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Analysis of End-to-End Communication System Network Model

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Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 515))

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Abstract

Deep learning has been very hot in the field of image recognition and natural language processing in recent years. In this article, we apply deep learning to the field of communication, using neural networks to build end-to-end communication systems under Gaussian channels, and jointly optimize the sender and receiver. The results show that the performance of the end-to-end communication system based on deep learning in Gaussian channel is better than the traditional communication system. Then we change the parameters and results of the network and analyze the results. Finally, we look forward to the next research direction.

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References

  1. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948)

    Google Scholar 

  2. Gagliardi, R.M., Karp, S.: Optical communications, 2nd edn. Wiley (1995)

    Google Scholar 

  3. Meyr, H., Moeneclaey, M., Fechtel, S.A.: Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing. Wiley (1998)

    Google Scholar 

  4. Schenk, T.: RF imperfections in high-rate wireless systems: impact and digital compensation. Springer Science & Business Media (2008)

    Google Scholar 

  5. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)

    Google Scholar 

  6. O’Shea, T.J., Karra, K., Clancy, T.C.: Learning to communicate: Channel auto-encoders, domain specific regularizes, and attention. In: IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 223–228 (2016)

    Google Scholar 

  7. O’Shea, T.J., Hoydis, J.: An introduction to machine learning communications systems. arXiv preprint arXiv:1702.00832 (2017)

  8. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of AISTATS 2010, vol. 9, pp. 249–256

    Google Scholar 

  9. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009). (Also published as a book. Now Publishers)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Information Science and Technology College, Dalian Maritime University, Dalian, China

    Kaiyao Zhang, Nan Wu & Xudong Wang

Authors
  1. Kaiyao Zhang
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  2. Nan Wu
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  3. Xudong Wang
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Corresponding author

Correspondence to Kaiyao Zhang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Physical and Electronic Information, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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© 2019 Springer Nature Singapore Pte Ltd.

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Cite this paper

Zhang, K., Wu, N., Wang, X. (2019). Analysis of End-to-End Communication System Network Model. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-13-6264-4_53

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  • DOI: https://doi.org/10.1007/978-981-13-6264-4_53

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6263-7

  • Online ISBN: 978-981-13-6264-4

  • eBook Packages: EngineeringEngineering (R0)

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