Blind equalization using neural networks and higher order statistics
Blind equalization has been one of the most active areas of research in recent years. The potential application of blind equalization in wireless communication is one of the main reasons for its popularity. This thesis compares four different methods of blind equalization for nonminimum phase system...
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sg-ntu-dr.10356-47172023-07-04T15:16:38Z Blind equalization using neural networks and higher order statistics Li, Rui. Saratchandran, Paramasivan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Blind equalization has been one of the most active areas of research in recent years. The potential application of blind equalization in wireless communication is one of the main reasons for its popularity. This thesis compares four different methods of blind equalization for nonminimum phase systems. Two Higher Order Statistics algorithms are used for channel identification. The first one is the Optimization al-gorithm and the second is Overdetermined Recursive Instrumental Variable (ORIV) algorithm. Two kinds of neural networks are used as equalizers to recover the trans-mitted signal. One is Multilayer Feedforward Network (MFN) based on Backpropa-gation algorithm, the other is Minimal Resource Allocation Network (MRAN) which is a newly developed Radial Basis Function Network that produces a parsimonious network structure. Master of Engineering 2008-09-17T09:57:14Z 2008-09-17T09:57:14Z 2000 2000 Thesis http://hdl.handle.net/10356/4717 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Li, Rui. Blind equalization using neural networks and higher order statistics |
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Blind equalization has been one of the most active areas of research in recent years. The potential application of blind equalization in wireless communication is one of the main reasons for its popularity. This thesis compares four different methods of blind equalization for nonminimum phase systems. Two Higher Order Statistics algorithms are used for channel identification. The first one is the Optimization al-gorithm and the second is Overdetermined Recursive Instrumental Variable (ORIV) algorithm. Two kinds of neural networks are used as equalizers to recover the trans-mitted signal. One is Multilayer Feedforward Network (MFN) based on Backpropa-gation algorithm, the other is Minimal Resource Allocation Network (MRAN) which is a newly developed Radial Basis Function Network that produces a parsimonious network structure. |
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Saratchandran, Paramasivan |
author_facet |
Saratchandran, Paramasivan Li, Rui. |
format |
Theses and Dissertations |
author |
Li, Rui. |
author_sort |
Li, Rui. |
title |
Blind equalization using neural networks and higher order statistics |
title_short |
Blind equalization using neural networks and higher order statistics |
title_full |
Blind equalization using neural networks and higher order statistics |
title_fullStr |
Blind equalization using neural networks and higher order statistics |
title_full_unstemmed |
Blind equalization using neural networks and higher order statistics |
title_sort |
blind equalization using neural networks and higher order statistics |
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2008 |
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http://hdl.handle.net/10356/4717 |
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1772828734789779456 |