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In this paper, we design and analyze distributed vector quantization (VQ) forcompressed measurements of correlated sparse sources over noisy channels.Inspired by the framework of compressed sensing (CS) for acquiring compressedmeasurements of the sparse sources, we develop optimized quantization schemesthat enable distributed encoding and transmission of CS measurements over noisychannels followed by joint decoding at a decoder. The optimality is addressedwith respect to minimizing the sum of mean-square error (MSE) distortionsbetween the sparse sources and their reconstruction vectors at the decoder. Wepropose a VQ encoder-decoder design via an iterative algorithm, and derive alower-bound on the end-to-end MSE of the studied distributed system. Throughseveral simulation studies, we evaluate the performance of the proposeddistributed scheme.

Distributed Quantization for Measurement of Correlated Sparse Sources over Noisy Channels