TY - GEN
T1 - Optimal quantization for compressive sensing under message passing reconstruction
AU - Kamilov, Ulugbek
AU - Goyal, Vivek K.
AU - Rangan, Sundeep
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - We consider the optimal quantization of compressive sensing measurements along with estimation from quantized samples using generalized approximate message passing (GAMP). GAMP is an iterative reconstruction scheme inspired by the belief propagation algorithm on bipartite graphs which generalizes approximate message passing (AMP) for arbitrary measurement channels. Its asymptotic error performance can be accurately predicted and tracked through the state evolution formalism. We utilize these results to design mean-square optimal scalar quantizers for GAMP signal reconstruction and empirically demonstrate the superior error performance of the resulting quantizers.
AB - We consider the optimal quantization of compressive sensing measurements along with estimation from quantized samples using generalized approximate message passing (GAMP). GAMP is an iterative reconstruction scheme inspired by the belief propagation algorithm on bipartite graphs which generalizes approximate message passing (AMP) for arbitrary measurement channels. Its asymptotic error performance can be accurately predicted and tracked through the state evolution formalism. We utilize these results to design mean-square optimal scalar quantizers for GAMP signal reconstruction and empirically demonstrate the superior error performance of the resulting quantizers.
UR - http://www.scopus.com/inward/record.url?scp=80054806044&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80054806044&partnerID=8YFLogxK
U2 - 10.1109/ISIT.2011.6034168
DO - 10.1109/ISIT.2011.6034168
M3 - Conference contribution
AN - SCOPUS:80054806044
SN - 9781457705953
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 459
EP - 463
BT - 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011
T2 - 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011
Y2 - 31 July 2011 through 5 August 2011
ER -