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Analysis of a Neural Detector Based on Self-Organizing Map in a 16 QAM System
Hua LIN Xiaoqiu WANG Jianming LU Takashi YAHAGI
Publication
IEICE TRANSACTIONS on Communications
Vol.E84-B
No.9
pp.2628-2634 Publication Date: 2001/09/01 Online ISSN:
DOI: Print ISSN: 0916-8516 Type of Manuscript: PAPER Category: Communication Devices/Circuits Keyword: self-organizing map, adaptive equalizer, QAM system, nonlinear distortion, LMS equalizer,
Full Text: PDF>>
Summary:
A signal suffers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, novel equalizer structures utilizing neural computation have been developed for compensating for nonlinear channel distortion. In this paper, we propose a neural detector based on self-organizing map (SOM) in a 16 QAM system. The proposed scheme uses the SOM algorithm and symbol-by-symbol detector to form a neural detector, and it adapts well to the changing channel conditions, including nonlinear distortions because of the topology-preserving property of the SOM algorithm. According to the theoretical analysis and computer simulation results, the proposed scheme is shown to have better performance than traditional linear equalizer when facing with nonlinear distortion.
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