Skip to main content
SpringerLink
Log in

Fractionally spaced equalizer based on dynamically varying modulus algorithm for spectrally efficient channel compensation in SC-FDMA based systems

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Orthogonal frequency division multiplexing (OFDM) based wireless communication systems are expected to satisfy the thirst for ever increasing demand on higher spectral efficiency. But, OFDM systems suffer from peak to average power ratio (PAPR) and inter carrier interference (ICI) problems. It is observed that when OFDM is used in the uplink, PAPR problem is more severe and the relative mobility of the user equipments with respect to the base station will cause Doppler spread which leads to ICI. One of the solutions to minimize PAPR and ICI is single carrier frequency division multiple access. But there is a tradeoff in spectral efficiency. The main objective of this paper is to evaluate the performance of fractionally spaced equalizer (FSE) for blind channel estimation based on higher order statistics and to identify any better alternative to improve its performance. Dynamically varying modulus algorithm (DVMA) based FSE is proposed in this paper which is a better alternative for supervised equalization. The simulation results prove that FSE blind equalizer based on DVMA outperform the conventional supervised and blind equalizers.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Abbreviations

GSM TDMA:

Global system for mobile communication time division multiple access

WCDMA:

Wideband code division multiple access

LTE:

Long term evolution

LTE-A:

Long term evolution-advanced

Wi-MAX:

Worldwide interoperability for microwave access

ISI:

Inter symbol interference

DL:

Downlink

CP:

Cyclic prefix

AMC:

Adaptive modulation and coding

MIMO:

Multi input and multi output

QAM:

Quadrature amplitude modulation

LS:

Least square

MMSE:

Minimum mean square error

RLS:

Recursive least square

LMS:

Least mean square

NLMS:

Normalized least mean square

SER:

Symbol error rate

CFO:

Carrier frequency offset

AWGN:

Additive white gaussian noise

References

  1. van Nee, R., & Prasad, R. (2000). OFDM for wireless multimedia communications. London: Artech House Publishers.

    Google Scholar 

  2. Sesia, S., Toufik, I., & Baker, M. (2009). LTE—The UMTS long term evolution from theory to practice. West Sussex, UK: Wiley.

    Book  Google Scholar 

  3. Cho, Y. S., Kim, J., Yang, W. Y., & Kang, C. G. (2010). MIMO-OFDM wireless communications with matlab. Singapore: Wiley.

    Book  Google Scholar 

  4. Myung, H. G., Lim, J., & Goodman, D. J. (2007). Single carrier FDMA for uplink wireless transmission IEEE. Vehicular Technology Magazine, 1(3), 9–17.

    Google Scholar 

  5. Masud Rana, Md., Kim, J., Cho, W. K. (2010). Training based channel estimation scheme for LTE SC-FDMA Systems Using LMS algorithm. Proceedings of International Conference on ICCEE.

  6. Lau, H. K., & Cheung, S. W. (1994). A pilot symbol-aided technique used for digital signals in multipath environments. IEEE ICC, 2, 1126–1130.

    Google Scholar 

  7. Temino, L.A.M.R.D., Manchon, C.N.I., Rom, C., Sorensen, T.B., Mogensen, P. (2008). Iterative channel estimation with robust wiener filtering in LTE downlink. Proceedings of International Conference on VTC, 1–5.

  8. Dimitris, G., & Manolakis, D. G. (2000). Statistical and adaptive signal processing. London: Artech House.

    Google Scholar 

  9. Haykin, S. (1996). Adaptive filter theory. Upper Saddle River, NJ: Prentice-Hall.

    Google Scholar 

  10. Yameogo, S., Palicot, J., Cariou, L. (2009).Blind time domain equalization of SC-FDMA signal. Proc. Vehicular Technology Conference, 1–4.

  11. Zeng, W. J., Li, X., Cheng, E. (2011).Blind and semi blind channel estimation for single carrier block transmission systems using few received blocks. IEEE International Conference on Communications (ICC), 1–5.

  12. Lyman, R.J., Edmonson,W.W.(1999). Decision-directed tracking of fading channels using linear prediction of the fading envelope. 33rd Asilomar Conference on Signal Processing, Systems, and Computers, 2, 1154–1158.

  13. Ran, J. J., Grunbeid, R., Rohling, H., et al. (2003). Decision-directed channel estimation method for OFDM systems with high velocities. IEEE VTC, 4, 2358–2361.

    Google Scholar 

  14. Xinmin, D., Haimovich, A. M., & Garcia-Frias, J. (2003). Decision-directed iterative channel estimation for MIMO systems. IEEE ICC, 4, 2326–2329.

    Google Scholar 

  15. Daofeng, X. Luxi, Y. (2004). Channel estimation for OFDM systems using superimposed training. International Conference in Central Asia on Internet, 26–29.

  16. Hoeher, P., Tufvesson, F. (1999). Channel estimation with superimposed pilot sequence. IEEEGLOBECOM, 2162–2166.

  17. Godard, D. N. (1980). Self-recovering equalization and carrier tracking in two-dimensional data communication systems. IEEE Transactions on Communications, 28(11), 1867–1875.

    Article  Google Scholar 

  18. Sato, Y. (1975). A method of self-recovering equalization for multilevel amplitude-modulation systems. IEEE Transactions on Communications, 23(6), 679–682.

    Article  Google Scholar 

  19. Johnson, R., Schniter, P., Endres J. T., et al. (1998). Blind equalization using the constant modulus criterion: A Review, IEEE, 86, 1927–1950.

  20. Rana, M. M., & Kim, J. (2011). LMS based blind channel estimation of SC-FDMA systems using variable step size and phase information. Electronics Letters, 47(5), 346–348.

    Article  Google Scholar 

  21. Mountassir, j., Nafornita, M. (2012). A blind channel estimation Technique based on denoising for LTE downlink and uplink system. Transactions on Electronics and Communications, 57(71).

  22. Bolcskei, H., Heath, R. W., Paulraj, A. J., et al. (2002). Blind channel identification and equalization in OFDM based multi antenna systems. IEEE Transactions on Signal Processing, 50(1), 96–109.

    Article  Google Scholar 

  23. Jiang, Du, Peng, Qicong, & Zhang, Hongying. (2003). Adaptive blind channel identification and equalization for MIMO wireless communication systems. Pearson, Indoor and Mobile Radio Communications, 3, 2078–2082.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics Engineering, VIT University, Vellore, Tamil Nadu, India

    K. Vinoth Babu, G. Ramachandra Reddy & J. Arun Prakash

Authors
  1. K. Vinoth Babu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Ramachandra Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Arun Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Vinoth Babu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vinoth Babu, K., Ramachandra Reddy, G. & Arun Prakash, J. Fractionally spaced equalizer based on dynamically varying modulus algorithm for spectrally efficient channel compensation in SC-FDMA based systems. Wireless Netw 20, 1387–1398 (2014). https://doi.org/10.1007/s11276-013-0678-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-013-0678-6

Keywords

Search

Navigation