  In this work, we propose two low-complexity set-membership normalized
least-mean-square (LCSM-NLMS1 and LCSM-NLMS2) algorithms to exploit the
sparsity of an unknown system. For this purpose, in the LCSM-NLMS1 algorithm,
we employ a function called the discard function to the adaptive coefficients
in order to neglect the coefficients close to zero in the update process.
Moreover, in the LCSM-NLMS2 algorithm, to decrease the overall number of
computations needed even further, we substitute small coefficients with zero.
Numerical results present similar performance of these algorithms when
comparing them with some state-of-the-art sparsity-aware algorithms, whereas
the proposed algorithms need lower computational cost.
