Massive MIMO Systems

  • Fernando Gregorio
  • Gustavo González
  • Christian Schmidt
  • Juan Cousseau
Part of the Signals and Communication Technology book series (SCT)


The development of massive multiple-input multiple-output (MaMIMO) techniques is motivated by the requirements of large spectral efficiency and reduced power consumption. The implementation of a large number of antennas offers a large spectral efficiency and link reliability. Moreover, the use of MaMIMO allows scaling down the transmitted power proportionally to the number of antennas used, which may lead to a significant improvement in terms of energy efficiency. Orthogonal frequency division multiplexing (OFDM) in combination with MaMIMO has a considerable potential to obtain very high data rates and high quality of service (QoS). However, MaMIMO systems require a mobile equipped with multiple antennas at the transmitter. This is a challenging issue in mobile devices mostly due to their size, cost, and computing power limitations. In MaMIMO, the radiated power per antenna decreases linearly with the number of antennas. Moreover, the effects of small-scale fading, non-coherent interference, and receiver noise are minimized. However, a massive number of antennas require a separate transceiver chain and power amplifier (PA) for each antenna (unless analog or hybrid analog-digital structures are used for beamforming purposes, in which case the number of RF chains can be reduced). In this situation, the size and costs of the analog front-end become a critical issue. The cost and size optimization implies the use of low-cost components which increase the imperfections that degrade the system performance. In this chapter, MaMIMO system performance, considering front-end RF imperfections and ADC/DAC with limited resolution, is carefully studied. Spectral and energy efficiency for the uplink and downlink scenarios are evaluated to quantify the overall system performance. Finally, low-resolution precoding techniques, antenna coupling, channel non-reciprocity, and channel estimation errors are also addressed.


