Research Article | OPEN ACCESS
MIP-Mitigated Sparse Channel Estimation Using Orthogonal Matching Pursuit Algorithm
1Guan Gui, 2Aihua Kuang, 3Ling Wang and 4, 5Aihua Zhang
1Department of Communication Engineering, Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan
2Department of Electronics, School of Electronics and Information Engineering of Zhengzhou, Zhengzhou, 450007, China
3Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
4School of Information Engineering, Zhengzhou University, Zhengzhou, 450001, China
5School of Electronic and Information Engineering, Zhongyuan University of Technology, ZhengZhou, 450007, China
Research Journal of Applied Sciences, Engineering and Technology 2013 1:180-186
Received: May 11, 2012 | Accepted: June 08, 2012 | Published: January 01, 2013
Abstract
Wireless communication requires accurate Channel State Information (CSI) for coherent detection. Due to the broadband signal transmission, dominant channel taps are often separated in large delay spread and thus are exhibited highly sparse distribution. Sparse Multi-Path Channel (SMPC) estimation using Orthogonal Matching Pursuit (OMP) algorithm has took advantage of simplification and fast implementation. However, its estimation performance suffers from large Mutual Incoherent Property (MIP) interference in dominant channel taps identification using Random Training Matrix (RTM), especially in the case of SMPC with a large delay spread or utilizing short training sequence. In this study, we propose a MIP mitigation method to improve sparse channel estimation performance. To improve the estimation performance, we utilize a designed Sensing Training Matrix (STM) to replace with RTM. Numerical experiments illustrate that the improved estimation method outperforms the conventional sparse channel methods which neglected the MIP interference in RTM.
Keywords:
Mutual Incoherent Property (MIP), Orthogonal Matching Pursuit (OMP), Sensing Training Matrix (STM), sparse channel estimation,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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