Skip to main content

Advertisement

SpringerLink
Log in

Channel estimation using spatial partitioning with coalitional game theory (SPCGT) in wireless communication

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

In 5G wireless communication estimation, of downlink channel with orthogonal frequency division multiplexing (OFDM) integrated with multiple input multiple output (MIMO) is a challenging task. This arises due to the utilization of orthogonal pilots in downlink which leads to pilot overhead. To overcome this challenge spatial-temporal sparsity features with compressive sensing utilized for estimation of channels. However, this leads to the challenge of spatial common sparsity in data transmission due to the overlapping of the antenna group which is not separated. To overcome those challenges, this paper developed a spatial partitioning coalitional game theory (SPCGT) for the MIMO-OFDM downlink channel. The performance of the proposed SPCGT is based on the spectral partitioning of the MIMO antenna array. Within MIMO partitioned antenna game theory is applied for reduction of pilot overhead with improved channel estimation. The proposed SPCGT model is aimed to reduce normalized mean square error (NMSE) and bit error rate (BER) for the estimation of the available channel. The performance of the proposed SPCGT is comparatively examined with the existing techniques in terms of the slow time and fast time varying channels. The NMSE and BER stated that the proposed SPCGT offers reduced NMSE and BER for the MIMO-OFDM system.

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

Similar content being viewed by others

References

  1. Dai, J., Zho, L., Chang, C., & Xu, W. (2020). Robust Bayesian learning approach for massive MIMO channel estimation. Signal Processing. https://doi.org/10.1016/j.sigpro.2019.107345.

    Article  Google Scholar 

  2. Qiao, G., Song, Q., Ma, L., Sun, Z., & Zhang, J. (2020). Channel prediction based temporal multiple sparse Bayesian learning for channel estimation in fast time-varying underwater acoustic OFDM communications. Signal Processing. https://doi.org/10.1016/j.sigpro.2020.107668.

    Article  Google Scholar 

  3. Shalavi, N., Atashbar, M., & Feghhi, M.M. (2020). Downlink channel estimation of FDD based massive MIMO using spatial partial-common sparsity modeling. Physical Communication, 42, 101138.

    Article  Google Scholar 

  4. Pasangi, P., Atashbar, M., & Feghhi, M. M. (2020). Blind downlink channel estimation of multi-user multi-cell massive MIMO system in the presence of the pilot contamination. AEU-International Journal of Electronics and Communications. https://doi.org/10.1016/j.aeue.2020.153099.

    Article  Google Scholar 

  5. Zhou, J., Yang, A., Guo, P., Zhuang, L., Guo, S., & Qiao, Y. (2020). Experimental estimation of inter-channel nonlinearity power by using differential pilot. Optical Fiber Technology, 58, 102256.

    Article  Google Scholar 

  6. Khan, M., Das, B., & Pati, B. B. (2020). Channel estimation strategies for underwater acoustic (UWA) communication: An overview. Journal of the Franklin Institute, 357, 7229–7265.

    Article  MathSciNet  Google Scholar 

  7. Ali, M. S., Li, Y., Chen, S., & Lin, F. (2020). On improved DFT-based low-complexity channel estimation algorithms for LTE-based uplink NB-IoT systems. Computer Communications, 149, 214–224.

    Article  Google Scholar 

  8. Wu, X., Zhu, W. P., & Yan, J. (2019). Channel estimation and tracking with nested sampling for fast-moving users in millimetre-wave communication. Digital Signal Processing, 94, 29–37.

    Article  Google Scholar 

  9. Han, J., Chepuri, S. P., & Leus, G. (2020). Joint channel and Doppler estimation for OSDM underwater acoustic communications. Signal Processing, 170, 107446.

    Article  Google Scholar 

  10. Das, D., & Subadar, R. (2019). Performance analysis of QAM for L-MRC receiver with estimation error over independent Hoyt fading channels. AEU-International Journal of Electronics and Communications, 107, 15–20.

    Article  Google Scholar 

  11. Fatema, N., Hua, G., Xiang, Y., Peng, D., & Natgunanathan, I. (2017). Massive MIMO linear precoding: a survey. IEEE Systems Journal, 12(4), 3920–3931.

    Article  Google Scholar 

  12. Flordelis, J., Rusek, F., Tufvesson, F., Larsson, E. G., & Edfors, O. (2018). Massive MIMO performance—TDD versus FDD: What do measurements say? IEEE Transactions on Wireless Communications, 17(4), 2247–2261.

