Hybrid Precoder and Combiner Designs for Decentralized Parameter Estimation in mmWave MIMO Wireless Sensor Networks

Hybrid precoder and combiner designs are conceived for decentralized parameter estimation in millimeter wave (mmWave) multiple-input-multiple-output (MIMO) wireless sensor networks (WSNs). More explicitly, efficient pre- and post-processing of the sensor observations and received signal are proposed...

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Vydáno v:IEEE internet of things journal Ročník 11; číslo 1; s. 1629 - 1643
Hlavní autoři: Maity, Priyanka, Srivastava, Suraj, Rajput, Kunwar Pritiraj, Venkategowda, Naveen K. D., Jagannatham, Aditya K., Hanzo, Lajos
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Decentralized parameter estimation
Estimation
hybrid transceiver design
Lower bounds
majorization theory
Matched pursuit
millimeter wave (mmWave) multiple-input–multiple-output (MIMO)
Millimeter wave communication
Millimeter waves
MIMO
MIMO communication
Parameter estimation
Radio frequency
Sensors
Signal reconstruction
Transceivers
Wireless sensor networks
wireless sensor networks (WSNs)
Radio frequency
Wireless sensor networks
Parameter estimation
wireless sensor networks (WSNs)
Estimation
millimeter wave (mmWave) multiple-input-multiple-output (MIMO)
Transceivers
Decentralized parameter estimation
hybrid transceiver design
Millimeter wave communication
MIMO communication
majorization theory
ISSN:2327-4662, 2327-4662
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Shrnutí:Hybrid precoder and combiner designs are conceived for decentralized parameter estimation in millimeter wave (mmWave) multiple-input-multiple-output (MIMO) wireless sensor networks (WSNs). More explicitly, efficient pre- and post-processing of the sensor observations and received signal are proposed for the minimum mean square error (MMSE) estimation of a parameter vector. The proposed techniques exploit the limited scattering nature of the mmWave MIMO channel for formulating the hybrid transceiver design framework as a multiple measurement vectors (MMVs)-based sparse signal recovery problem. This is then solved using the iterative appealingly low-complexity simultaneous orthogonal matching pursuit (SOMP). Tailor-made designs are presented for WSNs operating under both total and per-sensor power constraints, while considering ideal noiseless as well as realistic noisy sensors. Furthermore, both the Bayesian Cramer-Rao lower bound and the centralized MMSE bound are derived for benchmarking the proposed decentralized estimation schemes. Our simulation results demonstrate the efficiency of the designs advocated and verify the analytical findings.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3290108