A Secure-Transmission Maximization Scheme for SWIPT Systems Assisted by an Intelligent Reflecting Surface and Deep Learning

IEEE Access

Huynh Thanh Thien¹  Insoo Koo¹  Pham-Viet Tuan² 

[1] Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea;Faculty of Physics, University of Education, Hue University, Hue, Vietnam;

关键词: SWIPT;    intelligent reflecting surface;    eavesdropper;    secure transmissions;    alternating optimization;    average secrecy rate;   

DOI  :  10.1109/ACCESS.2022.3159679

来源: DOAJ

【 摘 要 】

Recently, the demand for spectral and energy efficiency has significantly been increased along with new breakthroughs in programmable meta-material techniques. The integration of an intelligent reflecting surface (IRS) into simultaneous wireless information and power transfer (SWIPT) systems has attracted much attention from operators in advanced wireless communication networks (WCNs) such as fifth-generation (5G) and sixth-generation (6G) networks. In addition, an IRS-assisted SWIPT system faces many security risks that can easily be compromised by eavesdroppers. In this paper, we investigate the physical-layer secure and transmission optimization problem in an IRS-assisted SWIPT system where a power-splitting (PS) scheme is installed in the user equipment (UE). In particular, our purpose is to maximize the system secrecy rate by jointly finding optimal solutions for transmitter power, PS factor of UE, and phase shifts matrix of IRS under the required minimum harvested energy and maximum transmitter power. We propose the alternating optimization (AO)-based scheme to obtain optimal solutions. The proposed AO-based scheme can effectively solve both convex and non-convex problems; however, applying them in practice still poses some difficulties due to the complexity and long computation time. This is because many mathematical transformations are used and the optimal solution needs a number of iterations to achieve convergence. Therefore, we also propose 5 types of data and DNN structures to potentially achieve efficiency in computations by using a deep learning (DL)-based approach. The simulation results indicate that the proposed IRS scheme provides an improvement in terms of the average secrecy rate (ASR) by up to 38.91% when the number of reflecting elements is high (30 elements) compared to a scheme without an IRS. We also observe that the DL-based approach not only provides similar performance to the AO-based scheme but it also significantly reduces computation time.

【 授权许可】

Unknown