Hybrid precoding for multiuser massive MIMO systems based on MMSE-PSO

  • Rongling Jian
  • Yueyun ChenEmail author
  • Zhan Liu
  • Yanqing Xia


Hybrid Precoding has been adopted as a promising technology for 5th generation wireless communication systems. In this paper, we propose a hybrid precoding scheme based on minimum mean square error (MMSE) and particle swarm optimization (PSO) for multiuser massive multiple-input multiple-output systems. The closed-form solutions of baseband precoding and the combiner are solved by convex optimization method. Meanwhile, the MMSE between the transmitted signal and the received signal is considered as an objective function of PSO, and the radio frequency precoding is obtained by updating the global optimal positions of the particles. The simulation results show that the proposed hybrid MMSE-PSO precoding significantly improves achievable rate and the system reliability.


Massive multiple-input multiple-output (Massive MIMO) Hybrid precoding Mean square error (MSE) Particle swarm optimization (PSO) 



This work was supported by National Science and Technology Major Project No. 2017ZX03001021-005.


  1. 1.
    Andrews, J. G., Buzzi, S., Wan, C., et al. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications,32(6), 1065–1082.CrossRefGoogle Scholar
  2. 2.
    Zheng, K., Zhao, L., Mei, J., et al. (2015). Survey of large-scale MIMO systems. IEEE Communications Surveys & Tutorials,17(3), 1738–1760.CrossRefGoogle Scholar
  3. 3.
    He, S., Wang, J., Huang, Y., et al. (2017). Codebook-based hybrid precoding for millimeter wave multiuser systems. IEEE Transactions on Signal Processing,65(20), 5289–5304.MathSciNetCrossRefGoogle Scholar
  4. 4.
    Nguyen, D. H. N., Le, L. B., & Le-Ngoc, T. (2016). Hybrid MMSE precoding for mmWave multiuser MIMO systems. In 2016 IEEE international conference on communications (ICC) (pp. 1–6). IEEE.Google Scholar
  5. 5.
    Lee, C. S., & Chung, W. H. (2017). Hybrid RF-baseband precoding for cooperative multiuser massive MIMO systems with limited RF chains. IEEE Transactions on Communications,65(4), 1575–1589.CrossRefGoogle Scholar
  6. 6.
    Zhu, X., Wang, Z., Dai, L., et al. (2016). Adaptive hybrid precoding for multiuser massive MIMO. IEEE Communications Letters,20(4), 776–779.CrossRefGoogle Scholar
  7. 7.
    Cai, J., Rong, B., & Sun, S. (2016). A low complexity hybrid precoding scheme for massive MIMO system. In 2016 16th international symposium on communications and information technologies (ISCIT) (pp. 638–641). IEEE.Google Scholar
  8. 8.
    Cong, J., Li, X., & Zhu, Y. (2017). Hybrid precoding for multi-user mmWave systems based on MMSE criterion. In 2017 23rd Asia-Pacific conference on communications (APCC) (pp. 1–5). IEEE.Google Scholar
  9. 9.
    Li, Z., Han, S., Sangodoyin, S., et al. (2018). Joint optimization of hybrid beamforming for multi-user massive MIMO downlink. IEEE Transactions on Wireless Communications, 17(6), 3600–3614.CrossRefGoogle Scholar
  10. 10.
    Payami, S., Ghoraishi, M., & Dianati, M. (2018). Hybrid beamforming for downlink massive MIMO systems with multiantenna user equipment. In Vehicular technology conference. IEEE.Google Scholar
  11. 11.
    Ayach, O. E., Rajagopal, S., Abu-Surra, S., et al. (2013). Spatially sparse precoding in millimeter wave MIMO systems. IEEE Transactions on Wireless Communications,13(3), 1499–1513.CrossRefGoogle Scholar
  12. 12.
    Zi, R., Ge, X., Thompson, J., et al. (2016). Energy efficiency optimization of 5G radio frequency Chain systems. IEEE Journal on Selected Areas in Communications,34(4), 758–771.CrossRefGoogle Scholar
  13. 13.
    Ha, V. N., Nguyen, D. H. N., & Frigon, J.-F. (2018). Energy-efficient hybrid precoding for mmWave multi-user systems. In 2018 IEEE international conference on communications (ICC) (pp. 1–6).Google Scholar
  14. 14.
    He, S., Qi, C., Wu, Y., et al. (2016). Energy-efficient transceiver design for hybrid sub-array architecture MIMO systems. IEEE Transactions on Wireless Communications,4, 9895–9905.Google Scholar
  15. 15.
    Ma, C., Shi, J., Huang, N., et al. (2016). Energy-efficient hybrid precoding for millimeter wave systems in MIMO interference channels. In Vehicular technology conference. IEEE.Google Scholar
  16. 16.
    Vizziello, Anna, Savazzi, Pietro, & Chowdhury, Kaushik R. (2018). A Kalman based hybrid precoding for multi-user millimeter wave MIMO systems. IEEE Access,6, 55712–55722.CrossRefGoogle Scholar
  17. 17.
    Huang, H., Song, Y., Yang, J., et al. (2019). Deep-learning-based millimeter-wave massive MIMO for hybrid precoding. IEEE Transactions on Vehicular Technology,68(3), 3027–3032.CrossRefGoogle Scholar
  18. 18.
    Elbir, A. M. (2019). CNN-based precoder and combiner design in mmWave MIMO systems. IEEE Communications Letters, 23(7), 1240–1243.CrossRefGoogle Scholar
  19. 19.
    Li, M., Liu, W., Tian, X., et al. (2018). Iterative hybrid precoder and combiner design for mmWave MIMO-OFDM systems. Wireless Networks, 25(8), 4829–4837.CrossRefGoogle Scholar
  20. 20.
    Magueta, R., Castanheira, D., Silva, A., et al. (2016). Hybrid iterative space-time equalization for multi-user mmW massive MIMO systems. IEEE Transactions on Communications,65(2), 608–620.CrossRefGoogle Scholar
  21. 21.
    Nickabadi, A., Ebadzadeh, M. M., & Safabakhsh, R. (2011). A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing,11(4), 3658–3670.CrossRefGoogle Scholar
  22. 22.
    Shelokar, P. S., Siarry, P., Jayaraman, V. K., et al. (2007). Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Applied Math and Computation,188(1), 129–142.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of Science and Technology BeijingBeijingChina

Personalised recommendations