Joint Transceiver Designs for MSE Minimization in MIMO Wireless Powered Sensor Networks

In this paper, we study vector parameter estimation in multiple-input multiple-output wireless-powered sensor networks (WPSNs) where sensor nodes operate by harvesting the radio frequency signals transmitted from energy access points (E-APs). We investigate a joint design of sensor data precoders, a...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2018-08, Vol.17 (8), p.5120-5131
Main Authors: Venkategowda, Naveen K. D., Hoon Lee, Inkyu Lee
Format: Article
Language:English
Subjects:
Algorithms
Complexity
Computer simulation
Covariance matrices
Covariance matrix
Distributed algorithms
Distributed estimation
Energy conservation
Energy harvesting
Energy utilization
Estimation
Nodes
Optimization
Parameter estimation
precoding
Radio signals
Sensors
Transceivers
Wireless communication
wireless power transfer
wireless sensor network
Wireless sensor networks
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Summary:In this paper, we study vector parameter estimation in multiple-input multiple-output wireless-powered sensor networks (WPSNs) where sensor nodes operate by harvesting the radio frequency signals transmitted from energy access points (E-APs). We investigate a joint design of sensor data precoders, a fusion rule, and energy covariance matrices to minimize the mean square error (MSE) of the parameter estimate based on a non-linear energy harvesting model. First, we propose a centralized algorithm to solve the MSE minimization problem. Next, to reduce the computational complexity at the fusion center (FC) and feedback overhead from the sensors to the FC, we present a distributed algorithm to locally compute the precoders and the energy covariance matrices. We employ the alternating direction method of multipliers technique to minimize the MSE in a distributed manner without any coordination from the FC. In the proposed distributed algorithm, each sensor node calculates its own precoders and determines the local information of the fusion rule, and then messages are broadcast to other sensor nodes and E-APs. Simulation results demonstrate that the distributed algorithm performs close to the centralized algorithm with reduced complexity. Moreover, the proposed methods exhibit superior estimation performance over conventional techniques in WPSNs.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2018.2838589