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Combining Recurrent Neural Networks with Self-Organizing Map for Channel Equalization
Xiaoqiu WANG Hua LIN Jianming LU Takashi YAHAGI
IEICE TRANSACTIONS on Communications
Publication Date: 2002/10/01
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Communication Devices/Circuits
recurrent neural network (RNN), self-organizing map (SOM), real-time recurrent learning (RTRL), winning neuron, quadrature amplitude modulation (QAM),
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Recently, neural networks (NNs) have been extensively applied to many signal processing problem due to their robust abilities to form complex decision regions. In particular, neural networks add flexibility to the design of equalizers for digital communication systems. Recurrent neural network (RNN) is a kind of neural network with one or more feedback loops, whereas self-organizing map (SOM) is characterized by the formation of a topographic map of the input patterns in which the spatial locations (i.e., coordinates) of the neurons in the lattice are indicative of intrinsic statistical features contained in the input patterns. In this paper, we propose a novel receiver structure by combining adaptive RNN equalizer with a SOM detector under serious ISI and nonlinear distortion in QAM system. According to the theoretical analysis and computer simulation results, the performance of the proposed scheme is shown to be quite effective in channel equalization under nonlinear distortion.