Rank optimization technique of physical layer secrecy capacity in MIMO wiretap channel
Khokher B. Rajesh G. Ananth C. Prabhu N. Rai H.M. Agarwal S. Agarwal N.
February 2026Springer
Wireless Networks
2026#32Issue 1433 - 445 pp.
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.
MIMO secrecy capacity , Optimal covariance matrix , Rank adaptive Monte-Carlo , Rank optimization
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Deparment of Artificial Intelligence and Machine Learning, New Horizon College of Engineering, Bangalore, 560103, India
Deparment of Electronics and Communication, New Horizon College of Engineering, Bangalore, 560103, India
Faculty of Intelligent Systems and Computer Technologies, Samarkand State University, Samarkand, 140104, Uzbekistan
Center for Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Chennai, India
Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan
School of Computer Science and Engineering, Yeungnam University, Gyeongsan, 38541, South Korea
School of Chemical Engineering, Yeungnam University, Gyeongsan, 38541, South Korea
Deparment of Artificial Intelligence and Machine Learning
Deparment of Electronics and Communication
Faculty of Intelligent Systems and Computer Technologies
Center for Research and Innovation
Department of Computer Science
School of Computer Science and Engineering
School of Chemical Engineering
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