TY - GEN
T1 - Sequential Decoding of Convolutional Codes for Synchronization Errors
AU - Banerjee, Anisha
AU - Lenz, Andreas
AU - Wachter-Zeh, Antonia
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85144590252&partnerID=8YFLogxK
U2 - 10.1109/ITW54588.2022.9965844
DO - 10.1109/ITW54588.2022.9965844
M3 - Conference contribution
AN - SCOPUS:85144590252
T3 - 2022 IEEE Information Theory Workshop, ITW 2022
SP - 630
EP - 635
BT - 2022 IEEE Information Theory Workshop, ITW 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Information Theory Workshop, ITW 2022
Y2 - 1 November 2022 through 9 November 2022
ER -