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It is well known that convolutional codes can be optimally decoded by using the Viterbi Algorithm (VA). We propose a decoding technique where the VA is applied to identify the error vector rather than the information message. We previously focused on convolutional coders of rate ½ [4] [5]. Here we generalize the method to codes of any rate. We show that, with the proposed 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 significant reduction of complexity. The higher the SNR, the greater the improvement for reduction in complexity. For instance, for SNR greater than 3 dB, a five fold reduction in complexity for the computation of ACS (Add Compare Select) is achieved.

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. 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, under certain channel conditions, optimum performance is achievable with an order of magnitude in complexity reduction. Besides, additional complexity reduction can be achieved by detecting the frames which have a low probability to be successfully decoded.

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