Low complexity syndrome algorithm for the decoding of convolutional codes

The decoding of convolutional codes in the maximum likelihood sense is carried out in a traditional way with the Viterbi algorithm (m). We proposed a soft and hard input decoder where the VA, associated with an relevant metric, is applied to identify the error vector rather than the information message. In this paper we show that, with this type of decoding, the exhaustive computation of a majority of ACS (Add Compare Select) is unnecessary. Moreover we show that optimal performance is achieved in the case of a hard input decoder and that performance closed to the optimum is achieved in the case of a soft input decoder while offering of a reduction of the complexity which is all the more important than the Ec/No ratio is (e. g. for ratio Ec/No greater than 3 dB, more than 80% of the ACS can be avoided). We also propose an algorithm allowing rejecting a frame without having to carry out any iteration of the VA.