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低压电力线载波通信压缩感知信道估计方法
引用本文:郭俊龙,王学伟,王琳. 低压电力线载波通信压缩感知信道估计方法[J]. 北京化工大学学报(自然科学版), 2016, 43(3): 110. DOI: 10.13543/j.bhxbzr.2016.03.018
作者姓名:郭俊龙  王学伟  王琳
作者单位:北京化工大学信息科学与技术学院,北京,100029;北京化工大学信息科学与技术学院,北京,100029;北京化工大学信息科学与技术学院,北京,100029
基金项目:国家自然科学基金(51577006)
摘    要:针对低压电力线载波通信多径信道,建立了正交频分复用(OFDM)基带传输系统和低压电力线载波通信多径信道传输特性模型,并根据传输特性模型与模型参数,实验得出了低压电力线载波通信信道的冲击响应 h(n);在此基础上,分析推导了满足压缩感知条件的 OFDM 低压电力线载波通信多径传输数学模型,提出了低压电力线载波通信压缩感知信道估计方法,解决了最小二乘(LS)信道估计方法和最小均方误差(MMSE)信道估计方法精度差的缺点。 实验结果表明:在采用相同导频数 128 和相同信噪比 SNR 为 14 dB 的情况下,压缩感知(CS)信道估计方法与 LS 及 MMSE 信道估计方法中较优者相比,得出的信道均方误差降低 16 dB,接收端的误码率降低 3 倍,并且随着信噪比的增加性能提升更明显。

关 键 词:压缩感知(CS)  信道估计  多径信道  正交频分复用(OFDM)
收稿时间:2016-01-18

Use of the CS estimation method for LV-PLC channels
GUO JunLong,WANG XueWei,WANG Lin. Use of the CS estimation method for LV-PLC channels[J]. Journal of Beijing University of Chemical Technology, 2016, 43(3): 110. DOI: 10.13543/j.bhxbzr.2016.03.018
Authors:GUO JunLong  WANG XueWei  WANG Lin
Affiliation:College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029,China
Abstract:In this paper, the basic principles of orthogonal frequency division multiplexing (OFDM) and compressive sensing are introduced, the multipath characteristics of a low-voltage power line carrier communication (LV-PLC) channel are analyzed, and an OFDM baseband transmission system and multipath channel transmission characteristic model of LV-PLC are established. Meanwhile, the impulse response h(n) of the LV-PLC channel is obtained according to model simulations. On this basis, an OFDM-based LV-PLC multipath transmission mathematical model that meets the compressed sensing conditions is deduced, and a CS estimation method for LV-PLC channels is proposed, which overcomes the poor precision of the LS and MMSE estimation methods. The results show that:in the presence of the same quantity of pilots (128) and SER is 14 dB, when compared with optimum results using the LS and MMSE estimation methods, the CS estimation method gives values of MSE and BER which are lower by 16 dB and a factor of three, respectively.
Keywords: compressive sensing (CS)   channel estimation   multipath channel   orthogonal frequency division multiplexing (OFDM)
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