Reduced symmetric self-constructing fuzzy neural network beamforming detectors

NCUIR > College of Electrical Engineering & Computer Science > Department of Communication Engineering > journal & Dissertation > Item 987654321/51524

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51524

Title:	Reduced symmetric self-constructing fuzzy neural network beamforming detectors
Authors:	Chang,YJ;Ho,CL
Contributors:	通訊工程學系
Keywords:	DECISION-FEEDBACK EQUALIZER;NONLINEAR CHANNEL EQUALIZER;ANTENNA-ARRAY;SYSTEMS;ALGORITHM;DESIGN
Date:	2011
Issue Date:	2012-03-27 18:55:16 (UTC+8)
Publisher:	國立中央大學
Abstract:	Beamforming technology has been widely used in smart antenna systems that can increase the user's capacity and coverage in modern communication products. In this study, a powerful reduced symmetric self-constructing fuzzy neural network (RS-SCFNN) beamforming detector is proposed for multi-antenna-assisted systems. A novel training algorithm for the RS-SCFNN beamformer is proposed based on clustering of array input vectors and an adaptive minimum bit-error rate method. An inherent symmetric property of the array input signal space is exploited to make training procedure of RS-SCFNN more efficient than that of standard SCFNN. In addition, the required amount of fuzzy rules can be greatly reduced in the RS-SCFNN structure. Simulation results demonstrate that RS-SCFNN beamformer provides superior performance to the classical linear ones and the other non-linear ones (including symmetric radial basis function, SCFNN and S-SCFNN), especially when supporting a large amount of users in the rank-deficient multi-antenna-assisted system.
Relation:	IET MICROWAVES ANTENNAS & PROPAGATION
Appears in Collections:	[Department of Communication Engineering] journal & Dissertation

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