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<p>Our paper analyzes the overall error performance of a hybrid satellite-terrestrial communication system that effectively employs non-orthogonal multiple access (NOMA). In this system model, a satellite serves as the source, while a relay and a destination are positioned on Earth. It harnesses two independent fading channels: the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\kappa - \mu \)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>κ</mi> <mo>-</mo> <mi>μ</mi> </mrow> </math></EquationSource> </InlineEquation> shadowed fading channel and the Nakagami-m fading channel, ensuring comprehensive coverage and reliability. To guarantee equitable transmission among multiple users, NOMA is seamlessly integrated into the system. Our analysis focuses on error performance, culminating in a derivation of the total error in terms of the symbol error probability (SEP). We also explore the impact of elevation angle, which significantly enhances overall transmission efficiency. Moreover, we implement advanced machine learning (ML) algorithms to efficiently handle the complex calculations required by the derived expressions, thus optimizing the error performance of our proposed system model.</p>

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Analysis of the error performance of NOMA-aided hybrid satellite-terrestrial communication system with machine learning

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • Priyanka Prasad,
  • M K Arti,
  • Aarti Jain

摘要

Our paper analyzes the overall error performance of a hybrid satellite-terrestrial communication system that effectively employs non-orthogonal multiple access (NOMA). In this system model, a satellite serves as the source, while a relay and a destination are positioned on Earth. It harnesses two independent fading channels: the \(\kappa - \mu \) κ - μ shadowed fading channel and the Nakagami-m fading channel, ensuring comprehensive coverage and reliability. To guarantee equitable transmission among multiple users, NOMA is seamlessly integrated into the system. Our analysis focuses on error performance, culminating in a derivation of the total error in terms of the symbol error probability (SEP). We also explore the impact of elevation angle, which significantly enhances overall transmission efficiency. Moreover, we implement advanced machine learning (ML) algorithms to efficiently handle the complex calculations required by the derived expressions, thus optimizing the error performance of our proposed system model.