STAR-RIS Enabled RSMA-Intelligent Autonomous Transport System: Joint Security and Covertness Analysis
The communication security and covertness of legitimate vehicles in the sixth-generation (6G) mobile communication intelligent automatic transportation systems (IATS) face significant challenges, as the communication equipment and wireless signals are complex and susceptible to information leakage. To address the above issues, this paper proposes a novel IATS that integrates simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with rate-splitting multiple access (RSMA). Concurrently, an eavesdropping vehicle, capable of monitoring and eavesdropping information from legitimate vehicles, is introduced to explore the joint security and covertness performance. A roadside device is also introduced to improve security and covertness performance by transmitting artificial noise. The closed-form expressions for outage probability (OP), detection error probability (DEP), and intercept probability (IP) are derived to characterize the reliability, covertness, and security of the proposed system. The impact of various system parameters on system performance has also been extensively conducted, including transmitted signal-to-noise ratio (SNR), interference power, the power allocation coefficients, and the number of elements in STAR-RIS. The simulation results reveal several key insights: 1) The OPs and IPs of the RSMA-IATS network gradually decrease and increase with the transmitted SNR, respectively; 2) Achieving an optimal balance in power allocation between monitoring and eavesdropping proves essential for effective eavesdropper management; 3) The energy efficiency (EE) of the proposed system exhibits dual peaks at higher STAR-RIS element numbers, contrasting with a single peak at lower element counts, underscoring the strategic deployment of STAR-RIS technology in RSMA-IATS network.
Authors
- Hao Deng (ORCID: https://orcid.org/0000-0002-2444-2693)
- Liang Yang (ORCID: https://orcid.org/0000-0002-6576-332X)
- Wanming Hao (ORCID: https://orcid.org/0000-0002-4465-3447)
- Junyao Zhang (ORCID: https://orcid.org/0009-0006-9892-8609)
- Xingwang Li (ORCID: https://orcid.org/0000-0002-0907-6517)
- Gaofeng Nie (ORCID: https://orcid.org/0000-0002-0880-1182)
- Peiqing Guo (ORCID: https://orcid.org/0009-0004-7528-130X)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- Hunan University (CN)
- Henan University (CN)
- Zhengzhou University (CN)
- Nanjing University of Posts and Telecommunications (CN)
- Henan Polytechnic University (CN)
Publication Details
- Journal
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2025-02-26
- DOI
- https://doi.org/10.1109/tits.2025.3543023
- Citations
- 4
- Primary Topic
- Big Data Technologies and Applications
- Type
- article
- Field-Weighted Citation Impact
- 16.68