  Sparse adaptive channel estimation problem is one of the most important
topics in broadband wireless communications systems due to its simplicity and
robustness. So far many sparsity-aware channel estimation algorithms have been
developed based on the well-known minimum mean square error (MMSE) criterion,
such as the zero-attracting least mean square (ZALMS), which are robust under
Gaussian assumption. In non-Gaussian environments, however, these methods are
often no longer robust especially when systems are disturbed by random
impulsive noises. To address this problem, we propose in this work a robust
sparse adaptive filtering algorithm using correntropy induced metric (CIM)
penalized maximum correntropy criterion (MCC) rather than conventional MMSE
criterion for robust channel estimation. Specifically, MCC is utilized to
mitigate the impulsive noise while CIM is adopted to exploit the channel
sparsity efficiently. Both theoretical analysis and computer simulations are
provided to corroborate the proposed methods.
