Channel-Optimized Vector Quantizer Design for Compressed Sensing Measurements

TL;DR

This paper develops a channel-optimized vector quantization method for transmitting compressed sensing measurements over noisy channels, minimizing mean-square error in sparse signal reconstruction.

Contribution

It introduces a new algorithm for training channel-optimized vector quantizers specifically designed for compressed sensing measurement transmission.

Findings

Derived optimal conditions for quantizer design.

Developed an end-to-end training algorithm.

Improved reconstruction accuracy over traditional methods.

Abstract

We consider vector-quantized (VQ) transmission of compressed sensing (CS) measurements over noisy channels. Adopting mean-square error (MSE) criterion to measure the distortion between a sparse vector and its reconstruction, we derive channel-optimized quantization principles for encoding CS measurement vector and reconstructing sparse source vector. The resulting necessary optimal conditions are used to develop an algorithm for training channel-optimized vector quantization (COVQ) of CS measurements by taking the end-to-end distortion measure into account.