Joint Source-Channel Vector Quantization for Compressed Sensing
2014 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 14, 3667-3681 p.Article in journal (Refereed) Published
We study joint source-channel coding (JSCC) of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a framework for realizing optimum JSCC schemes that enable encoding and transmitting CS measurements of a sparse source over discrete memoryless channels, and decoding the sparse source signal. For this purpose, the optimal design of encoder-decoder pair of a VQ is considered, where the optimality is addressed by minimizing end-to-end mean square error (MSE). We derive a theoretical lower bound on the MSE performance and propose a practical encoder-decoder design through an iterative algorithm. The resulting coding scheme is referred to as channel-optimized VQ for CS, coined COVQ-CS. In order to address the encoding complexity issue of the COVQ-CS, we propose to use a structured quantizer, namely low-complexity multistage VQ (MSVQ). We derive new encoding and decoding conditions for the MSVQ and then propose a practical encoder-decoder design algorithm referred to as channel-optimized MSVQ for CS, coined COMSVQ-CS. Through simulation studies, we compare the proposed schemes vis-a-vis relevant quantizers.
Place, publisher, year, edition, pages
2014. Vol. 62, no 14, 3667-3681 p.
Vector quantization, multi-stage vector quantization, joint source-channel coding, noisy channel, compressed sensing, sparsity, mean square error
Telecommunications Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-149203DOI: 10.1109/TSP.2014.2329649ISI: 000339042700014ScopusID: 2-s2.0-84903722424OAI: oai:DiVA.org:kth-149203DiVA: diva2:738777
QC 201408192014-08-192014-08-182014-08-19Bibliographically approved