BEAST decoding of block codes obtained via convolutional codes
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