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    Algorithms, Vol. 8, Pages 799-809: Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise Environments (2015)
    MDPI Publishing
    Details
    Publication Date: 2015-09-26
    Description: The sign least mean square with reweighted L1-norm constraint (SLMS-RL1) algorithm is an attractive sparse channel estimation method among Gaussian mixture model (GMM) based algorithms for use in impulsive noise environments. The channel sparsity can be exploited by SLMS-RL1 algorithm based on appropriate reweighted factor, which is one of key parameters to adjust the sparse constraint for SLMS-RL1 algorithm. However, to the best of the authors’ knowledge, a reweighted factor selection scheme has not been developed. This paper proposes a Monte-Carlo (MC) based reweighted factor selection method to further strengthen the performance of SLMS-RL1 algorithm. To validate the performance of SLMS-RL1 using the proposed reweighted factor, simulations results are provided to demonstrate that convergence speed can be reduced by increasing the channel sparsity, while the steady-state MSE performance only slightly changes with different GMM impulsive-noise strengths.
    Electronic ISSN: 1999-4893
    Topics: Computer Science
    Published by MDPI Publishing
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  • 2
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    Algorithms, Vol. 9, Pages 54: Sign Function Based Sparse Adaptive Filtering Algorithms for Robust Channel Estimation under Non-Gaussian Noise Environments (2016)
    MDPI Publishing
    Details
    Publication Date: 2016-08-13
    Description: Robust channel estimation is required for coherent demodulation in multipath fading wireless communication systems which are often deteriorated by non-Gaussian noises. Our research is motivated by the fact that classical sparse least mean square error (LMS) algorithms are very sensitive to impulsive noise while standard SLMS algorithm does not take into account the inherent sparsity information of wireless channels. This paper proposes a sign function based sparse adaptive filtering algorithm for developing robust channel estimation techniques. Specifically, sign function based least mean square error (SLMS) algorithms to remove the non-Gaussian noise that is described by a symmetric α-stable noise model. By exploiting channel sparsity, sparse SLMS algorithms are proposed by introducing several effective sparse-promoting functions into the standard SLMS algorithm. The convergence analysis of the proposed sparse SLMS algorithms indicates that they outperform the standard SLMS algorithm for robust sparse channel estimation, which can be also verified by simulation results.
    Electronic ISSN: 1999-4893
    Topics: Computer Science
    Published by MDPI Publishing
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