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...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
2004-05.
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Online Access: | Get fulltext Get fulltext |
LEADER | 00998 am a22001573u 4500 | ||
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001 | 259267 | ||
042 | |a dc | ||
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 |