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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Hlavní autoři:	Bowen Li, Suya Wu, Erin E. Tripp, Ali Pezeshki, Vahid Tarokh
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2024-01-01
Edice:	IEEE Access
Témata:	Adaptive filtering EM algorithm minimax concave penalty (MCP) sparse system identification
On-line přístup:	https://ieeexplore.ieee.org/document/10522635/
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