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Adaptive ‘soft’ sliding block decoding of convolutional code using the artificial neural network
Author(s) -
Rajbhandari Sujan,
Ghassemlooy Zabih,
Angelova Maia
Publication year - 2012
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.2523
Subject(s) - decoding methods , viterbi decoder , iterative viterbi decoding , sequential decoding , computer science , viterbi algorithm , soft decision decoder , convolutional code , soft output viterbi algorithm , block (permutation group theory) , algorithm , convolutional neural network , code (set theory) , artificial intelligence , block code , mathematics , set (abstract data type) , geometry , programming language
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. Copyright © 2012 John Wiley & Sons, Ltd.