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Guessing the Code: Learning Encoding Mappings Using the Back Propagation Algorithm

MacHireddy, A and Garani, SS (2019) Guessing the Code: Learning Encoding Mappings Using the Back Propagation Algorithm. In: 2019 International Joint Conference on Neural Networks, IJCNN 2019, 14 July 2019 - 19 July 2019, Budapest.

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Official URL: https://doi.org/10.1109/IJCNN.2019.8852203


Error correction codes such as low density parity check (LDPC) codes are popularly used to enhance the performance of digital communication systems. The current decoding framework relies on exchanging beliefs over a Tanner graph, which the encoder and decoder are aware of. However, this information may not be available readily, for example in covert communication. The main idea of this paper is to build a neural network to learn the encoder mappings in the absence of knowledge of the Tanner graph. We propose a scheme to learn the mappings using the back propagation algorithm. We investigate into the choice of different cost functions and the number of hidden neurons for learning the encoding function. The proposed scheme is capable of learning the parity check equations over a binary field towards identifying the validity of a codeword. Simulation results over synthetic data show that our algorithm is indeed capable of learning the encoder mappings and identifying the parity check equations. © 2019 IEEE.

Item Type: Conference Paper
Publication: Proceedings of the International Joint Conference on Neural Networks
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Backpropagation algorithms; Block codes; Cost functions; Decoding; Digital communication systems; Encoding (symbols); Error correction; Forward error correction; Mapping; Signal encoding, Covert communications; Encoding functions; Error correction codes; Linear block code; Low-density parity-check (LDPC) codes; Number of hidden neurons; Parity-check equations; Synthetic data, Learning algorithms
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited: 19 Dec 2022 06:54
Last Modified: 19 Dec 2022 06:54
URI: https://eprints.iisc.ac.in/id/eprint/78497

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