Optimum decoding of a class of product codes is investigated. The class is the one given by the serial concatenation of a binary single-parity-check code with a low-dimension binary linear block code. It was proved by Wolf that maximum likelihood decoding for this class of product codes can be efficiently performed through the Viterbi algorithm over a compact trellis representation of the code. In this letter, it is showed that the decoding complexity can be further reduced by formulating the decoding problem as a symbol-wise maximum-a-posteriori decision problem. Results illustrated for suitably designed codes show that the proposed algorithm significantly outperforms conventional iterative decoders. Finally, a generalization of the code construction, enjoying the same low-complexity decoding principle is presented and analyzed, achieving tangible coding gains at moderate error rates.
G. Liva, E. Paolini, M. Chiani (2014). On Optimum Decoding of Certain Product Codes. IEEE COMMUNICATIONS LETTERS, 18, 905-908 [10.1109/LCOMM.2014.2315812].
On Optimum Decoding of Certain Product Codes
PAOLINI, ENRICO;CHIANI, MARCO
2014
Abstract
Optimum decoding of a class of product codes is investigated. The class is the one given by the serial concatenation of a binary single-parity-check code with a low-dimension binary linear block code. It was proved by Wolf that maximum likelihood decoding for this class of product codes can be efficiently performed through the Viterbi algorithm over a compact trellis representation of the code. In this letter, it is showed that the decoding complexity can be further reduced by formulating the decoding problem as a symbol-wise maximum-a-posteriori decision problem. Results illustrated for suitably designed codes show that the proposed algorithm significantly outperforms conventional iterative decoders. Finally, a generalization of the code construction, enjoying the same low-complexity decoding principle is presented and analyzed, achieving tangible coding gains at moderate error rates.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.