Skip to main content
SpringerLink
Log in

Millimeter Wave Hybrid Precoding Based on Deep Learning

  • Conference paper
  • First Online:
Communications and Networking (ChinaCom 2021)

Included in the following conference series:

  • 1061 Accesses

Abstract

To overcome the high energy consumption, and insufficient use of spatial information in traditional hybrid precoding algorithms, a hybrid precoding algorithm based on deep learning was proposed for millimeter wave massive MIMO system. Firstly, the hybrid precoding design problem was transformed into an exhaustive search problem for the analog precoder/combiner using the equivalent channel matrix. Then, a deep learning model was constructed to learn how to optimize the cascaded hybrid precoder by an improved convolutional neural network model. Finally, the optimized cascaded hybrid precoder was used to predict the analog precoding/combiner matrix directly, and the digital precoding/combiner matrix was obtained by applying singular value decomposition (SVD) to the equivalent channel matrix. Simulation results show that the performance of the proposed cascaded hybrid precoder is close to that of the pure digital precoder and can maximize the achievable rate to enhance the spectral efficiency of the millimeter wave massive MIMO system.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Ghosh, A., Thomas, T.A., Cudak, M.C., et al.: Millimeter-wave enhanced local area systems: a high-data-rate approach for future wireless net-works. IEEE J. Sel. Areas Commun. 32(6), 1152–1163 (2014)

    Article  Google Scholar 

  2. Alkhateeb, A., Mo, J., Gonzalez-Prelcic, N., et al.: MIMO precoding and combining solutions for millimeter-wave systems. IEEE Commun. Mag. 52(12), 122–131 (2014)

    Article  Google Scholar 

  3. Heath, R.W., Gonzalez-Prelcic, N., Rangan, S., et al.: An overview of signal processing techniques for millimeter wave MIMO systems. IEEE J. Sel. Top. Signal Process. 10(3), 436–453 (2016)

    Article  Google Scholar 

  4. Gao, X., Dai, L., Han, S., et al.: Energy-efficient hybrid analog and digital precoding for mmWave MIMO systems with large antenna arrays. IEEE J. Sel. Areas Commun. 34(4), 998–1009 (2016)

    Article  Google Scholar 

  5. Alkhateeb, A., Leus, G., Heath, R.W.: Limited feedback hybrid precoding for multi-user millimeter wave systems. IEEE Trans. Wirel. Commun. 14(11), 6481–6494 (2015)

    Article  Google Scholar 

  6. Chen, C.E., Tsai, Y.C., Yang, C.H.: An iterative geometric mean decomposition algorithm for MIMO communications systems. IEEE Trans. Wirel. Commun. 14(1), 343–352 (2014)

    Article  Google Scholar 

  7. Jin, J., Zheng, Y.R., Chen, W., et al.: Hybrid precoding for millimeter wave MIMO systems: a matrix factorization approach. IEEE Trans. Wirel. Commun. 17(5), 3327–3339 (2018)

    Article  Google Scholar 

  8. Zhang, E., Huang, C.: On achieving optimal rate of digital precoder by RF-baseband codesign for MIMO systems. In: 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), pp. 1–5. IEEE (2014)

    Google Scholar 

  9. Wang, G., Ascheid, G.: Joint pre/post-processing design for large milli-meter wave hybrid spatial processing systems. In: European Wireless 2014; 20th European Wireless Conference, VDE, pp. 1–6 (2014)

    Google Scholar 

  10. Yu, X., Shen, J.C., Zhang, J., et al.: Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems. IEEE J. Sel. Top. Signal Process. 10(3), 485–500 (2016)

    Article  Google Scholar 

  11. Chen, C.H., Tsai, C.R., Liu, Y.H., et al.: Compressive sensing (CS) assisted low-complexity beamspace hybrid precoding for millimeter-wave MIMO systems. IEEE Trans. Signal Process. 65(6), 1412–1424 (2016)

    Article  MathSciNet  Google Scholar 

  12. Alkhateeb, A., El Ayach, O., Leus, G., et al.: Channel estimation and hybrid precoding for millimeter wave cellular systems. IEEE J. Sel. Top. Signal Process. 8(5), 831–846 (2014)

    Article  Google Scholar 

  13. El Ayach, O., Rajagopal, S., Abu-Surra, S., et al.: Spatially sparse pre-coding in millimeter wave MIMO systems. IEEE Trans. Wirel. Commun. 13(3), 1499–1513 (2014)

    Article  Google Scholar 

  14. Alkhateeb, A.: DeepMIMO: a generic deep learning dataset for millimeter wave and massive MIMO applications. arXiv preprint arXiv:1902.06435 (2019)

  15. Li, X., Alkhateeb, A.: Deep learning for direct hybrid precoding in millimeter wave massive MIMO systems. In: 2019 53rd Asilomar Conference on Signals, Systems, and Computers, pp. 800–805. IEEE (2019)

    Google Scholar 

  16. Huang, H., Song, Y., Yang, J., et al.: Deep-learning-based millimeter-wave massive MIMO for hybrid precoding. IEEE Trans. Veh. Technol. 68(3), 3027–3032 (2019)

    Article  Google Scholar 

  17. Bao, X., Feng, W., Zheng, J., et al.: Deep CNN and equivalent channel based hybrid precoding for mmWave massive MIMO systems. IEEE Access 8, 19327–19335 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China

    Qing Liu & Ken Long

Authors
  1. Qing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ken Long
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qing Liu .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Fudan University, Shanghai, China

    Jun Wun

  3. Zhejiang University, Hangzhou, China

    Jianwei Yin

  4. Tsinghua University, Beijing, China

    Feifei Shen

  5. Xidian University, X'ian, China

    Yulong Shen

  6. Hangzhou Dianzi University, Hangzhou, China

    Jun Yu

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

Liu, Q., Long, K. (2022). Millimeter Wave Hybrid Precoding Based on Deep Learning. In: Gao, H., Wun, J., Yin, J., Shen, F., Shen, Y., Yu, J. (eds) Communications and Networking. ChinaCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 433. Springer, Cham. https://doi.org/10.1007/978-3-030-99200-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-99200-2_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-99199-9

  • Online ISBN: 978-3-030-99200-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation