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Adaptive Filtering With Reduced Computational Complexity Using SOPOT Arithmetic
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DOI：10.1109/TCSI.2021.3119285.png)
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摘要
En 中文
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.
Keyword:
Approximation algorithms
Arithmetic
Finite impulse response filters
Convergence
Change detection algorithms
Computational complexity
Matching pursuit algorithms
Sums of signed-powers-of-two
adaptive filters
SPT-LMS
SPT-NLMS
SPT-RLS
