Detection for Uplink Massive MIMO System: A Survey

  • Lin Li
  • Weixiao MengEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 301)


In this paper, we make a compressive survey for the research on detection in uplink Massive multiple input and multiple output (MIMO) system. As one key technology in Massive MIMO system, which is also one primary subject for the fifth generation wireless communications, this research is significant to be developed. As a result of large scaled antennas, the channel gain matrix in Massive MIMO system is asymptotic diagonal orthogonal, and it is an non-deterministic polynomial hard problem to obtain the optimum bits error rate (BER) performance during finite polynomial complexity time. The traditional detection algorithms for MIMO system are not efficient any more due to poor BER performance or high computational complexity. The exiting detection algorithms for Massive MIMO system are able to solve this issue. However, there are still crucial problems for them, including employing the deep learning technology for detection in Massive MIMO system, and not work for the millimeter wave Massive MIMO system in the strong spatial correlation environment even exiting keyhole effect, which is not rich scattering, as well as application in Hetnets wireless communications, and etc. Therefore, the research on detection for uplink Massive MIMO system is still in its early stage, there are lots of significant and urgent issues to overcome in the future.


Massive MIMO Low complexity detection Optimum performance 


  1. 1.
    Shafi, M., et al.: 5G: a tutorial overview of standards, trials, challenges, deployment, and practice. IEEE J. Sel. Areas Commun. 35(6), 1201–1221 (2017)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Björnson, E., Hoydis, J., Sanguinetti, L.: Massive MIMO has unlimited capacity. IEEE Trans. Wireless Commun. 17(1), 574–590 (2018)CrossRefGoogle Scholar
  3. 3.
    Matthaiou, M., Smith, J.P., Ngo, Q.H., Tataria, H.: Does massive MIMO fail in ricean channels? IEEE Commun. Lett. 8(1), 61–64 (2019)CrossRefGoogle Scholar
  4. 4.
    Yang, S., Hanzo, L.: Fifty years of MIMO detection: the road to large-scale MIMOs. IEEE Commun. Surv. Tutorials 17(4), 1941–1988 (2015)CrossRefGoogle Scholar
  5. 5.
    Mann, P., Sah, K.A., Budhiraja, R., Chaturvedi, K.A.: Bit-level reduced neighborhood search for low-complexity detection in large MIMO systems. IEEE Wireless Commun. Lett. 7(2), 146–149 (2018)CrossRefGoogle Scholar
  6. 6.
    Sah, K.A., Chaturvedi, K.A.: An unconstrained likelihood ascent based detection algorithm for large MIMO systems. IEEE Trans. Wireless Commun. 16(4), 2262–2273 (2017)CrossRefGoogle Scholar
  7. 7.
    Elghariani, A., Zoltowski, M.: Low complexity detection algorithms in large-scale MIMO systems. IEEE Trans. Wireless Commun. 15(3), 1689–1702 (2016)CrossRefGoogle Scholar
  8. 8.
    Hedstrom, C.J., Yuen, H.C., Chen, R., Farhang-Boroujeny, B.: Achieving near MAP performance with an excited markov chain Monte Carlo MIMO detector. IEEE Trans. Wireless Commun. 16(12), 7718–7732 (2017)CrossRefGoogle Scholar
  9. 9.
    Yang, S., Xu, X., Alanis, D., Ng, X.S., Hanzo, L.: Is the low-complexity mobile-relay-aided FFR-DAS capable of outperforming the high-complexity CoMP? IEEE Trans. Veh. Technol. 65(4), 2154–2169 (2016)CrossRefGoogle Scholar
  10. 10.
    Sen, P., Yılmaz, ö. A.: A low-complexity graph-based LMMSE receiver for MIMO ISI channels with \(M\)-QAM modulation. IEEE Trans. Wireless Commun. 16(2), 1185–1195 (2017)CrossRefGoogle Scholar
  11. 11.
    Mandloi, M., Bhatia, V.: Multiple stage ant colony optimization algorithm for near-OPTD large-MIMO detection. In: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 914–918. Nice (2015)Google Scholar
  12. 12.
    Huang, H., Song, Y., Yang, J., Gui, G., Adachi, F.: Deep-learning-based millimeter-wave massive MIMO for hybrid precoding. IEEE Trans. Veh. Technol. 68(3), 3027–3032 (2019)CrossRefGoogle Scholar
  13. 13.
    Wang, T., Wen, C., Jin, S., Li, Y.G.: Deep learning-based CSI feedback approach for time-varying massive MIMO channels. IEEE Wireless Commun. Lett. 8(2), 416–419 (2019)CrossRefGoogle Scholar
  14. 14.
    Chen, R., Xu, H., Wang, X., Li, J.: On the performance of OAM in keyhole channels. IEEE Wireless Commun. Lett. 8(1), 313–316 (2019)CrossRefGoogle Scholar
  15. 15.
    Chen, C., Zhao, X., Yuan, J.: Coverage analysis of inter-tier interference cancellation for massive MIMO HetNet with repulsion. IEEE Commun. Lett. 23(2), 350–353 (2019)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Communications Research CenterHarbin Institute of TechnologyHarbinChina

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