Non-Adaptive Group Testing Framework based on Concatenation Code

TL;DR

This paper introduces an explicit non-adaptive group testing method using concatenation codes that efficiently identifies defective items with near-optimal test counts and fast decoding, especially for small numbers of defectives.

Contribution

It provides a strongly explicit construction of measurement matrices for non-adaptive group testing with optimal test complexity for up to two defectives and scalable schemes for larger numbers.

Findings

Number of tests for up to 2 defectives is approximately 16 log N.

Decoding time is sub-quadratic in log N, specifically O((log N)^2 / (log log N)^2).

The scheme scales to identify most defective items for larger d with O(d log N) tests.

Abstract

We consider an efficiently decodable non-adaptive group testing (NAGT) problem that meets theoretical bounds. The problem is to find a few specific items (at most $d$) satisfying certain characteristics in a colossal number of $N$ items as quickly as possible. Those $d$ specific items are called \textit{defective items}. The idea of NAGT is to pool a group of items, which is called \textit{a test}, then run a test on them. If the test outcome is \textit{positive}, there exists at least one defective item in the test, and if it is \textit{negative}, there exists no defective items. Formally, a binary $t \times N$ measurement matrix $\mathcal{M} = (m_{ij})$ is the representation for $t$ tests where row $i$ stands for test $i$ and $m_{ij} = 1$ if and only if item $j$ belongs to test $i$. There are three main objectives in NAGT: minimize the number of tests $t$, construct matrix $\mathcal{M}$, and identify defective items as quickly as possible. In this paper, we present a strongly explicit construction of $\mathcal{M}$ for when the number of defective items is at most 2, with the number of tests $t \simeq 16 \log{N} = O(\log{N})$. In particular, we need only $K \simeq N \times 16\log{N} = O(N\log{N})$ bits to construct such matrices, which is optimal. Furthermore, given these $K$ bits, any entry in the matrix can be constructed in time $O \left(\ln{N}/ \ln{\ln{N}} \right)$. Moreover, $\mathcal{M}$ can be decoded with high probability in time $O\left( \frac{\ln^2{N}}{\ln^2{\ln{N}}} \right)$. When the number of defective items is greater than 2, we present a scheme that can identify at least $(1-\epsilon)d$ defective items with $t \simeq 32 C(\epsilon) d \log{N} = O(d \log{N})$ in time $O \left( d \frac{\ln^2{N}}{\ln^2{\ln{N}}} \right)$ for any close-to-zero $\epsilon$, where $C(\epsilon)$ is a constant that depends only on $\epsilon$.