Skip to main content
SpringerLink
Log in

A Survey Channel Estimation for Intelligent Reflecting Surface (IRS)

  • Conference paper
  • First Online:
Cognitive Radio Oriented Wireless Networks and Wireless Internet (CROWNCOM 2021, WiCON 2021)

Abstract

An intelligent reflecting surface (IRS), is a new era of wireless communication towards intelligent and reconfigurable wireless networks. IRS can enhance communication quality between the network terminals with a small cost, low complexity, and low energy consumption when the direct connection has been blocked. To obtain the IRS features, the acquisition of channel state information (CSI) is substantial but it’s challenging in practice, due to the massive number of IRS elements without any capabilities of signal processing. To deal with this challenge in this survey, we first introduce an overview of channel estimation for IRS, then we address the main recent techniques proposed to estimate channels in IRS with various strategies in different applications. Furthermore, we summarize these recent works and list the main points that affect the estimation of the channel in IRS-aided communication system, and finally outline some future researches in IRS channel estimation and the conclusion of this survey.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhao, J.: A survey of intelligent reflecting surfaces (IRSs): Towards 6G wireless communication networks. https://arxiv.org/abs/1907.04789 (2019)

  2. Wu, Q., Zhang, R.: Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Commun. Mag. 58(1), 106–112 (2020). https://doi.org/10.1109/MCOM.001.1900107

    Article  Google Scholar 

  3. Zhang, L., et al.: Space-time-coding digital metasurfaces. Nat. Commun. 9(1), 4334 (2018)

    Article  Google Scholar 

  4. Zhao, J., et al.: Programmable time-domain digital-coding metasurface for non-linear harmonic manipulation and new wireless communication systems. Natl. Sci. Rev. 6(2), 231–238 (2018)

    Article  Google Scholar 

  5. Hu, S., Rusek, F., Edfors, O.: Beyond massive MIMO: The potential of data transmission with large intelligent surfaces. IEEE Trans. Signal Process. 66(10), 2746–2758 (2018)

    Article  MathSciNet  Google Scholar 

  6. Hu, S., Rusek, F., Edfors, O.: Beyond massive MIMO: The potential of positioning with large intelligent surfaces. IEEE Trans. Signal Process. 66(7), 1761–1774 (2018)

    Article  MathSciNet  Google Scholar 

  7. Han, Y., Tang, W., Jin, S., Wen, C.-K., Ma, X.: Large intelligent surface-assisted wireless communication exploiting statistical CSI. IEEE Trans. Veh. Technol. 68(8), 8238–8242 (2019). https://doi.org/10.1109/TVT.2019.2923997

    Article  Google Scholar 

  8. Nadeem, Q.U.A., Kammoun, A., Chaaban, A., Debbah, M., Alouini, M.S.: Asymptotic analysis of large intelligent surface assisted MIMO communication. https://arxiv.org/abs/1903.08127 (2019)

  9. Nie, S., Jornet, J.M., Akyildiz, I.F.: Intelligent environments based on ultra-massive MIMO platforms for wireless communication in millimeter wave and terahertz bands. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7849–7853 (2019)

    Google Scholar 

  10. Tan, X., Sun, Z., Jornet, J.M., Pados, D.: Increasing indoor spectrumsharing capacity using smart reflect-array. In: IEEE International Conference on Communications (ICC), pp. 1–6 (2016)

    Google Scholar 

  11. Zheng, B., Zhang, R.: Intelligent reflecting surface-enhanced OFDM: Channel estimation and reflection optimization. IEEE Wireless Commun. Lett. 9(4), 518–522 (2020)

    Article  Google Scholar 

  12. You, C., Zheng, B., Zhang, R.: Intelligent reflecting surface with discrete phase shifts: Channel estimation and passive beamforming. In: Proceedings IEEE International Conference. Communications (ICC), Dublin, Ireland, June, pp. 1–6 (2020)

    Google Scholar 

  13. He, Z.-Q., Yuan, X.: Cascaded channel estimation for large intelligent metasurface assisted massive MIMO. IEEE Wireless Commun. Lett. 9(2), 210–214 (2020)

    Article  Google Scholar 

  14. Mishra, D., Johansson, H.: Channel estimation and low-complexity beamforming design for passive intelligent surface assisted MISO wireless energy transfer. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019

    Google Scholar 

  15. Jensen, T.L., De Carvalho, E.: On optimal channel estimation scheme for intelligent reflecting surfaces based on a minimum variance unbiased estimator. https://arxiv.org/abs/1909.09440 (2019)

  16. Wang, P., Fang, J., Duan, H., Li, H.: Compressed channel estimation for intelligent reflecting surface-assisted millimeter wave systems. IEEE Signal Process. Lett. 27, 905–909 (2020). https://doi.org/10.1109/LSP.2020.2998357

    Article  Google Scholar 

  17. Zheng, B., You, C., Zhang, R.: Intelligent reflecting surface assisted multi-user OFDMA: Channel estimation and training design. http://arxiv.org/abs/2003.00648

  18. Wang, Z., Liu, L., Cui, S.: Channel estimation for intelligent reflecting surface assisted multiuser communications: Framework, algorithms, and analysis. IEEE Trans. Wireless Commun. (2020). https://doi.org/10.1109/TWC.2020.3004330

