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BEAST decoding - asymptotic complexity

Publiceringsår: 2005
Språk: Engelska
Publikation/Tidskrift/Serie: 2005 IEEE Information Theory Workshop
Dokumenttyp: Konferensbidrag


BEAST is a bidirectional efficient algorithm for searching trees that performs soft-decision maximum-likelihood (ML) decoding of block codes. The decoding complexity of BEAST is significantly reduced compared to the Viterbi algorithm. An analysis of the asymptotic BEAST decoding complexity verifies BEAST's high efficiency compared to other algorithms. The best of the obtained asymptotic upper bounds on the BEAST decoding complexity is better than previously known bounds for ML decoding in a wide range of code rates.



  • Technology and Engineering
  • maximum likelihood decoding
  • tree searching
  • block codes
  • decision trees
  • computational complexity


IEEE IT SOC Information Theory Workshop 2005 on Coding and Complexity
Rotorua, New Zealand
  • ISBN: 0-7803-9480-1

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