The University of Southampton
University of Southampton Institutional Repository

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

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.
0893-6080
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
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), 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.

Text
NN2008-21 - Version of Record
Restricted to Repository staff only
Request a copy

More information

Published date: 1 February 2008

Identifiers

Local EPrints ID: 448364
URI: http://eprints.soton.ac.uk/id/eprint/448364
ISSN: 0893-6080
PURE UUID: f87ad25f-c0f0-45d6-9628-f901a75ddb99
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 21 Apr 2021 16:31
Last modified: 05 May 2021 16:31

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×