Volume 4 Number 6 (Dec. 2012)
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IJCEE 2012 Vol.4(6): 854-856 ISSN: 1793-8163 DOI: 10.7763/IJCEE.2012.V4.619

A Consistent Binormalized Data-Reusing LMS Algorithm for Noisy FIR Models

Byung Hoon Kang, Nam Kyu Kwon, Hyon-Taek Choi, and Poo Gyeon Park
Abstract—This paper proposes a consistent binormalized data-reusing least mean square (LMS) algorithm for identifying finite impulse response models whose input and output are corrupted by additive white noise. The proposed algorithm exploits the stochastic properties of the noisy input to compensate a bias of estimation which is occurred by input noise. Furthermore, by reusing the input signal, the algorithm overcomes a decline of convergence performance with highly correlated input signal. The experimental results show that the proposed algorithm achieves consistent estimation with noisy input signal. Furthermore, the proposed algorithm gets faster convergence rate and smaller steady-state estimation errors than the ordinary consistent LMS algorithms when the input signal is highly correlated.

Index Terms—Normalized least mean square algorithm (nLMS), consistent estimation, input noise, bias compensation, FIR channel estimation.

Byung Hoon Kang and Nam Gyu Kwon are with the Department of Electrical Engineering, Pohang University of Science and Technology, San 31, Hyojadong, Namgu, Pohang, Kyungbuk, 790-784, Korea (e-mail: anbabo@postech.ac.kr, kwunnam@postech.ac.kr).
Hyun-Taek Choi is with the Korea Ocean Research & Development Institute, Korea (e-mail: htchoiphd@kordi.re.kr).
Poo Gyeon Park is with the Division of IT Convergence Engineering and the Department of Electrical Engineering, Pohang University of Science and Technology, San 31, Hyojadong, Namgu, Pohang, Kyungbuk, 790-784, Korea (e-mail: ppg@postech.ac.kr).

Cite: Byung Hoon Kang, Nam Kyu Kwon, Hyon-Taek Choi, and Poo Gyeon Park, "A Consistent Binormalized Data-Reusing LMS Algorithm for Noisy FIR Models," International Journal of Computer and Electrical Engineering vol. 4, no. 6, pp. 854-856, 2012.

General Information

ISSN: 1793-8163
Frequency: Semiyearly
Editor-in-Chief: Prof. Yucong Duan
Abstracting/ Indexing: EI (INSPEC, IET), Ulrich's Periodicals Directory, Google Scholar, EBSCO, Engineering & Technology Digital Library, ProQuest, and Electronic Journals Library
E-mail: ijcee@iap.org

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