Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems

Li-jun GE; Hui GUO; Yue LI; Lan ZHAO

doi:10.11959/j.issn.1000-436x.2017291

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Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems

Papers | 更新时间：2024-06-05

- Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems
- Journal on Communications Vol. 38, Issue 12, Pages: 57-62(2017)
- 作者机构：
  
  1. 天津工业大学电子与信息工程学院，天津 300387
  2. 天津市光电检测技术与系统重点实验室，天津 300387
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61302062);The Research Program of Application Foundation and Advanced Technology of Tianjin(13JCQNJC00900)
- DOI：10.11959/j.issn.1000-436x.2017291
  CLC： TN914.4
- Online First：2017-12，
  
  Published：25 December 2017
- 稿件说明：
移动端阅览
Li-jun GE, Hui GUO, Yue LI, et al. Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems[J]. Journal on Communications, 2017, 38(12): 57-62.
DOI：

Li-jun GE, Hui GUO, Yue LI, et al. Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems[J]. Journal on Communications, 2017, 38(12): 57-62. DOI： 10.11959/j.issn.1000-436x.2017291.

摘要

针对信道路径数未知的大规模多输入多输出（MIMO，multi-input multi-output）系统，提出一种稀疏度自适应的压缩感知信道估计方法——块稀疏自适应匹配追踪（BSAMP，block sparsity adaptive matching pursuit）算法。利用大规模MIMO系统子信道的联合稀疏性，通过设置阈值及寻找最大后向差分位置对支撑集原子进行快速初步选择，同时考虑了观测矩阵非正交性造成的能量弥散，提高算法的估计性能；通过正则化对原子进行二次筛选，以提高算法的稳定性。仿真表明，该算法能快速、准确地恢复稀疏度未知的大规模MIMO信道信息。

Abstract

A sparsity-adaptive channel estimation algorithm based on compressive sensing was proposed for massive MIMO systems when the number of channel multi-paths was unknown.By exploiting the joint sparsity characteristics of the sub-channels，the proposed block sparsity adaptive matching pursuit (BSAMP) algorithm first selected atoms by setting a threshold and finding the position of the maximum backward difference，which reduces the energy dispersion caused by the non-orthogonality of the observation matrix and improves the performance of the algorithm.Then a regularization method was utilized to improve the stability of the algorithm.Simulation results demonstrate that the proposed algorithm recovers the channel state information accurately and shows a high computational efficiency.

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references

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