Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

SpringerLink
Log in

Energy efficiency maximization of massive MIMO systems using RF chain selection and hybrid precoding

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Modern day millimeter wave communication systems prefer hybrid precoding architecture over digital architecture due to higher energy efficiency, lower power consumption and comparable spectral efficiency. Both energy efficiency and spectral efficiency defines the system performance of a hybrid precoder and are dependent on the number of available active RF chains. The aim to maximize energy efficiency without any obvious performance degradation in terms of spectral efficiency has created a tradeoff due to dependency of energy and spectral efficiency on RF chains. This tradeoff is being investigated in this paper by performing RF chain selection using evolutionary algorithms. We present a hybrid heuristic approach comprising of low computationally complex evolutionary algorithms for RF chain selection and successive interference cancellation for precoding. Furthermore, we have shown that for low SNR regime the analog percoding is optimal in terms of energy efficiency and for high SNR regime we can adopt the RF chain selection procedure to maximize the energy efficiency. Moreover, the channel irregularities do not effect our proposed scheme.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

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

References

  1. Rangan, S., Rappaport, T. S., & Erkip, E. Millimeter-wave cellular wireless networks: Potentials and challenges. In Proceedings of the 2014 Proceedings of the IEEE (pp. 366–385).

  2. Pi, Z., & Khan, F. (2011). An introduction to millimeter-wave mobile broadband systems. IEEE Communications Magazine, 49(6), 101–107.

    Article  Google Scholar 

  3. Andrews, J. G., et al. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications, 32(6), 1065–1082.

    Article  Google Scholar 

  4. Rappaport, T. S., et al. (2013). Millimeter wave mobile communications for 5G cellular: It will work! IEEE Access, 1, 335–349.

    Article  Google Scholar 

  5. Rappaport, T. S., et al. (2015). Wideband millimeter-wave propagation measurements and channel models for future wireless communication system design. IEEE Transactions on Communications, 63(9), 3029–3056.

    Article  Google Scholar 

  6. Heath, R. W., et al. (2016). An overview of signal processing techniques for millimeter wave MIMO systems. IEEE Journal of Selected Topics in Signal Processing, 10(3), 436–453.

    Article  Google Scholar 

  7. Han, S., et al. (2015). Large-scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5G. IEEE Communications Magazine, 53(1), 186–194.

    Article  Google Scholar 

  8. Sohrabi, F., & Yu, W. (2016). Hybrid digital and analog beamforming design for large-scale antenna arrays. IEEE Journal of Selected Topics in Signal Processing, 10(3), 501–513.

    Article  Google Scholar 

  9. Gao, X., et al. (2016). Energy-efficient hybrid analog and digital precoding for mmWave MIMO systems with large antenna arrays. IEEE Journal on Selected Areas in Communications, 34(4), 998–1009.

    Article  Google Scholar 

  10. Ayach, O. E., et al. (2014). Spatially sparse precoding in millimeter wave MIMO systems. IEEE Transactions on Wireless Communications, 13(3), 1499–1513.

    Article  Google Scholar 

  11. Yu, X., et al. (2016). Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems. IEEE Journal of Selected Topics in Signal Processing, 10(3), 485–500.

    Article  Google Scholar 

  12. Alluhaibi, O., Ahmed, Q. Z., Wang, J., & Zhu, H. (2017). Hybrid digital-to-analog precoding design for mm-wave systems. In IEEE international conference on communications (ICC), Paris (pp. 1–6).

  13. Khalid, S., Abbas, W. B., Kim, H. S., & Niaz, M. T. (2020). Evolutionary algorithm based capacity maximization of 5G/B5G hybrid pre-coding systems. Sensors, 20, 5338. https://doi.org/10.3390/s20185338

  14. Yi, X., Bo, L., Zhongjiang, Y., Jiancun, F., & Mao, Y. (2019). A general hybrid precoding method for mmwave massive MIMO systems. Radio Engineering, 28(2).

  15. Zhang, D., Wang, Y., Li, X., & Xiang, W. (2017). Hybridly connected structure for hybrid beamforming in mmwave massive MIMO systems. IEEE Transactions on Communications, 66(2), 662–674.

