Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach

Prashant Khanduri, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Pramod Kumar Varshney

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

This paper considers the problem of high-dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the fusion center (FC). We design optimal collaboration strategies which are universal for a class of deterministic signals. By establishing the equivalence between the collaboration strategy design problem and sparse principal component analysis (PCA), we solve the problem efficiently and evaluate the impact of collaboration on detection performance.

Original languageEnglish (US)
Article number7548304
Pages (from-to)1484-1488
Number of pages5
JournalIEEE Signal Processing Letters
Volume23
Issue number10
DOIs
StatePublished - Oct 1 2016

Keywords

  • Dimensionality reduction
  • multitask detection
  • sparse learning
  • universal collaboration

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering

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