Skip to main content
SpringerLink
Log in

Hybrid Type-2 Fuzzy Based Channel Estimation for MIMO-OFDM System with Doppler Offset Influences

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The channel estimation methods track and predict the variation in channel characteristics, so that the original signal can be obtained after nullifying the channel induced influences. The channel estimation methods impact the overall performance of the MIMO-OFDM system. When the communicating nodes are mobile, a complete estimation of the fast time varying channel is accomplished if the Doppler offset is evaluated along with the channel gain. However, most of the channel estimation approaches proposed in literature for MIMO-OFDM systems assume that the Doppler offset contributed by highly mobile communicating nodes is already known to the receiver. The estimation of the Doppler offset with the channel coefficients renders the channel estimation problem non linear. In this paper, the issue of this non linear channel estimation for high mobility communicating nodes with associated dynamic Doppler offset in a MIMO-OFDM system is addressed. In order to obtain complete information of the channel which includes the channel coefficients and the associated Doppler offsets, a hybrid interval type-2 fuzzy aided Kalman filter for channel estimation is proposed. The type-2 fuzzy based membership functions are used here opposed to the type-1 fuzzy membership functions because the type-2 fuzzy membership functions are capable of effective modeling even under high degree of uncertainties. Furthermore, a detailed computational complexity analysis of the proposed algorithm is presented which shows that the algorithm has moderate computational complexity and has good performance in fast time varying channel conditions with high node mobility in a MIMO-OFDM system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Kim, K., Pun, M., & Iltis, R. (2010). Joint carrier frequency offset and channel estimation for uplink MIMO-OFDMA systems using parallel Schmidt Rao-Blackwellized particle filters. IEEE Transactions on Communications, 58(9), 2697–2708.

    Article  Google Scholar 

  2. Simon, E., Ros, L., Hijazi, H., & Ghogho, M. (2012). Joint carrier frequency offset and channel estimation for OFDM systems via the EM algorithm in the presence of very high mobility. IEEE Transactions on Signal Processing, 603(2), 754–765.

    Article  MathSciNet  MATH  Google Scholar 

  3. Liu, Z., Ma, X., & Giannakis, G. B. (2002). Space–time coding and Kalman filtering for time-selective fading channel. IEEE Transactions on Communications, 50(2), 183–186.

    Article  Google Scholar 

  4. Komninakis, C., Fragouli, C., Sayed, A. H., & Wesel, R. D. (2002). Multi-input multi-output fading channel tracking and equalization using Kalman estimation. IEEE Transactions on Signal Processing, 50(5), 1065–1076.

    Article  Google Scholar 

  5. Min, C., Chang, N., Cha, J., & Kang, J. (2007). MIMO-OFDM downlink channel prediction for IEEE802.16e systems using Kalman filter. In Proceedings of the IEEE WCNC, Kowloon, Hongkong (pp. 942–946).

  6. Schafhuber, D., Matz, G., & Hlawatsch, F. (2003). Kalman tracking of time varying channels in wireless MIMO-OFDM systems. In Proceedings of the 36th Asilomar conference on signals, systems & computers (Vol. 2, pp. 1261–1265).

  7. Nevat, I., Peters, G. W., Doucet, A., & Yuan, J. (2014). Joint channel and Doppler offset estimation in dynamic cooperative relay networks. IEEE Transactions on Wireless Communications, 13(12), 6570–6579.

    Article  Google Scholar 

  8. Chen, B. S., Yang, C. Y., & Liao, W. J. (2012). Robust fast time-varying multipath fading channel estimation and equalization for MIMO-OFDM systems via a fuzzy method. IEEE Transactions on Vehicular Technology, 61(4), 1599–1609.

    Article  Google Scholar 

  9. Yang, L., & Shen, Q. (2013). Closed form fuzzy interpolation. Fuzzy Sets and Systems, 225, 1–22.

    Article  MathSciNet  MATH  Google Scholar 

  10. Shen, Q., & Yang, L. (2011). Generalisation of scale and move transformation-based fuzzy interpolation. Journal of Advanced Computational Intelligence and Intelligent Informatics, 15(3), 288–298.

    Article  Google Scholar 

  11. Chen, C., Quek, C., & Shen, Q. (2013). Scale and move transformation-based fuzzy rule interpolation with interval type-2 fuzzy sets. In Proceedings of the IEEE international conference on fuzzy systems (pp. 1–8).

  12. Chen, S., & Chang, Y. (2011). Fuzzy rule interpolation based on the ratio of fuzziness of interval type-2 fuzzy sets. Expert Systems with Applications, 38(10), 12202–12213.

    Article  Google Scholar 

  13. Chen, S., & Lee, L. (2011). Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on interval type-2 fuzzy sets. Expert Systems with Applications, 38(8), 9947–9957.

    Article  Google Scholar 

  14. Salmond, D. (2001). Target tracking: Introduction and Kalman tracking filters. Target Tracking: Algorithms and Applications, 2001(174), 1.

    Google Scholar 

  15. Shantaiya, S., et al. (2015). Multiple object tracking using Kalman filter and optical flow. European Journal of Advances in Engineering and Technology, 2(2), 34–39.

    Google Scholar 

  16. Mahfouz, S., et al. (2014). Target tracking using machine learning and Kalman filter in wireless sensor networks. IEEE Sensors Journal, 14(10), 3715–3725.

    Article  Google Scholar 

  17. Patel, H. A., & Thakore, D. G. (2013). Moving object tracking using Kalman filter. IJCSMC, 2(4), 326–332.

    Google Scholar 

  18. Haykin, S. (2002). Adaptive filter theory (4th ed.). Englewood Cliffs, NJ: Prentice-Hall.

    MATH  Google Scholar 

  19. Yang, L., Chen, C., Jin, N., Fu, X., & Shen, Q. (2014). Closed form fuzzy interpolation with interval type-2 fuzzy sets. In IEEE international conference on fuzzy systems (pp. 2184–2199).

