TY - GEN
T1 - Sublinear compressive sensing reconstruction via belief propagation decoding
AU - Pham, Hoa V.
AU - Dai, Wei
AU - Milenkovic, Olgica
PY - 2009
Y1 - 2009
N2 - We propose a new compressive sensing scheme, based on codes of graphs, that allows for joint design of sensing matrices and low complexity reconstruction algorithms. The compressive sensing matrices can be shown to offer asymptotically optimal performance when used in combination with OMP methods. For more elaborate greedy reconstruction schemes, we propose a new family of list decoding and multiple-basis belief propagation algorithms. Our sim ulation results indicate that the proposed CS scheme offers good complexity-performance tradeoffs for several classes of sparse signals.
AB - We propose a new compressive sensing scheme, based on codes of graphs, that allows for joint design of sensing matrices and low complexity reconstruction algorithms. The compressive sensing matrices can be shown to offer asymptotically optimal performance when used in combination with OMP methods. For more elaborate greedy reconstruction schemes, we propose a new family of list decoding and multiple-basis belief propagation algorithms. Our sim ulation results indicate that the proposed CS scheme offers good complexity-performance tradeoffs for several classes of sparse signals.
UR - http://www.scopus.com/inward/record.url?scp=70449475988&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449475988&partnerID=8YFLogxK
U2 - 10.1109/ISIT.2009.5205667
DO - 10.1109/ISIT.2009.5205667
M3 - Conference contribution
AN - SCOPUS:70449475988
SN - 9781424443130
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 674
EP - 678
BT - 2009 IEEE International Symposium on Information Theory, ISIT 2009
T2 - 2009 IEEE International Symposium on Information Theory, ISIT 2009
Y2 - 28 June 2009 through 3 July 2009
ER -