Adaptive ‘soft’ sliding block decoding of convolutional code using the artificial neural network

Sujan Rajbhandari, Zabih Ghassemlooy, Maia Angelova

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

A Viterbi algorithm (VA) is the optimal decoding strategy for the convolutional code. The Viterbi algorithm is complex and requires a large memory and delay. In this paper, an alternative sub-optimal decoder based on the artificial neural network (ANN) is proposed and studied using a sliding block decoding algorithm. The ANN is trained in a supervised manner and the system parameters are optimised using computer simulations for the optimum performance. Comparative study with the Viterbi decoder is carried out. The performance of the ANN decoder is found to be comparable to the Viterbi ‘soft’ decoding with much reduced decoding length. The key advantages of the proposed ANN decoder compared with other ANN decoders are the reduced decoding and training length, adaptive decoding, no iteration required and possibility of parallel decoding.
Original languageEnglish
Pages (from-to)672-677
JournalTransactions on Emerging Telecommunications Technologies
Volume23
Issue number7
DOIs
Publication statusPublished - 2012

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