2008 Special Issue: Adaptive nonlinear least bit error-rate detection for symmetrical RBF beamforming

  • Authors:
  • S. Chen;A. Wolfgang;C. J. Harris;L. Hanzo

  • Affiliations:
  • School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK

  • Venue:
  • Neural Networks
  • Year:
  • 2008

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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.