Sparse channel estimation algorithm based on compressed sensing in MIMO NC-OFDM system

Enqing CHEN; Xinli GAO; Xiaoqiang XIANG; Zhongyong WANG

doi:10.3969/j.issn.1000-0801.2016.02.006

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Sparse channel estimation algorithm based on compressed sensing in MIMO NC-OFDM system

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

- Sparse channel estimation algorithm based on compressed sensing in MIMO NC-OFDM system
- Telecommunication Science Vol. 32, Issue 2, Pages: 41-46(2016)
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- DOI：10.3969/j.issn.1000-0801.2016.02.006
  CLC： TN911
- Published Online：2016-02，
  
  Published：15 February 2016
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Enqing CHEN, Xinli GAO, Xiaoqiang XIANG, et al. Sparse channel estimation algorithm based on compressed sensing in MIMO NC-OFDM system[J]. Telecommunication science, 2016, 32(2): 41-46.
DOI：

Enqing CHEN, Xinli GAO, Xiaoqiang XIANG, et al. Sparse channel estimation algorithm based on compressed sensing in MIMO NC-OFDM system[J]. Telecommunication science, 2016, 32(2): 41-46. DOI： 10.3969/j.issn.1000-0801.2016.02.006.

摘要

多输入多输出不连续正交频分复用（MIMO NC-OFDM）系统是认知无线电（CR）系统的常用体制，由于授权用户占用而导致的载波不连续情况下的信道估计是影响该系统性能的关键技术问题。提出一种基于压缩感知（CS）的MIMO NC-OFDM 系统的信道估计方法——稀疏自适应匹配追踪（SAMP）算法。SAMP 算法在重构过程中先对信号稀疏度进行初始估计，然后自适应调整步长逐步逼近信号，相较于其他贪婪算法，能够在稀疏度未知的情况下准确重建稀疏信号。仿真结果表明，SAMP算法提高了重构精度，在实际应用中易于实现。

Abstract

Multiple-input multiple-output non-contiguous orthogonal frequency division multiplexing（MIMO NC-OFDM）system is a commonly used system in cognitive radio. One of the key technical that affects the MIMO NC-OFDM performance is channel estimation in the condition of non-continuous carrier caused by licensed users’ occupation. Sparsity adaptive matching pursuit（SAMP）algorithm was proposed as a new method to estimate sparse channel in MIMO NC-OFDM system. Compared with other state-of-the-art greedy algorithms，the most innovative feature of the SAMP is that it is capable to adjust the step length adaptively to approach the original signal and reconstruct the sparse signal without prior information of the sparsity. Simulation result shows that，the new channel estimation method outperforms many existing iterative algorithms in reconstruction performance and can be implemented easily in practical application.

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references

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