Kernel-based nonlinear beamforming construction using orthogonal forward selection with Fisher ratio class separability measure

This letter shows that the wireless communication system capacity is greatly enhanced by employing nonlinear beamforming and the optimal Bayesian beamformer outperforms the standard linear beamformer significantly in terms of a reduced bit error rate, at a cost of increased complexity. Block-data ad...

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Bibliographic Details
Main Authors: Chen, S. (Author), Hanzo, L. (Author), Wolfgang, A. (Author)
Format: Article
Language:English
Published: 2004-05.
Subjects:
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100 1 0 |a Chen, S.  |e author 
700 1 0 |a Hanzo, L.  |e author 
700 1 0 |a Wolfgang, A.  |e author 
245 0 0 |a Kernel-based nonlinear beamforming construction using orthogonal forward selection with Fisher ratio class separability measure 
260 |c 2004-05. 
856 |z Get fulltext  |u https://eprints.soton.ac.uk/259267/1/NBofsFRT.ps 
856 |z Get fulltext  |u https://eprints.soton.ac.uk/259267/2/01288112.pdf 
520 |a This letter shows that the wireless communication system capacity is greatly enhanced by employing nonlinear beamforming and the optimal Bayesian beamformer outperforms the standard linear beamformer significantly in terms of a reduced bit error rate, at a cost of increased complexity. Block-data adaptive implementation of the Bayesian beamformer is realized based on an orthogonal forward selection procedure with Fisher ratio for class separability measure. 
655 7 |a Article