Low complexity algorithm for optimal hard decoding of convolutional codes

It is well known that convolutional codes can be optimally decoded by the Viterbi algorithm (VA). We propose an optimal hard decoding technique where the VA is applied to identify the error vector rather than the information message. We show that, with this type of decoding, the exhaustive computation of a vast majority of state to state iterations is unnecessary. Hence, under certain channel conditions, optimum performance is achievable with an order of magnitude reduction in complexity. Besides, additional complexity reduction can be achieved by detecting the frames which have a low probability of being successfully decoded.