Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (19): 153-159.DOI: 10.3778/j.issn.1002-8331.1906-0176

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High-Order MIMO Systems Channel Estimation Algorithms Based on Compressive Sensing

JI Xiaohui, SUN Zeyu, YAN Ben, LI Chuanfeng   

  1. School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, Henan 471023, China
  • Online:2019-10-01 Published:2019-09-30



  1. 洛阳理工学院 计算机与信息工程学院 河南 洛阳 471023

Abstract: Large number of antennas in the communication network, it is difficult to complete the effective calculation and evaluation of the channel. High-order MIMO systems Channel Estimation algorithms based on Compressive Sensing(HMCE-CS) is proposed. Firstly, a simple pilot structure is designed to reduce computational complexity. Secondly, the length of the pilot sequence can be adaptively adjusted according to the variation of the channel sparsity, thereby saving the overhead of the pilot resources. Simulation experiments show that compared with the traditional channel estimation algorithm, the HMCE-CS algorithm can reduce the average pilot sequence length under the same channel estimation accuracy, or the HMCE-CS algorithm can be improved under the same pilot sequence length. The channel estimation accuracy verifies the effectiveness of the proposed algorithm.

Key words: multiple input multiple output system, compressive sensing, pilot sequence, sparse matrix

摘要: 针对通信网络中的天线数量巨大,很难完成对信道的有效计算与估值。借助于高阶多输入多输出(MIMO)系统的可靠性,提出了一种基于压缩感知的信道估值高阶MIMO系统。设计了一种简单的导频结构,以降低计算复杂度;利用导频序列长度可以随着信道稀疏度变化情况自适应的调整,从而节省了导频资源的开销。仿真实验表明,与传统的信道估计算法比较,在相同的信道估计精度条件下,HMCE-CS算法可以降低平均导频序列长度;或者在相同的导频序列长度条件下,HMCE-CS算法可以提高信道估计精度,验证了该算法的有效性。

关键词: 多输入多输出, 压缩感知, 导频序列, 稀疏矩阵