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

  • Authors:
  • R. Henning;C. Chakrabarti

  • Affiliations:
  • Arizona State University, Tempe, Arizona;Arizona State University, Tempe, Arizona

  • Venue:
  • Proceedings of the 2002 international symposium on Low power electronics and design
  • Year:
  • 2002

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Abstract

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.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.