Preprint Article Version 1 This version is not peer-reviewed

Machine Learning-Based Node Selection for Cooperative Non-Orthogonal Multi-Access System Under Physical Layer Security

Version 1 : Received: 30 November 2019 / Approved: 2 December 2019 / Online: 2 December 2019 (04:30:17 CET)

How to cite: 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. Preprints 2019, 2019120003 (doi: 10.20944/preprints201912.0003.v1). 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. Preprints 2019, 2019120003 (doi: 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.

Subject Areas

physical layer security (PLS); cooperative relay transmission; non-orthogonal multiple access (NOMA); fuzzy logic; feed forward neural networks (FFNN); secrecy capacity

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