Skip to main content

Advertisement

Log in

Iterative Time-Varying Channel Prediction Based on the Vector Prony Method

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper proposes channel prediction algorithms, based on the vector Prony method, tackling the channel state information (CSI) acquisition problem in fast time-varying uplink and downlink channels of the time division duplex (TDD) in massive multiple-input, multiple-output (MIMO) systems. For the uplink system, we show that the CSI obeys the Prony equation in theory, and we aim to overcome the instability of solving the Prony equation via iterating the Prony coefficients based on CSI re-estimated from signals received during data transmission. For the downlink system, a method to generate precoders is constructed based on the prediction of downlink CSI. Experiments are performed according to the fast fading full-dimensional multiple-input multiple-output (FD-MIMO) channel model of 3GPP Standards. The results demonstrate that performance is improved remarkably in uplink transmissions and approaches the theoretical optimal scheme in downlink transmissions.

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

Access this article

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.

Algorithm I
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availibility Statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. All results are included in this published article; the results raw output is available from the corresponding author on reasonable request.

Code Availability

Code is available from the corresponding author on reasonable request.

Abbreviations

CSI:

Channel State Information

TDD:

Time Division Duplex

MIMO:

Multiple-input Multiple-output

FD-MIMO:

Full-Dimensional Multiple-input Multiple-output

AR:

Autoregressive

SOS:

Sum-Of-Sinusoids

FIR:

Fnite Impulse Response

PAD:

Prony-based Angular-delay Domain

MU:

Mobile User

3GPP:

3Rd Generation Partnership Project

BS:

Base Station

UL:

Uplink

DL:

Downlink

AOA:

Azimuth Of Arrival angle

AOD:

Azimuth Of Departure angle

ZOA:

Zenith Of Arrival angle

ZOD:

Zenith Of Departure angle

MMSE:

Minimum Mean Square Error

LOS:

Loss Of Sight

ASA:

Azimuth Spread of Arrival angle

ASD:

Azimuth Spread of Departure angle

ZSA:

Zenith Spread of Arrival angle

ZSD:

Zenith Spread of Departure angle

ML:

Maximum Likehood

QAM:

Quadrature Amplitude Modulation

SNR:

Signal Noise Ratio

DMRS:

Demodulation Reference Signal

References

  1. Shaik, N., Malik, P. K. (2022). 5G massive MIMO-OFDM system model: Existing channel estimation algorithms and its review. In Smart antennas. EAI/Springer innovations in communication and computing (Springer).

  2. Duel-Hallen, A., Hu, S., & Hallen, H. (2000). Long-range prediction of fading signals: Enabling adaptive transmission for mobile radio channels. Special issue on advances in wireless and mobile communications. IEEE Signal Processing Magazine, 17(3), 62–75.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Chen, M., & Viberg, M. (2009). Long-range channel prediction based on nonstationary parametric modeling. IEEE Transactions on Signal Process, 57(2), 622–634.

    Article  MathSciNet  Google Scholar 

  6. Truong, K. T., & Heath, R. W. (2013). Effects of channel aging in massive MIMO systems. Journal of Communications and Networks, 15(4), 338–351.

    Article  Google Scholar 

  7. Papazafeiropoulos, A. K., & Ratnarajah, T. (2015). Deterministic equivalent performance analysis of time-varying massive MIMO systems. IEEE Transactions on Wireless Communications, 14(10), 5795–5809.

    Article  Google Scholar 

  8. Kong, C., Zhong, C., Papazafeiropoulos, A. K., Matthaiou, M., & Zhang, Z. (2015). Sum-rate and power scaling of massive MIMO systems with channel aging. IEEE Transactions on Communications, 63(12), 4877–4893.

    Article  Google Scholar 

  9. Adeogun, R. O., Teal, P. D., & Dmochowski, P. A. (2015). Extrapolation of MIMO mobile-to-mobile wireless channels using parametric-model-based prediction. IEEE Transactions on Vechicular Technology, 64(10), 4487–4498.

    Article  Google Scholar 

  10. Fu, L., Wang, Q., Huang, Y., & Yang, L. (2016). Performance analysis of low-complexity channel prediction for uplink massive MIMO. IET Communications, 10(14), 1744–1751.

    Article  Google Scholar 

  11. Peng, W., & Jiang, M. T. (2017). Channel prediction in time-varying massive MIMO environments. IEEE Access, 5, 23938–23946.

    Article  Google Scholar 

  12. Derevianko, N., & Plonka, G. (2021) Exact reconstruction of extended exponential sums using rational approximation of their Fourier coefficients. arXiv preprint arXiv:2103.07743

  13. Yin, H., Wang, H., Liu, Y., & Gesbert, D. (2020). Addressing the curse of mobility in massive MIMO with Prony-based angular-delay domain channel predictions. IEEE Journal of Selected Areas in Communications, 38, 2903–2917.

    Article  Google Scholar 

  14. Technical Specification Group Radio Access Network; Study on 3D channel model for LTE (release 12), document TR 36.873 V12.7.0, 3rd Generation Partnership Project (2017)

  15. Hou, X., & Kayama, H. (2011). Demodulation reference signal design and channel estimation for LTE-advanced uplink. In M. Almeida (Ed.), Advances in vehicular networking technologies. IntechOpen.

    Google Scholar 

Download references

Funding

This work was not supported by funding from any source.

Author information

Authors and Affiliations

Authors

Contributions

Yi Huang, Haiquan Wang, Haifan Yin, and Zhijin Zhao conceived the algorithm and designed the experiments; Yi Huang and Haiquan Wang designed the MATLAB programs; Yi Huang and Haiquan Wang performed the experiments; Yi Huang prepared figures and analyzed the experimental results; Haiquan Wang drafted the manuscript; Yi Huang, Haifan Yin, and Zhijin Zhao revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Haiquan Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no Conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Y., Wang, H., Yin, H. et al. Iterative Time-Varying Channel Prediction Based on the Vector Prony Method. Wireless Pers Commun 136, 103–122 (2024). https://doi.org/10.1007/s11277-024-11162-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-11162-8

Keywords

Navigation