Performance Bounds for Vector Quantized Compressive Sensing
2012 (English)In: 2012 International Symposium on Information Theory and Its Applications, ISITA 2012, IEICE , 2012, 289-293 p.Conference paper (Refereed)
In this paper, we endeavor for predicting the performance of quantized compressive sensing under the use of sparse reconstruction estimators. We assume that a high rate vector quantizer is used to encode the noisy compressive sensing measurement vector. Exploiting a block sparse source model, we use Gaussian mixture density for modeling the distribution of the source. This allows us to formulate an optimal rate allocation problem for the vector quantizer. Considering noisy CS quantized measurements, we analyze upper- and lower-bounds on reconstruction error performance guarantee of two estimators - convex relaxation based basis pursuit de-noising estimator and an oracle-assisted least-squares estimator.
Place, publisher, year, edition, pages
IEICE , 2012. 289-293 p.
Estimation, Information theory, Relaxation processes
Telecommunications Signal Processing
IdentifiersURN: urn:nbn:se:kth:diva-103493ISI: 000320850700061ScopusID: 2-s2.0-84873544340ISBN: 978-488552267-3OAI: oai:DiVA.org:kth-103493DiVA: diva2:560324
2012 International Symposium on Information Theory and Its Applications, ISITA 2012; Honolulu, HI; 28 October 2012 through 31 October 2012
QC 201301182012-10-122012-10-122014-06-03Bibliographically approved