Fast communication: Convergence analysis of sparse LMS algorithms with l1-norm penalty based on white input signal

Authors:
Kun Shi;Peng Shi
Affiliations:
Texas Instruments, Dallas, TX 75243, USA;Division of Statistics, Northern Illinois University, DeKalb, IL 60115, USA
Venue:
Signal Processing
Year:
2010

Citing 1
Cited 2

Sparse LMS for system identification

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing

Fast communication: Gradient optimization p-norm-like constraint LMS algorithm for sparse system estimation

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Transient analysis of zero attracting NLMS algorithm without Gaussian inputs assumption

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Abstract

The zero-attracting LMS (ZA-LMS) algorithm is one of the recently published sparse LMS algorithms. It usesan l"1-norm penalty in the standard LMS cost function. In this paper, we perform convergence analysis of the ZA-LMS algorithm based on white input signals. The stability condition is examined and the steady-state mean square deviation (MSD) is derived in terms of the system sparsity, system response length, and filter parameters (step size and zero-attractor controller). In addition, we propose a criterion for parameter selection such that the ZA-LMS algorithm outperforms the standard LMS algorithm. The results are demonstrated through computer simulations.