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<p><?tk 4?>To secure wireless transmission at the physical layer, issues of the multiple channel communication system, which is the most crucial evaluation in modern wireless communication, are a key component of 5G networks. The secrecy capacity of a Gaussian MIMO wiretap channel is approximated in this study by considering the rank and other parameters of the transmitter covariance matrix, while explicitly implementing secure communication based on information theory. The transfer covariance matrix and the privacy power should be calculated to find the best optimization algorithm. To provide optimal secrecy capacity across multiple random trials, the optimization approach is based on Rank-adaptive Monte Carlo, as well as Monte Carlo and Rank. This study introduces optimization of secrecy capacity in a Rank-Adaptive Monte Carlo (RAMC) algorithm in MIMO wiretap channels. This approach differs from the current convex optimization techniques. RAMC dynamically adjusts the covariance matrix rank to handle the non-convex case where <i>S1</i>–<i>S2</i> has multiple positive eigenvalues. Our approach proves rank-1 covariance matrices achieve optimal secrecy under total power constraints, outperforming full-rank solutions at high SNR. Numerical results validate RAMC’s efficiency for systems with ≤ 16 antennas and identify scalability limits for larger arrays.</p>

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Rank optimization technique of physical layer secrecy capacity in MIMO wiretap channel

  • 期刊论文

  • 发表日期：

    2025年12月5日

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  • Bhawna Khokher,
  • G. Rajesh,
  • Christo Ananth,
  • N. Prabhu,
  • Hari Mohan Rai,
  • Saurabh Agarwal,
  • Neha Agarwal

摘要

To secure wireless transmission at the physical layer, issues of the multiple channel communication system, which is the most crucial evaluation in modern wireless communication, are a key component of 5G networks. The secrecy capacity of a Gaussian MIMO wiretap channel is approximated in this study by considering the rank and other parameters of the transmitter covariance matrix, while explicitly implementing secure communication based on information theory. The transfer covariance matrix and the privacy power should be calculated to find the best optimization algorithm. To provide optimal secrecy capacity across multiple random trials, the optimization approach is based on Rank-adaptive Monte Carlo, as well as Monte Carlo and Rank. This study introduces optimization of secrecy capacity in a Rank-Adaptive Monte Carlo (RAMC) algorithm in MIMO wiretap channels. This approach differs from the current convex optimization techniques. RAMC dynamically adjusts the covariance matrix rank to handle the non-convex case where S1–S2 has multiple positive eigenvalues. Our approach proves rank-1 covariance matrices achieve optimal secrecy under total power constraints, outperforming full-rank solutions at high SNR. Numerical results validate RAMC’s efficiency for systems with ≤ 16 antennas and identify scalability limits for larger arrays.