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Adaptive Radial Basis Function Detector for Beamforming

Adaptive Radial Basis Function Detector for Beamforming
Adaptive Radial Basis Function Detector for Beamforming
We consider nonlinear detection in rank-deficient multiple-antenna assisted beamforming systems. By exploiting the inherent symmetry of the underlying optimal Bayesian detection solution, a symmetric radial basis function (RBF) detector is proposed and two adaptive algorithms are developed for training the proposed RBF detector. The first adaptive algorithm, referred to as the nonlinear least bit error, is a stochastic approximation to the Parzen window estimation of the detector output’s probability density function while the second algorithm is based on a clustering. The proposed adaptive solutions are capable of providing a signal to noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmarker, when supporting four users with the aid of two receive antennas or five users employing three antenna elements.
2967-2972
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Labib, K.
38d604a0-10c0-4b45-9203-9b65b3927750
Kang, R.
bda2bfc2-9b10-497d-98ef-c244d36e9392
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Labib, K.
38d604a0-10c0-4b45-9203-9b65b3927750
Kang, R.
bda2bfc2-9b10-497d-98ef-c244d36e9392
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1

Chen, S., Labib, K., Kang, R. and Hanzo, L. (2007) Adaptive Radial Basis Function Detector for Beamforming. IEEE ICC'07, United Kingdom. 24 - 28 Jun 2007. pp. 2967-2972 .

Record type: Conference or Workshop Item (Paper)

Abstract

We consider nonlinear detection in rank-deficient multiple-antenna assisted beamforming systems. By exploiting the inherent symmetry of the underlying optimal Bayesian detection solution, a symmetric radial basis function (RBF) detector is proposed and two adaptive algorithms are developed for training the proposed RBF detector. The first adaptive algorithm, referred to as the nonlinear least bit error, is a stochastic approximation to the Parzen window estimation of the detector output’s probability density function while the second algorithm is based on a clustering. The proposed adaptive solutions are capable of providing a signal to noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmarker, when supporting four users with the aid of two receive antennas or five users employing three antenna elements.

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More information

Published date: 2007
Additional Information: Event Dates: 24-28 June 2007
Venue - Dates: IEEE ICC'07, United Kingdom, 2007-06-24 - 2007-06-28
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 264241
URI: http://eprints.soton.ac.uk/id/eprint/264241
PURE UUID: 20d1acbc-c7a1-41cb-9650-be9f828c9b94
ORCID for L. Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 29 Jun 2007
Last modified: 12 Nov 2019 02:04

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Contributors

Author: S. Chen
Author: K. Labib
Author: R. Kang
Author: L. Hanzo ORCID iD

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