BEAST decoding of block codes obtained via convolutional codes
Author
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.
Publishing year
2005
Language
English
Pages
1880-1891
Publication/Series
IEEE Transactions on Information Theory
Volume
51
Issue
5
Document type
Journal article
Publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
Topic
- Electrical Engineering, Electronic Engineering, Information Engineering
Keywords
- bidirectional search of trees
- asymptotical decoding complexity
- decoding of block codes
- decoding
- convolutional codes
- maximum-likelihood (ML)
Status
Published
ISBN/ISSN/Other
- ISSN: 0018-9448