Group Testing with Side Information via Generalized Approximate Message Passing

Autor:	Cao, Shu-Jie, Goenka, Ritesh, Wong, Chau-Wai, Rajwade, Ajit, Baron, Dror
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Information Theory
Druh dokumentu:	Working Paper
DOI:	10.1109/TSP.2023.3287671
Popis:	Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given n samples, one per individual. Each individual is either infected or uninfected. These samples are arranged into m < n pooled samples, where each pool is obtained by mixing a subset of the n individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we incorporate side information (SI) collected from contact tracing (CT) into nonadaptive/single-stage group testing algorithms. We generate different types of possible CT SI data by incorporating different possible characteristics of the spread of disease. These data are fed into a group testing framework based on generalized approximate message passing (GAMP). Numerical results show that our GAMP-based algorithms provide improved accuracy. Comment: To appear in IEEE Trans. Signal Processing. arXiv admin note: substantial text overlap with arXiv:2106.02699, arXiv:2011.14186
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.03731 Zobrazit plný text záznamu View this record from Arxiv