Cluster Computing

, Volume 22, Supplement 4, pp 7713–7722 | Cite as

Dual QR decomposition in K-best sphere detection for Internet of Things networks

  • B. Syed Moinuddin BokhariEmail author
  • M. A. Bhagyaveni


Internet of Things (IoT) in 5G communications is becoming more popular due to its robust features. Multiple input multiple output (MIMO) in IoT is playing a vital role nowadays with large and massive concepts. At the same time detection in such scenario is becoming complex day by day. Sphere decoding algorithms is one of the MIMO detection schemes used for achieving near-optimal maximum likelihood detection (MLD) performance. Preprocessing algorithms such as lattice reduction and QR-decomposition (QRD) has been widely used along with the variants of MIMO detection for achieving better bit error rate (BER) performance. This research paper proposes a novel dual QRD (DQRD) algorithm to obtain a universal low complex-lattice reduction aided (UL-LRA)—K-best sphere detection (KSD) for MIMO networks. The proposed algorithm is evaluated under additive white Gaussian noise flat fading and indoor task group ac (TGac-channel D) channel model. The results proved that the BER performance was enhanced as well as the average runtime complexity of LRA–KSD was reduced by 45 and 39% in a flat faded and channel D respectively compared to the optimal MLD using 16-QAM.


IoT 5G MIMO Maximum likelihood detection (MLD) Quadrature amplitude modulation (QAM) Bit error rate (BER) Runtime AWGN 