    Article  Google Scholar 

  16. Liu, X., et al. (2019). Hybrid precoding for massive mmWave MIMO systems. IEEE Access, 7, 33577–33586.

    Article  Google Scholar 

  17. Asaad, S., Rabiei, A. M., & Mller, R. R. (2018). Massive MIMO With antenna selection: Fundamental limits and applications. IEEE Transactions on Wireless Communications, 17(12), 8502–8516.

    Article  Google Scholar 

  18. Khalid, S., Mehmood, R., Abbas, W. b., Khalid, F., & Naeem, M. (2020). Joint transmit antenna selection and precoding for millimeter wave massive MIMO systems. Physical Communication, 42.

  19. Khalid, S., Mehmood, R., Abbas, W., et al. (2021). Probabilistic distribution learning algorithm based transmit antenna selection and precoding for millimeter wave massive MIMO systems. Telecommunication Systems, 76, 449–460.

    Article  Google Scholar 

  20. Naeem, M., & Lee, D. C. (2011). Low-complexity joint transmit and receive antenna selection for MIMO systems. Engineering Applications of Artificial Intelligence, 24(6), 1046–1051.

  21. Naeem, M., & Lee, D. C. (2014). A joint antenna and user selection scheme for multiuser MIMO system. Applied Soft Computing, 23, 366–374.

  22. Maimaiti, S., Chuai, G., Gao, W., et al. (2019). A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems. Journal on Wireless Communications and Networking 208. https://doi.org/10.1186/s13638-019-1529-7

  23. Kaushik, A., Thompson, J., Vlachos, E., Tsinos, C., & Chatzinotas, (2019). Dynamic RF chain selection for energy efficient and low complexity hybrid beamforming in millimeter wave MIMO systems. IEEE Transactions on Green Communications and Networking, 3(4), 886–900

  24. Vlachos, E., & Thompson, J. (2021). Energy-efficiency maximization of hybrid massive MIMO precoding with random-resolution DACs via RF selection. IEEE Transactions on Wireless Communications, 20(2), 1093–1104.

    Article  Google Scholar 

  25. Li, X., Huang, Y., Heng, W., & Wu, J. (2021). Machine learning-inspired hybrid precoding for mmWave MU-MIMO systems with domestic switch network. Sensors, 21, 3019. https://doi.org/10.3390/s21093019

  26. Alkhateeb, A., et al. (2014). Channel estimation and hybrid precoding for millimeter wave cellular systems. IEEE Journal of Selected Topics in Signal Processing, 8(5), 831–846.

    Article  Google Scholar 

  27. Yang, X.-S. (2014). Nature-Inspired Optimization Algorithms, (1st edn). Elsevier. ISBN: 9780124167438

  28. del Valle, Y., Venayagamoorthy, G. K., & Mohagheghi, S. (2008). Particle swarm optimization: Basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 12(2) 171–195.

  29. Kennedy, J., & Eberhart, R. C. (1995). Particle SWARM optimization. In Proceedings of IEEE conference on neural networks, Piscataway (pp. 1942–1948).

  30. Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm optimization. In Proceedings of the conference on systems, man, and cybernetics, SMC97 (pp. 4104–4109).

  31. Huang, H., et al. (2019). Deep-learning-based millimeter-wave massive MIMO for hybrid precoding. IEEE Transactions on Vehicular Technology, 68(3), 3027–3032.

    Article  Google Scholar 

Download references

Funding

No funding is used for this research work.

Author information

Authors and Affiliations

  1. National University of Computer and Emerging Sciences (NUCES), Islamabad, Pakistan

    Salman Khalid, Waqas bin Abbas & Farhan Khalid

  2. COMSATS University, Wah Campus, Islamabad, Pakistan

    Rashid Mehmood & Muhammad Naeem

Authors
  1. Salman Khalid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rashid Mehmood
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Waqas bin Abbas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Farhan Khalid
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Muhammad Naeem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Salman Khalid.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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

Khalid, S., Mehmood, R., Abbas, W.b. et al. Energy efficiency maximization of massive MIMO systems using RF chain selection and hybrid precoding. Telecommun Syst 80, 251–261 (2022). https://doi.org/10.1007/s11235-022-00900-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-022-00900-7

Keywords

Search

Navigation