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
- Lexi Xu (ORCID: https://orcid.org/0000-0003-4338-7252)
- Xiaoqi Qin (ORCID: https://orcid.org/0000-0002-5788-0657)
- Dusit Niyato (ORCID: https://orcid.org/0000-0002-7442-7416)
- Yang Cao (ORCID: https://orcid.org/0000-0002-4744-7008)
- Nan Zhao (ORCID: https://orcid.org/0000-0002-6497-7799)
- Jingying Bao
Institutions
- 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)
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
- National Natural Science Foundation of China