  In this article, a fractional-norm constrained blind adaptive algorithm is
presented for sparse channel equalization. In essence, the algorithm improves
on the minimization of the constant modulus (CM) criteria by adding a sparsity
inducing \(\ell_p\)-norm penalty. Simulation results demonstrate that the
proposed regularized equalizer exploits the inherent channel sparsity
effectively and exhibits faster convergence compared to its counterparts.
