Recursive Least Squares With Minimax Concave Penalty Regularization for Adaptive System Identification
We develop a recursive least squares (RLS) type algorithm with a minimax concave penalty (MCP) for adaptive identification of a sparse tap-weight vector that represents a communication channel. The proposed algorithm recursively yields its estimate of the tap-vector, from noisy streaming observation...
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| Autors principals: | , , , , |
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| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
IEEE
2024-01-01
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| Col·lecció: | IEEE Access |
| Matèries: | |
| Accés en línia: | https://ieeexplore.ieee.org/document/10522635/ |
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