• DocumentCode
    2431085
  • Title

    A low-complexity implementation of sampling-based MIMO detection

  • Author

    Ding, Rui ; Gao, Xiqi ; You, Xiaohu

  • Author_Institution
    IEEE Nat. Mobile Commun. Res. Lab., Southeast Univ., Nanjing
  • fYear
    2008
  • fDate
    7-11 June 2008
  • Firstpage
    705
  • Lastpage
    710
  • Abstract
    A low-complexity implementation of sequential Monte Carlo (SMC) sampling-based detector is developed for multiple-input multiple-output (MIMO) communication systems. Unlike previous reports about SMC sampling that widely sequentially draw samples and process each sample independently, we present a novel sampling method which collaboratively processes all samples and extracts information from a collection of samples to establish the sampling space to draw next samples. Simultaneously, the proposed method adopts a reselection step to save storage resource and decrease computation burden. Simulations indicate that the proposed solution decreases the necessary amount of samples and improves the system performance compared with the classical SMC detector. The revised detector is also compared with sphere decoding (SD) that has the comparable computation burden, and simulation result shows that it can obtain the same performance as SD with lower complexity.
  • Keywords
    MIMO communication; Monte Carlo methods; signal detection; signal sampling; MIMO detection; SMC sampling-based detector; information extraction; multiple-input multiple-output communication; resource storage saving; sequential Monte Carlo method; Collaboration; Computational modeling; Data mining; Decoding; Detectors; MIMO; Monte Carlo methods; Sampling methods; Sliding mode control; System performance; Multiple-input multiple-output (MIMO); Sequential Monte Carlo (SMC); sampling-based detection; sphere decoding (SD);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks and Signal Processing, 2008 International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4244-2310-1
  • Electronic_ISBN
    978-1-4244-2311-8
  • Type

    conf

  • DOI
    10.1109/ICNNSP.2008.4590442
  • Filename
    4590442