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Maximum-likelihood detection based on branch and bound algorithm for MIMO systems

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Abstract

Maximum likelihood detection for MIMO systems can be formulated as an integer quadratic programming problem. In this paper, we introduce depth-first branch and bound algorithm with variable dichotomy into MIMO detection. More nodes may be pruned with this structure. At each stage of the branch and bound algorithm, active set algorithm is adopted to solve the dual subproblem. In order to reduce the complexity further, the Cholesky factorization update is presented to solve the linear system at each iteration of active set algorithm efficiently. By relaxing the pruning conditions, we also present the quasi branch and bound algorithm which implements a good tradeoff between performance and complexity. Numerical results show that the complexity of MIMO detection based on branch and bound algorithm is very low, especially in low SNR and large constellations.

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Authors and Affiliations

  1. Institute of Communications Engineering, PLA University of Science and Technology, Nanjing, 210007, China

    Li Zi & Cai YueMing

  2. National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, China

    Cai YueMing

Authors
  1. Li Zi
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  2. Cai YueMing
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Corresponding authors

Correspondence to Li Zi or Cai YueMing.

Additional information

Supported by Jiangsu Natural Science Fund Project (Grant No. BK2006002), and the Open Research Foundation of National Mobile Communications Research Laboratory, Southeast University (Grant No. N200601)

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Li, Z., Cai, Y. Maximum-likelihood detection based on branch and bound algorithm for MIMO systems. Sci. China Ser. F-Inf. Sci. 51, 306–319 (2008). https://doi.org/10.1007/s11432-008-0022-4

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  • DOI: https://doi.org/10.1007/s11432-008-0022-4

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