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

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.