Secure Integrated Sensing and Communication for Binary Input Additive White Gaussian Noise Channels
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Beitragende
Abstract
We study a secure integrated sensing and communication (ISAC) model motivated by the need to simultaneously exploit the sensitive attributes of wireless devices, such as their location, and communicate securely. Specifically, we consider a state-dependent binary-input two-user additive white Gaussian noise (AWGN) broadcast channel, in which the channel state se-quence consists of two components, each affecting a receiver, mod-eled as independent and identically distributed (i.i.d.) correlated phase shifts to approximate the location-dependent signatures of the receivers. The objective of the transmitter is to simultaneously estimate the channel states while reliably transmitting a secret message to one of the receivers, treating the other as a passive attacker. We characterize the exact secrecy-distortion region when 1) the channel output feedback is perfect, i.e., noiseless with a unit time delay; and 2) the channel is degraded. The characterized rate region offers an outer bound for more complex secure ISAC settings with noisy generalized output feedback and non-degraded channels. We also characterize the secrecy-distortion region for reversely-degraded channels. The results illustrate the benefits of jointly sensing the channel state and securely communicating messages as compared to separation-based methods.
Details
Originalsprache | Englisch |
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Titel | 2023 IEEE 3rd International Symposium on Joint Communications and Sensing, JC and S 2023 |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
Seiten | 1-6 |
ISBN (elektronisch) | 979-8-3503-4568-1 |
Publikationsstatus | Veröffentlicht - 2023 |
Peer-Review-Status | Ja |
Konferenz
Titel | 3rd IEEE International Symposium on Joint Communications and Sensing |
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Kurztitel | JC&S 2023 |
Veranstaltungsnummer | 3 |
Dauer | 5 - 7 März 2023 |
Webseite | |
Ort | Kongresszentrum Seefeld & online |
Stadt | Seefeld |
Land | Österreich |
Externe IDs
ORCID | /0000-0002-1702-9075/work/165878335 |
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