Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSP.2005.861069
Title: Convergence analysis of constrained joint adaptation in recording channels
Authors: Lim, S.C.
Mathew, G. 
Anthonio, M.
Keywords: Constrained joint adaptation
Least-mean-square (LMS) algorithm
Partial response (PR) equalization
Recording channels
Issue Date: Jan-2006
Source: Lim, S.C., Mathew, G., Anthonio, M. (2006-01). Convergence analysis of constrained joint adaptation in recording channels. IEEE Transactions on Signal Processing 54 (1) : 95-104. ScholarBank@NUS Repository. https://doi.org/10.1109/TSP.2005.861069
Abstract: Partial response (PR) equalization employing the linearly constrained least-mean-square (LCLMS) adaptive algorithm is widely used for jointly designing equalizer and PR target in recording channels. However, there is no literature on its convergence analysis. Further, existing analyses of the least-mean-square (LMS) algorithm assume that the input signals are jointly Gaussian, an assumption that is invalid for PR equalization with binary input. In this paper, we present a novel method to analyze the convergence of the LCLMS algorithm, without the Gaussian assumption. Our approach accommodates distinct step sizes for equalizer and PR target. It is shown that the step-size range required to guarantee stability of LCLMS with binary data is larger than that with Gaussian data. The analytical results are corroborated by extensive simulation studies. © 2006 IEEE.
Source Title: IEEE Transactions on Signal Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/55431
ISSN: 1053587X
DOI: 10.1109/TSP.2005.861069
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

1
checked on Dec 6, 2017

Page view(s)

31
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.