한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제14권8호
  • /
  • Pages.965-980
  • /
  • 2011
  • /
  • 1229-7771(pISSN)
  • /
  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
권호 표지
DOI QR코드

DOI QR Code

Blind Channel Equalization Using Conditional Fuzzy C-Means

  • Han, Soo-Whan (Dept. of Multimedia Engineering, Dongeui University)
  • 투고 : 2011.02.25
  • 심사 : 2011.06.21
  • 발행 : 2011.08.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, the use of conditional Fuzzy C-Means (CFCM) aimed at estimation of desired states of an unknown digital communication channel is investigated for blind channel equalization. In the proposed CFCM, a collection of clustered centers is treated as a set of pre-defined desired channel states, and used to extract channel output states. By considering the combinations of the extracted channel output states, all possible sets of desired channel states are constructed. The set of desired states characterized by the maximal value of the Bayesian fitness function is subsequently selected for the next fuzzy clustering epoch. This modification of CFCM makes it possible to search for the optimal desired channel states of an unknown channel. Finally, given the desired channel states, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In a series of simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The experimental studies demonstrate that the performance (being expressed in terms of accuracy and speed) of the proposed CFCM is superior to the performance of the existing method exploiting the "conventional" Fuzzy C-Means (FCM).

키워드

과제정보

연구 과제 주관 기관 : Dongeui University

참고문헌

  1. E. Biglieri, A. Gersho, R.D. Gitlin, and T.L. Lim, "Adaptive Cancellation of Nonlinear Intersymbol Interference for Voiceband Data Transmission," IEEE J. Selected Areas Commun. SAC-2, Vol.5, pp.765-777, 1984.
  2. Z. Ding and L. Ye, Blind Equalization and Identification, Marcel Dekker, New York, 2001.
  3. H. Gazzah and K. A. Meraim, "Blind ZF Equalization with Controlled Delay Robust to Order Over Estimation," Signal Processing, Vol.83, pp.1505-1518, 2003. https://doi.org/10.1016/S0165-1684(03)00070-7
  4. J. Zhu, XR Cao and RW. Liu, "A Blind Fractionally Spaced Equalizer Using Higher Order Statistics," IEEE Trans. Circuits Syst. II: Analog Digital Signal Process, Vol.46, pp. 755-764, 1999. https://doi.org/10.1109/82.769783
  5. E. Serpedin and G.B. Giannakis, "Blind Channel Identification and Equalization with Modulation-Induced Cyclostationarity," IEEE Trans. Signal Processing, Vol.46, pp.1930- 1944, 1998. https://doi.org/10.1109/78.700965
  6. Y. Fang, W.S. Chow, and K.T. Ng, "Linear Neural Network Based Blind Equalization," Signal Processing, Vol.76, pp.37-42, 1999. https://doi.org/10.1016/S0165-1684(98)00245-X
  7. Yun Ye and Saman S. Abeysekera, "Efficient Blind Estimation and Equalization of Non- Minimum Phase Communication Channels Via the Use of a Zero Forcing Equalizer," Signal Processing, Vol.86, pp.1019-1034, 2006. https://doi.org/10.1016/j.sigpro.2005.07.021
  8. S. Mo and B. Shafai, "Blind Equalization Using Higher Order Cumulants and Neural Networks," IEEE trans. Signal Processing, Vol.42, pp.3209-3217, 1994. https://doi.org/10.1109/78.330378
  9. X. H. Dai, "CMA-Based Nonlinear Blind Equaliser Modelled by a Two-Layer Feedforward Neural Network," IEE Proceedings: Communications, Vol.148, pp.243-248, 2001. https://doi.org/10.1049/ip-com:20010269
  10. R. Pandey and J. K. Gautam, "Blind Eualization of Nonlinear Communication Channels Using Modified FLANN," IETE Journal of Research, Vol.48, pp.127-132, 2002. https://doi.org/10.1080/03772063.2002.11416267
