Sequential Decoding of Short Length Binary Codes: Performance Versus Complexity

Sequential decoding of short length binary codes for the additive white Gaussian noise channel is considered. A variant of the variable-bias term (VBT) metric is introduced, producing useful trade-offs between performance and computational complexity. Comparisons are made with tail-biting convolutional codes decoded with a wrap-around Viterbi algorithm (WAVA) and with polar codes under successive-cancellation list (SCL) decoding. It is found that sequential decoding with the improved VBT metric has a better performance-complexity tradeoff than tail-biting codes under WAVA decoding (except at low complexities) but a worse performance-complexity tradeoff than polar codes under SCL decoding (except at high complexities).

Automated summary

In this study, sequential decoding of short binary codes for the additive white Gaussian noise channel was examined, with an improved variant of the VBT metric introduced to provide beneficial trade-offs between performance and computational complexity. It was observed that the sequential decoding with the enhanced VBT metric showed a better balance between performance and complexity compared to tail-biting convolutional codes decoded with a wrap-around Viterbi algorithm. However, the performance-complexity tradeoff for the sequential decoding was found to be inferior to polar codes under SCL decoding at high complexities.