An approach for adaptively approximating the viterbi algorithm to reduce power consumption while decoding convolutional codes

Russell Henning, Chaitali Chakrabarti

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Significant power reduction can be achieved by exploiting real-time variation in system characteristics. An approach is proposed and studied herein that exploits variation in signal transmission system characteristics to reduce power consumption while decoding convolutional codes. With this approach, Viterbi decoding is adaptively approximated by varying the pruning threshold of the T-algorithm and truncation length while employing trace-back memory management. A heuristic is given for finding and adaptively applying pairs of pruning threshold and truncation length values that significantly reduce power to variations in signal-to-noise ratio (SNR), code rate, and maximum acceptable bit-error rate (BER). The power reduction potential of different levels of adaptation is studied. High-level energy reduction estimates of 80% to 97% compared with Viterbi decoding are shown. Implementation insight and general conclusions about when applications can particularly benefit from this approach are given.

Original languageEnglish (US)
Pages (from-to)1443-1451
Number of pages9
JournalIEEE Transactions on Signal Processing
Volume52
Issue number5
DOIs
StatePublished - May 1 2004

Keywords

  • Convolutional code
  • Low power
  • T-algorithm
  • Viterbi algorithm

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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