Skip to main content
Springer Nature Link
Log in

Deep learning based user scheduling for massive MIMO downlink system

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

In this paper, we investigate a user scheduling algorithm for massive multiple-input multiple-output (MIMO) systems over more general correlated Rician fading channels. To achieve low latency and high throughput, a new user scheduling algorithm based on deep learning (DL) is proposed, which exploits only statistical channel state information. The proposed scheduling network is trained to grasp the mapping from the statistical signal and interference pattern to the user scheduling decision through supervised learning. It can predict the optimal scheduling scheme from statistical CSI without iterative calculation after offline training. Simulation results demonstrate the superior performance of the proposed algorithm in terms of calculation time, and it achieves almost the same throughput as the optimal scheduling algorithm which is obtained through exhaustive search. Furthermore, with the normalization of the input data, the proposed scheduling network is robust to the change of the channel environment and the number of transmit antennas.

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

Access this article

Log in via an institution

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.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Rusek F, Persson D, Lau B K, et al. Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Process Mag, 2013, 30: 40–60

    Article  Google Scholar 

  2. You X H, Pan Z W, Gao X Q, et al. The 5G mobile communication: The development trends and its emerging key techniques (in Chinese). Sci Sin Inform, 2014, 44: 551–563

    Article  Google Scholar 

  3. Wang D M, Zhang Y, Wei H, et al. An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications. Sci China Inf Sci, 2016, 59: 081301

    Article  Google Scholar 

  4. Marzetta T L. Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans Wirel Commun, 2010, 9: 3590–3600

    Article  Google Scholar 

  5. Li X, Liu Z Y, Qin N N, et al. FFR based joint 3D beamforming interference coordination for multi-cell FD-MIMO downlink transmission systems. IEEE Trans Veh Technol, 2020, 69: 3105–3118

    Article  Google Scholar 

  6. Adhikary A, Nam J, Ahn J-Y, et al. Joint spatial division and multiplexing: the large-scale array regime. IEEE Trans Inform Theory, 2013, 59: 6441–6463

    Article  MathSciNet  Google Scholar 

  7. Li X, Sun T T, Qin N N, et al. User scheduling for downlink FD-MIMO systems under Rician fading exploiting statistical CSI. Sci China Inf Sci, 2018, 61: 082302

    Article  Google Scholar 

  8. Han Y, Hsu T H, Wen C K, et al. Efficient downlink channel reconstruction for FDD multi-antenna systems. IEEE Trans Wirel Commun, 2019, 18: 3161–3176

    Article  Google Scholar 

  9. Wang T Q, Wen C K, Wang H Q, et al. Deep learning for wireless physical layer: opportunities and challenges. China Commun, 2017, 14: 92–111

    Article  Google Scholar 

  10. Wen C K, Shih W T, Jin S. Deep learning for massive MIMO CSI feedback. IEEE Wirel Commun Lett, 2018, 7: 748–751

    Article  Google Scholar 

  11. He H T, Wen C K, Jin S, et al. Deep learning-based channel estimation for beamspace mmWave massive MIMO systems. IEEE Wirel Commun Lett, 2018, 7: 852–855

    Article  Google Scholar 

  12. He H T, Jin S, Wen C K, et al. Model-driven deep learning for physical layer communications. IEEE Wirel Commun, 2019, 26: 77–83

    Article  Google Scholar 

  13. Goodfellow I, Bengio Y I, Courville A. Deep Learning. Cambridge: MIT Press, 2016

    MATH  Google Scholar 

  14. Ye H, Li G Y, Juang B H. Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wirel Commun Lett, 2018, 7: 114–117

    Article  Google Scholar 

  15. Xia W C, Zheng G, Zhu Y X, et al. A deep learning framework for optimization of MISO downlink beamforming. IEEE Trans Commun, 2020, 68: 1866–1880

    Article  Google Scholar 

  16. Cui W, Shen K M, Yu W. Spatial deep learning for wireless scheduling. IEEE J Sel Areas Commun, 2019, 37: 1248–1261

    Article  Google Scholar 

  17. Li X, Yu X X, Sun T T, et al. Joint scheduling and deep learning-based beamforming for FD-MIMO systems over correlated Rician fading. IEEE Access, 2019, 7: 118297–118309

    Article  Google Scholar 

  18. Taricco G. Optimum receiver design and performance analysis of arbitrarily correlated Rician fading MIMO channels with imperfect channel state information. IEEE Trans Inform Theory, 2010, 56: 1114–1134

    Article  MathSciNet  Google Scholar 

  19. Boukhedimi I, Kammoun A, Alouini M S. Multi-cell MMSE combining over correlated Rician channels in massive MIMO systems. IEEE Wirel Commun Lett, 2020, 9: 12–16

    Article  Google Scholar 

  20. Han Y, Zhang H C, Jin S, et al. Investigation of transmission schemes for millimeter-wave massive MU-MIMO systems. IEEE Syst J, 2017, 11: 72–83

    Article  Google Scholar 

  21. Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of International Conference on Machine Learning, Lille, 2015. 448–456

  22. He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016. 770–778

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61831013, 61971126, 61941104, 61921004).

Author information

Authors and Affiliations

  1. The National Communications Research Laboratory, Southeast University, Nanjing, 210096, China

    Xiaoxiang Yu, Jiajia Guo, Xiao Li & Shi Jin

Authors
  1. Xiaoxiang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiajia Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shi Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiao Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, X., Guo, J., Li, X. et al. Deep learning based user scheduling for massive MIMO downlink system. Sci. China Inf. Sci. 64, 182304 (2021). https://doi.org/10.1007/s11432-020-2993-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-2993-6

Keywords