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Sparsity-Constrained Kernel Recursive Generalized Maximum Versoria Criterion Algorithm
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DOI：10.1109/LSP.2023.3265604.png)
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Abstract
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This letter proposes a novel random Fourier features (RFF) based sparsity-aware Kernel recursive adaptive filtering algorithm which employs generalized maximum Versoria criterion (GMVC) as an adaptation cost and generalized Versoria zero attraction (GVZA) as a sparsifying-norm. The proposed RFF-based GVZA kernel recursive GMVC (RFF-GVZA-KRGMVC) algorithm is robust for nonlinear sparse channel estimation under non-Gaussian noise over both stationary and non-stationary environments. Simulations indicate that the proposed RFF-GVZA-KRGMVC approach delivers better convergence and bit error rate performance over the existing stochastic gradient based RFF-ZA-kernel-MVC (RFF-ZA-KMVC) algorithm at the cost of increased computational complexity. Furthermore, detailed convergence analysis is also performed for the proposed algorithm and corroborated by Monte-Carlo simulations.
Keywords:
Signal processing algorithms
Kernel
Convergence
Machine learning algorithms
Channel estimation
Computational complexity
Covariance matrices
Sparsity-constrained
RKHS
zero-attracting
maximum Versoria criterion
sparse channel estimation
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