• DocumentCode
    2554545
  • Title

    Decoders for low-density parity-check convolutional codes with large memory

  • Author

    Bates, Stephen ; Gunthorpe, Logan ; Pusane, Ali Emre ; Chen, Zhengang ; Zigangirov, Kamil ; Costello, Daniel J., Jr.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Alberta Univ., Edmonton, Alta.
  • fYear
    2006
  • fDate
    21-24 May 2006
  • Abstract
    Low-density parity-check convolutional codes offer the same good error-correcting performance as low-density parity-check block codes while having the ability to encode and decode arbitrary lengths of data. This makes these codes well suited to certain applications, such as forward error control on packet switching networks. In this paper we propose a decoder architecture for low-density parity-check convolutional codes with very large memories. These codes have very good error correcting properties and as such may be applicable in wireless sensor networks and space communication systems. We discuss a realization of this architecture for a (2048,3,6) code implemented on a field-programmable gate-array
  • Keywords
    block codes; convolutional codes; decoding; error correction codes; field programmable gate arrays; parity check codes; block codes; convolutional codes; decoder architecture; error-correcting performance; field-programmable gate-array; forward error control; low-density parity-check codes; packet switching networks; Block codes; Convolutional codes; Decoding; Error correction; Ethernet networks; Field programmable gate arrays; Mobile communication; Packet switching; Parity check codes; Wireless sensor networks; Convolutional codes; Data communication; Error correction coding; High-speed integrated circuits;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on
  • Conference_Location
    Island of Kos
  • Print_ISBN
    0-7803-9389-9
  • Type

    conf

  • DOI
    10.1109/ISCAS.2006.1693780
  • Filename
    1693780