On the Step-Size Bounds of Frequency-Domain Block LMS Adaptive Filters

2013 ◽  
Vol 20 (1) ◽  
pp. 23-26 ◽  
Author(s):  
Junghsi Lee ◽  
Hsu-Chang Huang
Keyword(s):  
Frequency Domain ◽  
Adaptive Filters ◽  
Step Size ◽  
Domain Block

scholarly journals Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights

2018 ◽  
Vol 8 (11) ◽  
pp. 2313 ◽  
Author(s):  
Feiran Yang ◽  
Yin Cao ◽  
Ming Wu ◽  
Felix Albu ◽  
Jun Yang
Keyword(s):  
Frequency Domain ◽  
Adaptive Filters ◽  
Noise Control ◽  
Active Noise Control ◽  
Least Mean Square ◽  
Step Size ◽  
Comprehensive Overview ◽  
Active Noise ◽  
Extensive Evaluation ◽  
Signal Path

This paper presents a comprehensive overview of the frequency-domain filtered-x least mean-square (FxLMS) algorithms for active noise control (ANC). The direct use of frequency-domain adaptive filters for ANC results in two kinds of delays, i.e., delay in the signal path and delay in the weight adaptation. The effects of the two kinds of delays on the convergence behavior and stability of the adaptive algorithms are analyzed in this paper. The first delay can violate the so-called causality constraint, which is a major concern for broadband ANC, and the second delay can reduce the upper bound of the step size. The modified filter-x scheme has been employed to remove the delay in the weight adaptation, and several delayless filtering approaches have been presented to remove the delay in the signal path. However, state-of-the-art frequency-domain FxLMS algorithms only remove one kind of delay, and some of these algorithms have a very high peak complexity and hence are impractical for real-time systems. This paper thus proposes a new delayless frequency-domain ANC algorithm that completely removes the two kinds of delays and has a low complexity. The performance advantages and limitations of each algorithm are discussed based on an extensive evaluation, and the complexities are evaluated in terms of both the peak and average complexities.

A Variable Step-Size for Sparse Nonlinear Adaptive Filters

2021 ◽  
Author(s):  
Alberto Carini ◽  
Markus V. S. Lima ◽  
Hamed Yazdanpanah ◽  
Simone Orcioni ◽  
Stefania Cecchi
Keyword(s):  
Adaptive Filters ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step

Analysis of the partitioned frequency-domain block LMS (PFBLMS) algorithm

2001 ◽  
Vol 49 (9) ◽  
pp. 1860-1874 ◽  
Author(s):  
Kheong Sann Chan ◽  
B. Farhang-Boroujeny
Keyword(s):  
Frequency Domain ◽  
Domain Block
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