Sparsity-constraint LMS algorithms for time-varying UWB channel estimation

Solomon Nunoo, Uche A.K. Chude-okonkwo, Razali Ngah

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)2408-2415
Number of pages8
JournalResearch Journal of Applied Sciences, Engineering and Technology
Issue number24
Publication statusPublished - 2014
Externally publishedYes


  • (N) LMS algorithms
  • Compressive sensing
  • Sparse channel estimation
  • Time-varying channels
  • Ultra wideband

ASJC Scopus subject areas

  • General Computer Science
  • General Engineering


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