Proportionate normalized least-mean-squares adaptation in echo cancelers

By D. Duttweiler. IEEE Transactions on Speech and Audio Processing, 2000

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Sparse solutions to linear inverse problems with multiple measurement vectors (2005, methodology): “For instance, these algorithms have been applied to biomagnetic inverse problems [2], [3], bandlimited extrapolation and spectral estimation [4], [5], direction-of-arrival estimation [6], [3], functional approximation [7], [8], channel equalization [9], echo cancellation [10], image restoration [11]”
Sparse Bayesian learning for basis selection (2004, background): “Example applications include biomagnetic imaging [4], channel equalization [5]–[7], bandlimited extrapolation and spectral estimation [8], [9], direction-of-arrival estimation [10], functional approximation [11]–[13], echo cancellation [ 14 ], [15], and image restoration [16].”
$l_{0}$ Norm Constraint LMS Algorithm for Sparse System Identification (2009, methodology): “Sparse systems, which exist in many applications, such as Digital TV transmission channels [1] and the echo paths [2], can be further divided to general sparse systems [Fig.”
Sparse LMS for system identification (2009, background): “O ther variants assign proportional step sizes of different taps accor ding to their magnitudes, such as the Proportionate Normalized LMS (PNLM S) and its variations [7, 8].”