Online Design of Optimal Precoders for High Dimensional Signal Detection

Prashant Khanduri, Lakshmi Narasimhan Theagarajan, Pramod Kumar Varshney

Research output: Contribution to journalArticle

Abstract

In this paper, we propose a novel methodology to design optimal precoders for distributed detection of high-dimensional signals. We consider a wireless sensor network (WSN) that consists of multiple sensors that are spatially distributed in a region of interest and a fusion center (FC). The sensors observe an unknown high-dimensional signal and forward their observations to the FC after precoding. The sensors collect data over both temporal and spatial domains. The FC performs a binary hypothesis test based on the data received from the sensors over noisy channels. In this setup, we present a technique to design optimal online linear precoding strategies with transmit power constraints. We show analytically that the error exponents achieved by the proposed precoders are independent of the signal dimension. In contrast, the error exponents of the state-of-the-art precoding strategies deteriorate with the increase in signal dimension. We verify our analysis via numerical simulations and show that the proposed precoders achieve better detection performance compared to those of other state-of-the-art techniques known in the literature.

Original languageEnglish (US)
Article number8744273
Pages (from-to)4122-4135
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume67
Issue number15
DOIs
StatePublished - Aug 1 2019

    Fingerprint

Keywords

  • dimensionality reduction
  • distributed hypothesis testing
  • precoder design
  • Spatio-temporal data

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this