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New Channel Estimation Method Using Singular Spectrum Analysis for OFDM Systems

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Abstract

This paper presents a new method for OFDM channel estimation (CE) using singular spectrum analysis (SSA). In this method, the conventional LMMSE procedure is used for CE and then SSA based optimum low rank approximation is performed on channel correlation matrix. Detailed simulations for bit error rate and mean square error have shown good performance for this algorithm compared to the well-known LSE, MMSE, and SVD based CE methods. In the proposed method, the SSA is also employed for denoising the signal aimed at further improvement in the performance of CE. SSA decomposes the received OFDM symbol into empirical orthogonal functions (EOFs). A de-noised signal is reconstructed using selective EOFs based on Pearson correlation coefficients. The proposed method has outperformed LSE and MMSE based methods and shown an average 2 dB improvements over SVD based method.

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References

  1. Prasad, R. (2004). OFDM for wireless communications systems. Norwood: Artech House, Inc. Publications.

    Google Scholar 

  2. Marchetti, N., Rahman, M. I., Kumar, S., & Prasad, R. (2009). OFDM: Principles and challenges. In V. Tarokh (Ed.), New directions in wireless communications research. Boston, MA: Springer.

    Google Scholar 

  3. Edfors, O., Sandell, M., van de Beek, J. J., Wilson, S. K., Börjesson, P. O., et al. (1997). OFDM channel estimation by singular value decomposition. IEEE Transactions on Communications, 46(7), 931–939.

    Article  Google Scholar 

  4. Dong, X., Lu, W. S., Soong, A. C. K., et al. (2007). Linear interpolation in pilot symbol assisted channel estimation for OFDM. IEEE Transactions on Wireless Communications, 6(7), 1910–1920.

    Article  Google Scholar 

  5. Qi, C., Wu, L., et al. (2012). A study of deterministic pilot allocation for sparse channel estimation in OFDM systems. IEEE Communications Letters, 16(5), 742–744.

    Article  Google Scholar 

  6. Dai, L., Wang, Z., Yang, Z., et al. (2013). Spectrally efficient time-frequency training OFDM for mobile large-scale MIMO systems. IEEE Journal on Selected Areas in Communications, 31(2), 251–263.

    Article  Google Scholar 

  7. Zhang, W., Xia, X. G., Ching, P. C., et al. (2006). Optimal training and pilot pattern design for OFDM systems in Rayleigh fading. IEEE Transactions on Broadcasting, 52(4), 505–514.

    Article  Google Scholar 

  8. Xie, H., Andrieux, G., Wang, Y., Diouris, J., Feng, S., et al. (2014). Efficient time domain threshold for sparse channel estimation in OFDM system. AEU-International Journal of Electronics and Communications, 68, 277–281.

    Article  Google Scholar 

  9. Van de Beek, J. J., Edfors, O., Sandell, M., et al. (2000). Analysis of DFT-based channel estimators for OFDM. Wireless Personal Communications, 12(1), 55–70.

    Article  Google Scholar 

  10. Pallaviram, S., Chandra Mohan, B., et al. (2015). A pilot aided channel estimator using DFT based time interpolator for massive MIMO-OFDM systems. AEU-International Journal of Electronics and Communications, 69, 321–327.

    Article  Google Scholar 

  11. Hoeher, P., & Tufvesson, F., et al. (1999). Channel estimation with superimposed pilot sequence. In IEEE global telecommunications conference (GLOBECOM’99) (pp. 2162–2166).

  12. Al-Naffouri, T., et al. (2007). An EM-based forward-backward Kalman filter for the estimation of time-variant channels in OFDM. IEEE Transactions on Signal Processing, 55(7), 3924–3930.

    Article  MathSciNet  MATH  Google Scholar 

  13. Liu, Y., Tan, Z., Hu, H., Cimini, L. J., Li, Y., et al. (2014). Channel estimation for OFDM. IEEE Communications Surveys & Tutorials, 16(4), 1891–1908.

    Article  Google Scholar 

  14. Mokhtar, B., Bouzidi, D. A., et al. (2015). New design of pilot patterns for joint semi-blind estimation of CFO and channel for OFDM systems. AEU-International Journal of Electronics and Communications, 69, 759–764.

    Article  Google Scholar 

  15. Liang, Y., Luo, H., Yan, C., & Huang, J., et al. (2006). Channel estimation using adaptive filters in MIMO-OFDM Systems. In International conference on wireless communications, networking and mobile computing, 2006 (WiCOM 2006) (pp. 1–4).

  16. Zhang, W., Gao, F., Yin, Q., et al. (2015). Blind channel estimation for MIMO-OFDM systems with low order signal constellation. IEEE Communications Letters, 19(3), 499–502.

    Article  Google Scholar 

  17. Beygiharchegani, S., Mitra, U., et al. (2015). Multi-scale multi-lag channel estimation using low rank approximation for OFDM. IEEE Transactions on Signal Processing, 63(18), 4744–4755.

    Article  MathSciNet  Google Scholar 

  18. Liu, H., Cong, W., Wang, L., Li, D., et al. (2017). Symbol error rate performance of nonlinear OFDM receiver with peak value threshold over frequency selective fading channel. AEU-International Journal of Electronics and Communications, 74, 163–170.

    Article  Google Scholar 

  19. Kari, D., et al. (2017). Robust adaptive algorithms for underwater acoustic channel estimation and their performance analysis. Digital Signal Processing, 68, 57–68.

    Article  Google Scholar 

  20. Pallaviram, S., & Chandra Mohan, B. (2017). A survey on OFDM channel estimation techniques based on denoising strategies. Engineering Science and Technology, an International Journal, 20, 629–636.

    Article  Google Scholar 

  21. Krishna, E. H., Sivani, K., & Reddy, K. A. (2014). On the use of singular spectrum analysis for OFDM channel estimation. In Proceedings of international conference on electronics and communication systems (ICECS), Coimbatore, India, 13–14 February, 2014 (pp. 1–4).

  22. Broomhead, D. S., King, G., et al. (1986). Extracting qualitative dynamics from experimental data. Physica D: Nonlinear Phenomena, 20, 217–236.

    Article  MathSciNet  MATH  Google Scholar 

  23. Broomhead, D. S., King, G., et al. (1986). On the qualitative analysis of experimental dynamical systems. In S. Sarkar (Ed.), Nonlinear phenomena and chaos (pp. 113–144). Bristol: Adam Hilger.

    Google Scholar 

  24. Allen, M., Smith, L. A., et al. (1997). Optimal filtering in singular spectrum analysis. Physics Letters, 234, 419–428.

    Article  Google Scholar 

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Authors and Affiliations

  1. University College of Engineering, KU, Kothagudem, Telangana, 507 101, India

    E. Hari Krishna

  2. Kakatiya Institute of Technology and Science, Warangal, Telangana, 506 015, India

    K. Sivani & K. Ashoka Reddy

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  1. E. Hari Krishna
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  2. K. Sivani
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  3. K. Ashoka Reddy
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Correspondence to K. Ashoka Reddy.

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Hari Krishna, E., Sivani, K. & Ashoka Reddy, K. New Channel Estimation Method Using Singular Spectrum Analysis for OFDM Systems. Wireless Pers Commun 101, 2193–2207 (2018). https://doi.org/10.1007/s11277-018-5811-5

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  • DOI: https://doi.org/10.1007/s11277-018-5811-5

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