Multi-objective optimization based GA in PLS of IRS-assisted PDNOMA communication

• Published in: IEEE access Vol. 12; p. 1
• Main Authors: Kieu-Xuan, Thuc, Nguyen-Thi, Hong, Le-Thi, Anh
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 6G mobile communication; Communications systems; Eavesdropping; Energy efficiency; Genetic algorithms; IRS-assisted PDNOMA; multi-objective optimization; Multiple objective analysis; NOMA; non-dominated sorting genetic algorithm; Nonorthogonal multiple access; Optimization; Performance evaluation; Reconfigurable intelligent surfaces; Relay; Relaying; Relays; secrecy energy efficiency; Security; Sorting algorithms; Wireless communication; worst secrecy capacity
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3417619

The intelligent reflecting surface (IRS) supports communication systems well, especially in physical layer security for the cooperative power domain non-orthogonal multiple access (PDNOMA). In this work, we investigate the secrecy performance of PDNOMA with the assistance of the IRS and a multiple-relaying network in the presence of an eavesdropper. Three selection strategies are considered at the relaying network to boost the system's performance: the first method is based on the best relay selection, the second on the max-min concept, and the third on harmonious characteristics. Moreover, the phase shift of IRS element and power allocation for each NOMA user can be controlled to improve the secrecy quality and reduce the influence of an eavesdropper (E). Besides, applying the technique of transmitting artificial noise (AN) from the source is also considered in this paper to interfere with the signal at E. Furthermore, in this paper, we determine two primary metrics to evaluate the secrecy performance of our proposed system: the worst secrecy capacity and secrecy energy efficiency. The balance of these two metrics needs to be assured to improve the secrecy performance. Thus, in this paper, we consider the multi-object problem and propose the genetic algorithm-based approach, a non-dominated sorting genetic algorithm with three procedures (NSGA-II), to solve this problem. Then, to highlight the proposed algorithm's outstanding performance, we compare it with other algorithms, Reference point based NSGA-II (R-NSGA-II) and the exhaustive search (ES). Additionally, the impacts of critical system parameters are investigated for both cases as IRS and none-IRS assistance comprises three relaying selection techniques, the number of IRS elements, the strength of AN signal, the distances of source-relay link, relay-IRS link, and IRS-Eavesdropper link. Finally, the summaries of these archived results show the benefits of our proposed model in different cases of the deployment of the IRS and without the IRS.