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Multiple hyperplane detector for implementing the asymptotic Bayesian decision feeback equalizer

Multiple hyperplane detector for implementing the asymptotic Bayesian decision feeback equalizer
Multiple hyperplane detector for implementing the asymptotic Bayesian decision feeback equalizer
A detector based on multiple-hyperplane partitioning of the signal space is derived for realizing the optimal Bayesian decision
feedback equaliser (DFE). It is known that the optimal Bayesian decision boundary separating any two neighbouring signal classes is asymptotically piecewise linear and consists of several hyperplanes, when the signal to noise ratio (SNR) tends to infinity. The proposed technique determines these hyperplanes and uses them to partition the observation space. The resulting detector can closely approximate the optimal Bayesian detector, at an advantage of considerably reduced decision complexity.
361-365
IEEE
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a

Chen, Sheng, Hanzo, Lajos and Mulgrew, B. (2001) Multiple hyperplane detector for implementing the asymptotic Bayesian decision feeback equalizer. In ICC 2001: IEEE International Conference on Communications. IEEE. pp. 361-365 . (doi:10.1109/ICC.2001.936963).

Record type: Conference or Workshop Item (Paper)

Abstract

A detector based on multiple-hyperplane partitioning of the signal space is derived for realizing the optimal Bayesian decision
feedback equaliser (DFE). It is known that the optimal Bayesian decision boundary separating any two neighbouring signal classes is asymptotically piecewise linear and consists of several hyperplanes, when the signal to noise ratio (SNR) tends to infinity. The proposed technique determines these hyperplanes and uses them to partition the observation space. The resulting detector can closely approximate the optimal Bayesian detector, at an advantage of considerably reduced decision complexity.

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Published date: 11 June 2001
Venue - Dates: ICC 2001: IEEE International Conference on Communications, , Helsinki, Finland, 2001-06-11 - 2001-06-15

Identifiers

Local EPrints ID: 454145
URI: http://eprints.soton.ac.uk/id/eprint/454145
PURE UUID: 03c1bc92-f318-4309-b935-2d2e2417503e
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 01 Feb 2022 17:43
Last modified: 02 Feb 2022 02:32

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Contributors

Author: Sheng Chen
Author: Lajos Hanzo ORCID iD
Author: B. Mulgrew

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