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In this to analyze the performance of Maximization for Smart Reflecting Surface (SRS) systems with MIMO systems. Reducing evaluation complexity is essential in optimizing the segment shifts of SRS systems due to the presence of a significant number of reflecting elements (REs) in SRS-assisted communication systems. This letter examines the Modified Dimension-wise Sinusoidal Maximization (MDSM) algorithm, which is a simple algorithm. Its purpose is to find the best variable SRS phase shifts that maximize the sum capability of MIMO network. An important benefit of this strategy is the objective purpose of the optimization challenge varies sinusoidally based on the phase shift of every reflecting element. The MDSM algorithm not only achieves superior results in optimizing the phase shift problem in the SRS phase, but it also exhibits faster convergence along with lower computational complexity in every iteration. This research will employ the Matlab tool for implementation.

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Smart Reflection Surface with Low Complexity and Maximum Sum Capacity Using MIMO System

  • 会议论文

  • 发表日期：

    2025年

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  • Shaik Karimullah,
  • Vinit Kumar Gunjan,
  • Fahimuddin Shaik,
  • Mudassir Khan

摘要

In this to analyze the performance of Maximization for Smart Reflecting Surface (SRS) systems with MIMO systems. Reducing evaluation complexity is essential in optimizing the segment shifts of SRS systems due to the presence of a significant number of reflecting elements (REs) in SRS-assisted communication systems. This letter examines the Modified Dimension-wise Sinusoidal Maximization (MDSM) algorithm, which is a simple algorithm. Its purpose is to find the best variable SRS phase shifts that maximize the sum capability of MIMO network. An important benefit of this strategy is the objective purpose of the optimization challenge varies sinusoidally based on the phase shift of every reflecting element. The MDSM algorithm not only achieves superior results in optimizing the phase shift problem in the SRS phase, but it also exhibits faster convergence along with lower computational complexity in every iteration. This research will employ the Matlab tool for implementation.