Nonadaptive Noise-Resilient Group Testing with Order-Optimal Tests and Fast-and-Reliable Decoding

TL;DR

This paper introduces Gacha GT, a nonadaptive, noise-resilient group testing scheme that uses a novel Reed--Solomon code design to achieve near-optimal test complexity and fast decoding within a broad parameter range.

Contribution

The paper presents Gacha GT, a unified scheme that meets all key criteria for group testing in a large parameter region, with specialized versions outperforming existing methods.

Findings

Satisfies all key criteria for a wide parameter range

Uses a redesigned Reed--Solomon code for efficient list-decoding

Achieves near-optimal test complexity and fast decoding

Abstract

Group testing (GT) is the Boolean version of spare signal recovery and, due to its simplicity, a marketplace for ideas that can be brought to bear upon related problems, such as heavy hitters, compressed sensing, and multiple access channels. The definition of a "good" GT varies from one buyer to another, but it generally includes (i) usage of nonadaptive tests, (ii) limiting to $O(k \log n)$ tests, (iii) resiliency to test noise, (iv) $O(k \mathrm{poly}(\log n))$ decoding time, and (v) lack of mistakes. In this paper, we propose $Gacha~GT$. Gacha is an elementary and self-contained, versatile and unified scheme that, for the first time, satisfies all criteria for a fairly large region of parameters, namely when $\log k < \log(n)^{1-1/O(1)}$. Outside this parameter region, Gacha can be specialized to outperform the state-of-the-art partial-recovery GTs, exact-recovery GTs, and worst-case GTs. The new idea Gacha brings to the market is a redesigned Reed--Solomon code for probabilistic list-decoding at diminishing code rates over reasonably-large alphabets. Normally, list-decoding a vanilla Reed--Solomon code is equivalent to the nontrivial task of identifying the subsets of points that fit low-degree polynomials. In this paper, we explicitly tell the decoder which points belong to the same polynomial, thus reducing the complexity and enabling the improvement on GT.