  1. 1.
    Wang, J., Daneshrad, B.: A comparative study of MIMO detection algorithms for wideband spatial multiplexing systems. In: Proceedings of IEEE Wireless Communication and Networking conference, 2005, vol. 1, pp. 408–413 (2005)Google Scholar
  2. 2.
    Waters, D.W.: Signal detection strategies and algorithms for multiple-input multiple-output channels. Doctoral of philosophy thesis, Georgia Institute of Technology (2005)Google Scholar
  3. 3.
    Kim, T.K., Kim, H.M., Im, G.H.: Enhanced QRD-M algorithm for soft-output MIMO detection. In: Proceedings of Signal Processing for Communications Symposium-Global Communications Conference (GLOBECOM), pp. 3572–3576. IEEE, Anaehim, CA (2012)Google Scholar
  4. 4.
    Zhu, X., Murch, R.D.: Performance analysis of maximum likelihood detection in a MIMO antenna system. IEEE Trans. Commun. 50(2), 187–191 (2002)CrossRefGoogle Scholar
  5. 5.
    Hasibbi, B., Vikalo, H.: On the sphere-decoding algorithm I. Expected complexity. IEEE Trans. Commun. 53(8), 2806–2818 (2005)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Huang, C.J., Sung, C.S., Lee, T.S.: A near-ML complex K-best decoder with efficient search design for MIMO systems. EURASIP J. Adv. Signal Process. 2010, 1–18 (2010)Google Scholar
  7. 7.
    Piao, C., et al.: Dynamic K-best sphere decoding algorithms for MIMO detection. Sci. Res. Publ. 5(38), 103–107 (2013)Google Scholar
  8. 8.
    Lou, X., et al.: Research on low complexity K-best sphere decoding algorithm for MIMO systems. Wirel. Pers. Commun. 84, 53–56 (2015)CrossRefGoogle Scholar
  9. 9.
    Guo, X., Yuan, S.: A new tree pruning SD algorithm to eliminate interference. Int. J. Digit. Content Technol. Appl. JDCTA 7(7), 476–482 (2013)Google Scholar
  10. 10.
    Thomas, R., Knopp, R., Maharaj, B.T., Cottatellucci, L.: Detection using Block QR decomposition for MIMO HetNets. In: Proceedings of Asilomar Conference on Signals, Systems and Computers, Pacific Groove, CA (2014)Google Scholar
  11. 11.
    Lenstra, A.K., Lenstra Jr., H.W., Lováz, L.: Factoring polynomials with rational coefficients. Math. Ann. 261, 515–534 (1982)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ling, C., Mow, W.H., Howgrave-Graham, N.: Reduced and fixed-complexity variants of the LLL algorithm for communications. IEEE Trans. Commun. 61(3), 1040–1050 (2013)CrossRefGoogle Scholar
  13. 13.
    Zhang, W., Qiao, S., Wei, Y.: A diagonal lattice reduction algorithm for MIMO detection. IEEE Signal Process. Lett. 19(5), 311–314 (2012)CrossRefGoogle Scholar
  14. 14.
    Wübben, D., Seethaler, D., Jaldén, J., Matz, G.: Lattice reduction - a survey with applications in wireless communications. In: IEEE Signal processing Magazine, pp. 70–91 (2011)CrossRefGoogle Scholar
  15. 15.
    Demmel, J.W.: Applied Numerical Algebra, 2nd edn. SIAM Publications, Prentice-Hall, Englewood Cliffs, NJ (1997)CrossRefGoogle Scholar
  16. 16.
    Ding, Z., Dai, L., Poor, V.H.: MIMO-NOMA design for small packet transmission in the Internet of Things. In: IEEE Access: Internet of Things (IoT) in 5G Wireless Communications, vol. 4, pp. 1393–1405 (2016)CrossRefGoogle Scholar
  17. 17.
    Arar, M., Yongacoglu, A.: Parallel low-complexity MIMO detection algorithm using QR decomposition and Alamouti space-time code. In: Proceedings of European Wireless Conference, pp. 141–148 (2010)Google Scholar
  18. 18.
    Syed Moinuddin Bokhari, B., Bhagyaveni, M.A.: Performance evaluation of K-best sphere detector using recursive Block QR decomposition. In: Proceedings of International Conference on Next Generation Computing and Communication Technologies (ICNGCCT-15), Dubai, pp. 134–138 (2015)Google Scholar
  19. 19.
    Oestges, C.: Validity of the Kronecker model for MIMO correlated channels. In: Proceedings of IEEE Vehicular Technology Conference, pp. 2818–2822, Melbourne, VIC (2006)Google Scholar
  20. 20.
    Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, Amendment 4: Enhancement for Very High Throughput for Operations in Bands below 6 GHz. IEEE P802.11ac /D3.0 (2012)Google Scholar
  21. 21.
    Chockalingam, A., Sundarrajan, B.: Large Mimo Systems, 1st edn. Cambridge University Press, New York (2014)Google Scholar
  22. 22.
    Mahmoud, H.A., Arslan, H.: A low-complexity high-speed QR decomposition implementation for MIMO receivers. In: Proceedings of International symposium on Circuits and Systems, pp. 33–36. IEEE, Taipei (2009)Google Scholar
  23. 23.
    Timothy, S.: Numerical Analysis. Pearson Education Inc., George Mason University, Upper Saddle River (2006)Google Scholar
  24. 24.
    Hammarling, S., Lucas, C.: Updating the QR factorization and the least squares problem, Manchester Institute for Mathematical Sciences, School of Mathematics (2008)Google Scholar
  25. 25.
    Singhal, K.A., Datta, T., Chockalingam, A.: Lattice reduction aided detection in large-MIMO systems. In: Proceedings of IEEE Signal Processing Advances in Wireless Communication (SPAWC) Darmstadt, pp. 594–598 (2013)Google Scholar
  26. 26.
    Seethaler, D., Matz, G., Hlawatsch, F.: Low-complexity MIMO data detection using Seysen’s lattice reduction algorithm. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), pp. 53-56. Honolulu, HI (2007)Google Scholar
  27. 27.
    Li, Q., Wang, Z.: Reduced complexity K-best sphere decoder design for MIMO systems. Circuits Syst. Signal Process 27, 491–505 (2008)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Guo, Z., Nilsson, P.: Algorithm and implementation of the K-best sphere decoding for MIMO detection. In: IEEE Journal on Selected Areas in Communications, pp. 491–503 (2006)Google Scholar
  29. 29.
    Syed Moinuddin Bokhari, B., Bhagyaveni, M.A.: Low complex lattice reduction aided- zero forcing MIMO detection in Heterogeneous networks. In: Proceedings of International Journal of Engineering Research & Technology (IJERT), pp. 510–514 (2015)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringCollege of Engineering, Guindy Campus, Anna UniversityChennaiIndia

Personalised recommendations