# Sparse Graph Codes for Non-adaptive Quantitative Group Testing

## TL;DR

This paper introduces a non-adaptive quantitative group testing algorithm using sparse graph codes and BCH codes, achieving near-optimal test efficiency and high probability of exact defective item recovery in large-scale scenarios.

## Contribution

The paper proposes a novel non-adaptive QGT scheme with sparse graph codes and BCH codes, providing probabilistic guarantees and analyzing test complexity.

## Key findings

- Requires at most c(t)K(t log2(ℓN/(c(t)K)+1)+1)+1 tests for large N,K
- Achieves minimum tests with t=2
- Decoding complexity is O(K log(N/K)) for t ≤ 4

## Abstract

This paper considers the problem of Quantitative Group Testing (QGT). Consider a set of $N$ items among which $K$ items are defective. The QGT problem is to identify (all or a sufficiently large fraction of) the defective items, where the result of a test reveals the number of defective items in the tested group. In this work, we propose a non-adaptive QGT algorithm using sparse graph codes over bi-regular bipartite graphs with left-degree $\ell$ and right degree $r$ and binary $t$-error-correcting BCH codes. The proposed scheme provides exact recovery with probabilistic guarantee, i.e. recovers all the defective items with high probability. In particular, we show that for the sub-linear regime where $\frac{K}{N}$ vanishes as $K,N\rightarrow\infty$, the proposed algorithm requires at most ${m=c(t)K\left(t\log_2\left(\frac{\ell N}{c(t)K}+1\right)+1\right)+1}$ tests to recover all the defective items with probability approaching one as ${K,N\rightarrow\infty}$, where $c(t)$ depends only on $t$. The results of our theoretical analysis reveal that the minimum number of required tests is achieved by $t=2$. The encoding and decoding of the proposed algorithm for any $t\leq 4$ have the computational complexity of $\mathcal{O}(K\log^2 \frac{N}{K})$ and $\mathcal{O}(K\log \frac{N}{K})$, respectively. Our simulation results also show that the proposed algorithm significantly outperforms a non-adaptive semi-quantitative group testing algorithm recently proposed by Abdalla \emph{et al.} in terms of the required number of tests for identifying all the defective items with high probability.