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

Rajbhandari, Sujan, Ghassemlooy, Zabih and Angelova, Maia (2012) Adaptive ‘soft’ sliding block decoding of convolutional code using the artificial neural network. Transactions on Emerging Telecommunications Technologies, 23 (7). pp. 672-677. ISSN 2161-3915

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Official URL: http://dx.doi.org/10.1002/ett.2523

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.

Item Type: Article
Uncontrolled Keywords: convolutional code, artificial neural network, block decoding
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics and Information Sciences
Depositing User: Ellen Cole
Date Deposited: 29 May 2012 15:45
Last Modified: 10 Aug 2015 11:16
URI: http://nrl.northumbria.ac.uk/id/eprint/7460

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