RIS-assisted Physical Layer Security

📅 2025-01-30
📈 Citations: 0
Influential: 0
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🤖 AI Summary
This work addresses the limited physical-layer secrecy capacity in multi-eavesdropper scenarios. We propose a reconfigurable intelligent surface (RIS)-aided semantic security enhancement scheme. By modeling the RIS-assisted eavesdropping channel, we exploit the RIS to achieve spatial beam separation at the transmitter and employ an orthogonal combiner at the legitimate receiver to jointly process direct and reflected signals. For the first time under the semantic security framework, we derive the achievable secrecy rate for RIS-enabled channels and design a joint optimization algorithm to maximize the secrecy rate by co-designing the RIS phase-shift matrix and the receiver’s combining weights. Simulation results demonstrate that the proposed scheme significantly improves secrecy capacity—particularly under low signal-to-noise ratio (SNR) and in multi-eavesdropper settings—while exhibiting robustness against variations in eavesdropper count, location, and total transmit power.

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Application Category

📝 Abstract
We propose a reconfigurable intelligent surface (RIS)-assisted wiretap channel, where the RIS is strategically deployed to provide a spatial separation to the transmitter, and orthogonal combiners are employed at the legitimate receiver to extract the data streams from the direct and RIS-assisted links. Then we derive the achievable secrecy rate under semantic security for the RIS-assisted channel and design an algorithm for the secrecy rate optimization problem. The simulation results show the effects of total transmit power, the location and number of eavesdroppers on the security performance.
Problem

Research questions and friction points this paper is trying to address.

Reconfigurable Intelligent Surface (RIS)
Physical Layer Security
Multiple Eavesdroppers
Innovation

Methods, ideas, or system contributions that make the work stand out.

Reconfigurable Intelligent Surfaces (RIS)
Physical Layer Security
Intelligent Mirror-Assisted Information Path
X
Xudong Li
Technische Universität Berlin
M
Matthias Frey
The University of Melbourne
Ehsan Tohidi
Ehsan Tohidi
Head of Sustainable and Modular Networks (SMN) group, Fraunhofer HHI
Wireless CommunicationsSignal ProcessingMachine learning
I
Igor Bjelaković
Technische Universität Berlin, Fraunhofer Heinrich Hertz Institute
S
Sławomir Stańczak
Technische Universität Berlin, Fraunhofer Heinrich Hertz Institute