A Constrained Decision Feedback Equalizer for Reduced Complexity Maximum Likelihood Sequence Estimation

Wen-Rong WU; Yih-Ming TSUIE

A Constrained Decision Feedback Equalizer for Reduced Complexity Maximum Likelihood Sequence Estimation

Wen-Rong WU, Yih-Ming TSUIE

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The maximum likelihood sequence estimator (MLSE) is usually implemented by the Viterbi algorithm (VA). The computational complexity of the VA grows exponentially with the length of the channel response. With some performance reduction, a decision-feedback equalizer (DFE) can be used to shorten the channel response. This greatly reduces the computational requirement for the VA. However, for many real-world applications, the complexity of the DFE/MLSE approach may be still too high. In this paper, we propose a constrained DFE that offers much lower VA computational complexity. The basic idea is to pose some constraints on the DFE such that the postcursors of the shortened channel response have only discrete values. As a result, the multiplication operations can be replaced by shift operations making the VA almost multiplication free. This will greatly facilitate the real world applications of the MLSE algorithm. Simulation results show that while the proposed algorithm basically offers the same performance as the original MLSE performance, the VA is much more efficient than the conventional approach.

Publication: IEICE TRANSACTIONS on Communications Vol.E85-B No.1 pp.231-238

Publication Date: 2002/01/01

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Type of Manuscript: PAPER

Category: Wireless Communication Technology

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Wen-Rong WU, Yih-Ming TSUIE, "A Constrained Decision Feedback Equalizer for Reduced Complexity Maximum Likelihood Sequence Estimation" in IEICE TRANSACTIONS on Communications, vol. E85-B, no. 1, pp. 231-238, January 2002, doi: .
Abstract: The maximum likelihood sequence estimator (MLSE) is usually implemented by the Viterbi algorithm (VA). The computational complexity of the VA grows exponentially with the length of the channel response. With some performance reduction, a decision-feedback equalizer (DFE) can be used to shorten the channel response. This greatly reduces the computational requirement for the VA. However, for many real-world applications, the complexity of the DFE/MLSE approach may be still too high. In this paper, we propose a constrained DFE that offers much lower VA computational complexity. The basic idea is to pose some constraints on the DFE such that the postcursors of the shortened channel response have only discrete values. As a result, the multiplication operations can be replaced by shift operations making the VA almost multiplication free. This will greatly facilitate the real world applications of the MLSE algorithm. Simulation results show that while the proposed algorithm basically offers the same performance as the original MLSE performance, the VA is much more efficient than the conventional approach.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e85-b_1_231/_p

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@ARTICLE{e85-b_1_231,
author={Wen-Rong WU, Yih-Ming TSUIE, },
journal={IEICE TRANSACTIONS on Communications},
title={A Constrained Decision Feedback Equalizer for Reduced Complexity Maximum Likelihood Sequence Estimation},
year={2002},
volume={E85-B},
number={1},
pages={231-238},
abstract={The maximum likelihood sequence estimator (MLSE) is usually implemented by the Viterbi algorithm (VA). The computational complexity of the VA grows exponentially with the length of the channel response. With some performance reduction, a decision-feedback equalizer (DFE) can be used to shorten the channel response. This greatly reduces the computational requirement for the VA. However, for many real-world applications, the complexity of the DFE/MLSE approach may be still too high. In this paper, we propose a constrained DFE that offers much lower VA computational complexity. The basic idea is to pose some constraints on the DFE such that the postcursors of the shortened channel response have only discrete values. As a result, the multiplication operations can be replaced by shift operations making the VA almost multiplication free. This will greatly facilitate the real world applications of the MLSE algorithm. Simulation results show that while the proposed algorithm basically offers the same performance as the original MLSE performance, the VA is much more efficient than the conventional approach.},
keywords={},
doi={},
ISSN={},
month={January},}

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TY - JOUR
TI - A Constrained Decision Feedback Equalizer for Reduced Complexity Maximum Likelihood Sequence Estimation
T2 - IEICE TRANSACTIONS on Communications
SP - 231
EP - 238
AU - Wen-Rong WU
AU - Yih-Ming TSUIE
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E85-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2002
AB - The maximum likelihood sequence estimator (MLSE) is usually implemented by the Viterbi algorithm (VA). The computational complexity of the VA grows exponentially with the length of the channel response. With some performance reduction, a decision-feedback equalizer (DFE) can be used to shorten the channel response. This greatly reduces the computational requirement for the VA. However, for many real-world applications, the complexity of the DFE/MLSE approach may be still too high. In this paper, we propose a constrained DFE that offers much lower VA computational complexity. The basic idea is to pose some constraints on the DFE such that the postcursors of the shortened channel response have only discrete values. As a result, the multiplication operations can be replaced by shift operations making the VA almost multiplication free. This will greatly facilitate the real world applications of the MLSE algorithm. Simulation results show that while the proposed algorithm basically offers the same performance as the original MLSE performance, the VA is much more efficient than the conventional approach.
ER -