Impulsive noise mitigation on MIMO power line based on sparse Bayesian learning

被引：0
|
作者：
Guo, Tao [1 ]
Hu, Guorong [1 ]
机构：
[1] Institute of Microelectronics of Chinese Academy of Sciences, Beijing
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2014年 / 14期
关键词：
Impulse noise; Noise mitigation; Power line communication; Signal to noise ratio (SNR); Sparse Bayesian learning; Symbol error rate (SER);
D O I：
10.7500/AEPS20130715011
中图分类号：
学科分类号：
摘要：
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 SNR. © 2014 State Grid Electric Power Research Institute Press
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页码：95 / 100and135
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