• DocumentCode
    697769
  • Title

    Genetic algorithm-aided fixed-point design of E-UTRA PRACH detector on multi-core DSP

  • Author

    Rongrong Qian ; Tao Peng ; Yuan Qi ; Wenbo Wang

  • Author_Institution
    Key Lab. of Universal Wireless Commun., Beijing Univ. of Posts & Telecommun. (BUPT), Beijing, China
  • fYear
    2009
  • fDate
    24-28 Aug. 2009
  • Firstpage
    1007
  • Lastpage
    1011
  • Abstract
    This paper presents a new genetic algorithm (GA)-aided methodology of software implementation in Digital Signal Processors (DSPs) under both the computational accuracy and bus bandwidth constraints. The design issue is firstly stated as two classes of fixed-point design problems, one of which is then formulated to a constrained integer programming (CIP) problem. And the genetic algorithm is proposed to treat with such CIP problem for the sake of efficiency. Then the fixed-point evolved (E)-UTRA PRACH detector is presented, which further underlines the feasibility and convenience of applying this methodology to practice. Finally, the numeric results justify the proposed GA-aided approach and demonstrate that a speedup by a factor of 33 can be achieved compared to the exhaustive search for the solution of E-UTRA PRACH detector design problem.
  • Keywords
    constraint theory; digital signal processing chips; genetic algorithms; integer programming; integrated circuit design; multiprocessing systems; CIP problem; E-UTRA PRACH detector design problem; GA-aided methodology; bus bandwidth constraints; computational accuracy; constrained integer programming; digital signal processors; fixed-point design problems; fixed-point evolved E-UTRA PRACH detector; genetic algorithm-aided fixed-point design; multicore DSP; software implementation; Algorithm design and analysis; Bandwidth; Detectors; Digital signal processing; Genetic algorithms; Hardware; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing Conference, 2009 17th European
  • Conference_Location
    Glasgow
  • Print_ISBN
    978-161-7388-76-7
  • Type

    conf

  • Filename
    7077341