Deep Learning for Wireless Communications
- URL: http://arxiv.org/abs/2005.06068v1
- Date: Tue, 12 May 2020 21:58:44 GMT
- Title: Deep Learning for Wireless Communications
- Authors: Tugba Erpek, Timothy J. O'Shea, Yalin E. Sagduyu, Yi Shi, T. Charles
Clancy
- Abstract summary: We first describe how deep learning is used to design an end-to-end communication system using autoencoders.
Next, we present the benefits of deep learning in spectrum situation awareness.
Finally, we discuss how deep learning applies to wireless communication security.
- Score: 3.7506111080592386
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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