Low-complexity sparse channel estimation for massive MIMO systems

Xin FANG; Yunju LIU; Haiyan CAO; Peng PAN

doi:10.11959/j.issn.1000-0801.2016149

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Low-complexity sparse channel estimation for massive MIMO systems

research and development | 更新时间：2024-06-05

- Low-complexity sparse channel estimation for massive MIMO systems
- Observation and communication Vol. 32, Issue 5, Pages: 89-95(2016)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.1000-0801.2016149
  CLC： TN911.22
- Published Online：2016-05，
  
  Published：20 May 2016
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Xin FANG, Yunju LIU, Haiyan CAO, et al. Low-complexity sparse channel estimation for massive MIMO systems[J]. Observation and communication, 2016, 32(5): 89-95.
DOI：

Xin FANG, Yunju LIU, Haiyan CAO, et al. Low-complexity sparse channel estimation for massive MIMO systems[J]. Observation and communication, 2016, 32(5): 89-95. DOI： 10.11959/j.issn.1000-0801.2016149.

摘要

针对大规模多输入多输出（MIMO）系统信道估计算法复杂度高的缺陷，结合无线通信信道固有的稀疏性提出了一种低复杂度的稀疏信道估计算法。该算法是在传统的离散傅里叶变换（DFT）信道估计的基础上利用分离算法将信道抽头与噪声空间分离开来，使得信道估计时只需要计算信道抽头的部分，因此算法的计算复杂度被大大降低。仿真结果表明，该算法在保持低复杂度的同时，可获得接近最小均方误差（MMSE）性能。

Abstract

Due to the high computational complexity of massive MIMO system

a low-complexity sparse channel estimation algorithm was proposed utilizing the inherent sparsity of the wireless communication channel to improve the estimation performance.The proposed algorithm separated channel tap from noise space based on the traditional discrete Fourier transform by adopting integral separation algorithm.This channel estimation algorithm need only calculate the channel tap

thus markedly reducing complexity of the algorithm.Numerical simulations show that proposed algorithm can approach to the performance of the minimum mean-square error estimator while maintaining lower complexity.

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references

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