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Development and Analysis of Deep Learning Architectures pp 223–266Cite as

Deep Learning for Wireless Communications

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 867))

Abstract

Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high degrees of freedom. Deep learning has a strong potential to overcome this challenge via data-driven solutions and improve the performance of wireless systems in utilizing limited spectrum resources. In this chapter, we first describe how deep learning is used to design an end-to-end communication system using autoencoders. This flexible design effectively captures channel impairments and optimizes transmitter and receiver operations jointly in single-antenna, multiple-antenna, and multiuser communications. Next, we present the benefits of deep learning in spectrum situation awareness ranging from channel modeling and estimation to signal detection and classification tasks. Deep learning improves the performance when the model-based methods fail. Finally, we discuss how deep learning applies to wireless communication security. In this context, adversarial machine learning provides novel means to launch and defend against wireless attacks. These applications demonstrate the power of deep learning in providing novel means to design, optimize, adapt, and secure wireless communications.

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Author information

Authors and Affiliations

  1. Virginia Tech, Arlington, VA, USA

    Tugba Erpek, Timothy J. O’Shea & T. Charles Clancy

  2. DeepSig, Inc., Arlington, VA, USA

    Timothy J. O’Shea

  3. Intelligent Automation, Inc., Rockville, MD, USA

    Yalin E. Sagduyu & Yi Shi

  4. Virginia Tech, Blacksburg, VA, USA

    Yi Shi

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  1. Tugba Erpek
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  2. Timothy J. O’Shea
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  3. Yalin E. Sagduyu
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  4. Yi Shi
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  5. T. Charles Clancy
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Correspondence to Tugba Erpek .

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Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Erpek, T., O’Shea, T.J., Sagduyu, Y.E., Shi, Y., Clancy, T.C. (2020). Deep Learning for Wireless Communications. In: Pedrycz, W., Chen, SM. (eds) Development and Analysis of Deep Learning Architectures. Studies in Computational Intelligence, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-31764-5_9

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