Title :
Symbol-by-symbol MAP decoding of convolutional codes using reciprocal dual convolutional codes
Author_Institution :
Dept. of Commun. Eng., Tech. Univ. Munchen, Germany
fDate :
29 Jun-4 Jul 1997
Abstract :
A new symbol-by-symbol maximum a-posteriori (MAP) decoding algorithm for high-rate convolutional codes using reciprocal dual convolutional codes is presented. It fulfills all the requirements for iterative (turbo) decoding of parallel concatenated convolutional codes. The computational complexity is low due to the fact that the number of codewords of the low-rate reciprocal dual code is much less than that of the high-rate code. For an efficient implementation forward and backward recursions are introduced
Keywords :
channel capacity; computational complexity; convolutional codes; dual codes; iterative methods; maximum likelihood decoding; maximum likelihood estimation; backward recursion; codewords; computational complexity; efficient code implementation; forward recursion; high-rate convolutional codes; iterative decoding; low-rate reciprocal dual code; maximum a-posteriori decoding algorithm; parallel concatenated convolutional codes; reciprocal dual convolutional codes; symbol-by-symbol MAP decoding; turbo decoding; Block codes; Boundary conditions; Computational complexity; Concatenated codes; Convolutional codes; Iterative algorithms; Iterative decoding; Matrices;
Conference_Titel :
Information Theory. 1997. Proceedings., 1997 IEEE International Symposium on
Conference_Location :
Ulm
Print_ISBN :
0-7803-3956-8
DOI :
10.1109/ISIT.1997.613141
