Skip to main content
Springer Nature Link
Log in

Adaptive Long-Range Prediction of Three-State LMS Channel Model

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In order to provide the accurate real-time knowledge of future channel state information for the adaptive transmission of narrowband land mobile satellite (LMS) communication systems, an adaptive long-range prediction (ALRP) method, suitable for a three-state LMS channel model at S-band, is proposed. The very slow shadowing conditions’ variations of satellite propagation channel can be described as a three-state Gilbert–Elliot channel model based on Markov chain. The channel state of future long-range is predicted based on weighting prediction while the parameters of linear auto-regression model are updated using an iterative adaptive tracking algorithm. The future channel fading series are predicted by the latest observations within current channel state. Simulation results show that the proposed method can be used to predict the future long-range channel state and fading series of three-state Gilbert–Elliot channel model accurately; and it is observed from the mean square error results that the prediction performance of the ALRP method is much more accurate than that of long-range prediction method. Moreover, the method has advantage of real-time and low-complexity which will make the adaptive transmission techniques feasible for LMS channel models.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Heidari, A., McAvoy, D., & Khandani, A.K. (2006). Adaptive long-range prediction of mobile fading. In Proceedings of the 23rd Biennial symposium on communications (pp. 219–222). Kingston.

  2. Duel-Hallen, A. (2007). Fading channel prediction for mobile radio adaptive transmission systems. Proceedings of the IEEE, 95(12), 2299–2313.

    Article  Google Scholar 

  3. Godwin, J.P., Loner, P.J., Williams, J.C. (2006) Adaptive transmission system. U.S. Patent 7,047,029. May 16, 2006.

  4. Loo, C. (1985). A statistical model for a land mobile satellite link. IEEE Transactions on Vehicular Technology, 34(3), 122–127.

    Article  Google Scholar 

  5. Corazza, G. E., & Vatalaro, F. (1994). A statistical model for land mobile satellite channels and its application to nongeostationary orbit systems. IEEE Transactions on Vehicular Technology, 43(3), 738–742.

    Article  Google Scholar 

  6. Lutz, E., Cygan, D., Dippold, M., et al. (1991). The land mobile satellite communication channel-recording, statistics, and channel model. IEEE Transactions on Vehicular Technology, 40(2), 375–386.

    Article  Google Scholar 

  7. Fontán, F. P., Vázquez-Castro, M., Cabado, C. E., et al. (2001). Statistical modeling of the LMS channel. IEEE Transactions on Vehicular Technology, 50(6), 1549–1567.

    Article  Google Scholar 

  8. Eyceoz, T., Duel-Hallen, A., & Hallen, H. (1998). Deterministic channel modeling and long range prediction of fast fading mobile radio channels. IEEE Communications Letters, 2(9), 254–256.

    Article  Google Scholar 

  9. Eyceoz, T., Hu, S., & Duee-Hallen, A. et al. (1999). “Adaptive prediction, tracking and power adjustment for frequency non-selective fast fading channels”, Communication Theory Mini-Conference, Vancouver: IEEE (pp. 1–5), June 1999.

  10. Duel-Hallen, A., Hu, S., & Hallen, H. (2000). Long-range prediction of fading signals. IEEE Signal Processing Magazine, 17(3), 62–75.

    Article  Google Scholar 

  11. Heidari, A., Khandani, A. K., & McAvoy D. Adaptive channel prediction system and method: U.S. Patent Application 13/558,906. July 26, 2012.

  12. Heidari, A., Khandani, A. K., & McAvoy, D. (2010). Adaptive modelling and long-range prediction of mobile fading channels. IET Communications, 4(1), 39–50.

    Article  Google Scholar 

  13. Schmidt, J. F., Cousseau, J. E., Wichman, R., et al. (2011). Low-complexity channel prediction using approximated recursive DCT. IEEE Transactions on Circuits and Systems I: Regular Papers, 58(10), 2520–2530.

    Article  MathSciNet  Google Scholar 

  14. Zhi-gang, Z. P. C. (2011). Markov process based satellite mobile channel model and long term prediction method (in Chinese). Journal of Electronics and Information Technology, 33(12), 2948–2953.

    Google Scholar 

  15. Zhou, J., Jiao, J., Yang, Z., et al. (2013). Research on Ka-band adaptive erasure codes in deep space communications (in Chinese). Journal of Astronautics, 34(1), 92–98.

    Google Scholar 

Download references

Acknowledgments

This paper is funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation, the China Postdoctoral Science Foundation funded project (2011M500640), and Postdoctoral Science Foundation of Heilongjiang Province (LBH-Z10206) and China Fundamental Research Funds for Central Universities (HEUCF130802).

Author information

Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Dan-feng Zhao, Xi Liao & Yang Wang

Authors
  1. Dan-feng Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xi Liao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yang Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xi Liao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, Df., Liao, X. & Wang, Y. Adaptive Long-Range Prediction of Three-State LMS Channel Model. Wireless Pers Commun 82, 745–756 (2015). https://doi.org/10.1007/s11277-014-2250-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-014-2250-9

Keywords