基于稀疏贝叶斯学习的MIMO电力线脉冲噪声消除

GUO Tao

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基于稀疏贝叶斯学习的MIMO电力线脉冲噪声消除

更新时间：2025-08-13

- 基于稀疏贝叶斯学习的MIMO电力线脉冲噪声消除
- Automation of Electric Power Systems Vol. 38, Issue 14, Pages: 95-100(2014)
- 作者机构：
  
  中国科学院微电子研究所,北京市,100029
- 作者简介：
- 基金信息：
- DOI：
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- Published：2014
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GUO Tao. [J]. Automation of electric power systems, 2014, 38(14): 95-100.
DOI：

GUO Tao. [J]. Automation of electric power systems, 2014, 38(14): 95-100. DOI：

摘要

为提高多输入多输出(MIMO)电力线通信系统对抗脉冲噪声的能力

基于稀疏贝叶斯学习的理论

利用脉冲噪声在电力线上的相关性

提出了一种消除MIMO电力线脉冲噪声的方案。方案使用全部子载波来联合估计脉冲噪声和可用子载波上的信号

无需训练脉冲噪声的统计信息。仿真中脉冲噪声拟合采用Bivariate Middleton Class A模型

结果表明该方案抗脉冲噪声性能比只使用空子载波的多观测向量稀疏贝叶斯学习（MSBL）方案提升了11 dB。

Abstract

In order to enhance the ability of multiple-input multiple-output (MIMO) power line communication system against the impulsive noise

a scheme is proposed to mitigating the impulsive noise impact on MIMO power line communications based on sparse Bayesian learning and correlation of impulsive noise on power lines. Under this scheme

all of the subcarriers are used to jointly estimate the impulsive noise and the signals on the available subcarriers. There is no need for information of training impulsive noise. The Bivariate Middleton Class A model is used in the case study to fit the impulsive noise

and the results show that the performance of the proposed scheme against the impulsive noise is better than the multiple measurement vector sparse Bayesion learning (MSBL) scheme using null subcarriers with an improvement of 11 dB signal to noise ratio (SNR).

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