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Nonlinear beamforming for multiple-antenna assisted QPSK wireless systems

Nonlinear beamforming for multiple-antenna assisted QPSK wireless systems
Nonlinear beamforming for multiple-antenna assisted QPSK wireless systems
A nonlinear beamforming aided detector is proposed for multiple-antenna assisted quadrature phase shift keying systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a symmetric radial basis function (SRBF) detector is developed which is capable of approaching the
optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be implemented effectively by estimating the channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variation enhanced
clustering algorithm to directly identify the SRBF centre vectors required for realising the optimal Bayesian detector.
4230-4234
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Tan, Shuang
e7f739b4-0aa4-494f-ac0d-cd000d10c576
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Tan, Shuang
e7f739b4-0aa4-494f-ac0d-cd000d10c576

Chen, Sheng, Hanzo, Lajos and Tan, Shuang (2008) Nonlinear beamforming for multiple-antenna assisted QPSK wireless systems. In IEEE International Conference on Communications. pp. 4230-4234 .

Record type: Conference or Workshop Item (Paper)

Abstract

A nonlinear beamforming aided detector is proposed for multiple-antenna assisted quadrature phase shift keying systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a symmetric radial basis function (SRBF) detector is developed which is capable of approaching the
optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be implemented effectively by estimating the channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variation enhanced
clustering algorithm to directly identify the SRBF centre vectors required for realising the optimal Bayesian detector.

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Published date: 19 May 2008

Identifiers

Local EPrints ID: 452320
URI: http://eprints.soton.ac.uk/id/eprint/452320
PURE UUID: 82567bf2-523d-4306-a6ea-ebf5586323a0
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 07 Dec 2021 17:31
Last modified: 25 Jan 2022 02:31

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Contributors

Author: Sheng Chen
Author: Lajos Hanzo ORCID iD
Author: Shuang Tan

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