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<p>The rapid development of ultra large-scale MIMO (Multiple Input Multiple Output) systems has posed challenges to traditional channel state information (CSI) feedback methods. The increase in the number of antennas significantly increases the amount of data required for feedback, resulting in higher feedback overhead and affecting system performance. In response to this issue, this article used compressive sensing technology to reduce the amount of CSI feedback data, thereby reducing feedback overhead and optimizing system performance. To this end, this article constructed a super large-scale MIMO system model and studies channel characteristics. Gaussian random measurement matrix is selected for channel sampling, and sparse reconstruction is achieved by combining orthogonal matching pursuit (OMP) algorithm. Through simulation experiments, it was found that under different channel conditions, the OMP algorithm reduced the amount of data fed back by 25% -50% compared to the Least Square (LS) algorithm. When processing large-scale data, the OMP algorithm not only improves efficiency, but also significantly reduces computational complexity and resource consumption. Under ideal channel conditions, the system exhibits extremely high reliability, with almost zero error rate and packet loss rate. This study provides an effective solution for CSI feedback in ultra large-scale MIMO systems.</p>

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Channel state feedback in near field ultra large-scale MIMO systems based on compressed sensing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月21日

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  • Guozhi Rong,
  • Rugui Yao,
  • Yifeng He

摘要

The rapid development of ultra large-scale MIMO (Multiple Input Multiple Output) systems has posed challenges to traditional channel state information (CSI) feedback methods. The increase in the number of antennas significantly increases the amount of data required for feedback, resulting in higher feedback overhead and affecting system performance. In response to this issue, this article used compressive sensing technology to reduce the amount of CSI feedback data, thereby reducing feedback overhead and optimizing system performance. To this end, this article constructed a super large-scale MIMO system model and studies channel characteristics. Gaussian random measurement matrix is selected for channel sampling, and sparse reconstruction is achieved by combining orthogonal matching pursuit (OMP) algorithm. Through simulation experiments, it was found that under different channel conditions, the OMP algorithm reduced the amount of data fed back by 25% -50% compared to the Least Square (LS) algorithm. When processing large-scale data, the OMP algorithm not only improves efficiency, but also significantly reduces computational complexity and resource consumption. Under ideal channel conditions, the system exhibits extremely high reliability, with almost zero error rate and packet loss rate. This study provides an effective solution for CSI feedback in ultra large-scale MIMO systems.