Reweighted l 1-norm penalized LMS for sparse channel estimation and its analysis

@inproceedings{Taheri2014ReweightedL1,
  title={Reweighted l 1-norm penalized LMS for sparse channel estimation and its analysis},
  author={Omid Taheri and Sergiy A. Vorobyov},
  year={2014}
}
A new reweighted l1 norm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper. Since standard LMS algorithm does not take into account the sparsity information about the channel impulse response (CIR), sparsity-aware modifications of the LMS algorithm aim at outperforming the standard LMS by introducing a penalty term to the standard LMS cost function which forces the solution to be sparse. Our reweighted l1 norm penalized LMS algorithm… CONTINUE READING

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