Sparse channel estimation based on a reweighted least-mean mixed-norm adaptive filter algorithm

A sparsity-aware least-mean mixed-norm (LMMN) adaptive filter algorithm is proposed for sparse channel estimation applications. The proposed algorithm is realized by incorporating a sum-log function constraint into the cost function of a LMMN which is a mixed norm controlled by a scalar-mixing parameter. As a result, a shrinkage is given to enhance the performance of the LMMN algorithm when the majority of the channel taps are zeros or near-zeros. The channel estimation behaviors of the proposed reweighted sparse LMMN algorithm is investigated and discussed in comparison with those of the standard LMS and the least-mean square/fourth (LMS/F) and previously sparse LMS/F algorithms. The simulation results show that the proposed reweighted sparse LMMN algorithm is superior to aforementioned algorithms with respect to the convergence speed and steady-state error floor.

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