Convergence Analysis of Constrained Joint Adaptation in Recording Channels
| Authors: | Mathew George, National University of Singapore and Data Storage Institute, Singapore
Sze Chieh Lim, National University of Singapore, Singapore |
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| Topic: | 1.1 Modelling, Identification & Signal Processing |
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| Session: | Recursive Estimation Methods |
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| Keywords: | Adaptation, adaptive equalization, constraints, LMS algorithm, mean-square error, partial response channels, recording channels, PR target |
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Abstract
Although partial response (PR) equalization employing the linearly constrained least-mean-square (LCLMS) algorithm is widely used in recording channels, there is no literature on its convergence analysis. Existing analyses of the LMS algorithm assume that the input signals are jointly Gaussian, which is an invalid assumption for PR equalization with binary input. In this paper, we present a convergence analysis of the LCLMS algorithm, without the Gaussian assumption. An approximate expression is derived for the misadjustment. It is shown that the step-size range required to guarantee stability is larger for binary data compared to Gaussian data.
