Low complexity algorithm for soft decoding of convolutional codes

It is well known that convolutional codes can be optimally decoded by the Viterbi algorithm (VA). We propose a soft decoding technique where the VA is applied to identify the se error vector rather than the information message. In this paper, we show that, with this type of decoding, the exhaustive computation of a vast majority of state to state iterations is unnecessary. Hence, performance close to optimum is achievable with a reduction of complexity. The gain in complexity of the proposed scheme is all the more important as the SINR is high. For instance, for SNR greater than 3 dB. more than 80% of id-rations and more than 84% of computations for ACS (add compare select) can be avoided.