Average power allocation based sum-rate optimization in massive MIMO systems



This paper exploits variations in the average channel gains in multi-cell multi-user massive multiple input multiple output (MIMO) systems. An average transmit power-control-based sum-rate optimization scheme is presented for the uplink of the system. The matched filtering (MF) and the zero forcing (ZF) processors are considered with perfect and imperfect channel state information at receiver (CSIR) under frequency flat Rayleigh fading channel. An average power-control-based system model is constructed for analyzing the sum-rate and formulating an optimization problem. A discrete level combinatorial optimization is performed for MF and ZF sum-rate under perfect and imperfect CSIR. The numerical results show a significant improvement in the sum-rate and power consumption. A low complexity algorithm for numerical optimization of the sum-rate is proposed. The performance of algorithm is quantified with different scenarios including different number of users, macro cells, and micro cells with low and high inter-cell interference powers. The evaluation results show that the improvement in sum-rate and energy efficiency increases with inter-cell interference power and the number of MTs.


Massive MIMO Sum-rate Power-control Match filtering Zero forcing 


  1. 1.
    Marzetta TL (2010) Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans Wirel Commun 9(11):3590–3600CrossRefGoogle Scholar
  2. 2.
    Lu L et al (2014) An overview of massive MIMO: Benefits and challenges. IEEE J Sel Topics Signal Process 8(5):742–758CrossRefGoogle Scholar
  3. 3.
    Larsson EG, Edfors O, Tufvesson F, Marzetta TL (2014) Massive MIMO for next generation wireless systems. IEEE Commun Mag 52(2):186–195CrossRefGoogle Scholar
  4. 4.
    Björnson E, Larsson EG, Marzetta TL (2016) Massive MIMO: ten myths and one critical question. IEEE Commun Mag 54(2):114–123CrossRefGoogle Scholar
  5. 5.
    Chockalingam A, Rajan BS (2014) Large MIMO systems. Cambridge University Press, CambridgeGoogle Scholar
  6. 6.
    Ngo HQ, Larsson EG, Marzetta TL (2013) The multicell multiuser MIMO uplink with very large antenna arrays and a finite-dimensional channel. IEEE Trans Commun 61(6):2350–2361CrossRefGoogle Scholar
  7. 7.
    Spencer QH, Peel CB, Swindlehurst AL, Haardt M (2004) An introduction to the multi-user MIMO downlink. IEEE Commun Mag 42(10):60–67CrossRefGoogle Scholar
  8. 8.
    de Lamare RC (2013) Massive MIMO systems: Signal processing challenges and research trends. arXiv:1310.7282
  9. 9.
    Rusek F, Persson D, Lau BK, Larsson EG, Marzetta TL, Edfors O et al (2013) Scaling up MIMO opportunities and challenges with very large arrays. IEEE Sig Process 30(1):40–60CrossRefGoogle Scholar
  10. 10.
    Edelman A, Rao NR (2005) Random matrix theory. Cambridge university press, CambridgeMATHGoogle Scholar
  11. 11.
    Tulino A, Verdu S (2004) Random matrix theory and wireless communications. Foundation and Trends in Communications and Information Theory. Now Publishers, Inc., DelftMATHGoogle Scholar
  12. 12.
    Ngo HQ, Larsson EG, Marzetta TL (2013) Energy and spectral efficiency of very large multiuser MIMO systems. IEEE Trans Commun 61(4):1436–1449CrossRefGoogle Scholar
  13. 13.
    Ngo HQ, Matthaiou M, Duong TQ, Larsson EG (2013) Uplink performance analysis of multicell MU-SIMO systems with ZF receivers. IEEE Trans Veh Technol 62(9):4471–4482CrossRefGoogle Scholar
  14. 14.
    Kong C, Zhong C, Papazafeiropoulos AK, Matthaiou M, Zhang Z (2015) Sum-rate and power scaling of massive MIMO systems with channel aging. IEEE Trans Commun 63(12):4879–4893CrossRefGoogle Scholar
  15. 15.
    Liu L, Matolak DW, Tao C, Li Y, Chen H (2016) Sum-rate capacity investigation of multiuser massive MIMO uplink systems in semi-correlated channels. In: Vehicular Technology Conference (VTC Spring). IEEE, NanjingGoogle Scholar
  16. 16.
    Erceg V, Greenstein LJ, Tjandra SY, Parkoff SR, Gupta A, Kulic B et al (1999) An empirically based path loss model for wireless channels in suburban environments. IEEE J Sel Areas Commun 17(7):1205–211CrossRefGoogle Scholar
  17. 17.
    Zarei S, Aulin J, Gerstacker W, Schober R (2017) Max-min multicell-aware precoding and power allocation for downlink massive MIMO systems. IEEE Sig Process Lett 24(10):1433–1437CrossRefGoogle Scholar
  18. 18.
    Nayebi E, Ashikhmin A, Marzetta TL, Yang H, Rao BD (2017) Precoding and power optimization in cell-free massive MIMO systems. IEEE Trans Wirel Commun 16(7):4445–4459CrossRefGoogle Scholar
  19. 19.
    Zhang J, Jiang Y, Li P, Zheng F, You X (2016) Energy efficient power allocation in massive MIMO systems based on standard interference function. In: Vehicular Technology Conference (VTC Spring). IEEE, NanjingGoogle Scholar
  20. 20.
    Björnson E, Sanguinetti L, Hoydis J, Debbah M (2015) Optimal design of energy-efficient multi-user MIMO systems: Is massive MIMO the answer. IEEE Trans Wirel Commun 14(6):3059–3075CrossRefGoogle Scholar
  21. 21.
    Nguyen TM, Ha VN, Le LB (2015) Resource allocation optimization in multi-user multi-cell massive MIMO networks considering pilot contamination. IEEE Access 3:1272– 1287CrossRefGoogle Scholar
  22. 22.
    Dai Y, Dong X (2016) Power allocation for multi-pair massive MIMO two-way AF relaying with linear processing. IEEE Trans Wireless Commun 15(9):5932–5946CrossRefGoogle Scholar
  23. 23.
    Li Y, Fan P, Leukhin A, Liu L (2017) On the spectral and energy efficiency of full-duplex small-cell wireless systems with massive MIMO. IEEE Trans. Veh. Technol 66(3):2339–2353CrossRefGoogle Scholar

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© Institut Mines-Télécom and Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringIndian Institute of Technology JodhpurRajasthanIndia

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