Retrieving Data Permutations from Noisy Observations: High and Low Noise Asymptotics

Document Type

Conference Proceeding

Publication Date

7-12-2021

Abstract

This paper considers the problem of recovering the permutation of an n-dimensional random vector X observed in Gaussian noise. First, a general expression for the probability of error is derived when a linear decoder (i.e., linear estimator followed by a sorting operation) is used. The derived expression holds with minimal assumptions on the distribution of X and when the noise has memory. Second, for the case of isotropic noise (i.e., noise with a diagonal scalar covariance matrix), the rates of convergence of the probability of error are characterized in the high and low noise regimes. In the low noise regime, for every dimension n, the probability of error is shown to behave proportionally to \sigma, where \sigma is the noise standard deviation. Moreover, the slope is computed exactly for several distributions and it is shown to behave quadratically in n. In the high noise regime, for every dimension n, the probability of correctness is shown to behave as 1/\sigma, and the exact expression for the rate of convergence is also provided.

Identifier

85115104228 (Scopus)

ISBN

[9781538682098]

Publication Title

IEEE International Symposium on Information Theory Proceedings

External Full Text Location

https://doi.org/10.1109/ISIT45174.2021.9518137

ISSN

21578095

First Page

1100

Last Page

1105

Volume

2021-July

Grant

CCF-1849757

Fund Ref

National Science Foundation

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