Skip to main content

Advertisement

Doubly Selective Channel Estimation Using Superimposed Training and Exponential Bases Models

Article metrics

  • 849 Accesses

  • 7 Citations

Abstract

Channel estimation for single-input multiple-output (SIMO) frequency-selective time-varying channels is considered using superimposed training. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). A periodic (nonrandom) training sequence is arithmetically added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission. A two-step approach is adopted where in the first step we estimate the channel using CE-BEM and only the first-order statistics of the data. Using the estimated channel from the first step, a Viterbi detector is used to estimate the information sequence. In the second step, a deterministic maximum-likelihood (DML) approach is used to iteratively estimate the SIMO channel and the information sequences sequentially, based on CE-BEM. Three illustrative computer simulation examples are presented including two where a frequency-selective channel is randomly generated with different Doppler spreads via Jakes' model.

References

  1. 1.

    Giannakis GB, Tepedelenlioglu C: Basis expansion models and diversity techniques for blind identification and equalization of time-varying channels. Proceedings of the IEEE 1998, 86(10):1969–1986. 10.1109/5.720248

  2. 2.

    Proakis JG: Digital Communications. 4th edition. McGraw-Hill, New York, NY, USA; 2001.

  3. 3.

    Tugnait JK, Tong L, Ding Z: Single-user channel estimation and equalization. IEEE Signal Processing Magazine 2000, 17(3):16–28.

  4. 4.

    Ma X, Giannakis GB, Ohno S: Optimal training for block transmissions over doubly selective wireless fading channels. IEEE Transactions on Signal Processing 2003, 51(5):1351–1366. 10.1109/TSP.2003.810304

  5. 5.

    Orozco-Lugo AG, Lara MM, McLernon DC: Channel estimation using implicit training. IEEE Transactions on Signal Processing 2004, 52(1):240–254. 10.1109/TSP.2003.819993

  6. 6.

    Tugnait JK, Luo W: On channel estimation using superimposed training and first-order statistics. IEEE Communications Letters 2003, 7(9):413–415. 10.1109/LCOMM.2003.817325

  7. 7.

    Zhou GT, Viberg M, McKelvey T: A first-order statistical method for channel estimation. IEEE Signal Processing Letters 2003, 10(3):57–60. 10.1109/LSP.2002.807864

  8. 8.

    Tugnait JK, Luo W: On channel estimation using superimposed training and first-order statistics. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '03), April 2003, Hong Kong 4: 624–627.

  9. 9.

    Baissas M-AR, Sayeed AM: Pilot-based estimation of time-varying multipath channels for coherent CDMA receivers. IEEE Transactions on Signal Processing 2002, 50(8):2037–2049. 10.1109/TSP.2002.800400

  10. 10.

    Barhumi I, Leus G, Moonen M: Time-varying FIR equalization for doubly selective channels. IEEE Transactions on Wireless Communications 2005, 4(1):202–214.

  11. 11.

    Leus G: Semi-blind channel estimation for rapidly time-varying channels. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), March 2005, Philadelphia, Pa, USA 3: 773–776.

  12. 12.

    Ma X, Giannakis GB: Maximum-diversity transmissions over doubly selective wireless channels. IEEE Transactions on Information Theory 2003, 49(7):1832–1840. 10.1109/TIT.2003.813485

  13. 13.

    Tsatsanis MK, Giannakis GB: Modeling and equalization of rapidly fading channels. International Journal of Adaptive Control & Signal Processing 1996, 10(2–3):159–176. 10.1002/(SICI)1099-1115(199603)10:2/3<159::AID-ACS346>3.0.CO;2-M

  14. 14.

    Farhang-Boroujeny B: Pilot-based channel identification: proposal for semi-blind identification of communication channels. Electronics Letters 1995, 31(13):1044–1046. 10.1049/el:19950739

  15. 15.

    Tugnait JK, Meng X: On superimposed training for channel estimation: performance analysis, training power allocation, and frame synchronization. IEEE Transactions on Signal Processing 2006, 54(2):752–765.

  16. 16.

    Schniter P: Low-complexity equalization of OFDM in doubly selective channels. IEEE Transactions on Signal Processing 2004, 52(4):1002–1011. 10.1109/TSP.2004.823503

  17. 17.

    Meng X, Tugnait JK: Superimposed training-based doubly-selective channel estimation using exponential and polynomial bases models. In Proceedings of the 38th Annual Conference on Information Sciences & Systems (CISS '04), March 2004, Princeton, NJ, USA. Princeton University;

  18. 18.

    Stoica P, Moses RL: Introduction to Spectral Analysis. Prentice-Hall, Englewood Cliffs, NJ, USA; 1997.

  19. 19.

    Porat B: Digital Processing of Random Signals. Prentice-Hall, Englewood Cliffs, NJ, USA; 1994.

  20. 20.

    Seshadri N: Joint data and channel estimation using blind trellis search techniques. IEEE Transactions on Communications 1994, 42(2–4, part 2):1000–1011.

  21. 21.

    Zheng YR, Xiao C: Simulation models with correct statistical properties for Rayleigh fading channels. IEEE Transactions on Communications 2003, 51(6):920–928. 10.1109/TCOMM.2003.813259

Download references

Author information

Correspondence to Jitendra K Tugnait.

Rights and permissions

Reprints and Permissions

About this article

Keywords

  • Quantum Information
  • Information Sequence
  • Channel Estimation
  • Training Sequence
  • Doppler Spread