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<p>Conventional Non-Orthogonal Multiple Access (NOMA) systems inherently remain susceptible to eavesdropping attacks owing to their broadcasting nature. Thus, they consistently pose a major challenge for confidentiality in secure Physical Layer Security (PLS)-enabled 5G and beyond communications. In order to overcome this challenge, the proposed research work presents a beamforming-assisted artificial noise-based transmission strategy for cooperative downlink NOMA networks, termed as BAANBT-PLS-NOMA. This work subsequently involves decode-and-forward (DF) relaying along with artificial noise injection in order to degrade the eavesdropper’s channel. Further power allocation is carried out using the Improved Salp Swarm Algorithm (ISSA) with provisions for load balancing and interference awareness. Additionally, the Improved Firefly Algorithm (IFA) is used to optimize precoding techniques in order to enhance signal separation capability during multi-layer transmission. Finally, essential secrecy and reliability measures like packet delivery ratio (PDR), bit error rate (BER), spectral efficiency, and outage probability are evaluated by Monte Carlo simulation analysis. Performance results indicate that the proposed model attains a PDR value of almost 96.36% at 50&#xa0;s with strong robustness against eavesdropping and better spectral efficiency under dynamic channel conditions. Moreover, the proposed framework enhances the physical layer security without the use of conventional computation-intensive cryptographic approaches. It has the potential to provide a reliable and energy-efficient solution for future intelligent wireless communication systems.</p>

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Beamforming-Assisted Artificial Noise-Based Transmission for Enhanced PLS-NOMA Systems Using Improved Salp Swarm and Firefly Algorithms

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Kirti Prakash,
  • R.C.S Chauhan

摘要

Conventional Non-Orthogonal Multiple Access (NOMA) systems inherently remain susceptible to eavesdropping attacks owing to their broadcasting nature. Thus, they consistently pose a major challenge for confidentiality in secure Physical Layer Security (PLS)-enabled 5G and beyond communications. In order to overcome this challenge, the proposed research work presents a beamforming-assisted artificial noise-based transmission strategy for cooperative downlink NOMA networks, termed as BAANBT-PLS-NOMA. This work subsequently involves decode-and-forward (DF) relaying along with artificial noise injection in order to degrade the eavesdropper’s channel. Further power allocation is carried out using the Improved Salp Swarm Algorithm (ISSA) with provisions for load balancing and interference awareness. Additionally, the Improved Firefly Algorithm (IFA) is used to optimize precoding techniques in order to enhance signal separation capability during multi-layer transmission. Finally, essential secrecy and reliability measures like packet delivery ratio (PDR), bit error rate (BER), spectral efficiency, and outage probability are evaluated by Monte Carlo simulation analysis. Performance results indicate that the proposed model attains a PDR value of almost 96.36% at 50 s with strong robustness against eavesdropping and better spectral efficiency under dynamic channel conditions. Moreover, the proposed framework enhances the physical layer security without the use of conventional computation-intensive cryptographic approaches. It has the potential to provide a reliable and energy-efficient solution for future intelligent wireless communication systems.