Sequential Decoding of Convolutional Codes for Synchronization Errors

Anisha Banerjee, Andreas Lenz, Antonia Wachter-Zeh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this work, a sequential decoder for convolutional codes over channels that are vulnerable to insertion, deletion, and substitution errors, is described and analyzed. The decoder expands the code trellis by introducing a new channel state variable, called drift state, as proposed by Davey-MacKay. A suitable decoding metric on that trellis for sequential decoding is derived, in a manner that generalizes the original Fano metric. Under low-noise environments, this approach reduces the decoding complexity by a couple orders of magnitude in comparison to Viterbi's algorithm. An analytical method to determine the computational cutoff rate is also suggested. This analysis is supported with numerical evaluations of bit error rates and computational complexity, which are compared with respect to optimal Viterbi decoding.

Original languageEnglish
Title of host publication2022 IEEE Information Theory Workshop, ITW 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages630-635
Number of pages6
ISBN (Electronic)9781665483414
DOIs
StatePublished - 2022
Event2022 IEEE Information Theory Workshop, ITW 2022 - Mumbai, India
Duration: 1 Nov 20229 Nov 2022

Publication series

Name2022 IEEE Information Theory Workshop, ITW 2022

Conference

Conference2022 IEEE Information Theory Workshop, ITW 2022
Country/TerritoryIndia
CityMumbai
Period1/11/229/11/22

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