Adaptive Filtering With Reduced Computational Complexity Using SOPOT Arithmetic

Implementing finite impulse response (FIR) adaptive filters by employing the sums of signed-powers-of-two (SOPOT) arithmetic may lead to simpler hardware and consequently reduced power consumption. In this paper, one evaluates the effects of SOPOT arithmetic on the adaptive filter's recursion a...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Vol. 69; no. 2; pp. 746 - 756
Main Authors: Coelho, Luiz Felipe da Silveira, Lovisolo, Lisandro, Tcheou, Michel Pompeu
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Adaptive filters
Algorithms
Approximation algorithms
Arithmetic
Change detection algorithms
Complexity
Computational complexity
Convergence
Finite impulse response filters
Impulse response
Iterative methods
Matching pursuit algorithms
Numerical stability
Power consumption
SPT-LMS
SPT-NLMS
SPT-RLS
Stability analysis
Sums of signed-powers-of-two
System identification
ISSN:1549-8328, 1558-0806
Online Access:Get full text
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Summary:Implementing finite impulse response (FIR) adaptive filters by employing the sums of signed-powers-of-two (SOPOT) arithmetic may lead to simpler hardware and consequently reduced power consumption. In this paper, one evaluates the effects of SOPOT arithmetic on the adaptive filter's recursion algorithms. The filters' coefficients and algorithms' underlying variables are fully operated using SOPOT arithmetic in the whole iterative process. More specifically, one evaluates convergence rate, numerical stability, and accuracy since using few signed-powers-of-two (SPT) terms propagates numerical errors during the adaptive cycle that may impair the algorithm behavior. The SOPOT approximations are obtained through the technique known as Matching Pursuits with Generalized Bit-Plane (MPGBP) algorithm, with notable cost-performance trade-off and low computational complexity. Results are provided for the Least-Mean-Squares (LMS), the Normalized Least-Mean-Squares (NLMS) and the Recursive-Least-Squares (RLS) algorithms, considering adaptive filters employed for system identification and change detection.
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ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2021.3119285