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Cited by 371

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Fundamental Limits of Communication With Low Probability of Detection

Open access
Wang, Ligong, Wornell, Gregory W., Zheng, Lizhong·2016·IEEE Transactions on Information Theory

Abstract

This paper considers the problem of communication over a discrete memoryless channel (DMC) or an additive white Gaussian noise (AWGN) channel subject to the constraint that the probability that an adversary who observes the channel outputs can detect the communication is low. In particular, the relative entropy between the output distributions when a codeword is transmitted and when no input is provided to the channel must be sufficiently small. For a DMC whose output distribution induced by the “off” input symbol is not a mixture of the output distributions induced by other input symbols, it is shown that the maximum amount of information that can be transmitted under this criterion scales like the square root of the blocklength. The same is true for the AWGN channel. Exact expressions for the scaling constant are also derived.

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Metadata

Authors
Wang, L., Wornell, G. W., & Zheng, L.
Venue
IEEE Transactions on Information Theory
Published
2016-06
Volume
62
Issue
6
Pages
3493-3503
ISSN
  • 0018-9448 (ISSN-L)
  • 1557-9654
OpenAlex
W3104101844
Cited by
371
References
4
FWCI
13.87
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Fundamental Limits of Communication With Low Probability of Detection. https://app.cochat.ai/papers/10.1109/tit.2016.2548471
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Wang, L., Wornell, G. W., & Zheng, L. (2016). Fundamental Limits of Communication With Low Probability of Detection. IEEE Transactions on Information Theory. https://doi.org/10.1109/tit.2016.2548471

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.