학술논문

Outage Constrained Max–Min Secrecy Rate Optimization for IRS-Aided SWIPT Systems With Artificial Noise
Document Type
Periodical
Source
IEEE Internet of Things Journal IEEE Internet Things J. Internet of Things Journal, IEEE. 11(6):9814-9828 Mar, 2024
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Array signal processing
MISO communication
Wireless communication
Optimization
Minimax techniques
Simultaneous wireless information and power transfer
Covariance matrices
Beamforming
intelligent reflecting surface (IRS)
max–min security rate
secrecy outage probability (SOP)
simultaneous wireless information and power transfer (SWIPT)
Language
ISSN
2327-4662
2372-2541
Abstract
This study focuses on an intelligent reflecting surface (IRS) enabled simultaneous wireless information and power transfer (SWIPT) system with the coexistence of legitimate users (LUs) and Eavesdroppers (Eves). The main objective is to jointly optimize the transmit beamforming and artificial noise covariance matrix at the access point, the phase shift matrix at the IRS, and the power splitting ratio at the LUs, to maximize the system’s min-secrecy rate. Due to the imperfect channel state information of Eves, an outage rate constraint is contained. The formulated problem is a challenging nonconvex optimization problem since it involves nonconvex objective function and constraints, and the outage rate constraint does not have simple closed form expression. To address this problem, an algorithm based on the alternating optimization method is proposed, which breaks down the nonconvex problem into three subproblems. The algorithm employs several techniques to solve these subproblems. Specifically, the outage rate constraint is approximated using the Bernstein-type inequality. And the Taylor formula, semi-definite relaxation, and successive convex approximation methods are employed to transform the nonconvex subproblems into convex ones. Simulation results demonstrate the effectiveness of the proposed algorithm compared to baseline algorithms under different conditions.