On an Improved K-Best Algorithm with High Performance and Low Complexity for MIMO Systems

  • Jia-lin YangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 670)


Multiple-input multiple-output (MIMO) techniques are significantly advanced in contemporary high-rate wireless communications. The computational complexity and bit-error-rate (BER) performance are main issues in MIMO systems. An algorithm is proposed based on a traditional K-Best algorithm, coupling with the fast QR decomposition algorithm with an optimal detection order for the channel decomposition, the Schnorr–Euchner strategy for solving the zero floating-point and sorting all the branches’ partial Euclidean distance, the sphere decoding algorithm for reducing the search space. The improved K-Best algorithm proposed in this paper has the following characteristics: (i) The searching space for the closest point to a region is smaller compared to that of the traditional K-Best algorithm in each dimension; (ii) it can eliminate the survival candidates at early stages, and (iii) it obtains better performance in the BER and the computational complexity.


MIMO K-Best algorithm Fast QR decomposition BER performance Computational complexity 


  1. 1.
    Shabany, M., Gulak, P. G.: A 675 Mbps, 4 × 4 64-QAM K-Best MIMO detector in 0.13 CMOS. IEEE Trans. V. L. Scale Int. Syst. 20(1), 135–147 (2012)Google Scholar
  2. 2.
    Liu, L., Lofgren, J., Nilsson, P.: Low-complexity likelihood information generation for spatial-multiplexing MIMO signal detection. IEEE Trans. Veh. Technol. 61(2), 607–617 (2012)Google Scholar
  3. 3.
    Mondal, S., Eltawil, A. M., Salama, K. N.: Architectural optimizations for low-power K-Best MIMO decoders. IEEE Trans. Veh. Technol. 58(7), 3145–3153 (2009)Google Scholar
  4. 4.
    Shiue, M. T., Long, S. S., Jao, C. K.: Design and implementation of power -efficient K-Best MIMO detector for configurable antennas. IEEE Trans. V. L. Scale Int. Syst. 22(11), 2418–2422 (2012)Google Scholar
  5. 5.
    Kim, T. H., Park, I. C.: Small-area and low-energy-best MIMO detector using relaxed tree expansion and early forwarding. IEEE Trans. Circuits & Syst. 57(10), 2753–2761 (2010)Google Scholar
  6. 6.
    Chen, S., Zhang, T., Xin, Y.: Relaxed-Best MIMO signal detector design and VLSI implementation. IEEE Trans. V. L. Scale Int. Syst. 15(3), 328–337 (2007)Google Scholar
  7. 7.
    El-Mashed, M. G., El-Rabaie, S.: Signal detection enhancement in LTE-A downlink physical layer using OSIC-based K-Best algorithm. Physical Commun. 14, 24–31(2015)Google Scholar
  8. 8.
    Qi, X. F., Holt, K.: A lattice-reduction -aided soft demapper for high-rate coded MIMO-OFDM systems. IEEE Signal Process. Lett. 14(5), 305–308 (2007)Google Scholar
  9. 9.
    Ghaderipoor, A., Tellambura, C.: A statistical pruning strategy for schnorr- euchner sphere decoding. IEEE Commun. Lett. 12(2), 121–123 (2008)Google Scholar
  10. 10.
    Li, Q.W., Wang, Z. F.: Improved K-Best sphere decoding algorithm for MIMO systems. In: Proc. 2006 IEEE Int. Symp. Circuits Syst., pp. 110–113. Island of Kos, Greece (2006)Google Scholar
  11. 11.
    Kora, A. D., Saemi, A., Cances, J. P.: New list sphere decoding (LSD) and iterative synchronization algorithms for MIMO- OFDM detection with LDPC FEC. IEEE Trans. Veh. Technol. 57(6), 3510–3524 (2008)Google Scholar
  12. 12.
    Amiri, K., Dick, C., Rao, R., Cavallaro, J. R.: Novel sort-free detector with modified real-valued decomposition (M-RVD) ordering in MIMO systems. In: Proc. 2007 IEEE Global Telecommun., pp. 1–5. New Orleans, USA (2007)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.20th Research Institute of Electronic Technology CorporationXi’anChina

Personalised recommendations