Low Complexity Lattice Reduction Aided Detectors for High Load Massive MIMO Systems

  • Thanh-Binh NguyenEmail author
  • Minh-Tuan Le
  • Vu-Duc Ngo


In this paper, a very low complexity Lattice Reduction technique, called Dual Shortest Longest Vector algorithm (SLV), is adopted to improve the Bit Error Rate (BER) performance of the Minimum Mean Square Error (MMSE) detector in high-load Massive MIMO systems, whereby resulting in the so-called SLV-aided MMSE (MMSE–SLV) detector. An efficient combination scheme of Generalized Group Detection (GGD) algorithm and the MMSE–SLV, called MMSE–GGD–SLV, is further proposed to enhance BER performance of the system more significantly. In order to do so, we first convert the Group Detection approach to the generalized one (GGD) by creating an arbitrary number of sub-systems. Then, an additional operation, i.e., channel matrix sorting, is applied to the GGD to reduce the error propagation between sub-systems. To make the detection complexities of the MMSE–GGD–SLV detector more practical, the MMSE–SLV detection procedure is only applied to the first sub-system. Various BER performance simulations and complexity analysis show that both the MMSE–GGD–SLV and the MMSE–SLV detectors noticeably outperform their conventional MMSE counterpart, yet at the cost of higher detection complexities. However, their complexities are kept at acceptable levels, which are much lower than those of the conventional BLAST detector. Therefore, the proposed detectors are very good candidates for signal recovery in high load Massive MIMO systems.


Massive MIMO Low complexity receiver Group detection Lattice reduction aided detector 



  1. 1.
    Ngo, H. Q. (2015). Massive MIMO: Fundamentals and system designs (Vol. 1642). Linköping: Linköping University Electronic Press.Google Scholar
  2. 2.
    Marzetta, T. L. (2015). Massive mimo: An introduction. Bell Labs Technical Journal, 20, 11–22.CrossRefGoogle Scholar
  3. 3.
    Marzetta, T. L., Larsson, E. G., Yang, H., & Ngo, H. Q. (2016). Fundamentals of massive MIMO. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  4. 4.
    Liu, T., Tong, J., Guo, Q., Xi, J., Yu, Y., & Xiao, Z. (2017). Energy efficiency of uplink massive mimo systems with successive interference cancellation. IEEE Communications Letters, 21(3), 668–671.CrossRefGoogle Scholar
  5. 5.
    Choi, J. W., & Shim, B. (2014). New approach for massive mimo detection using sparse error recovery. In Global communications conference (GLOBECOM), 2014 IEEE, IEEE (pp. 3754–3759).Google Scholar
  6. 6.
    Chockalingam, A., & Rajan, B. S. (2014). Large MIMO systems. Cambridge: Cambridge University Press.Google Scholar
  7. 7.
    Nguyen, T. B., Nguyen, T. D., Le, M. T., & Ngo, V. D. (2017). Efficiency zero forcing detectors based on group detection algorithm for massive mimo systems. In: 2017 International conference on advanced technologies for communications (ATC) (pp. 48–53).Google Scholar
  8. 8.
    Zhou, Q., & Ma, X. (2013). Element-based lattice reduction algorithms for large mimo detection. IEEE Journal on Selected Areas in Communications, 31(2), 274–286.CrossRefGoogle Scholar
  9. 9.
    Li, T., Patole, S., & Torlak, M. (2014). A multistage linear receiver approach for mmse detection in massive mimo. In 2014 48th Asilomar conference on signals, systems and computers (pp. 2067–2072). IEEE.Google Scholar
  10. 10.
    Hassibi, B. (2000). A fast square-root implementation for blast. In Conference record of the thirty-fourth asilomar conference on signals, systems and computers (Vol. 2, pp. 1255–1259). IEEE.Google Scholar
  11. 11.
    Ma, X., & Zhang, W. (2008). Performance analysis for mimo systems with lattice-reduction aided linear equalization. IEEE Transactions on Communications, 56(2), 309–318.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Bai, L., Choi, J., & Yu, Q. (2014). Low complexity MIMO receivers. Berlin: Springer.CrossRefGoogle Scholar
  13. 13.
    Tran, X. N., Ho, H. C., Fujino, T., & Karasawa, Y. (2008). Performance comparison of detection methods for combined stbc and sm systems. IEICE Transactions on Communications, 91(6), 1734–1742.CrossRefGoogle Scholar
  14. 14.
    Nguyen, T. B., Le, M. T., Ngo, V. D., & Nguyen, V. G. (2018). Parralel group detection approach for massive mimo systems. In 2018 International conference on advanced technologies for communications (ATC) (pp. 160–165).Google Scholar
  15. 15.
    Golub, G. H., & Van Loan, C. F. (2012). Matrix computations (Vol. 3). Baltimore: JHU Press.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Radio ElectronicsLe Quy Don Technical UniversityHanoiVietnam
  2. 2.MobiFone Research and Development CenterMobiFone CorporationHanoiVietnam
  3. 3.Hanoi University of Science and TechnologyHanoiVietnam

Personalised recommendations