Title :
BEAST decoding - asymptotic complexity
Author :
Bocharova, Irina E. ; Kudryashov, Boris D. ; Johannesson, Rolf
Author_Institution :
Dept. of Inf. Syst., St. Petersburg State Univ. of Aerosp. Instrum., Russia
fDate :
29 Aug.-1 Sept. 2005
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.
Keywords :
block codes; computational complexity; decision trees; maximum likelihood decoding; tree searching; BEAST decoding; asymptotic complexity; block codes; decoding complexity; maximum-likelihood decoding; searching trees; soft-decision decoding; AWGN; Aerospace electronics; Algorithm design and analysis; Block codes; Convolutional codes; Information systems; Information technology; Maximum likelihood decoding; Upper bound; Viterbi algorithm;
Conference_Titel :
Information Theory Workshop, 2005 IEEE
Print_ISBN :
0-7803-9480-1
DOI :
10.1109/ITW.2005.1531846
