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

Low-Complexity Set-Membership Normalized LMS Algorithm for Sparse System Modeling