Sparse signal recovery via tree search matching pursuit

Jaeseok Lee, Jun Won Choi, Byonghyo Shim

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Recently, greedy algorithm has received much attention as a cost-effective means to reconstruct the sparse signals from compressed measurements. Much of previous work has focused on the investigation of a single candidate to identify the support (index set of nonzero elements) of the sparse signals. Well-known drawback of the greedy approach is that the chosen candidate is often not the optimal solution due to the myopic decision in each iteration. In this paper, we propose a tree search based sparse signal recovery algorithm referred to as the tree search matching pursuit (TSMP). Two key ingredients of the proposed TSMP algorithm to control the computational complexity are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In numerical simulations of Internet of Things (IoT) environments, it is shown that TSMP outperforms conventional schemes by a large margin.

Original languageEnglish
Article number7742718
Pages (from-to)699-712
Number of pages14
JournalJournal of Communications and Networks
Volume18
Issue number5
DOIs
Publication statusPublished - 2016 Oct 1
Externally publishedYes

Keywords

  • Compressive sensing
  • greedy algorithm
  • sparse recovery
  • tree pruning
  • tree search.

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

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