215128
20190317000355.0
978-1-5090-1806-2
000390098702187
ISI
CONF
Converse Bounds for Noisy Group Testing with Arbitrary Measurement Matrices
New York
2016
Ieee
2016
5
Conference Papers
IEEE International Symposium on Information Theory
We consider the group testing problem, in which one seeks to identify a subset of defective items within a larger set of items based on a number of noisy tests. While matching achievability and converse bounds are known in several cases of interest for i.i.d.~measurement matrices, less is known regarding converse bounds for arbitrary measurement matrices. We address this by presenting two converse bounds for arbitrary matrices and general noise models. First, we provide a strong converse bound ($\mathbb{P}[\mathrm{error}] \to 1$) that matches existing achievability bounds in several cases of interest. Second, we provide a weak converse bound ($\mathbb{P}[\mathrm{error}] \not\to 0$) that matches existing achievability bounds in greater generality.
Group testing
information-theoretic limits
converse bounds
Fano's inequality
Scarlett, Jonathan
248798
248483
Cevher, Volkan
199128
243957
International Symposium on Information Theory (ISIT)
Barcelona
July 10-15, 2016
2868-2872
2016 Ieee International Symposium On Information Theory
Publisher's version
253393
Publisher's version
http://infoscience.epfl.ch/record/215128/files/GT_ISIT.pdf
LIONS
252306
U12179
oai:infoscience.tind.io:215128
STI
conf
GLOBAL_SET
248798
248798
248798
248798
EPFL-CONF-215128
EPFL
PUBLISHED
REVIEWED
CONF