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Article Information:
Sparsity-constraint LMS Algorithms for Time-varying UWB Channel Estimation
Solomon Nunoo, 1Uche A.K. Chude-okonkwo and 1Razali Ngah
Corresponding Author: Solomon Nunoo
Submitted: August 03, 2014
Accepted: September 14, 2014
Published: December 25, 2014 |
Abstract:
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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.
Key words: Compressive sensing, (N) LMS algorithms, sparse channel estimation, time-varying channels, ultra wideband, ,
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Cite this Reference:
Solomon Nunoo, 1Uche A.K. Chude-okonkwo and 1Razali Ngah, . Sparsity-constraint LMS Algorithms for Time-varying UWB Channel Estimation. Research Journal of Applied Sciences, Engineering and Technology, (24): 2408-2415.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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