Distributed Quantization for Compressed Sensing

TL;DR

This paper introduces a distributed vector quantization framework for compressed sensing measurements of correlated sparse sources, optimizing encoding and decoding to minimize reconstruction error at a fusion center.

Contribution

It develops a novel distributed quantizer design for compressed sensing, including an iterative algorithm for practical implementation and a performance lower-bound.

Findings

Derived a lower bound on end-to-end performance.

Proposed an iterative algorithm for encoder-decoder design.

Validated the effectiveness of the approach through simulations.

Abstract

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.