Security Enhancing for IRS-Aided NOMA Networks via Constructive Interference Precoding

In this paper, we propose a constructive interference precoding (CIP) enabled secure intelligent reflecting surface (IRS)-non-orthogonal multiple access (NOMA) scheme, lever-aging both the inter-user interference and artificial noise to boost the legitimate transmission of far user, while inducing the eavesdropper to decode the deceptive information. Based on this CIP-NOMA scheme, we minimize the transmit power under the perfect channel state information (CSI), subject to the CIP constraint for the far user and eavesdropper, while guaranteeing the quality of service and successive interference cancellation for the near user and the IRS unit modulus constraint. To handle this non-convex problem, an alternating optimization algorithm is proposed. Specifically, by alternately optimizing the precoding vectors at the base station and the IRS reflecting matrix via the successive convex approximation and the penalty-based algorithm, respectively, a reliable solution can be obtained. Simulation results demonstrate that the proposed scheme can achieve better security performance with less energy consumption compared to the conventional NOMA.

Authors

Institutions

Publication Details

Published
2024-11-27
DOI
https://doi.org/10.1109/mape62875.2024.10813944
Citations
1
Primary Topic
Advanced Wireless Communication Technologies
Type
article
Field-Weighted Citation Impact
0.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Security Enhancing for IRS-Aided NOMA Networks via Constructive Interference Precoding

Lexi Xu, Xiaoqi Qin, Dusit Niyato, Yang Cao et al.
1 citations
Advanced Wireless Communication Technologies
0.21
article

Security Enhancing for IRS-Aided NOMA Networks via Constructive Interference Precoding

Lexi Xu, Xiaoqi Qin, Dusit Niyato, Yang Cao, Nan Zhao, Jingying Bao
article en
1 citations

Abstract

In this paper, we propose a constructive interference precoding (CIP) enabled secure intelligent reflecting surface (IRS)-non-orthogonal multiple access (NOMA) scheme, lever-aging both the inter-user interference and artificial noise to boost the legitimate transmission of far user, while inducing the eavesdropper to decode the deceptive information. Based on this CIP-NOMA scheme, we minimize the transmit power under the perfect channel state information (CSI), subject to the CIP constraint for the far user and eavesdropper, while guaranteeing the quality of service and successive interference cancellation for the near user and the IRS unit modulus constraint. To handle this non-convex problem, an alternating optimization algorithm is proposed. Specifically, by alternately optimizing the precoding vectors at the base station and the IRS reflecting matrix via the successive convex approximation and the penalty-based algorithm, respectively, a reliable solution can be obtained. Simulation results demonstrate that the proposed scheme can achieve better security performance with less energy consumption compared to the conventional NOMA.

Beijing University of Posts and Telecommunications (CN), Singapore University of Technology and Design (SG), Nanyang Technological University (SG), Dalian University of Technology (CN), State Key Laboratory of Networking and Switching Technology, China United Network Communications Group (China) (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 44%
Advanced Wireless Communication Technologies
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Security Enhancing for IRS-Aided NOMA Networks via Constructive Interference Precoding — Lexi Xu, Xiaoqi Qin, et al. · (2024) | TGRS Research Map | TGRS