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Minimum Probability of Error-Based Equalization Algorithms for Fading Channels


Novel channel equalizer algorithms are introduced for wireless communication systems to combat channel distortions resulting from multipath propagation. The novel algorithms are based on newly derived bounds on the probability of error (PE) and guarantee better performance than the traditional zero forcing (ZF) or minimum mean square error (MMSE) algorithms. The new equalization methods require channel state information which is obtained by a fast adaptive channel identification algorithm. As a result, the combined convergence time needed for channel identification and PE minimization still remains smaller than the convergence time of traditional adaptive algorithms, yielding real-time equalization. The performance of the new algorithms is tested by extensive simulations on standard mobile channels.



  1. Steele R, Hanzo L (Eds): Mobile Radio Communications. John Wiley & Sons, New York, NY, USA; 1999.

    Google Scholar 

  2. Gerstacker WH, Schober R: Equalization concepts for EDGE. IEEE Transactions on Wireless Communications 2002,1(1):190-199. 10.1109/7693.975457

    Article  Google Scholar 

  3. Proakis JG: Digital Communications. McGraw-Hill, New York, NY, USA; 1995.

    MATH  Google Scholar 

  4. Shimbo O, Celebiler MI: The probability of error due to intersymbol interference and Gaussian noise in digital communication systems. IEEE Transactions on Communications 1971,19(2):113-119. 10.1109/TCOM.1971.1090619

    Article  Google Scholar 

  5. Yeh C-C, Barry JR: Adaptive minimum bit-error rate equalization for binary signaling. IEEE Transactions on Communications 2000,48(7):1226-1235. 10.1109/26.855530

    Article  Google Scholar 

  6. Chen S, Mulgrew B, Hanzo L: Least bit error rate adaptive nonlinear equalisers for binary signalling. IEE Proceedings: Communications 2003,150(1):29-36. 10.1049/ip-com:20030284

    Article  Google Scholar 

  7. Chandra Kumar P, Saratchandran P, Sundararajan N: Minimal radial basis function neural networks for nonlinear channel equalisation. IEE Proceedings: Vision, Image and Signal Processing 2000,147(5):428-435. 10.1049/ip-vis:20000459

    Google Scholar 

  8. Bhatia V, Mulgrew B, Georgiadis ATh: Minimum BER DFE equalizer in alpha stable noise. Proceedings of the 12th European Signal Processing Conference (EUSIPCO '04), September 2004, Vienna, Austria

    Google Scholar 

  9. Yen RY, Cha MT, Young M-H: A stochastic unbiased minimum mean error rate algorithm for decision feedback equalizers. Proceedings of the 24th IASTED International Conference on Signal Processing, Pattern Recognition, and Applications (SPPRA '06), February 2006, Innsbruck, Austria 160-164.

    Google Scholar 

  10. Dua A, Desai UB, Mallik RK: Minimum probability of error-based methods for adaptive multiuser detection in multipath DS-CDMA channels. IEEE Transactions on Wireless Communications 2004,3(3):939-948. 10.1109/TWC.2004.827759

    Article  Google Scholar 

  11. Chen S, Samingan AK, Mulgrev B, Hanzo L: Adaptive minimum-BER linear multiuser detection. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '01), May 2001, Salt Lake City, Utah, USA 4: 2253-2256.

    Google Scholar 

  12. Hjørungnes A, Debbah M: Minimum BER FIR receiver filters for DS-CDMA systems. Proceedings of IEEE Global Telecommunications Conference (GLOBECOM '05), November-December 2005, St. Louis, Mo, USA 4: 2287-2291.

    Google Scholar 

  13. Dong B, Wang X: Sampling-based soft equalization for frequency-selective MIMO channels. IEEE Transactions on Communications 2005,53(2):278-288. 10.1109/TCOMM.2004.841996

    MathSciNet  Article  Google Scholar 

  14. Yu JX, Li Y, Luo Z-Q, Yoshida S: Split soft-decision equalization for wireless channels with large delay spread. IEEE Transactions on Communications 2005,53(2):269-277. 10.1109/TCOMM.2004.842000

    Article  Google Scholar 

  15. Ng SX, Yee M-S, Hanzo L: Coded modulation assisted radial basis function aided turbo equalization for dispersive Rayleigh-fading channels. IEEE Transactions on Wireless Communications 2004,3(6):2198-2206. 10.1109/TWC.2004.837410

    Article  Google Scholar 

  16. Laot C, Le Bidan R, Leroux D: Low-complexity MMSE turbo equalization: a possible solution for EDGE. IEEE Transactions on Wireless Communications 2005,4(3):965-974.

    Article  Google Scholar 

  17. Choi S, Lee T-W: A negentropy minimization approach to adaptive equalization for digital communication systems. IEEE Transactions on Neural Networks 2004,15(4):928-936. 10.1109/TNN.2004.828758

    Article  Google Scholar 

  18. Savazzi P, Favalli L, Costamagna E, Mecocci A: A suboptimal approach to channel equalization based on the nearest neighbor rule. IEEE Journal on Selected Areas in Communications 1998,16(9):1640-1648. 10.1109/49.737633

    Article  Google Scholar 

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Correspondence to Janos Levendovszky.

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Levendovszky, J., Kovacs, L. & van der Meulen, E.C. Minimum Probability of Error-Based Equalization Algorithms for Fading Channels. J Wireless Com Network 2007, 014683 (2007).

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  • Fading Channel
  • Channel State
  • Channel State Information
  • Minimum Mean Square Error
  • Adaptive Algorithm