Probabilistic Sorting Memory Constrained Tree Search Algorithm for MIMO System

  • Xiaoping Jin
  • Zheng GuoEmail author
  • Ning Jin
  • Zhengquan Li
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)


Considering the shortcomings of large storage space requirements and high complexity in multiple-symbol differential detection algorithm in current Multiple Input Multiple Output (MIMO) system, this paper proposes a probabilistic sorting memory constrained tree search algorithm (PSMCTS) by using performance advantage of sorting algorithm and storage advantage of memory constrained tree search (MCTS). Based on PSMCTS, a pruning PSMCTS named PPSMCTS is put forward. Simulation results show that the performance of PSMCTS is approach to that of ML algorithm under fixed memory situations, while the computational complexity is lower than that of MCTS algorithm in small storage capacity conditions under low signal noise ratio (SNR) region. PPSMCTS has more prominent advantages on reduction of computational complexity than PSMCTS algorithm. Theoretical analysis and simulation demonstrate that the two proposed algorithms can effectively inherit the good feature of MCTS algorithm, which are suitable for hardware implementation.


MIMO Probabilistic sorting Memory constrained tree search Pruning algorithm 



This work was supported by Zhejiang Provincial Natural Science Foundation of China (no. LY17F010012), the Natural Science Foundation of China (no. 61571108), the open Foundation of State key Laboratory Of Networking and Switching Technology (Beijing University of Posts and Telecommunication no. SKLNST-2016-2-14).

Authors’ Contributions

Xiaoping Jin conceived the idea of the system model and designed the proposed schemes. Zheng Guo has done a part of basic work in this article. Ning Jin performed simulations of the proposed schemes. Zhengquan Li provided substantial comments on the work and supported and supervised the research. All of the authors participated in the project, and they read and approved the final manuscript.

Competing Interests

The authors declare that they have no competing interests.


  1. 1.
    Wei, R.Y.: Differential encoding by a look-up table for quadrature-amplitude modulation. IEEE Trans. Commun. 59(1), 84–94 (2011)CrossRefGoogle Scholar
  2. 2.
    Kim, J.-S., Moon, S.-H., Lee, I.: A new reduced complexity ML detection scheme for MIMO systems. IEEE Trans. Commun. 58(4), 1302–1310 (2010)CrossRefGoogle Scholar
  3. 3.
    Bello, I.A., Halak, B., El-Hajjar, M., Zwolinski, M.: A survey of VLSI implementations of tree search algorithms for MIMO detection. Circ. Syst. Signal Process. 35(10), 3644–3674 (2016)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Schenk, A., Fischer, R.F.H.: A stopping radius for the sphere decoder: complexity reduction in multiple-symbol differential detection. In: International ITG Conference on Source and Channel Coding, pp. 1–6. IEEE (2010)Google Scholar
  5. 5.
    Takahashi, T., Fukuda, T., Sun, C.: An appropriate radius for reduced-complexity sphere decoding. In: International Conference on Communications, Circuits and Systems (ICCCAS), 28–30 July 2010, Chengdu, China, pp. 41–44 (2010)Google Scholar
  6. 6.
    Jin, N., Jin, X.P., Ying, Y.G., Wang, S., Lou, X.Z.: Research on low-complexity breadth-first detection for multiple-symbol differential unitary space-time modulation systems. IET Commun. 5(13), 1868–1878 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Mao, X., Ren, S.: Adjustable reduced metric-first tree search. In: International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), 23–25 September 2011, Wuhan, China, pp. 1–4 (2011)Google Scholar
  8. 8.
    Kim, T., Park, I.: High-throughput and area efficient MIMO symbol detection based on modified Dijkstra search. IEEE Trans. Circuits Syst. I Regul. Pap. 57(7), 1756–1766 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Jasika, N., Alispahic, N., Elma, A.: Dijkstra’s shortest path algorithm serial and parallel execution performance analysis. In: MIPRO 2012 Proceedings of the 35th International Convention, 21–25 May 2012, Opatija, pp. 1811–1815 (2012)Google Scholar
  10. 10.
    Suh, S., Barry, J.R.: Reduced-complexity MIMO detection via a slicing breadth-first tree search. IEEE Trans. Wirel. Commun. 16(3), 1782–1790 (2017)CrossRefGoogle Scholar
  11. 11.
    Sah, A.K., Chaturvedi, A.K.: Stopping rule-based iterative tree search for low-complexity detection in MIMO systems. IEEE Trans. Wirel. Commun. 16(1), 169–179 (2017)CrossRefGoogle Scholar
  12. 12.
    Dai, Y., Yan, Z.: Memery constrained tree search detection and new ordering schemes. IEEE J. Sel. Top. Signal Process. 3(6), 1026–1037 (2009)CrossRefGoogle Scholar
  13. 13.
    Chang, R.Y., Chung, W.-H.: Efficient tree-search MIMO detection with probabilistic node ordering. In: IEEE International Conference on Communications, 5–9 June, 2011, Kyoto, pp. 1–5 (2011)Google Scholar
  14. 14.
    Chang, R.Y., Chung, W.-H.: Best-first tree search with probabilistic node ordering for MIMO detection: generalization and performance-complexity tradeoff. IEEE Trans. Wirel. Commun. 11(2), 780–789 (2012)CrossRefGoogle Scholar
  15. 15.
    Cui, T., Tellambura, C.: Bound-intersection detection for multiple-symbol differential unitary space–time modulation. IEEE Trans. Commun. 53(12), 2114–2123 (2005)CrossRefGoogle Scholar
  16. 16.
    Li, Y., Wei, J.B.: Multiple symbol differential detection algorithm based on the sphere decoding in unitary space time modulation system. Sci. China Ser. F-Inf. Sci. 39(5), 569–578 (2009)Google Scholar
  17. 17.
    Hu, X., Gao, Y., Pan, Y.: Error rates calculation and performance analysis of (2,1) STBC systems. In: 7th International Conference on Signal Processing Proceedings ICSP, 31 August–4 September 2004, Beijing, pp. 1902–1905 (2004)Google Scholar
  18. 18.
    Cui, T., Tellambura, C.: On multiple symbol detection for diagonal DUSTM over ricean channels. IEEE Trans. Wirel. Commun. 7(4), 1146–1151 (2008)CrossRefGoogle Scholar
  19. 19.
    Bhukania, B., Schniter, P.: On the robustness of decision-feedback detection of DPSK and differential unitary space-time modulation in Rayleigh-fading channels. IEEE Trans. Wirel. Commun. 3(5), 1481–1489 (2004)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Xiaoping Jin
    • 1
  • Zheng Guo
    • 1
    Email author
  • Ning Jin
    • 1
  • Zhengquan Li
    • 2
    • 3
  1. 1.Department of Information EngineeringChina Jiliang UniversityHang ZhouChina
  2. 2.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina
  3. 3.National Mobile Communications Research LaboratorySoutheast UniversityNanjingChina

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