Neural network decoder for (7, 4) hamming code Online publication date: Thu, 01-Oct-2020
by Aldrin Claytus Vaz; C. Gurudas Nayak; Dayananda Nayak
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 19, No. 4, 2020
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.
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