Title: Neural network decoder for (7, 4) hamming code

Authors: Aldrin Claytus Vaz; C. Gurudas Nayak; Dayananda Nayak

Addresses: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal – 576104, Karnataka, India ' Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal – 576104, Karnataka, India ' Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal – 576104, Karnataka, India

Abstract: To ensure the accuracy, integrity, and fault-tolerance in the data to be transmitted, error correcting codes (ECC) are used. To decode the received data and correct the errors, different techniques have been developed. In this paper, artificial neural networks (ANN) have been used instead of traditional error-correcting techniques, because of their real-time operation, self-organisation, and adaptive learning and to project what will most likely happen on the analogy of human brain. A decoding approach based on the backpropagation algorithm for feed-forward ANN has been simulated using MATLAB for (7, 4) hamming code. The designed ANN is trained on all possible combinations of code words such that it can detect and correct up to 1-bit error. The synaptic weights are updated during each training cycle of the network. The simulation results show that the proposed technique is correctly able to detect and correct 1-bit error in the received data.

Keywords: artificial neural network; ANN; back propagation algorithm; error correcting code; hamming code.

DOI: 10.1504/IJISTA.2020.110035

International Journal of Intelligent Systems Technologies and Applications, 2020 Vol.19 No.4, pp.405 - 420

Received: 18 Dec 2018
Accepted: 07 Nov 2019
Published online: 01 Oct 2020 *

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