Salem, M.A.; Abd.Aziz, A.B.; Al-Selwi, H.F.; Alias, M.Y.B.; Kim Geok, T.; Mahmud, A.; Bin-Ghoot, A.S. Machine Learning-Based Node Selection for Cooperative Non-Orthogonal Multi-Access System Under Physical Layer Security. Preprints2019, 2019120003. https://doi.org/10.20944/preprints201912.0003.v1
APA Style
Salem, M.A., Abd.Aziz, A.B., Al-Selwi, H.F., Alias, M.Y.B., Kim Geok, T., Mahmud, A., & Bin-Ghoot, A.S. (2019). Machine Learning-Based Node Selection for Cooperative Non-Orthogonal Multi-Access System Under Physical Layer Security. Preprints. https://doi.org/10.20944/preprints201912.0003.v1
Chicago/Turabian Style
Salem, M.A., Azwan Mahmud and Ahmed Salem Bin-Ghoot. 2019 "Machine Learning-Based Node Selection for Cooperative Non-Orthogonal Multi-Access System Under Physical Layer Security" Preprints. https://doi.org/10.20944/preprints201912.0003.v1
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 ($C_S$) 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 term the of secrecy performance.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