  11. T. Stathaki and A. Scohyers, "A Constrained Optimization Approach to the Blind Estimation of Volterra Kernels," Proc. IEEE Int. Conf. on ASSP, Vol.3, pp.2373-2376, 1997.
  12. G.K. Kaleh and R. Vallet, "Joint Parameter Estimation and Symbol Detection for Linear or Nonlinear Unknown Channels," IEEE Trans. Commun., Vol.42, pp.2406-2413, 1994. https://doi.org/10.1109/26.297849
  13. D. Erdogmus, D. Rende, J.C. Principe, and T.F. Wong, "Nonlinear Channel Equalization Using Multilayer Perceptrons with Information the Oretic Criterion," Proc. of IEEE workshop Neural Networks and Signal Processing, MA, U.S.A., pp. 443-451, 2001.
  14. I. Santamaria, C. Pantaleon, L. Vielva and J. Ibanez, "Blind Equalization of Constant Modulus Signals Using Support Vector Machines," IEEE Trans. Signal Processing, Vol.52, pp.1773-1782, 2004. https://doi.org/10.1109/TSP.2004.827176
  15. R. Lopez-Valcarce and S. Dasgupta, "Blind Equalization of Nonlinear Channels from Second-Order Statistics," IEEE Transactions on Signal Processing, Vol.49, pp.3084-3097, 2001. https://doi.org/10.1109/78.969516
  16. G. Raz and B. Van Veen, "Blind Equalization and Identification of Nonlinear and IIR Systems-a Least Squares Approach," IEEE Trans. Signal Processing, Vol.48, pp.192-200, 2000. https://doi.org/10.1109/78.815489
  17. H. Lin and K. Yamashita, "Hybrid Simplex Genetic Algorithm for Blind Equalization Using RBF Networks," Mathematics and Computers in Simulation, Vol.59, pp.293-304, 2002. https://doi.org/10.1016/S0378-4754(01)00364-0
  18. S. Han, W. Pedrycz, and C. Han, "Nonlinear Channel Blind Equalization Using Hybrid Genetic Algorithm with Simulated Annealing," Mathematical and Computer Modeling, Vol.41, pp.697-709, 2005. https://doi.org/10.1016/j.mcm.2004.05.006
  19. S. Park and S. Han, "A Study on Blind Nonlinear Channel Equalization Using Modified Fuzzy C-Means," Journal of Korea Multimedia Society, Vol.10, No.10, pp.1284-1294, 2007.
  20. S. Han, I. Lee, and W. Pedrycz, "Modified Fuzzy C-Means and Bayesian Equalizer for Nonlinear Blind Channel," Applied Soft Computing, Vol.9, pp.1090-1096, 2009. https://doi.org/10.1016/j.asoc.2009.02.006
  21. W. Pedrycz, "Conditional Fuzzy Clustering in the Design of Radial Basis Function Neural Networks," IEEE Trans. Neural Networks, Vol.9, pp.601-612, 1998. https://doi.org/10.1109/72.701174
  22. K. Yoon, K. Kwak, and S. Kim, "Nonlinear Channel Equalization Using Fuzzy Clustering Adaptive Neuro-Fuzzy Filter," Journal of Korea Electronics Engineers Society, Vol.38, pp.35-41, 2001.
  23. R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973.
  24. S. Chen, B. Mulgrew, and S. McLaughlin, "Adaptive Bayesian Equalizer with Decision Feedback," IEEE Trans. Signal Processing, Vol.41, pp.2918-2927, 1993. https://doi.org/10.1109/78.236513
  25. S. Chen, B. Mulgrew, and M. Grant, "A Clustering Technique for Digital Communication Channel Equalization Using Radial Basis Function Network," IEEE Trans. Neural Networks, Vol.4, pp.570-579, 1993. https://doi.org/10.1109/72.238312
  26. H. Lin and K. Yamashita, "Blind Equalization Using Parallel Bayesian Decision Feedback Equalizer," Mathematics and Computers in Simulation, Vol.56, pp.247-257, 2001. https://doi.org/10.1016/S0378-4754(01)00276-2
  27. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
  28. S.K. Patra and B. Mulgrew, "Fuzzy Techniques for Adaptive Nonlinear Equalization," Signal Processing, Vol.80, pp.985-1000, 2000. https://doi.org/10.1016/S0165-1684(00)00015-3