Optimized Transmission for Parameter Estimation in Wireless Sensor Networks

A central problem in analog wireless sensor networks is to design the gain or phase-shifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed par...

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Bibliographic Details
Published in:IEEE transactions on signal and information processing over networks 2020, Vol.6, p.35-47
Main Authors: Khobahi, Shahin, Soltanalian, Mojtaba, Jiang, Feng, Swindlehurst, A. Lee
Format: Article
Language:English
Subjects:
alternating direction method of multipliers (ADMM)
Array signal processing
Computer networks
Computing costs
consensus algorithms
Design optimization
Distributed beamforming
Estimation
fusion center
Iterative methods
Nodes
Optimization
Parameter estimation
Parameterization
Relaying
Remote sensors
Routing
Sensors
signal recovery
Task analysis
waveform design on graphs
Wireless networks
Wireless sensor networks
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Summary:A central problem in analog wireless sensor networks is to design the gain or phase-shifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an over-parametrization of the original design problem, we devise a cyclic optimization approach that can handle tuning both gains and phase-shifts of the sensor nodes, even in intricate scenarios involving sensor selection or discrete phase-shifts. Each iteration of the proposed design framework consists of a combination of the Gram-Schmidt process and power method-like iterations, and as a result, enjoys a low computational cost. Along with formulating the design problem for a fusion center, we further present a consensus-based framework for decentralized estimation of deterministic parameters in a distributed network, which results in a similar sensor gain design problem. The numerical results confirm the computational advantage of the suggested approach in comparison with the state-of-the-art methods-an advantage that becomes more pronounced when the sensor network grows large.
ISSN:2373-776X
2373-7778
DOI:10.1109/TSIPN.2019.2945631