Advertisement

A Review on Deep Learning-Based Channel Estimation Scheme

  • Amish RanjanEmail author
  • Abhinav Kumar Singh
  • B. C. Sahana
Conference paper
  • 12 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1154)

Abstract

In this review paper, we have tried to review the maximum amount of research work done till date on deep learning-based algorithms for channel estimation in different wireless systems of communication. Based on the numerical analysis of different papers, this review paper will prove that the DL-based approach is the new trend in channel estimation as it is highly outperforming the conventional schemes. In this paper, we have also tried to cover the basic concept of channel estimation.

Keywords

Deep learning Machine learning Channel estimation OFDM MIMO 

References

  1. 1.
    Zhaoa, L., Zhang, P., Dong Q., Huang X., Zhao J. and Zeyu, Su.: Research of the Channel Estimation in Wireless Communication Systems ITM Web of Conferences, vol. 25, EDP Sciences (2019)Google Scholar
  2. 2.
    David, N., Wiese, T., Utschick, W.: Learning the MMSE channel estimator. IEEE Trans. Signal Process. 66(11), 2905–2917 (2018)Google Scholar
  3. 3.
    Mo, J., Schniter, P., Prelcic, N.G., Robert W.H.,: Channel estimation in millimeter wave MIMO systems with one-bit quantization. In: 48th Asilomar Conference on Signals, Systems and Computers, IEEE (2014)Google Scholar
  4. 4.
    Du, Z., Song, X., Cheng, J., Beaulieu, N.C.: Maximum likelihood-based channel estimation for macrocellular OFDM uplinks in dispersive time-varying channels. IEEE Trans. Wirel. Commun. 10(1), 176–187 (2010)Google Scholar
  5. 5.
    Ma, J., Li, P.: Data-aided channel estimation in large antenna systems. IEEE Trans. Signal Process. 62(12), 3111–3124 (2014)Google Scholar
  6. 6.
    Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K.-C., Hanzo, L.: Machine learning paradigms for next-generation wireless networks. IEEE Wirel. Commun. 24(2), 98–105 (2016)Google Scholar
  7. 7.
    Er, M.J., Zhou, Y.: Theory and novel applications of machine learning. InTech (2009)Google Scholar
  8. 8.
    Olutayo, O.O., Stanley, H.M.: Review of channel estimation for wireless communication systems. IETE Tech Rev. 29(4), 282–298 (2012)Google Scholar
  9. 9.
    Han, S., Ahn, S., Oh, E., Hong, D.: Effect of channel-estimation error on BER performance in cooperative transmission. IEEE Trans. Veh. Technol. 58(4), 2083–2088 (2008)Google Scholar
  10. 10.
    Soltani, M., Vahid, P., Ali, M., Hamid, S.: Deep learning-based channel estimation. IEEE Commun. Lett. 23(4), 652–655 (2019)Google Scholar
  11. 11.
    Dong, C., Chen, C.L., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307(2015)Google Scholar
  12. 12.
    He, H.: Deep learning-based channel estimation for beamspace mmWave massive MIMO systems. IEEE Wirel. Commun. Lett. 7(5), 852–855 (2018)Google Scholar
  13. 13.
    Kai, Z., Zuo, W., Chen, Y., Deyu, M., Lei, Z.: Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)Google Scholar
  14. 14.
    Gao, X., Dai, L., Han, S., Chih-Lin, I., Wang, X.: Reliable beamspace channel estimation for millimeter-wave massive MIMO systems with lens antenna array. IEEE Trans. Wirel. Commun. 16(9) 6010–6021 (2017)Google Scholar
  15. 15.
    Jie, Y., Wen, C.-K., Jin, S., Gao, F.: Beamspace channel estimation in mm wave systems via cosparse image reconstruction technique. IEEE Trans. Commun. 66(10), 4767–4782 (2018)Google Scholar
  16. 16.
    Christopher, A.M., Maleki, A., Richard, G.B.: From denoising to compressed sensing. IEEE Trans. Inf. Theory 62(9), 5117–5144 (2016)Google Scholar
  17. 17.
    Xu, J., Zhu, P., Li, J., You, X.: Deep learning based pilot design for multi-user distributed massive MIMO systems. IEEE Wirel. Commun. Lett. (2019)Google Scholar
  18. 18.
    Chun, C.-J., Kang, J.-M., Kim, I.-M.: Deep learning based joint pilot design and channel estimation for multiuser MIMO channels. arXiv:1812.04120 (2018)
  19. 19.
    Kang, J.-M., Chun, C.-J., Kim, I.-M.: Deep-learning-based channel estimation for wireless energy transfer. IEEE Commun. Lett. 22(11), 2310–2313 (2018)Google Scholar
  20. 20.
    Ying, S., Prabhu, B., Daniel, P.P..: Majorization-minimization algorithms in signal processing, communications, and machine learning. IEEE Trans. Signal Process. 65(3), 794–816 (2016)Google Scholar
  21. 21.
    Ralph W.G.: A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik 35, 237–246 (1972)Google Scholar
  22. 22.
    Chun, C.-J., Kang, J.-M., Kim, I.-M.: Deep Learning Based Channel Estimation for Massive MIMO Systems. IEEE Wirel. Commun. Lett. (2019)Google Scholar
  23. 23.
    O’Shea, T., Jakob, H.: An introduction to deep learning for the physical layer. IEEE Trans. Cognit. Commun. Netw. 3(4), 563–575 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Amish Ranjan
    • 1
    Email author
  • Abhinav Kumar Singh
    • 1
  • B. C. Sahana
    • 2
  1. 1.University College of Engineering and Technology, VBUHazaribagIndia
  2. 2.National Institute of TechnologyPatnaIndia

Personalised recommendations