Graded quantization for multiple description coding of compressive measurements

TL;DR

This paper introduces Graded Quantization (CS-GQ), a novel multiple description coding method for compressed sensing measurements, enhancing robustness against packet losses in resource-constrained and unreliable communication scenarios.

Contribution

The paper presents CS-GQ, a new MDC approach tailored for compressed sensing, with an optimized decoding algorithm and demonstrated superior performance over existing methods.

Findings

CS-GQ effectively improves robustness against packet losses.

The proposed decoding algorithm enhances signal reconstruction quality.

Simulations show CS-GQ outperforms other schemes in test metrics.

Abstract

Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios like sensor networks. However, such scenarios are often coupled with unreliable communication channels and providing robust transmission of the acquired data to a receiver is an issue. Multiple description coding (MDC) effectively combats channel losses for systems without feedback, thus raising the interest in developing MDC methods explicitly designed for the CS framework, and exploiting its properties. We propose a method called Graded Quantization (CS-GQ) that leverages the democratic property of compressive measurements to effectively implement MDC, and we provide methods to optimize its performance. A novel decoding algorithm based on the alternating directions method of multipliers is derived to reconstruct signals from a limited number of received descriptions. Simulations are performed to assess the performance of CS-GQ against other methods in presence of packet losses. The proposed method is successful at providing robust coding of CS measurements and outperforms other schemes for the considered test metrics.