Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Particle Filtering for Joint Symbol Detection, Frequency Offset and Channel Estimation in Time-Varying MIMO Channels with Multiple Frequency Offsets

Abstract

We develop three methods for the joint symbol detection, frequency offset and channel estimation in time-varying multiple-input multiple-output channels with multiple frequency offsets between transmit and receive antennas. These methods are based on particle filtering. The first method utilizes the posterior proposal distribution (PD) to generate particles, which is optimal PD because it minimizes the variance of the importance weights, conditionally on the observations and past particles. Second, we develop an improved sampling strategy, which exploits the discrete nature of the symbol variable. The improved sampling strategy has same computational complexity as the posterior PD, while its performance is significantly improved. Finally, we derive a suboptimal complexity-reduced method, which utilizes the artificial sequential structure of the Bell-Labs layered space–time detection scheme to compress the sample space of symbol variable. Compared to the posterior PD, the computational complexity of the suboptimal method is largely reduced, while it still significantly outperforms the posterior PD. Simulation results are provided to illustrate the performance of these methods.

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

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

References

  1. 1.

    Foschini, G. J., & Gans, M. (1998). On limits of wireless communications in a fading environment when using multiple antennas. Wireless Personal Communications, 45(1), 311–335.

  2. 2.

    Besson, O., & Stoica, P. (2003). On parameter estimation of MIMO flat fading channels with frequency offsets. IEEE Transactions on Signal Processing, 51(3), 602–613.

  3. 3.

    Pham, T., Nallanathan, A., & Liang, Y. (2008). Joint channel and frequency offset estimation in distributed MIMO flat-fading channels. IEEE Transactions on Wireless Communications, 7(2), 648–656.

  4. 4.

    Tian, Y., Lei, X., Xiao, Y., & Li, S. (2010). SAGE based joint timing-frequency offsets and channel estimation in distributed MIMO systems. Computer Communications, 33, 2125–2131.

  5. 5.

    Zhang, J., Zheng, Y. R., Xiao, C., & Letaief, K. B. (2010). Channel equalization and symbol detection for single-carrier MIMO systems in the presence of multiple carrier frequency offsets. IEEE Transactions on Vehicular Technology, 59(4), 2021–2030.

  6. 6.

    Jamalabdollahi, M., & Salari, S. (2013). RLS-based estimation and tracking of frequency offset and channel coefficients in MIMO-OFDM systems. Wireless Personal Communications, 71(2), 1159–1174.

  7. 7.

    Luo, R., Yang, J., & Li, R. (2015). A new timing and frequency synchronization algorithm for distributed MIMO-OFDM systems. Wireless Personal Communications, 82(3), 1685–1696.

  8. 8.

    Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.

  9. 9.

    Cappé, O., Godsill, S. J., & Moulines, E. (2007). An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE, 95(5), 899–924.

  10. 10.

    Chen, R., Wang, X., & Liu, J. S. (2000). Adaptive joint detection and decoding in flat-fading channels via Mixture Kalman filtering. IEEE Transactions on Information Theory, 46(6), 2079–2094.

  11. 11.

    Komninakis, C., Fragouli, C., Sayeed, 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.

  12. 12.

    Roman, T., Enescu, M., & Koivunen, V. (2004). Joint time-domain tracking of channel and frequency offsets for MIMO-OFDM systems. Wireless Personal Communications, 31, 181–200.

  13. 13.

    Kim, K. J., Pun, M., & Iltis, R. A. (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.

  14. 14.

    Simon, E. P., Ros, L., Hijazi, H., Fang, J., Gaillot, D. P., & Berbineau, M. (2011). Joint carrier frequency offset and fast time-varying channel estimation for MIMO-OFDM systems. IEEE Transactions on Vehicular Technology, 60(3), 955–965.

  15. 15.

    Vázquez, M. A., Bugallo, M. F., & Míguez, J. (2008). Sequential Monte Carlo methods for the complexity-constrained MAP equalization of dispersive MIMO channels. Signal Processing, 88, 1017–1034.

  16. 16.

    Han, Y. (2012). A Rao-Blackwellized particle filter for adaptive beamforming with strong interference. IEEE Transactions on Signal Processing, 60(6), 2952–2961.

  17. 17.

    Hlinka, O., Hlawatsch, F., & Djurić, P. M. (2013). Distributed particle filtering in agent networks: A survey, classification, and comparison. IEEE Signal Processing Magazine, 30, 61–81.

  18. 18.

    Berntorp, K., Robertsson, A., & Årzén, K. (2014). Rao-Blackwellized particle filters with out-of-sequence measurement processing. IEEE Transactions on Signal Processing, 62(24), 6454–6467.

  19. 19.

    Petetin, Y., & Desbouvries, F. (2015). Bayesian conditional Monte Carlo algorithms for nonlinear time-series state estimation. IEEE Transactions on Signal Processing, 63(14), 3586–3598.

  20. 20.

    Zhou, L., Li, G., Zheng, Z., & Xiao, T. (2015). A trust region-based particle filter algorithm for indoor tracking. Wireless Personal Communications, 80(2), 739–750.

  21. 21.

    Huang, Y., Zhang, J., & Djurić, P. M. (2005). Bayesian detection for BLAST. IEEE Transactions on Signal Processing, 53(3), 1086–1096.

  22. 22.

    Dong, B., Wang, X., & Doucet, A. (2003). A new class of soft MIMO demodulation algorithms. IEEE Transactions on Signal Processing, 51(11), 2752–2763.

  23. 23.

    Jakes, W. C. (1974). Microwave mobile communications. New York: Wiley.

  24. 24.

    Baddour, K. E., & Beaulieu, N. C. (2005). Autoregressive modeling for fading channel simulation. IEEE Transactions on Wireless Communications, 4(4), 1650–1662.

  25. 25.

    Yu, Y., & Cheng, Q. (2006). Particle filters for maneuvering target tracking problem. Signal Processing, 86, 195–203.

  26. 26.

    Li, T., Bolić, M., & Djurić, P. M. (2015). Resampling methods for particle filtering. IEEE Signal Processing Magazine, 32, 70–86.

  27. 27.

    Omura, J. (1969). On the Viterbi decoding algorithm. IEEE Transactions on Information Theory, 15, 177–179.

Download references

Author information

Correspondence to Yihua Yu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yu, Y. Particle Filtering for Joint Symbol Detection, Frequency Offset and Channel Estimation in Time-Varying MIMO Channels with Multiple Frequency Offsets. Wireless Pers Commun 96, 1277–1298 (2017). https://doi.org/10.1007/s11277-017-4237-9

Download citation

Keywords

  • Channel estimation
  • Frequency offset
  • Multi-input multi-output (MIMO)
  • Particle filter
  • Symbol detection