High throughput sequential decoding with state estimation

R. Xu*, K. Morris

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)


Sequential decoding can achieve high throughput convolutional decoding with much lower computational complexity when compared with the Viterbi algorithm (VA) at a relatively high signal-to-noise ratio (SNR). A parallel bidirectional Fano algorithm (BFA) decoding architecture is investigated in this paper. In order to increase the utilisation of the parallel BFA decoders, and thus improve the decoding throughput, a state estimation method is proposed which can effectively partition a long codeword into multiple short sub-codewords. The parallel BFA decoding with state estimation architecture is shown to achieve 30-55% decoding throughput improvement compared with the parallel BFA decoding scheme without state estimation. Compared with the VA, the parallel BFA decoding only requires 3-30% computational complexity of that required by the VA with a similar error rate performance.

Original languageEnglish
Pages (from-to)2033-2039
Number of pages7
JournalIET Communications
Issue number13
Publication statusPublished - 5 Sep 2012




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