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BEAST decoding of block codes obtained via convolutional codes

In English

Författare

Summary, in English

BEAST is a bidirectional efficient algorithm for searching trees. In this correspondence, BEAST is extended to maximum-likelihood (ML) decoding of block codes obtained via convolutional codes. First it is shown by simulations that the decoding complexity of BEAST is significantly less than that of the Viterbi algorithm. Then asymptotic upper bounds on the BEAST decoding complexity for three important ensembles of codes are derived. They verify BEAST's high efficiency compared to other algorithms. For high rates, the new asymptotic bound for the best ensemble is in fact better than previously known bounds.

Publiceringsår

2005

Språk

Engelska

Sidor

1880-1891

Publikation/Tidskrift/Serie

IEEE Transactions on Information Theory

Volym

51

Issue

5

Dokumenttyp

Artikel i tidskrift

Förlag

IEEE - Institute of Electrical and Electronics Engineers Inc.

Ämne

  • Electrical Engineering, Electronic Engineering, Information Engineering

Nyckelord

  • bidirectional search of trees
  • asymptotical decoding complexity
  • decoding of block codes
  • decoding
  • convolutional codes
  • maximum-likelihood (ML)

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 0018-9448