Advertisement

Cluster Computing

, Volume 22, Supplement 4, pp 8581–8588 | Cite as

Interference management based on power control and MU-MIMO

  • Yang WangEmail author
  • Jianbiao He
  • Bo Zou
Article
  • 46 Downloads

Abstract

Improvement of spectrum efficiency is a valuable research topic in mobile communication system, which impact cell edge user experience especially. According to current research results, three methods to improve inter-cell spectrum efficiency base on inter-cell interference management, including inter-cell interference randomization, inter-cell interference cancellation and inter-cell interference co-ordination. In this paper, one combinatorial method based on uplink power control and SINR prediction with non-ideal MU-MIMO CSI feedback is proposed for optimal management of inter-cell interference co-ordination. The simulation results show that this method performs very well in complex inter-cell interference scenario, and the average spectrum efficiency is improved more than 50%.

Keywords

Interference management Inter-cell interference co-ordination Power control MU-MIMO Inter-cell spectrum efficiency 

Notes

Acknowledgements

This paper was supported by Guangdong IIOT(M-S) Engineering Technology Center (No.2015-1487) and Shenzhen IIOT engineering Laboratory (Shenzhen Polytech, No.2017-713).

References

  1. 1.
    Andrews, J.G., et al.: What will 5G be? IEEE J. Sel. Areas Commun. 32(6), 1065–1082 (2014)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Ghosh, A.: The 5G mmWave radio revolution. Microw. J. 59(9), 22–36 (2016)Google Scholar
  3. 3.
    Ghosh, A., et al.: Millimeter-wave enhanced local area systems: a high-data-rate approach for future wireless networks. IEEE J. Sel. Areas Commun. 32(6), 1152–1163 (2014)CrossRefGoogle Scholar
  4. 4.
    Björnson, E., Larsson, E.G., Marzetta, T.L.: Massive MIMO: ten myths and one critical question. IEEE Commun. Mag. 54(2), 114–123 (2016)CrossRefGoogle Scholar
  5. 5.
    Du, J., Valenzuela, R.A.: How much spectrum is too much in millimeter wave wireless access. IEEE J. Sel. Areas Commun. 35(7), 1444–1458 (2017)CrossRefGoogle Scholar
  6. 6.
    Rangan, S., Rappaport, T.S., Erkip, E.: Millimeter-wave cellular wireless networks: potentials and challenges. Proc. IEEE 102(3), 366–385 (2014)CrossRefGoogle Scholar
  7. 7.
    Heath Jr., R.W., González-Prelcic, N., Rangan, S., Roh, W., Sayeed, A.M.: An overview of signal processing techniques for millimeter wave MIMO systems. IEEE J. Sel. Topics Signal Process. 10(3), 436–453 (2016)CrossRefGoogle Scholar
  8. 8.
    Song, L., Han, Z., Zhang, Z., Jiao, B.: Non-cooperative feedback-rate control game for channel state information in wireless networks. IEEE J. Select. Areas Commun. 30(1), 188–197 (2012)CrossRefGoogle Scholar
  9. 9.
    Adhikary, A., et al.: Joint spatial division and multiplexing for mm-Wave channels. IEEE J. Sel. Areas Commun. 35(7), 1239–1255 (2014)CrossRefGoogle Scholar
  10. 10.
    Andrews, J.G., Bai, T., Kulkarni, M.N., Alkhateeb, A., Gupta, A.K., Heath Jr., R.W.: Modeling and analyzing millimeter wave cellular systems. IEEE Trans. Commun. 65(1), 403–430 (2017)Google Scholar
  11. 11.
    Gao, Z., Dai, L., Mi, D., Wang, Z., Imran, M.A., Shaki, M.Z.: MmWave massive-MIMO-based wireless backhaul for the 5G ultra-dense network. IEEE Wireless Commun. 22(5), 13–21 (2015)CrossRefGoogle Scholar
  12. 12.
    Pi, Z., Choi, J., Heath Jr., R.W.: Millimeter-wave gigabit broad band revolution toward 5G: fixed access and backhaul. IEEE Commun. Mag. 54(4), 138–144 (2016)CrossRefGoogle Scholar
  13. 13.
    Zhang, Y.-P., Wang, P., Goldsmith, A.: Rainfall effect on the performance of millimeter-wave MIMO systems. IEEE Trans. Wireless Commun. 14(9), 4857–4866 (2015)CrossRefGoogle Scholar
  14. 14.
    Sun, C., Gao, X.Q., Jin, S., Matthaiou, M., Ding, Z., Xiao, C.: Beam division multiple access transmission for massive MIMO communications. IEEE Trans. Commun. 63(6), 2170–2184 (2017)CrossRefGoogle Scholar
  15. 15.
    Björnson, E., Matthaiou, M., Debbah, M.: Massive MIMO with non-ideal arbitrary arrays: hardware scaling laws and circuit-aware design. IEEE Trans. Wireless Commun. 14(8), 4353–4368 (2015)CrossRefGoogle Scholar
  16. 16.
    Roivainen, A., Dias, C.F., Tervo, N., Hovinen, V., Sonkki, M., Latva-Aho, M.: Geometry-based stochastic channel model for two-story lobby environment at 10 GHz. IEEE Trans. Antennas Propag. 64(9), 3990–4003 (2016)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Zhang, C., Huang, Y., Jing, Y., Jin, S., Yang, L.: Sum-rate analysis for massive MIMO downlink with joint statistical beamforming and user scheduling. IEEE Trans. Wireless Commun. 16(4), 2181–2194 (2017)CrossRefGoogle Scholar
  18. 18.
    Huang, J., Wang, C.-X., Feng, R., Sun, J., Zhang, W., Yang, Y.: Multi-frequency mmWave massive MIMO channel measurements and characterization for 5G wireless communication systems. IEEE J. Sel. Areas Commun. 35(7), 1591–1605 (2017)CrossRefGoogle Scholar
  19. 19.
    MacCartney, G.R., Rappaport, T.S.: Rural macrocell path loss models for millimeter wave wireless communications. IEEE J. Sel. Areas Commun. 35(7), 1663–1677 (2017)CrossRefGoogle Scholar
  20. 20.
    Ai, B., et al.: On indoor millimeter wave massive MIMO channels: measurement and simulation. IEEE J. Sel. Areas Commun. 35(7), 1678–1690 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronical and Communication EngineeringShenzhen PolytechnicShenzhenChina

Personalised recommendations