A distributed tracking algorithm for reconstruction of graph signals

Xiaohan Wang, Mengdi Wang, Yuantao Gu

Research output: Contribution to journalArticle

37 Scopus citations

Abstract

The rapid development of signal processing on graphs provides a new perspective for processing large-scale data associated with irregular domains. In many practical applications, it is necessary to handle massive data sets through complex networks, in which most nodes have limited computing power. Designing efficient distributed algorithms is critical for this task. This paper focuses on the distributed reconstruction of a time-varying bandlimited graph signal based on observations sampled at a subset of selected nodes. A distributed least square reconstruction (DLSR) algorithm is proposed to recover the unknown signal iteratively, by allowing neighboring nodes to communicate with one another and make fast updates. DLSR uses a decay scheme to annihilate the out-of-band energy occurring in the reconstruction process, which is inevitably caused by the transmission delay in distributed systems. Proof of convergence and error bounds for DLSR are provided in this paper, suggesting that the algorithm is able to track time-varying graph signals and perfectly reconstruct time-invariant signals. The DLSR algorithm is numerically experimented with synthetic data and real-world sensor network data, which verifies its ability in tracking slowly time-varying graph signals.

Original languageEnglish (US)
Article number7042257
Pages (from-to)728-740
Number of pages13
JournalIEEE Journal on Selected Topics in Signal Processing
Volume9
Issue number4
DOIs
StatePublished - Jun 1 2015

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Distributed algorithm
  • graph signal
  • sampling and reconstruction
  • signal processing on graph
  • time-varying signal

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