• DocumentCode
    3218522
  • Title

    On the VLSI Implementation of low complexity K-best MIMO decoders

  • Author

    Mondal, Sudip ; Salama, Khaled N. ; Eltawil, Ahmed

  • Author_Institution
    Electr., Comput. & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA
  • fYear
    2008
  • fDate
    14-17 Dec. 2008
  • Firstpage
    337
  • Lastpage
    340
  • Abstract
    The VLSI implementation of maximum likelihood (ML) detection for higher order multiple input multiple output (MIMO) systems continues to be a major challenge. Battery driven handheld devices impose strict area and power constraints while demanding guaranteed performance over a wide range of operating conditions. This paper presents a modified, low complexity K-best detector for a 4×4, 64 QAM MIMO system , which uses a modified path extension algorithm to bring down the computational complexity by more than 50%. It also exploits the structure of a QAM constellation to achieve low power operation by reducing the number of costly multiplication operations while computing the path metrics. The two new approaches, combined with a resource shared architecture leads to the implementation of a very efficient FPGA based system, where we verify the performance of the algorithm.
  • Keywords
    MIMO communication; VLSI; computational complexity; field programmable gate arrays; maximum likelihood detection; FPGA based system; K-best MIMO decoders; VLSI; computational complexity; maximum likelihood detection; multiple input multiple output systems; Batteries; Computational complexity; Computer architecture; Detectors; Handheld computers; MIMO; Maximum likelihood decoding; Maximum likelihood detection; Quadrature amplitude modulation; Very large scale integration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Microelectronics, 2008. ICM 2008. International Conference on
  • Conference_Location
    Sharjah
  • Print_ISBN
    978-1-4244-2369-9
  • Electronic_ISBN
    978-1-4244-2370-5
  • Type

    conf

  • DOI
    10.1109/ICM.2008.5393766
  • Filename
    5393766