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Symmetric self-constructing fuzzy neural network beamformers trained with cluster-based minimum bit-error rate method

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2 Author(s)
Yao-Jen Chang ; Department of Communication Engineering, National Central University, Chung-Li city, Taiwan ; Chia-Lu Ho

In this paper, a powerful symmetric self-constructing fuzzy neural network (S-SCFNN) beamformer is proposed for multi-antenna assisted systems. A novel training algorithm for the S-SCFNN beamformer is proposed based on partition of the array input signal space and a cluster-based minimum bit-error rate method. An inherent symmetric property of the array input signal space is exploited to make the training procedure of S-SCFNN more efficient compared to that of standard SCFNN. Simulation results demonstrate that the S-SCFNN beamformer provides superior performance to the classical linear and nonlinear ones, especially when supporting a large amount of users in the rank-deficient multi-antenna assisted system.

Published in:

Electronics and Information Engineering (ICEIE), 2010 International Conference On  (Volume:2 )

Date of Conference:

1-3 Aug. 2010