On the sparsity-aware partial-update NLMS algorithms for UWB channel estimation

Partial updating of filter coefficients is an effective method for reducing computational load and power consumption in adaptive filter implementation. In this paper, we present a class of MMax partial update NLMS algorithms suitable for estimating UWB channels, whose characteristics have shown to vary between highly sparse and dense depending on the channel environment under consideration and the measured bandwidth. Simulation results show improved performance of the proposed algorithms in terms of convergence speed, and computational complexity