Linear MMSE Precoder Combiner Designs for Decentralized Estimation in Wireless Sensor Networks

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

Research output: Contribution to journalConference Articlepeer-review

3 Scopus citations

Abstract

This work considers the design of linear minimum mean square error (MMSE) precoders and combiners for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) wireless sensor network. The proposed designs that minimize the mean squared error (MSE) of the parameter estimate at the fusion center are based on majorization theory, which leads to non-iterative closed-form solutions for the precoders and combiners. Various scenarios are considered for parameter estimation such as networks with ideal high precision sensors as well as noisy non-ideal sensors. Moreover, inter parameter correlation is also incorporated, which makes the analysis comprehensive. The Bayesian Cramer-Rao bound (BCRB) and centralized MMSE bound are determined to characterize the estimation performance. Simulation results demonstrate the improved performance and also corroborate our analytical formulations.

Original languageEnglish (US)
Article number9322457
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
StatePublished - 2020
Externally publishedYes
Event2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: Dec 7 2020Dec 11 2020

Keywords

  • Wireless sensor networks
  • linear decentralized estimation
  • majorization theory
  • precoder-combiner design

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing

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