Adaptive nonlinear least bit error-rate detection for symmetrical RBF beamforming
Adaptive nonlinear least bit error-rate detection for symmetrical RBF beamforming
A powerful symmetrical radial basis function (RBF) aided detector is proposed for nonlinear detection in so-called rank-deficient multipleantenna assisted beamforming systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, the proposed RBF detector becomes capable of approaching the optimal Bayesian detection performance using channel-impaired training data. A novel nonlinear least bit error algorithm is derived for adaptive training of the symmetrical RBF detector based on a stochastic approximation to the Parzen window estimation of the detector output’s probability density function. The proposed adaptive solution is capable of providing a signal-to-noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmark, when supporting four users with the aid of two receive antennas or seven users employing four receive antenna elements.
Radial basis function network, Symmetry, Probability density function, Recursive learning, Stochastic algorithm, Multiple-antenna system, Beamforming
358-367
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Wolfgang, Andreas
029e48ea-f4a9-4334-8e9f-1d4b9fe1b0e3
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
1 February 2008
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Wolfgang, Andreas
029e48ea-f4a9-4334-8e9f-1d4b9fe1b0e3
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, Sheng, Wolfgang, Andreas, Harris, Chris J. and Hanzo, Lajos
(2008)
Adaptive nonlinear least bit error-rate detection for symmetrical RBF beamforming.
Neural Networks, 21 (2-3), .
(doi:10.1016/j.neunet.2007.12.014).
Abstract
A powerful symmetrical radial basis function (RBF) aided detector is proposed for nonlinear detection in so-called rank-deficient multipleantenna assisted beamforming systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, the proposed RBF detector becomes capable of approaching the optimal Bayesian detection performance using channel-impaired training data. A novel nonlinear least bit error algorithm is derived for adaptive training of the symmetrical RBF detector based on a stochastic approximation to the Parzen window estimation of the detector output’s probability density function. The proposed adaptive solution is capable of providing a signal-to-noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmark, when supporting four users with the aid of two receive antennas or seven users employing four receive antenna elements.
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Accepted/In Press date: 11 December 2007
Published date: 1 February 2008
Keywords:
Radial basis function network, Symmetry, Probability density function, Recursive learning, Stochastic algorithm, Multiple-antenna system, Beamforming
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 265323
URI: http://eprints.soton.ac.uk/id/eprint/265323
PURE UUID: 8c810f59-bfbf-4e01-8261-609c3a881f65
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Date deposited: 14 Mar 2008 10:28
Last modified: 18 Mar 2024 02:34
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Contributors
Author:
Sheng Chen
Author:
Andreas Wolfgang
Author:
Chris J. Harris
Author:
Lajos Hanzo
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