Performance evaluation of adaptive filters for sparse wireless channel estimation

The progressive increase of data rates in wireless communication systems has induced channel models with sampled impulse responses which are mostly sparse. This paper presents a unified derivation of adaptive filters exploiting sparsity in the complex domain, and compares the performance of classic and state-of-the-art adaptive algorithms for estimating sparse wireless channels as well as their tracking ability in this inherently time-varying environment. Simulation results confirm the efficiency of the sparsity-aware algorithms.