본문 바로가기
HOME> 논문 > 논문 검색상세

논문 상세정보

Theoretical Derivation of Minimum Mean Square Error of RBF based Equalizer

Lee Jung-Sik    (School of Electronics & Information Eng., Kunsan National University  );
  • 초록

    In this paper, the minimum mean square error(MSE) convergence of the RBF equalizer is evaluated and compared with the linear equalizer based on the theoretical minimum MSE. The basic idea of comparing these two equalizers comes from the fact that the relationship between the hidden and output layers in the RBF equalizer is also linear. As extensive studies of this research, various channel models are selected, which include linearly separable channel, slightly distorted channel, and severely distorted channel models. In this work, the theoretical minimum MSE for both RBF and linear equalizers were computed, compared and the sensitivity of minimum MSE due to RBF center spreads was analyzed. It was found that RBF based equalizer always produced lower minimum MSE than linear equalizer, and that the minimum MSE value of RBF equalizer was obtained with the center spread which is relatively higher(approximately 2 to 10 times more) than variance of AWGN. This work provides an analytical framework for the practical training of RBF equalizer system.


  • 주제어

    equalizer .   linear channel .   RBF .   neural network.  

  • 참고문헌 (12)

    1. G.J. Gibson, S.Siu, and C.F.N. Cowan, 'Application of Multilayer Perceptrons as Adaptive Channel Equalizers,' IEEE Int. Conf. Acoust. Speech, Signal Processing, Glasgow, Scotland, pp. 1183-1186, 1989 
    2. P. R Chang and B. C. Wang 'Adaptive Decision Feedback Equalization for Digital Channels using Multilayer Neural Networks,' IEEE J. Selected Areas Commun., Vol. 13, pp.316-324, Feb. 1995 
    3. K. A. Al-Mashouq, I. S. Reed, ''The Use of Neural Nets to Combine Equalization with Decoding for Severe Intesymbol Interference Channels,' IEEE Trans. Neural Networks, Vol.5, pp..982-988, Nov. 1994 
    4. S. Haykin, Adpative Filter Theory, third edition, Prentice Hall, 1996 
    5. B. Mulgrew, 'Applying Radial Basis Functions,' IEEE Sig. Proc. Mag., pp.50-65, Mar. 1996 
    6. J. Lee, A Radial Basis Function Equalizer with Reduced Number of Centers, Ph.D. dissertation, Florida Institute of Technology, 1996 
    7. S. Chen, B. Mulgrew, and P. M Grant, 'A Clustering Technique for Digital Communication Channel Equalization using Radial Basis Function Networks,' IEEE Trans. Neural Networks, Vol.4, pp.570-579, Jill. 1993 
    8. S. K. Patra and B. Mulgrew, 'Computational Aspects of Adaptive Radial Basis Function Equalizer Design,' IEEE Int. Symposium on Circuits and Systems, Hong Kong. pp.521-524, Jun. 1997 
    9. J. Lee, C.B. Beach, and N. Tepedelenlioglu, 'A Practical Radial Basis Function Equalizer,' IEEE Trans. Neural Networks, Vol.10, pp.450-455, Mar. 1991 
    10. M Ibnkahla, 'Applications of Neural Networks to Digital Communications- a Survey,' Signal Processing, pp. 1185-1215, Mar. 1999 
    11. J. G. Proakis, Digital Communications, third edition, McGraw Hill, 1995 
    12. E. Lee and D. Messerschmitt, Digital Communication, second edition, Springer, 1993 

 활용도 분석

  • 상세보기

    amChart 영역
  • 원문보기

    amChart 영역

원문보기

무료다운로드
  • NDSL :
유료다운로드

유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.

원문복사신청을 하시면, 일부 해외 인쇄학술지의 경우 외국학술지지원센터(FRIC)에서
무료 원문복사 서비스를 제공합니다.

NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.

이 논문과 함께 출판된 논문 + 더보기