Joint channel and impulsive noise estimation method for MIMO-OFDM systems

Xinrong LYU; Youming LI; Qiang GUO

doi:10.11959/j.issn.1000-436x.2021238

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Joint channel and impulsive noise estimation method for MIMO-OFDM systems

Papers | 更新时间：2024-06-05

- Joint channel and impulsive noise estimation method for MIMO-OFDM systems
- Journal on Communications Vol. 42, Issue 12, Pages: 54-64(2021)
- 作者机构：
  
  1. 宁波大学科学技术学院，浙江宁波 315300
  2. 宁波大学信息科学与工程学院，浙江宁波 315211
  3. 哈尔滨工程大学信息与通信工程学院，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
  
  The International Cooperation Project of the Ministry of Science and Technology(2018YFE0206500);The National Natural Science Foundation of China(61571250);The Natural Science Foundation of Zhejiang Province(LY22F010018);The Foundation of Ningbo Jiangbei District Science and Technology Bureau(201801A04)
- DOI：10.11959/j.issn.1000-436x.2021238
  CLC： TN911.4
- Online First：2021-12，
  
  Published：25 December 2021
- 稿件说明：
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Xinrong LYU, Youming LI, Qiang GUO. Joint channel and impulsive noise estimation method for MIMO-OFDM systems[J]. Journal on Communications, 2021, 42(12): 54-64.
DOI：

Xinrong LYU, Youming LI, Qiang GUO. Joint channel and impulsive noise estimation method for MIMO-OFDM systems[J]. Journal on Communications, 2021, 42(12): 54-64. DOI： 10.11959/j.issn.1000-436x.2021238.

摘要

针对 MIMO-OFDM 系统中的脉冲噪声问题，提出了一种基于多测量向量压缩感知理论的信道与脉冲噪声联合估计方法。该方法将信道冲激响应和脉冲噪声联合组成一个具有行稀疏性的待估计矩阵，构建了一个基于全部子载波的多测量向量压缩感知模型。由于数据子载波中未知的发射符号导致观察矩阵的部分元素不确定，因此将发射符号视作未知参数，利用稀疏贝叶斯学习理论和期望最大值算法实现了一种能联合估计信道、脉冲噪声和发射符号的迭代方法。与现有方法相比，所提方法不仅能够充分利用全部子载波信息，而且不需要信道和脉冲噪声的先验统计信息。仿真结果表明，所提方法在信道估计及误比特率性能上有明显改善。

Abstract

Aiming at the impulsive noise occurring in MIMO-OFDM systems， a joint channel and impulsive noise estimation method based on the multiple measurement vector compressed sensing theory was proposed.The channel impulse response and the impulsive noise were combined to form a row sparse matrix to be estimated， and a multiple measurement vector compressed sensing model based on all subcarriers was constructed.As the measurement matrix was partially unknown due to the presence of unknown transmitted symbols in data tones， the multiple response sparse Bayesian learning theory and expectation maximization framework were adopted to jointly estimate the channel impulse response， the impulsive noise， and the data symbols which were regarded as unknown parameters.Compared with the existing methods， the proposed method not only utilizes all subcarriers but also does not use any a priori information of the channel and impulsive noise.The simulation results show that the proposed method achieves significant improvement on the channel estimation and bit error rate performance.

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references

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