Next Article in Journal
A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis
Previous Article in Journal
Control for Ship Course-Keeping Using Optimized Support Vector Machines
Article Menu

Export Article

Open AccessArticle
Algorithms 2016, 9(3), 54; doi:10.3390/a9030054

Sign Function Based Sparse Adaptive Filtering Algorithms for Robust Channel Estimation under Non-Gaussian Noise Environments

1
School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
2
College of Computer Science, Chongqing University, Chongqing 400044, China
3
Institute of Signal Transmission and Processing, College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Academic Editor: Paul M. Goggans
Received: 24 June 2016 / Revised: 26 July 2016 / Accepted: 9 August 2016 / Published: 12 August 2016
View Full-Text   |   Download PDF [2500 KB, uploaded 12 August 2016]   |  

Abstract

Robust channel estimation is required for coherent demodulation in multipath fading wireless communication systems which are often deteriorated by non-Gaussian noises. Our research is motivated by the fact that classical sparse least mean square error (LMS) algorithms are very sensitive to impulsive noise while standard SLMS algorithm does not take into account the inherent sparsity information of wireless channels. This paper proposes a sign function based sparse adaptive filtering algorithm for developing robust channel estimation techniques. Specifically, sign function based least mean square error (SLMS) algorithms to remove the non-Gaussian noise that is described by a symmetric α-stable noise model. By exploiting channel sparsity, sparse SLMS algorithms are proposed by introducing several effective sparse-promoting functions into the standard SLMS algorithm. The convergence analysis of the proposed sparse SLMS algorithms indicates that they outperform the standard SLMS algorithm for robust sparse channel estimation, which can be also verified by simulation results. View Full-Text
Keywords: robust sparse channel estimation; sign function based least mean square error (SLMS); sparsity-promoting function; non-Gaussian noise; convergence analysis robust sparse channel estimation; sign function based least mean square error (SLMS); sparsity-promoting function; non-Gaussian noise; convergence analysis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhang, T.; Gui, G. Sign Function Based Sparse Adaptive Filtering Algorithms for Robust Channel Estimation under Non-Gaussian Noise Environments. Algorithms 2016, 9, 54.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top