Abstract
In this paper the delayed least-mean-square (DLMS) algorithm is proposed for training a transversal filter-based decision feedback equalizer (DFE). Delays in the filter coefficient update process are used to pipeline the DFE, thereby increasing the throughput rate, for a given speed of hardware. The filter structures selected for the feedforward and feedback section of the DFE facilitate the use of a shared error signal, thereby reducing communication costs. The new resulting structure is highly modular and is very suitable for very large scale integration (VLSI) implementation. A pipelined form for the normalized least-mean-square algorithm (NMLS) is also obtained which removes the dependency of the convergence speed on the input signal power. The convergence and residual mean-square error characteristics of the different pipelined filters are compared
Translated title of the contribution | Pipelined DFE architectures using delayed coefficient adaptation |
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Original language | English |
Article number | 7 |
Pages (from-to) | 868 - 873 |
Journal | IEEE Transactions on Circuits and Systems II - Analogue and Digital Signal Processing |
Volume | 45 |
Issue number | 7 |
DOIs | |
Publication status | Published - Jul 1998 |
Bibliographical note
Rose publication type: Journal articleTerms of use: Copyright © 1998 IEEE. Reprinted from IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, [see also Circuits and Systems II: Express Briefs, IEEE Transactions on]. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.
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Keywords
- adaptive filters
- delayed least-mean-squares