By Topic

Side information based orthogonal matching pursuit in distributed compressed sensing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Wenbo Zhang ; Key Lab. of Universal Wireless Commun., Beijing Univ. of Posts & Telecommun., Beijing, China ; Cong Ma ; Weiliang Wang ; Yu Liu
more authors

Compressed sensing (CS) theory shows that it is possible to reconstruct exactly a sparse signal from fewer linear measurements than that would be expected from traditional sampling theory. Orthogonal matching pursuit (OMP) is a kind of greedy pursuit algorithms that could implement CS signal recovery. However, in distributed compressed sensing (DCS) scenario, an emerging field based on the correlation among sources, original OMP has to be modified in order to satisfy the limited power restrictions. In this paper, by considering the reconstructed signal as side information (SI), we propose a new jointly decoding algorithm based on OMP and deduce the theoretical measurement rate of each signal. Compared with conventional decoding algorithms, a certain amount of saving in time and number of measurements is obtained and illustrated in the simulation results.

Published in:

Network Infrastructure and Digital Content, 2010 2nd IEEE International Conference on

Date of Conference:

24-26 Sept. 2010