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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Bibliografische Detailangaben

Hauptverfasser:	Bowen Li, Suya Wu, Erin E. Tripp, Ali Pezeshki, Vahid Tarokh
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2024-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Adaptive filtering EM algorithm minimax concave penalty (MCP) sparse system identification
Online-Zugang:	https://ieeexplore.ieee.org/document/10522635/
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