Advertisement

Optimal ZF Precoder Under per Antenna Power with Conjugate Beamforming for MU Massive MIMO Systems

  • James Kweku Nkrumah Nyarko
  • Christian Ango Mbom
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 237)

Abstract

In this paper, we deliberate on multiuser massive multiple-input multiple-output (MU-MIMO) system in designing optimal zero forcing (ZF) precoder under per antenna power constraint. MU massive MIMO with non-square matrix is restrained by the large channel matrix dimension, conjugate beamforming maximization approach is developed to align the channel matrix for the optimal ZF precoder. We further introduced complex lattice reduction (CLR) to transform the lattice bases of the channel matrix and shorten the basis vector, thus meliorates the orthogonality of the conjugate beamforming. Simulation results show LR-based optimal ZF precoder outperforms other precoding schemas. The LR-based optimal ZF precoder improved the beamforming for the base station (BS) to focus on the users, thus improving spatial multiplexing gain and diversity order. As BS antennas and users turn large, the sum rate over the subchannels depends on the dominance of users (that is BS antennas to user antennas ratio) for the channel gain. Thus performance of the LR-based precoder schema under per antenna power can help save power in practical massive MIMO implementation.

Keywords

MU massive MIMO Zero forcing (ZF) precoder Conjugate beamforming Lattice reduction (LR) Per antenna power 

References

  1. 1.
    Rusek, F., Person, D., Lau, B.K., Larsson, E.G., Marzetta, T.L.: Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Sig. Proc. Mag. 30, 40–60 (2013)CrossRefGoogle Scholar
  2. 2.
    Ngo, H.Q., Larsson, E.G., Marzetta, T.L.: Energy and spectral efficiency of very large MU MIMO systems. IEEE Trans. Commun 61(4), 1436–1449 (2013)CrossRefGoogle Scholar
  3. 3.
    Zu, K., Lamare, R.C.: Low - complexity lattice reduction-aided regularized block diagonalization for MU-MIMO system. IEEE Commun. Lett. 16(6), 925–928 (2012)CrossRefGoogle Scholar
  4. 4.
    Serbetli, S., Yener, A.: Transceiver optimization for multiuser MIMO systems. IEEE Trans. Sig. Process. 52(1), 214–226 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Yao, R., Nan, H., Xu, J., Li, G.: Optimal BD-ZF precoder for multi-user MIMO downlink transmission. Electron. Lett. 51(14), 1121–1123 (2015)CrossRefGoogle Scholar
  6. 6.
    Luo, Z.Q., Yu, W.: An introduction to convex optimization for communications and signal processing. IEEE J. Sel. Areas Commun. 24(8), 1426–1438 (2006)CrossRefGoogle Scholar
  7. 7.
    Boyed, S., Vandenerghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  8. 8.
    Perez-Cruz, F., Rodrigues, M.R.D., Verdu, S.: MIMO Guassian channel with arbitrary inputs: optimal precoding and power allocation. IEEE Trans. Inf. Theory 56(3), 1070–1083 (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Kaviani, S., Krzymien, W.A.: On the optimality of multiuser ZF precoding in MIMO broadcast channels. In: IEEE VTC Spring (2009)Google Scholar
  10. 10.
    Vu, M.: MISO capacity with per-antenna power constraint. IEEE Trans. Commun. 59(5), 1268–1274 (2011)CrossRefGoogle Scholar
  11. 11.
    Zu, K., Song, B., Haardt, M., Lamare R.C.: Flexible coordinated beamforming with lattice reduction for MU massive MIMO systems. In: IEEE EUSIPCO (2014)Google Scholar
  12. 12.
    Lutkepohl, H.: Handbook of Matrices. Wiley, Hoboken (1996)zbMATHGoogle Scholar
  13. 13.
    Bremner, M.R.: Lattice Basis Reduction: An Introduction to the LLL Algorithm and Its Applications. Taylor & Francis Group, London (2012)zbMATHGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • James Kweku Nkrumah Nyarko
    • 1
  • Christian Ango Mbom
    • 1
  1. 1.School of Electronics and Information EngineeringNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China

Personalised recommendations