Enhanced channel estimation in OFDM systems with neural network technologies

  • Chia-Hsin Cheng
  • Yao-Hung Huang
  • Hsing-Chung Chen
Methodologies and Application
  • 1 Downloads

Abstract

Orthogonal frequency division multiplexing (OFDM) provides an effective and low complexity means of eliminating inter-symbol interference for transmission over frequency selective fading channels. In OFDM systems, channel state information (CSI) is required for the OFDM receiver to perform coherent detection or diversity combining, if multiple transmit and receive antennas are deployed. In practice, CSI can be reliably estimated at the receiver by transmitting pilots along with data symbols. In this paper, we investigate and compare various efficient pilot-based channel estimation schemes by neural network technologies for OFDM systems. We present further the application of functional link neural fuzzy network (FLNFN) for channel estimation in the investigated OFDM systems. We compared bit error rates of the proposed neural network with that of the other neural network technologies, the least square (LS) algorithm, and the minimum mean square error (MMSE) algorithm. Our results demonstrate that the proposed FLNFN algorithm can enhance the performance of channel estimation in existing OFDM channel environments.

Keywords

Functional link neural fuzzy network OFDM Channel estimation Least square Minimum mean square error Back-propagation neural network 

Notes

Acknowledgements

This work was partially supported by the Ministry of Science and Technology (MOST), Taiwan, Republic of China, under Grant MOST 106-2632-E-468-003, and Asia University, Taiwan, and China Medical University Hospital, China Medical University, Taiwan (Grant No. ASIA-106-CMUH-04).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no competing interests.

