Sequential Decoding Algorithms of Convolutional Codes: Implementation, Improvement and Comparison

Authors

1 Doctoral student of telecommunications, Sharif University of Technology, Tehran, Iran

2 Associate Professor, Sharif University of Technology, Tehran, Iran

Abstract

Error correction capability of convolutional codes is improved by increasing code constraint
length. However, increasing the constraint length results in high complexity of optimum Viterbi
decoding algorithm because the number of computations in Viterbi algorithm is exponentially
proportional to the constraint length. Consequently, decoding of high constraint length convolutional
codes using Viterbi algorithm may practically be impossible. Sub-optimum decoding algorithms such
as Fano and Stack algorithms have been proposed to feasible fast sequential decoding of high
constraint length convolutional codes. In this paper, we introduce different methods of implementing
Fano and Stack algorithms and propose some techniques to improve their speed and required
memory. We compare the introduced implementations of the algorithms in terms of error correction
capability, decoding time and required memory. Furthermore, we use simulation results to show that
if the communication error is low, Fano and Stack algorithms can provide the same error correction
capability as the optimum Viterbi algorithm in very short decoding time

Keywords

References

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Electronic and Cyber Defense
Volume 3, Issue 2 - Serial Number 2
January 2020
Pages 61-73

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History

  • Receive Date: 21 March 2015
  • Revise Date: 21 June 2023
  • Accept Date: 19 September 2018
  • Publish Date: 23 July 2015

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