• DocumentCode
    3122109
  • Title

    Best rate 1/2 convolutional codes for turbo equalization with severe ISI

  • Author

    Anderson, John B. ; Zeinali, Mehdi

  • Author_Institution
    Electr. & Inf. Tech. Dept., Lund Univ., Lund, Sweden
  • fYear
    2012
  • fDate
    1-6 July 2012
  • Firstpage
    2366
  • Lastpage
    2370
  • Abstract
    We give a procedure to find good convolutional codes for use with iterative decoding (turbo equalization) of the coded AWGN intersymbol interference (ISI) channel. The method is based on the input-output bit error rate characteristic of the convolutional BCJR module in the iterative decoder, in combination with the code minimum distance. Both are essential to find best codes. Best feedforward and recursive systematic codes are listed for memories 2-4, and some individual good codes at memory 5. Codes are tested over a root raised-cosine faster than Nyquist channel with severe ISI. Turbo BER performance reaches 0.8-1.6 dB from capacity with a 4-16 state codes. Feedforward codes perform better than recursive systematic.
  • Keywords
    AWGN channels; channel coding; convolutional codes; equalisers; error statistics; intersymbol interference; iterative decoding; turbo codes; ISI; Nyquist channel; best feedforward codes; best rate 1/2 convolutional codes; code minimum distance; coded AWGN intersymbol interference channel; convolutional BCJR module; input-output bit error rate characteristic; iterative decoding; recursive systematic codes; root raised-cosine; turbo BER performance; turbo equalization; AWGN channels; Bit error rate; Convolutional codes; Decoding; Feedforward neural networks; Iterative decoding; Signal to noise ratio;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory Proceedings (ISIT), 2012 IEEE International Symposium on
  • Conference_Location
    Cambridge, MA
  • ISSN
    2157-8095
  • Print_ISBN
    978-1-4673-2580-6
  • Electronic_ISBN
    2157-8095
  • Type

    conf

  • DOI
    10.1109/ISIT.2012.6283937
  • Filename
    6283937