Cooperative Satellite-Terrestrial Networks With Imperfect CSI and Multiple Jammers: Performance Analysis and Deep Learning Evaluation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256204" target="_blank" >RIV/61989100:27240/24:10256204 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/24:10256204
Result on the web
<a href="https://ieeexplore.ieee.org/document/10701552" target="_blank" >https://ieeexplore.ieee.org/document/10701552</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JSYST.2024.3463715" target="_blank" >10.1109/JSYST.2024.3463715</a>
Alternative languages
Result language
angličtina
Original language name
Cooperative Satellite-Terrestrial Networks With Imperfect CSI and Multiple Jammers: Performance Analysis and Deep Learning Evaluation
Original language description
This article introduces novel and deep learning approaches for the security analysis of a hybrid satellite-terrestrial cooperative network. More specifically, a satellite transmits information to a ground user through multiple relays in the presence of an eavesdropper. To prevent potential eavesdropping, multiple friendly jammers are employed to disrupt the reception process of the eavesdropper by artificial noise. Within this setting, we then derive the closed-form expressions of the outage probability (OP) and secrecy outage probability (SOP) of the considered system in the presence of imperfect channel state information. Important to mention is the fact that in complex systems (e.g., with multiple jammers, multiple relays, and considering the independent but nonidentically distributed Rician nature of satellite links), analytical approaches may not be effective due to their complex mathematical derivations. As such, we develop a highly effective yet low-complexity deep learning approach to estimate the OP and SOP of the system. Through extensive Monte Carlo simulations, we evaluate the OP and SOP of the system in various settings and demonstrate the effectiveness of the proposed solutions. Interestingly, the proposed deep learning method can achieve comparable performance to that of the analytical approach.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Systems Journal
ISSN
1932-8184
e-ISSN
1937-9234
Volume of the periodical
18
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
2062-2073
UT code for WoS article
001328976800001
EID of the result in the Scopus database
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