Scholarlink

<p>Accurate channel modeling and estimation are essential for enabling extremely large-scale multiple-input multiple-output (XL-MIMO) in 6G networks with ultra-high spectral efficiency. As the aperture of antenna arrays expands significantly in XL-MIMO systems, the electromagnetic propagation field transitions from the far-field to the near-field. To address this, we propose a Gradient Descent-based XL-MIMO channel estimation algorithm (GD-SOMP), which enhances estimation accuracy by transforming discrete sampling points into a continuous representation, aligning better with actual near-field XL-MIMO scenarios. Numerical simulation results demonstrate that the GD-SOMP algorithm achieves superior root mean square error (NMSE) performance compared to conventional near-field channel estimation methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Gradient descent based polarization channel estimation in extremely largescale MIMO systems

  • 期刊论文

  • 发表日期：

    2025年8月13日

查看全文
  • Hongmei Wang,
  • Shuo Liu,
  • Jinling Song,
  • Shiyin Li,
  • Faguang Wang,
  • Minghui Min

摘要

Accurate channel modeling and estimation are essential for enabling extremely large-scale multiple-input multiple-output (XL-MIMO) in 6G networks with ultra-high spectral efficiency. As the aperture of antenna arrays expands significantly in XL-MIMO systems, the electromagnetic propagation field transitions from the far-field to the near-field. To address this, we propose a Gradient Descent-based XL-MIMO channel estimation algorithm (GD-SOMP), which enhances estimation accuracy by transforming discrete sampling points into a continuous representation, aligning better with actual near-field XL-MIMO scenarios. Numerical simulation results demonstrate that the GD-SOMP algorithm achieves superior root mean square error (NMSE) performance compared to conventional near-field channel estimation methods.