Soft-Decision Decoding for LDPC Code-Based Quantitative Group Testing

TL;DR

This paper introduces a belief-propagation-based soft-decision decoding method for LDPC code-based quantitative group testing, significantly improving detection accuracy over previous hard-decision approaches.

Contribution

It presents a novel soft-information decoder for LDPC-based group testing, enhancing performance beyond existing hard-decision methods.

Findings

Soft-decision decoder outperforms hard-decision decoder in simulations

Improved misdetection rate with the proposed method

Demonstrates the effectiveness of belief propagation in this context

Abstract

We consider the problem of identifying defective items in a population with non-adaptive quantitative group testing. For this scenario, Mashauri et al. recently proposed a low-density parity-check (LDPC) code-based quantitative group testing scheme with a hard-decision decoding approach (akin to peeling decoding). This scheme outperforms generalized LDPC code-based quantitative group testing schemes in terms of the misdetection rate. In this work, we propose a belief-propagation-based decoder for quantitative group testing with LDPC codes, where the messages being passed are purely soft. Through extensive simulations, we show that the proposed soft-information decoder outperforms the hard-decision decoder Mashauri et al.