    Article  Google Scholar 

  19. Chen, J., Liang, Y.C., Cheng, H.V., Yu, W.: Channel estimation for reconfigurable intelligent surface aided multi-user MIMO systems. https://arxiv.org/abs/1912.03619

  20. You, C., Zheng, B., Zhang, R.: Wireless communication via double IRS: channel estimation and passive beamforming designs. IEEE Wireless Commun. Lett. 10(2), 431–435 (2021). https://doi.org/10.1109/LWC.2020.3034388

    Article  Google Scholar 

  21. Elbir, A.M., Papazafeiropoulos, A., Kourtessis, P., Chatzinotas, S.: Deep channel learning for large intelligent surfaces aided mm-wave massive MIMO systems. IEEE Wireless Commun. Lett. 9(9), 1447–1451 (2020)

    Article  Google Scholar 

  22. Elbir, A.M., Coleri, S.: Federated learning for channel estimation in conventional and IRS-assisted massive MIMO. https://arxiv.org/abs/2008.10846 (2020)

  23. Ning, B., Chen, Z., Chen, W., Du, Y.: Channel estimation and transmission for intelligent reflecting Surface assisted THz communications. arXiv e-prints (2019)

    Google Scholar 

  24. Taha, A., Alrabeiah, M., Alkhateeb, A.: Enabling large intelligent surfaces with compressive sensing and deep learning. arXiv preprint https://arxiv.org/abs/1904.10136, Apr 2019

  25. Taha, A., Zhang, Y., Mismar, F.B. and Alkhateeb, A.: Deep reinforcement learning for intelligent reflecting surfaces: towards standalone operation.https://arxiv.org/abs/2002.11101, May 2020

  26. Liu, S., Gao, Z., Zhang, J., Renzo, M.D., Alouini, M.: Deep denoising neural network assisted compressive channel estimation for mmWave intelligent reflecting surfaces. IEEE Trans. Veh. Technol. 69(8), 9223–9228 (2020)

    Article  Google Scholar 

  27. Alexandropoulos, G.C., Vlachos, E.: A hardware architecture for reconfigurable intelligent surfaces with minimal active elements for explicit channel estimation. arXiv e-prints (2020)

    Google Scholar 

  28. Zhao, M.M., Wu, Q., Zhao, M.J., Zhang, R.: Two-timescale beamforming optimization for intelligent reflecting surface enhanced wireless network. In: 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 1–5 (2020). https://doi.org/10.1109/SAM48682.2020.9104346

  29. You, C., Zheng, B., Zhang, R.: Fast beam training for IRS-assisted multiuser communications. IEEE Wireless Commun. Lett. 9(11), 1845–1849 (2020)

    Article  Google Scholar 

  30. Wu, Q., Zhang, R.: Intelligent reflecting surface enhanced wireless network: joint active and passive beamforming design. IEEE Global Commun. Conf. (GLOBECOM) 2018, 1–6 (2018). https://doi.org/10.1109/GLOCOM.2018.8647620

    Article  Google Scholar 

  31. Lin, J., et al.: Channel estimation for wireless communication systems assisted by large intelligent surfaces. arXiv preprint https://arxiv.org/abs/1911.02158 (2019)

  32. Wei, L., Huang, C., Alexandropoulos, G.C., Yuen, C.: Parallel factor decomposition channel estimation in RIS-assisted multi-user MISO communication. In: 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 1–5 (2020). https://doi.org/10.1109/SAM48682.2020.9104305

  33. Hu, R., Tong, J., Xi, J., Guo, Q., Yu, Y.: Matrix completion-based channel estimation for MmWave communication systems with array-inherent impairments. IEEE Access 6, 62915–62931 (2018). https://doi.org/10.1109/ACCESS.2018.2877432

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported in part by Shanghai Rising-Star Program under Grant 19QA1409100, in part by the National Natural Science Foundation of China under Grants 62071332, 61631017, and U1733114, and in part by the Fundamental Research Funds for the Central Universities. I would like to express my very great appreciation to Pr. Huang for his valuable and constructive suggestions during the planning and development of this research work. His willingness to give his time so generously has been very much appreciated.

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Shanghai, China

    Jiwayria M. S. D. Babiker & Xinlin Huang

Authors
  1. Jiwayria M. S. D. Babiker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinlin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinlin Huang .

Editor information

Editors and Affiliations

  1. Hebei Normal University, Shijiazhuang, China

    Huilong Jin

  2. Hebei Normal University, Shijiazhuang, China

    Chungang Liu

  3. United International University, Dhaka, Bangladesh

    Al-Sakib Khan Pathan

  4. Lakehead University, Thunder Bay, ON, Canada

    Zubair Md. Fadlullah

  5. Lakehead University, Thunder Bay, ON, Canada

    Salimur Choudhury

Rights and permissions

Reprints and permissions

Copyright information

© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Babiker, J.M.S.D., Huang, X. (2022). A Survey Channel Estimation for Intelligent Reflecting Surface (IRS). In: Jin, H., Liu, C., Pathan, AS.K., Fadlullah, Z.M., Choudhury, S. (eds) Cognitive Radio Oriented Wireless Networks and Wireless Internet. CROWNCOM WiCON 2021 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-030-98002-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-98002-3_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98001-6

  • Online ISBN: 978-3-030-98002-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation