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    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
    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.
    Appears in Collections:[通訊工程學系] 期刊論文

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