Low-power approach for decoding convolutional codes with adaptive Viterbi algorithm approximations

Significant power reduction can be achieved by exploiting real-time variation in system characteristics while decoding convolutional codes. The approach proposed herein adaptively approximates Viterbi decoding by varying truncation length and pruning threshold of the T-algorithm while employing trace-back memory management. Adaptation is performed according to variations in signal-to-noise ratio, code rate, and maximum acceptable bit error rate. Potential energy reduction of 70 to 97.5% compared to Viterbi decoding is demonstrated. The superiority of adaptive T-algorithm decoding compared to fixed T-algorithm decoding is studied. General conclusions about when applications can particularly benefit from this approach are given.