• DocumentCode
    3029937
  • Title

    A simplified MMSE PSIC MIMO detector for turbo receivers

  • Author

    Han Juan ; Wang Qiuju ; Ren Wei ; Tang Shan ; Shi Jinglin

  • Author_Institution
    Inst. of Comput. Technol., Wireless Commun. Technol. Res. Center, China
  • fYear
    2012
  • fDate
    8-10 Aug. 2012
  • Firstpage
    119
  • Lastpage
    123
  • Abstract
    In this paper, a low-complexity MIMO detector with parallel soft symbol interference cancellation and an MMSE filter, is proposed for a turbo receiver in BICM MIMO systems. The detector is utilized in non-first iterative process of Turbo receiver to suppress residual interference and noise. The complexity of the algorithm can be greatly reduced as the matrix inverse for weighting vector of MMSE is not necessary due to the approximation that the components of residual interference of PSIC and the noise are modeled as uncorrelated Gaussian random variables. Monte Carlo simulation results confirm that the proposed algorithm can achieve almost the same performance as the traditional MMSE PSIC, but with much lower complexity.
  • Keywords
    Gaussian processes; MIMO communication; Monte Carlo methods; interference suppression; least mean squares methods; radio receivers; BICM MIMO systems; Gaussian random variables; MMSE PSIC MIMO detector; MMSE filter; Monte Carlo simulation; low-complexity MIMO detector; matrix inverse; noise suppression; non-first iterative process; parallel soft symbol interference cancellation; turbo receivers; Complexity theory; Detectors; Estimation; Interference; Iterative decoding; MIMO; Receivers; BICM MIMO; MMSE; PSIC; Residual interference; Turbo Receiver;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications and Networking in China (CHINACOM), 2012 7th International ICST Conference on
  • Conference_Location
    Kun Ming
  • Print_ISBN
    978-1-4673-2698-8
  • Electronic_ISBN
    978-1-4673-2697-1
  • Type

    conf

  • DOI
    10.1109/ChinaCom.2012.6417460
  • Filename
    6417460