Scholarlink

<p>In massive multi-input multi-output systems, hybrid precoding is employed to minimize the overall number of radio frequency chains. In spite of this, the conventional method of hybrid precoding (HP), which employs a SVD approach, necessitates complex bit assignment in order to match the SNR of multiple sub-channels. In actual systems, this method results in considerable coding/decoding complexity. Geometric mean decomposition based hybrid precoding (GMD-HP) is a novel decomposition approach that is used to get over the challenging bit allocates procedure in SVD-HP. In GMD-HP, bit assignment becomes simpler since all subchannels have equal SNRs. Although the difficulty of GMD-HP is lowered in terms of bit allocation, the design complexity is still equivalent to SVD-HP. The advancement of analog and digital precoders is the cause of this high degree of design complexity. Because the analog precoder architecture, which is built on the orthogonal matching pursuit (OMP) algorithm, is very non-convex in nature, it has a high level of complexity. The present research examined the performance of 2 various precoding strategies: Singular Value Decomposition based HP and Geometric Mean Decomposition-HP to beam steering and spatial sparse precoding. The signal-to-noise ratio vs. spectral efficiency simulation results of the proposed Matrix Decomposition based precoding techniques are compared.</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

Design of Hybrid Precoder for Massive MIMO System Based on Geometric Mean Decomposition Method

  • 期刊论文

  • 发表日期：

    2026年3月30日

查看全文
  • Mahesh Mudavath,
  • S. Sandhya Rani,
  • Madhuri Gummineni,
  • Bhukya Vijay Kumar,
  • G. Koteswara Rao

摘要

In massive multi-input multi-output systems, hybrid precoding is employed to minimize the overall number of radio frequency chains. In spite of this, the conventional method of hybrid precoding (HP), which employs a SVD approach, necessitates complex bit assignment in order to match the SNR of multiple sub-channels. In actual systems, this method results in considerable coding/decoding complexity. Geometric mean decomposition based hybrid precoding (GMD-HP) is a novel decomposition approach that is used to get over the challenging bit allocates procedure in SVD-HP. In GMD-HP, bit assignment becomes simpler since all subchannels have equal SNRs. Although the difficulty of GMD-HP is lowered in terms of bit allocation, the design complexity is still equivalent to SVD-HP. The advancement of analog and digital precoders is the cause of this high degree of design complexity. Because the analog precoder architecture, which is built on the orthogonal matching pursuit (OMP) algorithm, is very non-convex in nature, it has a high level of complexity. The present research examined the performance of 2 various precoding strategies: Singular Value Decomposition based HP and Geometric Mean Decomposition-HP to beam steering and spatial sparse precoding. The signal-to-noise ratio vs. spectral efficiency simulation results of the proposed Matrix Decomposition based precoding techniques are compared.