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Data compression methods based on Neural Networks

Published: 13 April 2022 Publication History

Abstract

The modern stage of the development of telecommunication systems is characterized by the widespread development of wired and wireless data transmission networks. The growth in the number of users of such networks, the emergence of new multimedia services impose high demands on speed, reliability, and delay time in information processing. One of the factors in increasing the data transfer rate is the on-the-fly compression of information at the link level. The paper describes a method for compressing data in a communication channel using error-correcting BCH codes and a feed-forward neural network autoencoder. This method converts a binary vector of user data into BCH codewords, which are used to train the autoencoder. This ultimately makes it possible to reduce the number of transmitted bits in the communication channel.

References

[1]
[1] A. Koucheryavy, A. Vladyko, R. Kirichek, ”State of the art and research challenges for public flying ubiquitous sensor networks”, Internet of Things, Smart Spaces, and Next Generation Networks and Systems, 2015, pp. 299-308.
[2]
[2] A. Yastrebova, R. Kirichek, Y. Koucheryavy, A. Borodin, and A. Koucheryavy, ”Future networks 2030: Architecture & requirements”, 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2018, pp. 1-8.
[3]
[3] A. Gaweda, J. Kacprzyk, G. Yen, L. Rutkowski, Advances in Data Analysis with Computational Intelligence Methods: Dedicated to Professor Jacek Żurada, 1rd ed., NY: Springer, ISBN: 978-0471016335, 2018.
[4]
[4] H. Adeli, S. Hung, Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems, 1rd ed., New Jersey: Wiley, ISBN: 978-0471016335, 1994.
[5]
[5] S. Haykin, Neural Networks: A Comprehensive Foundation, New York: Macmillan College Publishing Company, ISBN: 978-0132733502, 1994.
[6]
[6] R. Callan, The Essence of Neural Networks, New Jersey: Prentice Hall, ISBN: 978-0139087325, 1998.
[7]
[7] M. Htay, S. Iyengar, Si-Qing Zheng, ”Correcting Errors in Linear Codes with Neural Network”, Proceedings of the 27th Southeastern Symposium on System Theory (SSST’95), pp. 386–391, 1995.
[8]
[8] S. Ahmed, ”Linear block code decoder using neural network”, IEEE International Joint Conference on Neural Networks (IJCNN’08), pp. 1111–1114, 2008.
[9]
[9] G. Zeng, D. Hush, N. Ahmed, ”An application of neural net in decoding error-correcting codes”, IEEE International Symposium on Circuits and Systems, pp.782–785, 1989.
[10]
[10] K. Mehrotra, C. Mohan, S. Ranka, ”Bounds on the number of samples needed for neural learning”, IEEE Transactions on Neural Networks, vol. 2, pp. 548–558, 1991.
[11]
[11] A. Claytus Vaz, G. Nayak, D. Nayak, ”Hamming Code Performance Evaluation using Artificial Neural Network Decoder”, 15th International Conference on Engineering of Modern Electric Systems (EMES), Oradea, Romania, pp. 37–40, June 2019.
[12]
[12] N. Kamiya, ”On Algebraic Soft-Decision Decoding Algorithms for BCH Codes”, IEEE Transactions on Information Theory, vol. 47, n. 1, pp. 45–58, 2001.
[13]
[13] M. Al-gaashani, M. S. A Muthanna, K. Abdukodir, A. Muthanna, and R. Kirichek, ”Intelligent System Architecture for Smart City and its Applications Based Edge Computing”, 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2020, pp. 269-274.
[14]
[14] S. Vladimirov, R. Kirichek, and V. Vishnevsky, ”Network Coding for the Interaction of Unmanned Flying Platforms in Data Acquisition Networks”, The 4th International Conference on Future Networks and Distributed Systems (ICFNDS), 2020, pp. 1-7.
[15]
[15] S. Vladimirov, V. Vishnevsky, A. Larionov, R. Kirichek, ”The Model of WBAN Data Acquisition Network Based on UFP”, International Conference on Distributed Computer and Communication Networks (DCCN-2020), 2020, pp. 220-231.
[16]
[16] V.M. Vishnevsky, B.N. Tereschenko, D.A. Tumchenok, A.M. Shirvanyan, A. Sokolov, ”Principles of building a power transmission system for tethered unmanned telecommunication platforms”, International Conference on Distributed Computer and Communication Networks, 2019, pp. 94-110.
[17]
[17] A. Berezkin, A. Zadorozhnaya, D. Kukunin, D. Matveev, E. Kraeva, ”Models and Methods for Decoding of Error-Correcting Codes based on a Neural Network”, The 13th International Congress on ultra modern telecommunications and control systems, 2021.
[18]
[18] Mingqiang Zhang and Haixia Zhang and Dongfeng Yuan and Minggao Zhang, ”Compressive Sensing and Autoencoder Based Compressed Data Aggregation for Green IoT Networks”, Global Communications Conference, 2019
[19]
[19] Yangming Zhou and Chengyou Wang and Xiao Zhou, ”Based Color Image Compression Algorithm Using an Efficient Lossless Encoder”, International Conference on Signal Processing, 2018
[20]
[20] Tongyang Xu and Izzat Darwazeh, ”Design and Prototyping of Neural Network Compression for Non-Orthogonal IoT Signals”, Wireless Communications and Networking Conference, 2019
[21]
[21] Pavi Saraswat and Kanika Garg and Rajan Tripathi and Ayush Agarwal, ”Encryption Algorithm Based on Neural Network”, 4th International Conference on Internet of Things: Smart Innovation and Usages, 2019
[22]
[22] Faisal Nadeem Khan and Alan Pak Tao Lau, ”Robust and efficient data transmission over noisy communication channels using stacked and denoising autoencoders”, Institute of Electrical and Electronics Engineers, 2019
[23]
[23] Sajid Majeed and Yusra Mansoor and Sana Qabil and Farooq Majeed and Behraj Khan, ”Comparative analysis of the denoising effect of unstructured vs. convolutional autoencoders”, International Conference on Emerging Trends in Smart Technologies, 2020

Cited By

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  • (2025)A Multiple Compression Approach using Attribute-based SignaturesOpen Research Europe10.12688/openreseurope.19247.15(49)Online publication date: 10-Feb-2025
  • (2023)Towards a Signature Based Compression Technique for Big Data Storage2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW58674.2023.00022(100-104)Online publication date: Apr-2023
  • (2022)Neural Network Coding in Data Compression Systems in Communication Channels2022 International Conference on Information, Control, and Communication Technologies (ICCT)10.1109/ICCT56057.2022.9976532(1-5)Online publication date: 3-Oct-2022

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cover image ACM Other conferences
ICFNDS '21: Proceedings of the 5th International Conference on Future Networks and Distributed Systems
December 2021
847 pages
ISBN:9781450387347
DOI:10.1145/3508072
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 13 April 2022

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Author Tags

  1. autoencoder
  2. compress
  3. error-correcting codes
  4. neural networks

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Cited By

View all
  • (2025)A Multiple Compression Approach using Attribute-based SignaturesOpen Research Europe10.12688/openreseurope.19247.15(49)Online publication date: 10-Feb-2025
  • (2023)Towards a Signature Based Compression Technique for Big Data Storage2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW58674.2023.00022(100-104)Online publication date: Apr-2023
  • (2022)Neural Network Coding in Data Compression Systems in Communication Channels2022 International Conference on Information, Control, and Communication Technologies (ICCT)10.1109/ICCT56057.2022.9976532(1-5)Online publication date: 3-Oct-2022

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