  1. 1.
    E.G. Larsson, O. Edfors, F. Tufvesson, T.L. Marzetta, Massive MIMO for next generation wireless systems. IEEE Commun. Mag. 52(2), 186–195 (2014)CrossRefGoogle Scholar
  2. 2.
    F. Rusek, D. Persson, B.K. Lau, E.G. Larsson, T.L. Marzetta, O. Edfors, F. Tufvesson, Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Process. Mag. 30(1), 40–60 (2013)CrossRefGoogle Scholar
  3. 3.
    L. Lu, G.Y. Li, A.L. Swindlehurst, A. Ashikhmin, R. Zhang, An overview of massive MIMO: benefits and challenges. IEEE J. Sel. Top. Signal Process. 8(5), 742–758 (2014)CrossRefGoogle Scholar
  4. 4.
    H.Q. Ngo, E.G. Larsson, T.L. Marzetta, Energy and spectral efficiency of very large multiuser MIMO systems. IEEE Trans. Commun. 61(4), 1436–1449 (2013)CrossRefGoogle Scholar
  5. 5.
    C.-S. Park, Y.-S. Byun, A.M. Bokiye, Y.-H. Lee, Complexity reduced zero-forcing beamforming in massive MIMO systems, in Information Theory and Applications Workshop (ITA) (2014), pp. 1–5Google Scholar
  6. 6.
    E. Björnson, J. Hoydis, M. Kountouris, M. Debbah, Massive MIMO systems with non-ideal hardware: energy efficiency, estimation, and capacity limits. IEEE Trans. Inf. Theory 60(11), 7112–7139 (2014)MathSciNetCrossRefGoogle Scholar
  7. 7.
    N. Jindal, MIMO broadcast channels with finite-rate feedback. IEEE Trans. Inf. Theory 52(11), 5045–5060 (2006)MathSciNetCrossRefGoogle Scholar
  8. 8.
    O. Raeesi, A. Gokceoglu, P.C. Sofotasios, M. Renfors, M. Valkama, Modeling and estimation of massive MIMO channel non-reciprocity: sparsity-aided approach, in 2017 25th European Signal Processing Conference (EUSIPCO) (2017), pp. 2596–2600Google Scholar
  9. 9.
    O. Raeesi, A. Gokceoglu, Y. Zou, E. Björnson, M. Valkama, Performance analysis of multi-user massive MIMO downlink under channel non-reciprocity and imperfect CSI. IEEE Trans. Commun. 66(6), 2456–2471 (2018)CrossRefGoogle Scholar
  10. 10.
    Y. Zou, O. Raeesi, R. Wichman, A. Tolli, M. Valkama, Analysis of channel non-reciprocity due to transceiver and antenna coupling mismatches in TDD precoded multi-user MIMO-OFDM downlink, in 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall) (2014), pp. 1–7Google Scholar
  11. 11.
    J. Choi, Downlink multiuser beamforming with compensation of channel reciprocity from RF impairments. IEEE Trans. Commun. 63(6), 2158–2169 (2015)CrossRefGoogle Scholar
  12. 12.
    J. Vieira, F. Rusek, F. Tufvesson, Reciprocity calibration methods for massive MIMO based on antenna coupling, in 2014 IEEE Global Communications Conference (2014), pp. 3708–3712Google Scholar
  13. 13.
    H. Wei, D. Wang, H. Zhu, J. Wang, S. Sun, X. You, Mutual coupling calibration for multiuser massive MIMO systems. IEEE Trans. Wireless Commun. 15(1), 606–619 (2016)CrossRefGoogle Scholar
  14. 14.
    T.L. Marzetta, Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans. Wireless Commun. 9(11), 3590–3600 (2010)CrossRefGoogle Scholar
  15. 15.
    O. Elijah, C.Y. Leow, T.A. Rahman, S. Nunoo, S.Z. Iliya, A comprehensive survey of pilot contamination in massive MIMO 5G system. IEEE Commun. Surv. Tuts. 18(2), 905–923 (2016)CrossRefGoogle Scholar
  16. 16.
    F. Rusek, D. Persson, B.K. Lau, E.G. Larsson, T.L. Marzetta, O. Edfors, F. Tufvesson, Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Process. Mag. 30(1), 40–60 (2013)CrossRefGoogle Scholar
  17. 17.
    K. Upadhya, S.A. Vorobyov, M. Vehkaper, Downlink performance of superimposed pilots in massive MIMO systems. IEEE Trans. Wireless Commun. 17(10), 6630–6644 (2018)CrossRefGoogle Scholar
  18. 18.
    E. Björnson, E.G. Larsson, M. Debbah, Massive MIMO for maximal spectral efficiency: how many users and pilots should be allocated? IEEE Trans. Wireless Commun. 15(2), 1293–1308 (2016)CrossRefGoogle Scholar
  19. 19.
    K. Upadhya, S.A. Vorobyov, M. Vehkapera, Superimposed pilots: an alternative pilot structure to mitigate pilot contamination in massive MIMO, in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2016), pp. 3366–3370Google Scholar
  20. 20.
    K. Upadhya, S.A. Vorobyov, An array processing approach to pilot decontamination for massive MIMO, in 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (2015), pp. 453–456Google Scholar
  21. 21.
    H.Q. Ngo, E.G. Larsson, EVD-based channel estimation in multicell multiuser MIMO systems with very large antenna arrays, in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2012), pp. 3249–3252Google Scholar
  22. 22.
    R.R. Müller, L. Cottatellucci, M. Vehkaperä, Blind pilot decontamination. IEEE J. Sel. Top. Signal Process. 8(5), 773–786 (2014)CrossRefGoogle Scholar
  23. 23.
  24. 24.
    M. Sarajli, L. Liu, O. Edfors, When are low resolution ADCs energy efficient in massive MIMO? IEEE Access 5, 14837–14853 (2017)CrossRefGoogle Scholar
  25. 25.
    D. Verenzuela, E. Björnson, M. Matthaiou, Hardware design and optimal ADC resolution for uplink massive MIMO systems, in 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), (2016), pp. 1–5Google Scholar
  26. 26.
    M. Sarajlic, L. Liu, O. Edfors, An energy efficiency perspective on massive MIMO quantization, in 2016 50th Asilomar Conference on Signals, Systems and Computers (2016), pp. 473–478Google Scholar
  27. 27.
    Y. Li, C. Tao, G. Seco-Granados, A. Mezghani, A.L. Swindlehurst, L. Liu, Channel estimation and performance analysis of one-bit massive MIMO systems. IEEE Trans. Signal Process. 65(15), 4075–4089 (2017)MathSciNetCrossRefGoogle Scholar
  28. 28.
    J.J. Bussgang, Cross correlation function of amplitude-distorted Gaussian input signals, Res. Lab Electron., M.I.T., Cambridge, MA, Tech. Rep. 216, vol. 3 (1952)Google Scholar
  29. 29.
    S. Jacobsson, G. Durisi, M. Coldrey, C. Studer, Linear precoding with low-resolution DACs for massive MU-MIMO-OFDM downlink. IEEE Trans. Wireless Commun. 18(3), 1595–1609 (2019)CrossRefGoogle Scholar
  30. 30.
    C. Desset, L. Van der Perre, Validation of low-accuracy quantization in massive MIMO and constellation EVM analysis, in 2015 European Conference on Networks and Communications (EuCNC) (2015), pp. 21–25Google Scholar
  31. 31.
    E. Björnson, L. Sanguinetti, J. Hoydis, M. Debbah, Optimal design of energy-efficient multi-user MIMO systems: is massive MIMO the answer? IEEE Trans. Wireless Commun. 14(6), 3059–3075 (2015).CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Fernando Gregorio
    • 1
  • Gustavo González
    • 1
  • Christian Schmidt
    • 1
  • Juan Cousseau
    • 1
  1. 1.Dpto. de Ing. Eléctrica y de Computadoras Universidad Nacional del Sur (UNS), Instituto de Inv. en Ing. Eléctrica “Alfredo Desages” (IIIE), UNS-CONICETBahía BlancaArgentina

Personalised recommendations