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Blind Neural Equalizer using Higher-Order Statistics
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  • Blind Neural Equalizer using Higher-Order Statistics
  • Blind Neural Equalizer using Higher-Order Statistics
저자명
Lee. Jung-Sik
간행물명
International journal of fuzzy logic and intelligent systems
권/호정보
2002년|2권 3호|pp.174-178 (5 pages)
발행정보
한국지능시스템학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

This paper discusses a blind equalization technique for FIR channel system, that might be minimum phase or not, in digital communication. The proposed techniques consist of two parts. One is to estimate the original channel coefficients based on fourth-order cumulants of the channel output, the other is to employ RBF neural network to model an inverse system fur the original channel. Here, the estimated channel is used as a reference system to train the RBF. The proposed RBF equalizer provides fast and easy teaming, due to the structural efficiency and excellent recognition-capability of R3F neural network. Throughout the simulation studies, it was found that the proposed blind RBF equalizer performed favorably better than the blind MLP equalizer, while requiring the relatively smaller computation steps in tranining.