  20. Mendel, J. M. (2001). Uncertain rule-based fuzzy logic systems: Introduction and new directions. Upper Saddle River, NJ: Prentice-Hall.

    MATH  Google Scholar 

  21. Hagras, H. (2004). A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Transactions on Fuzzy Systems, 12, 524–539.

    Article  Google Scholar 

  22. Hagras, H. (2007). Type-2 FLCs: A new generation of fuzzy controllers. IEEE Computational Intelligence Magazine, 2(1), 30–43.

    Article  Google Scholar 

  23. Wu, D., & Tan, W. W. (2006). Genetic learning and performance evaluation of type-2 fuzzy logic controllers. Engineering Applications of Artificial Intelligence, 19(8), 829–841.

    Article  Google Scholar 

  24. Castillo, O., & Melin, P. (2008). Type-2 fuzzy logic theory and applications. Berlin: Springer.

    Book  MATH  Google Scholar 

  25. Wu, D., & Tan, W. W. (2006). A simplified type-2 fuzzy controller for real-time control. ISA Transactions, 15(4), 503–516.

    Google Scholar 

  26. Liang, Q., & Mendel, J. M. (2000). Interval type-2 fuzzy logic systems: Theory and design. IEEE Transactions on Fuzzy Systems, 8(5), 535–550.

    Article  Google Scholar 

  27. Wu, D., & Mendel, J. M. (2009). Enhanced Karnik–Mendel algorithms. IEEE Transactions on Fuzzy Systems, 17(4), 923–934.

    Article  Google Scholar 

  28. Duran, K., Bernal, H., & Melgarejo, M. (2008). Improved iterative algorithm for computing the generalized centroid of an interval type-2 fuzzy set. In Proceedings of the NAFIPS, New York (pp. 1–5).

  29. Melgarejo, M. (2007). A fast recursive method to compute the generalized centroid of an interval type-2 fuzzy set. In Proceedings of the NAFIPS, SanDiego, CA (pp. 190–194).

  30. Wu, D., & Nie, M. (2011). Comparison and practical implementation of type-reduction algorithms for type-2 fuzzy sets and systems. In Proceedings of the IEEE international conference on fuzzy systems, Taipei, Taiwan (pp. 2131–2138).

  31. Yeh, C. Y., Jeng, W. H., & Lee, S. J. (2011). An enhanced type-reduction algorithm for type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 19(2), 227–240.

    Article  Google Scholar 

  32. Nie, M., & Tan, W. W. (2008). Towards an efficient type-reduction method for interval type-2 fuzzy logic systems. In IEEE international conference on fuzzy systems (IEEE world congress on computational intelligence), Hong Kong (pp. 1425–1432).

  33. Li, J., John, R., Coupland, S., & Kendall, G. (2017). On Nie–Tan operator and type-reduction of interval type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 26(2), 1036–1039.

    Article  Google Scholar 

  34. Mendel, J. M. (1971). Computational requirements of a discrete Kalman filter. IEEE Transactions on Automatic Control, 16(6), 748–758.

    Article  Google Scholar 

  35. Vaidehi, V., & Krishnan, C. N. (1998). Computational complexity of the Kalman tracking algorithm. IETE Journal of Research, 44(3), 125–134.

    Article  Google Scholar 

  36. Chandrasekar, J., Kim, I. S., & Bernstein, D. S. (2007). Reduced-order Kalman filtering for time-varying systems. In IEEE conference on decision and control (pp. 6214–6219). https://doi.org/10.1109/cdc.2007.4434882.

  37. Chandrasekar, J., Barerro, O., Moor, B. D., & Bernstein, D. S. (2007). Kalman filtering with constrained output injection. International Journal of Control. https://doi.org/10.1080/00207170701373633.

    MathSciNet  MATH  Google Scholar 

  38. Berberidis, D., & Giannakis, G. B. (2016). Data sketching for large-scale Kalman filtering. In IEEE international conference on acoustics, speech and signal processing (pp. 6195–6199). https://doi.org/10.1109/icassp.2016.7472868.

  39. Wu, D. (2013). Approaches for reducing the computational cost of interval type-2 fuzzy logic systems: Overview and comparisons. IEEE Transactions on Fuzzy Systems, 21(1), 80–99.

    Article  Google Scholar 

  40. 3GPP, Physical Layer Aspects for Evolved UTRA, TR 25.814, v7.0.0.

  41. Zhou, X., Lamahewa, T., & Sadeghi, P. (2009). Kalman filter-based channel estimation for amplify and forward relay communications. In Proceedings of the Asilomar conference on signals, systems and computers (pp. 1498–1502).

  42. Lindbom, L. (1993). Simplified Kalman estimation of fading mobile radio channels: High performance at LMS computational load. Proceedings of the ICASSP, 3, 352–355.

    Article  Google Scholar 

  43. Sun, X., Jin, L., & Xiong, M. (2008). Extended Kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks. PLoS ONE, 3(11), 3758.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Guru Nanak Dev University, Regional Campus, Jalandhar, India

    Harmandar Kaur

  2. Department of Electronics and Communication Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Mamta Khosla & Rakesh Kumar Sarin

Authors
  1. Harmandar Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mamta Khosla
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rakesh Kumar Sarin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harmandar Kaur.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, H., Khosla, M. & Sarin, R.K. Hybrid Type-2 Fuzzy Based Channel Estimation for MIMO-OFDM System with Doppler Offset Influences. Wireless Pers Commun 108, 1131–1143 (2019). https://doi.org/10.1007/s11277-019-06460-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06460-5

Keywords

Search

Navigation