Probabilistic Sorting Memory Constrained Tree Search Algorithm for MIMO System
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.
KeywordsMIMO 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).
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.
The authors declare that they have no competing interests.
- 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.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
- 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
- 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
- 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
- 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.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