000150523 001__ 150523
000150523 005__ 20251017144642.0
000150523 0247_ $$2doi$$a10.1109/TVT.2024.3523524
000150523 0248_ $$2sideral$$a142581
000150523 037__ $$aART-2024-142581
000150523 041__ $$aeng
000150523 100__ $$aYang, Xingyu
000150523 245__ $$aGame-Guided Matching Theory-Based Resource Allocation for Secure Semantic Communications
000150523 260__ $$c2024
000150523 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150523 5203_ $$aSemantic communication (SemCom) has become one of the most promising techniques in breaking the performance bottleneck for sixth-generation wireless networks, but the SemCom performance is easily degraded under malicious jamming and eavesdropping attacks over open wireless links. Therefore, this paper designs a reliable and secure SemCom approach against a hybrid attacker based on resource allocation, with the objective to jointly optimize channel selection and the number of transmitted semantic symbols to maximize secrecy semantic transmission rate (SS-R) under different quality of service requirements. We adopt a hierarchical framework based on Stackelberg game to model the interactions between legitimate users and the hybrid attacker. Furthermore, we model the optimization problem as a many-to-one matching problem with externalities, and propose two swap-based resource allocation algorithms, aiming to maximize the overall SS-R and meet fairness awareness. The two proposed algorithms are able to guide the iteration properly based on the dynamic of attack behaviors, which improves the secure resource allocation efficiency. Simulation results show that the proposed approaches outperform the baselines and benchmarks under different scenarios.
000150523 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000150523 590__ $$a7.1$$b2024
000150523 592__ $$a2.156$$b2024
000150523 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b41 / 366 = 0.112$$c2024$$dQ1$$eT1
000150523 591__ $$aTRANSPORTATION SCIENCE & TECHNOLOGY$$b13 / 77 = 0.169$$c2024$$dQ1$$eT1
000150523 591__ $$aTELECOMMUNICATIONS$$b16 / 120 = 0.133$$c2024$$dQ1$$eT1
000150523 593__ $$aAerospace Engineering$$c2024$$dQ1
000150523 593__ $$aApplied Mathematics$$c2024$$dQ1
000150523 593__ $$aElectrical and Electronic Engineering$$c2024$$dQ1
000150523 593__ $$aComputer Networks and Communications$$c2024$$dQ1
000150523 593__ $$aAutomotive Engineering$$c2024$$dQ1
000150523 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000150523 700__ $$aYang, Helin
000150523 700__ $$aJiang, Yifu
000150523 700__ $$aAlphones, Arokiaswami
000150523 700__ $$aXiao, Liang
000150523 773__ $$g(2024), 1-6$$pIEEE trans. veh. technol.$$tIEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY$$x0018-9545
000150523 8564_ $$s1159676$$uhttps://zaguan.unizar.es/record/150523/files/texto_completo.pdf$$yPostprint
000150523 8564_ $$s3488171$$uhttps://zaguan.unizar.es/record/150523/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000150523 909CO $$ooai:zaguan.unizar.es:150523$$particulos$$pdriver
000150523 951__ $$a2025-10-17-14:32:31
000150523 980__ $$aARTICLE