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QAM equalization and symbol detection in OFDM systems using extreme learning machine

  • Extreme Learning Machine’s Theory & Application
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Abstract

This paper presents a new learning-based framework to jointly solve equalization and symbol detection problems in orthogonal frequency division multiplexing systems with quadrature amplitude modulation. The framework utilizes extreme learning machine (ELM), a recent addition to the class of supervised learning algorithms, to achieve fast training, high performance, and low error rates. The proposed ELM scheme employs infinitely differentiable nonlinear activation functions in least-square solution to learn the channel response, which is the equalization part. In addition to equalization, ELM performs symbol detection. Existing learning-based schemes require an additional symbol slicer for the symbol detection. The proposed framework does not experience training bottleneck imposed by gradient descent–based approaches. Simulation results show that the proposed framework outperforms other learning-based equalizers in terms of symbol error rate and training speeds.

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References

  1. Zhao Y, Haggman S (2001) Intercarrier interference self cancelation scheme for OFDM mobile communication systems. IEEE Trans Commun 49:1185–1191

    Article  MATH  Google Scholar 

  2. Piazzo L, Mandarini P (2002) Analysis of phase noise effects in OFDM modems. IEEE Trans Commun 50:1696–1705

    Article  Google Scholar 

  3. Shentu J, Panta K, Armstrong J (2003) Effects of phase noise on performance of OFDM systems using an ICI cancelation scheme. IEEE Trans Broadcasting 49:221–224

    Article  Google Scholar 

  4. Fazel K, Kaiser S (2003) Multi-carrier and spread spectrum systems. Wiley, England

    Book  Google Scholar 

  5. Bahai ARS, Saltzberg BR, Ergen M (2004) Multi carrier digital communications: theory and applications of OFDM. Springer, New York

    Google Scholar 

  6. Panayirci E, Senil H, Poor HV (2010) Joint channel estimation, equalization, and data detection for OFDM systems in the presence of very high mobility. IEEE Trans Signal Process 58:4225–4238

    Article  MathSciNet  Google Scholar 

  7. Muck M, de Courville M, Duhamel P (2006) A pseudorandom postfix OFDM modulator-semi-blind channel estimation and equalization. IEEE Trans Signal Process 54:1005–1017

    Article  Google Scholar 

  8. Wu C, Shiue M, Wang C (2010) Joint carrier synchronization and equalization algorithm for packet-based OFDM systems over the multipath fading channel. IEEE Trans Vehicular Technol 59:248–260

    Article  Google Scholar 

  9. Barhumi I, Leus G, Moonen M (2006) Equalization for OFDM over doubly selective channels. IEEE Trans Signal Process 54:1445–1458

    Article  Google Scholar 

  10. Cui T, Tellambura C (2006) Joint data detection and channel estimation for OFDM systems. IEEE Trans Commun 54:670–679

    Article  Google Scholar 

  11. Wu H, Huang X, Wu Y, Wang X (2008) Theoretical studies and efficient algorithm of semi-blind ICI equalization for OFDM. IEEE Trans Wireless Commun 7:3791–3798

    Article  Google Scholar 

  12. Gao F, Nallanathan A (2007) Blind channel estimation for OFDM systems via a generalized precoding. IEEE Trans Vehicular Technol 56:1155–1164

    Article  Google Scholar 

  13. Doukopoulos XG, Moustakides GV (2006) Blind adaptive channel estimation in OFDM systems. IEEE Trans Wireless Commun 5:1716–1725

    Article  Google Scholar 

  14. Rugini L, Banelli P, Leus G (2005) Simple equalization of time-varying channels for OFDM. IEEE Commun Lett 9:619–621

    Article  Google Scholar 

  15. Proakis JG (1989) Digital communication, 2nd edn. McGraw-Hill, New York

    Google Scholar 

  16. Haykin S (1998) Digital communication. Wiley, New York

    Google Scholar 

  17. Kechriotis G, Zervas E, Manolakos ES (1994) Using recurrent neural networks for adaptive communication channel equalization. IEEE Trans Neural Netw 5:267–277

    Article  Google Scholar 

  18. Kim T, Adali T (2003) Approximation by fully complex multilayer perceptrons. Neural Comput 15:1641–1666

    Article  MATH  Google Scholar 

  19. Kim T, Adali T (2001) Complex backpropagation neural network using elementary transcendental activation functions. In: Proceedings of IEEE international conference on acoustics, speech, signal process (ICASSP), Salt Lake City, pp 1281–1284

  20. Jianping D, Sundararajan N, Saratchandran P (2002) Communication channel equalization using complex-valued minimal radial basis function neural networks. IEEE Trans Neural Netw 13:687–696

    Article  Google Scholar 

  21. Cha I, Kassam SA (1995) Channel equalization using adaptive complex radial basis function networks. IEEE J Sel Area Commun 13:122–131

    Article  Google Scholar 

  22. Li M, Huang G, Saratchandran P, Sundararajan N (2005) Fully complex extreme learning machine. Neurocomputing 68:306–314

    Article  Google Scholar 

  23. Moustafa M, El-Ramly S (2009) Channel estimation and equalization using backpropagation neural networks in OFDM systems. In: Proceedings of IFIP international conference on wireless and optical communcation networks, Cairo, Egypt, pp 1–4

  24. Lerkvaranyu S, Dejhan K, Miyanaga Y (2004) M-QAM demodulation in an OFDM system with RBF neural network. In: Proceedings of 47th midwest symposium on circuits and systems (MWSCAS), Hiroshima, Japan, pp II-581-II-584

  25. Raivio K, Henriksson J, Simula O (1998) Neural detection of QAM signal with strongly nonlinear receiver. Neurocomputing 21:159–171

    Article  MATH  Google Scholar 

  26. Huang GB, Zhu QY, Siew CK (2005) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Article  Google Scholar 

  27. Huang GB, Wang DH, Yuan L (2011) Extreme learning machines: a survey. Int J Mach Learn Cyber 2:107–122

    Article  Google Scholar 

  28. Wang X, Lu J, Lin H, Zhang N, Sekiya H, Yahagi T (2002) Combining RNN Equalizer with SOM Detector. In: Proceedings of 6th international conference on signal processing (ICSP02), pp 1291–1294

  29. Harada H, Prasad R (2002) Simulation and software radio for mobile communication. Artech House, Boston

    Google Scholar 

  30. Mitchell T (1997) Machine learning. McGraw-Hill, USA

    MATH  Google Scholar 

  31. Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38:367–378

    Article  MATH  Google Scholar 

  32. Zhao G, Shen Z, Miao C, Man Z (2009) On improving the conditioning of extreme learning machine: a linear case. In: Proceedings of the 7th international conference on Information, communcation, and signal processing (ICICS2009), Macau, pp 1–5

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Acknowledgments

Authors would like to thank Dr. Q.M. Jonathan Wu and Dr. Rashid Minhas for their valuable discussions and help to improve the quality of this manuscript.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada

    Ishaq Gul Muhammad, Kemal E. Tepe & Esam Abdel-Raheem

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  1. Ishaq Gul Muhammad
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  2. Kemal E. Tepe
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  3. Esam Abdel-Raheem
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Correspondence to Ishaq Gul Muhammad.

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Muhammad, I.G., Tepe, K.E. & Abdel-Raheem, E. QAM equalization and symbol detection in OFDM systems using extreme learning machine. Neural Comput & Applic 22, 491–500 (2013). https://doi.org/10.1007/s00521-011-0796-y

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  • DOI: https://doi.org/10.1007/s00521-011-0796-y

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