Active RIS Empowered Secure MISO Systems: AN and RIF Approaches
In this paper, we explore the physical layer security (PLS) of an active reconfigurable intelligent surface (RIS) assisted multi-input single-output (MISO) system in the presence of a passive eavesdropper (Eve), whose channel is unknown to the legitimate transmitter. In this practical scenario, we first propose an artificial noise (AN) based PLS approach, which can effectively deteriorate the eavesdropping by jointly designing the disturbance signal and the active RIS. However, the AN scheme requires a vast amount of energy to emit the noise signal, which can be potentially suppressed by Eve using more antennas. To tackle this issue, we then introduce another novel reconfigurable intelligent fading (RIF) approach, which utilizes the active RIS to rapidly manipulate the propagation environment, so that Eve suffers a fast fading channel, while the legitimate user (LU) still experiences a slow fading channel. Since Eve encounters difficulties in effectively estimating her fast fading channel, she cannot perform reliable legitimate information detection in a coherent manner, thereby significantly reducing the risk of information leakage and enabling secure communications. Simulation results demonstrate the effectiveness of both proposed approaches. Moreover, the RIF scheme achieves a substantial improvement in security performance compared to the AN scheme, particularly for the cases with limited transmit power.
Authors
- Jinjin Chu (ORCID: https://orcid.org/0000-0001-7901-4357)
- Qian Liu (ORCID: https://orcid.org/0000-0002-3162-935X)
- Rang Liu (ORCID: https://orcid.org/0000-0001-7151-7224)
- Ming Li
- Ming Yu (ORCID: https://orcid.org/0000-0002-0934-5895)
- Peishi Li
Institutions
- University of California, Irvine (US)
- Dalian University of Technology (CN)
Publication Details
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1109/vtc2024-fall63153.2024.10757472
- Primary Topic
- IoT Networks and Protocols
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China