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

Mohammed Ahmed Salem(1*), Azlan Bin Abd Aziz(2), Hatem Fahd Al-Selwi(3), Mohamad Yusoff Bin Alias(4), Tan Kim Geok(5), Azwan Mahmud(6), Ahmed Salem Bin-Ghooth(7)

(1) Multimedia University (MMU), Malaysia
(2) Multimedia University (MMU), Malaysia
(3) University Teknical Malaysia Melaka (UTeM), Malaysia
(4) Faculty of Engineering, Multimedia University (MMU), Malaysia
(5) Multimedia University (MMU), Malaysia
(6) Multimedia University (MMU), Malaysia
(7) Multimedia University (MMU), Malaysia
(*) Corresponding author


DOI: https://doi.org/10.15866/irecap.v10i5.18594

Abstract


Cooperative non-orthogonal multi access communication is a promising paradigm for future wireless networks because of its advantages in terms of energy efficiency, wider coverage, and interference mitigation. This paper studies 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 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 (
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Keywords


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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References


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