Robust Linear Transceiver Designs for Vector Parameter Estimation in MIMO Wireless Sensor Networks under CSI Uncertainty

Kunwar Pritiraj Rajput, Yogesh Verma, Naveen K.D. Venkategowda, Aditya K. Jagannatham, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

This work conceives the robust linear transceivers for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) multi-sensor network under imperfect channel state information (CSI) at the fusion center (FC). Both the popular stochastic (S-) and norm ball CSI uncertainty (N-CSIU) models are considered for robust design. The proposed techniques are based on two design criterion, the first being, minimizing the mean squared error (MSE) of the estimate at the FC subject to total network power or individual sensor power constraints. Second, minimizing the total power consumption in the network while meeting a predefined level of MSE performance. Furthermore, the framework for precoder and combiner optimization is based on results from majorization theory, which leads to non-iterative closed-form solutions for the transceivers. While the most general scenario with correlated parameters and arbitrary observation SNR is considered to begin with, scenarios with uncorrelated parameters and high observation SNR are also considered as special cases, which makes the analysis comprehensive. Simulation results are presented to demonstrate the efficacy of the proposed schemes.

Original languageEnglish (US)
Article number9444772
Pages (from-to)7347-7362
Number of pages16
JournalIEEE Transactions on Vehicular Technology
Volume70
Issue number8
DOIs
StatePublished - Aug 2021

Keywords

  • Wireless sensor networks
  • decentralized estimation
  • parameter estimation
  • robust transceiver design
  • stochastic robust design
  • worst-case design

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Automotive Engineering

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