    Article  Google Scholar 

  13. Shalavi, N., Atashbar, M., & Feghhi, M. M. (2020). Downlink channel estimation of FDD based massive MIMO using spatial partial-common sparsity modeling. Physical Communication, 42, 101138.

    Article  Google Scholar 

  14. Lu, W., Wang, Y., Wen, X., Hua, X., Peng, S., & Zhong, L. (2019). Compressive downlink channel estimation for FDD massive MIMO using weighted $ l_ p $ minimization. IEEE Access, 7, 86964–86978.

    Article  Google Scholar 

  15. Uwaechia, A. N., Mahyuddin, N. M., Ain, M. F., Latiff, N. M. A., & Za’bah, N. F. (2019). Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels. Physical Communication, 36, 100771.

    Article  Google Scholar 

  16. Kuai, X., Chen, L., Yuan, X., & Liu, A. (2019). Structured turbo compressed sensing for downlink massive MIMO-OFDM channel estimation. IEEE Transactions on Wireless Communications, 18(8), 3813–3826.

    Article  Google Scholar 

  17. Chen, L., Liu, A., & Yuan, X. (2017). Structured turbo compressed sensing for massive MIMO channel estimation using a Markov prior. IEEE Transactions on Vehicular Technology, 67(5), 4635–4639.

    Article  Google Scholar 

  18. Apelfröjd, R., Zirwas, W., & Sternad, M. (2019). Low-overhead cyclic reference signals for channel estimation in FDD massive MIMO. IEEE Transactions on Communications, 67(5), 3279–3291.

    Article  Google Scholar 

  19. Dai, J., Liu, A., & Lau, V. K. (2018). FDD massive MIMO channel estimation with arbitrary 2D-array geometry. IEEE Transactions on Signal Processing, 66(10), 2584–2599.

    Article  MathSciNet  Google Scholar 

  20. Choi, J. W., Shim, B., Ding, Y., Rao, B., & Kim, D. I. (2017). Compressed sensing for wireless communications: Useful tips and tricks. IEEE Communications Surveys & Tutorials, 19(3), 1527–1550.

    Article  Google Scholar 

  21. Zhu, F., Wu, N., & Liang, Q. (2017). Channel estimation for massive MIMO with 2-D nested array deployment. Physical Communication, 25, 432–437.

    Article  Google Scholar 

  22. Araújo, D. C., De Almeida, A. L., Da Costa, J. P., & de Sousa, R. T. (2019). Tensor-based channel estimation for massive MIMO-OFDM systems. IEEE Access, 7, 42133–42147.

    Article  Google Scholar 

  23. Lian, L., Liu, A., & Lau, V. K. (2019). Exploiting dynamic sparsity for downlink FDD-massive MIMO channel tracking. IEEE Transactions on Signal Processing, 67(8), 2007–2021.

    Article  MathSciNet  Google Scholar 

  24. Wu, S., Ni, Z., Meng, X., & Kuang, L. (2016). Block expectation propagation for downlink channel estimation in massive MIMO systems. IEEE Communications Letters, 20(11), 2225–2228.

    Article  Google Scholar 

  25. Qiao, G., Song, Q., Ma, L., Liu, S., Sun, Z., & Gan, S. (2018). Sparse Bayesian learning for channel estimation in time-varying underwater acoustic OFDM communication. IEEE Access, 6, 56675–56684.

    Article  Google Scholar 

  26. Yin, Y., Liu, S., Qiao, G., Yang, Y., & Yang, Y. (2015). OFDM demodulation using virtual time reversal processing in underwater acoustic communications. Journal of Computational Acoustics, 23(4), 1540011.

    Article  Google Scholar 

  27. Zhou, Y. H., Tong, F., & Zhang, G. Q. (2017). Distributed compressed sensing estimation of underwater acoustic OFDM channel. Applied Acoustics, 117, 160–166.

    Article  Google Scholar 

  28. Qiao, G., Song, Q., Ma, L., Sun, Z., & Zhang, J. (2020). Channel prediction based temporal multiple sparse bayesian learning for channel estimation in fast time-varying underwater acoustic OFDM communications. Signal Processing., 175, 107668. https://doi.org/10.1016/j.sigpro.2020.107668.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, 641202, India

    S. Dhanasekaran

  2. Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, 641004, India

    J. Ramesh

Authors
  1. S. Dhanasekaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Ramesh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Dhanasekaran.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dhanasekaran, S., Ramesh, J. Channel estimation using spatial partitioning with coalitional game theory (SPCGT) in wireless communication. Wireless Netw 27, 1887–1899 (2021). https://doi.org/10.1007/s11276-020-02528-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-020-02528-4

Keywords

Search

Navigation