References

  1. Amiri A, Niaki STA, Moghadam AT (2015) A probabilistic artificial neural network-based procedure for variance change point estimation. Soft Comput 19(3):691–700CrossRefGoogle Scholar
  2. Chen CH, Lin CJ, Lin CT (2007) A recurrent functional-link-based neural fuzzy system and its applications. In: IEEE symposium on computational intelligence in image and signal processing, (CIISP 2007), Honolulu, HI 1–5 April 2007, pp 415–420Google Scholar
  3. Chen CH, Lin CJ, Lin CT (2008) A functional-link-based neurofuzzy network for nonlinear system control. IEEE Trans Fuzzy Syst 16:1362–1378CrossRefGoogle Scholar
  4. Cheng CH, Cheng YP, Huang YH, Li WC (2013) Using back propagation neural network for channel estimation and compensation in OFDM systems. In: 2013 Seventh international conference on complex, intelligent, and software intensive systems, Taichung, 3–5, pp 340–345Google Scholar
  5. Cheng CH, Huang YF, Huang YH, Chen HC, Yao TY (2015) Neural network-based estimation for OFDM channels. In: Proceedings of 2015 IEEE 29th international conference on advanced information networking and applications (AINA), Gwangiu, South Korea, March 24–27, pp 600–604Google Scholar
  6. Cheng CH, Huang YH, Chen HC (2016) Channel estimation in OFDM systems using neural network technology combined with a genetic algorithm. Soft Comput 20(10):4139–4148CrossRefGoogle Scholar
  7. Cho YS, Kim J, Yang WY, Kang CG (2010) MIMO-OFDM wireless communications with MATLAB. Wiley (Asia) Pte Ltd, SingaporeCrossRefGoogle Scholar
  8. Coleri S, Ergen M, Puri A, Bahai A (2002) Channel estimation techniques based on pilot arrangement in OFDM systems. IEEE Trans Broadcast 48(3):223–229CrossRefGoogle Scholar
  9. Cui T, Tellambura C (2004) Channel estimation for OFDM systems based on adaptive radial basis function networks. In: IEEE 60th vehicular technology conference, 2004. VTC2004-Fall, 2004, pp 608–611Google Scholar
  10. Gao X, Dai L, Yuen C, Zhang Y (2014) Low-complexity MMSE signal detection based on Richardson method for large-scale MIMO systems. In: 2014 IEEE vehicular technology conference (VTC Fall), pp 1–5Google Scholar
  11. Godoy J, Lanbert A, Villagra J (2012) Development of an particle swarm algorithm for vehicle localization. In: Proceedings of IEEE intelligent vehicles symposium, Alcala de Henares, 3–7 June 2012, pp 1114–1119Google Scholar
  12. Heiskala J, Terry J (2001) OFDM wireless LANs: a theoretical and practical guide, 1st edn. Part of the Kaleidoscope series. SamsGoogle Scholar
  13. Ibrahim NK, Abdullah RSAR, Saripan MI (2009) Artificial neural network approach in radar target classification. J Comput Sci 5:23–32CrossRefGoogle Scholar
  14. Jang JSR, Tsai SC, Mizutani E (1997) Neural-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle RiverGoogle Scholar
  15. Jeremic A, Thomas TA, Nehorai A (2004) OFDM channel estimation in the presence of interference. IEEE Trans Signal Process 52(12):3429–3439MathSciNetCrossRefMATHGoogle Scholar
  16. Kang F, Xu Q, Li J (2016) Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Appl Math Model 40(11):6105–6120MathSciNetCrossRefGoogle Scholar
  17. Kang F, Liu J, Li J, Li S (2017) Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Struct Control Health Monit 24(10):e1997CrossRefGoogle Scholar
  18. Lin CT, Lee CSG (1996) Neural fuzzy system: a neuro-fuzzy synergism to intelligent systems. Prentice Hall, Upper Saddle RiverGoogle Scholar
  19. Lippmann R (1987) An introduction to computing with neural nets. IEEE ASSP Mag 4(2):4–22CrossRefGoogle Scholar
  20. Misra BB, Dehuri S (2007) Functional link artificial neural network for classification task in data mining. J Comput Sci 3(12):948–955CrossRefGoogle Scholar
  21. Nawaz SJ, Mohsin S, Ikaram AA (2009) Neural network based MIMO-OFDM channel equalizer using comb-type pilot arrangement. In: 2009 International conference on future computer and communication, Kuala Lumpur, pp 36–41Google Scholar
  22. Pao YH (1989) Adaptive pattern recognition and neural networks. Addison-WesleyGoogle Scholar
  23. Pao YH, Takefuji Y (1992) Functional-link net computing: theory, system architecture, and functionalities. Computer 25(5):76–79CrossRefGoogle Scholar
  24. Patra JC, Bornand C (2010) Nonlinear dynamic system identification using Legendre neural network. In: Proceedings of the 2010 international joint conference on neural networks (IJCNN), Barcelona, pp 1–7Google Scholar
  25. Patra JC, Pal RN (1995) Functional link artificial neural network-based adaptive channel equalization of nonlinear channels with QAM signal 1995. In: IEEE international conference on systems, man and cybernetics. Intelligent systems for the 21st century, vol 3, Vancouver, BC, 22–25 Oct 1995, pp 2081–2086Google Scholar
  26. Patra JC, Pal RN (1995) A functional link artificial neural network for adaptive channel equalization. Signal Process 43:181–195CrossRefMATHGoogle Scholar
  27. Ramezani F, Nikoo M, Nikoo M (2015) Artificial neural network weights optimization based on social-based algorithm to realize sediment over the river. Soft Comput 19(2):375–387CrossRefGoogle Scholar
  28. Ruan G, Tan Y (2010) A three-layer back-propagation neural network for spam detection using artificial Immune concentration. Softw Comput 14(2):139–150CrossRefGoogle Scholar
  29. Taşpinar N, Seyman MN (2010) Back propagation neural network approach for channel estimation in OFDM system. In: 2010 IEEE international conference on wireless communications, networking and information security, Beijing, China, pp 265–268Google Scholar
  30. Wu M, Yin B, Vosoughi A, Studer C, Cavallaro JR, Dick C (2013) Approximate matrix inversion for high-throughput data detection in the large-scale MIMO uplink. In: 2013 IEEE international symposium on circuits and systems (ISCAS), pp 2155–2158Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringNational Formosa UniversityYunlinTaiwan
  2. 2.Department of Computer Science and Information EngineeringAsia UniversityTaichungTaiwan
  3. 3.Department of Medical Research, China Medical University HospitalChina Medical UniversityTaichungTaiwan

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