Distributed Quantization for Compressed Sensing
2014 (English)Conference paper (Refereed)
We study distributed coding of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a distributed framework for realizing optimized quantizer that enables encoding CS measurements of correlated sparse sources followed by joint decoding at a fusion center. The optimality of VQ encoder-decoder pairs is addressed by minimizing the sum of mean-square errors between the sparse sources and their reconstruction vectors at the fusion center. We derive a lower-bound on the end-to-end performance of the studied distributed system, and propose a practical encoder-decoder design through an iterative algorithm.
Place, publisher, year, edition, pages
2014. 6439-6443 p.
, International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Compressed sensing, correlation, distributed source coding, mean square error, vector quantization
Telecommunications Signal Processing
Research subject Transport Science
IdentifiersURN: urn:nbn:se:kth:diva-143356DOI: 10.1109/ICASSP.2014.6854844ISI: 000343655306095ScopusID: 2-s2.0-84905252077ISBN: 978-1-4799-2893-4OAI: oai:DiVA.org:kth-143356DiVA: diva2:706406
2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014; Florence; Italy; 4 May 2014 through 9 May 2014
ProjectsCompressed sensing, distributed source coding, vector quantization, correlation, mean square error
QC 201501222014-03-202014-03-202015-01-22Bibliographically approved