record.url?scp=85018515386&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018515386&partnerID=8YFLogxK
U2 - 10.1109/JSTSP.2017.2678106
DO - 10.1109/JSTSP.2017.2678106
M3 - Article
AN - SCOPUS:85018515386
VL - 11
SP - 549
EP - 562
JO - IEEE Journal on Selected Topics in Signal Processing
JF - IEEE Journal on Selected Topics in Signal Processing
SN - 1932-4553
IS - 3
M1 - 7870580
ER -