Joint Collaboration and Compression Design for Random Signal Detection in Wireless Sensor Networks

Xiancheng Cheng, Baocheng Geng, Prashant Khanduri, Baixiao Chen, Pramod Varshney

Research output: Contribution to journalArticlepeer-review

Abstract

In this work, we propose a joint collaboration-compression framework for the random signal detection problem in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a linear collaboration matrix) with each other. Then a subset of sensors linearly compress their aggregated information before communicating with the fusion center (FC). We propose a novel metric called generalized deflection coefficient (GDC) for evaluating the detection performance which is shown to be tightly upper bounded by the Kullback-Leibler divergence for Gaussian observations. We jointly design the linear collaboration and compression strategies under power constraints via alternating maximization of the proposed GDC metric. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.

Original languageEnglish (US)
Article number9508151
Pages (from-to)1630-1634
Number of pages5
JournalIEEE Signal Processing Letters
Volume28
DOIs
StatePublished - 2021

Keywords

  • Distributed sensor networks
  • generalized deflection coefficient
  • random signal detection

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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