Cooperative Satellite-Terrestrial Networks With Imperfect CSI and Multiple Jammers: Performance Analysis and Deep Learning Evaluation

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27740/24:10256204

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Cooperative Satellite-Terrestrial Networks With Imperfect CSI and Multiple Jammers: Performance Analysis and Deep Learning Evaluation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Cooperative Satellite-Terrestrial Networks With Imperfect CSI and Multiple Jammers: Performance Analysis and Deep Learning Evaluation

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Systems Journal

ISSN

1932-8184

e-ISSN

1937-9234

Svazek periodika

18

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

2062-2073

Kód UT WoS článku

001328976800001

EID výsledku v databázi Scopus

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