Research Article | OPEN ACCESS
Sparsity-constraint LMS Algorithms for Time-varying UWB Channel Estimation
1, 2Solomon Nunoo, 1Uche A.K. Chude-okonkwo and 1Razali Ngah
1Wireless Communication Centre, Universiti Teknologi Malaysia, Johor, Malaysia
2Department of Electrical and Electronic Engineering, University of Mines and Technology, Tarkwa, Ghana
Research Journal of Applied Sciences, Engineering and Technology 2014 24:2408-2415
Received: August 03, 2014 | Accepted: September 14, 2014 | Published: December 25, 2014
Abstract
Sparsity constraint channel estimation using compressive sensing approach has gained widespread interest in recent times. Mostly, the approach utilizes either the l1-norm or l0-norm relaxation to improve the performance of LMS-type algorithms. In this study, we present the adaptive channel estimation of time-varying ultra wideband channels, which have shown to be sparse, in an indoor environment using sparsity-constraint LMS and NLMS algorithms for different sparsity measures. For a less sparse CIR, higher weightings are allocated to the sparse penalty term. Simulation results show improved performance of the sparsity-constraint algorithms in terms of convergence speed and mean square error performance.
Keywords:
Compressive sensing, (N) LMS algorithms, sparse channel estimation, time-varying channels, ultra wideband,
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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.
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The authors have no competing interests.
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