Citation

Salem, Mohammed Ahmed and Abd Aziz, Azlan and Al Selwi, Hatem Fahd and Alias, Mohamad Yusoff and Tan, Kim Geok and Mahmud, Azwan and Ghoot, Ahmed Salem (2019) Machine Learning-Based Node Selection for Cooperative Non-Orthogonal Multi-Access System Under Physical Layer Security. Electrical & electronic engineering on (IRECAP), 10 (5). pp. 311-324. ISSN 2039-5086

Text 105.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Cooperative non-orthogonal multi access communication is a promising paradigm for the future wireless networks because of its advantages in terms of energy efficiency, wider coverage, and interference mitigating. In this paper, we study the secrecy performance of a downlink cooperative non-orthogonal multi access (NOMA) communication system under the presence of an eavesdropper node. Smart node selection based on feed forward neural networks (FFNN) is proposed in order to improve the physical layer security (PLS) of a cooperative NOMA network. The selected cooperative relay node is employed to enhance the channel capacity of the legal users, where the selected cooperative jammer is employed to degrade the capacity of the wiretapped channel. Simulations of the secrecy performance metric namely the secrecy capacity (CS ) are presented and compared with the conventional technique based on fuzzy logic node selection technique. Based on our simulations and discussions the proposed technique outperforms the existing technique in terms of the secrecy performance.

Item Type:	Article
Uncontrolled Keywords:	Physical layer security (PLS), cooperative relay transmission, non-orthogonal multiple access 13 (NOMA), fuzzy logic, feed forward neural networks (FFNN) secrecy capacity
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2021 02:40
Last Modified:	30 Sep 2021 02:40
URII:	http://shdl.mmu.edu.my/id/eprint/8440

Downloads

Edit (login required)