BEAST decoding - asymptotic complexity
(2005) IEEE IT SOC Information Theory Workshop 2005 on Coding and Complexity- Abstract
- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/628993
- author
- Bocharova, Irina LU ; Johannesson, Rolf LU and Kudryashov, Boris LU
- organization
- publishing date
- 2005
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- maximum likelihood decoding, tree searching, block codes, decision trees, computational complexity
- host publication
- 2005 IEEE Information Theory Workshop
- conference name
- IEEE IT SOC Information Theory Workshop 2005 on Coding and Complexity
- conference location
- Rotorua, New Zealand
- conference dates
- 2005-08-29 - 2005-09-01
- external identifiers
-
- scopus:33749079690
- ISBN
- 0-7803-9480-1
- DOI
- 10.1109/ITW.2005.1531846
- language
- English
- LU publication?
- yes
- id
- 79279884-0ca2-4afb-8485-b0815ceec4b6 (old id 628993)
- date added to LUP
- 2016-04-04 13:34:23
- date last changed
- 2022-01-30 00:29:51
@inproceedings{79279884-0ca2-4afb-8485-b0815ceec4b6, abstract = {{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.}}, author = {{Bocharova, Irina and Johannesson, Rolf and Kudryashov, Boris}}, booktitle = {{2005 IEEE Information Theory Workshop}}, isbn = {{0-7803-9480-1}}, keywords = {{maximum likelihood decoding; tree searching; block codes; decision trees; computational complexity}}, language = {{eng}}, title = {{BEAST decoding - asymptotic complexity}}, url = {{http://dx.doi.org/10.1109/ITW.2005.1531846}}, doi = {{10.1109/ITW.2005.1531846}}, year = {{2005}}, }