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Adaptive nonlinear least bit error-rate detection for symmetric RBF beamforming

Adaptive nonlinear least bit error-rate detection for symmetric RBF beamforming
Adaptive nonlinear least bit error-rate detection for symmetric 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.
358-367
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Wolfgang, A.
e87811dd-7028-4ac3-90cc-62003ff22202
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Wolfgang, A.
e87811dd-7028-4ac3-90cc-62003ff22202
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1

Chen, S., Wolfgang, A., Harris, C.J. and Hanzo, L. (2008) Adaptive nonlinear least bit error-rate detection for symmetric RBF beamforming. Neural Networks : the official journal of the International Neural Network Society, 21 (2-3), 358-367.

Record type: Article

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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Published date: March 2008
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 265323
URI: https://eprints.soton.ac.uk/id/eprint/265323
PURE UUID: 8c810f59-bfbf-4e01-8261-609c3a881f65
ORCID for L. Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 14 Mar 2008 10:28
Last modified: 25 Oct 2019 00:39

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