Adaptive nonlinear least bit error-rate detection for symmetrical RBF beamforming.
Neural Netw
; 21(2-3): 358-67, 2008.
Article
in En
| MEDLINE
| ID: mdl-18207699
ABSTRACT
A powerful symmetrical radial basis function (RBF) aided detector is proposed for nonlinear detection in so-called rank-deficient multiple-antenna 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Computer Communication Networks
/
Signal Processing, Computer-Assisted
/
Neural Networks, Computer
/
Nonlinear Dynamics
Type of study:
Diagnostic_studies
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Neural Netw
Journal subject:
NEUROLOGIA
Year:
2008
Document type:
Article
Affiliation country:
United kingdom
Publication country:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA