Adaptive nonlinear least bit error-rate detection for symmetric RBF beamforming
Chen, S., Wolfgang, A., Harris, C.J. and Hanzo, L. (2008) Adaptive nonlinear least bit error-rate detection for symmetric RBF beamforming. Neural Networks, 21, (2-3), 358-367.
Available under License Creative Commons Public Domain Dedication.
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.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||14 Mar 2008 10:28|
|Last Modified:||02 Mar 2012 12:41|
|Contributors:||Chen, S. (Author)
Wolfgang, A. (Author)
Harris, C.J. (Author)
Hanzo, L. (Author)
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)