4.7 Article

Artificial Noise and RIS-Aided Physical Layer Security: Optimal RIS Partitioning and Power Control

Journal

IEEE WIRELESS COMMUNICATIONS LETTERS
Volume 12, Issue 6, Pages 992-996

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2023.3256001

Keywords

Quality of service; Communication system security; Physical layer security; Wireless networks; Transmitting antennas; Signal to noise ratio; Jamming; Artificial noise; physical layer security; optimization; partitioning; power control; reconfigurable intelligent surface (RIS); secrecy capacity

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This letter proposes a virtual partitioning method for reconfigurable intelligent surfaces (RIS) to enhance the intended signal for a legitimate user and increase the impact of artificial noise (AN) on an illegitimate user. The optimization of partitioning ratio and transmit power levels is performed to maximize secrecy capacity and establish a secure link. Simulation results demonstrate the effectiveness of the proposed RIS-partitioning method.
The synergism of reconfigurable intelligent surfaces (RIS) and artificial noise (AN) shows significant promise in improving physical layer security in wireless networks. Accordingly, this letter proposes the virtual partitioning of RIS elements into two parts such that the phase shifts of the different partitions are configured to improve the intended signal at a legitimate user and enhance the impact of AN on an illegitimate user, respectively. To this aim, two problems are defined to jointly optimize the partitioning ratio, and signal/noise transmit power levels for two main objectives. First, we maximize secrecy capacity by satisfying users' quality of service (QoS). Second, we optimize transmit power to establish a secure link by satisfying the QoS of the legitimate user. We provide closed-form solutions subject to the rate constraints on both legitimate and illegitimate users. Simulation results validate the closed-from solutions and show that the proposed RIS-partitioning method dramatically improves SC compared to benchmark methods.

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