THESIS
2022
1 online resource (xvi, 136 pages) : illustrations (chiefly color)
Abstract
The next-generation wireless networks are required to support the continual exponential
growth of mobile data traffic and a plethora of applications, which arouses unprecedented
challenges to mathematical modeling and optimization. Recently, there is a surge of
interests in deep learning-based communication systems, which do not require tractable
mathematical models. These studies lead to promising results for various applications in
wireless communications, e.g., resource management, channel estimation, and joint source-channel
coding. However, the existing works adopted neural network architectures from
applications such as computer vision and applied them as black boxes. Consequently,
they often require huge amounts of training samples, yield poor performance in large-scale
networks,...[
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The next-generation wireless networks are required to support the continual exponential
growth of mobile data traffic and a plethora of applications, which arouses unprecedented
challenges to mathematical modeling and optimization. Recently, there is a surge of
interests in deep learning-based communication systems, which do not require tractable
mathematical models. These studies lead to promising results for various applications in
wireless communications, e.g., resource management, channel estimation, and joint source-channel
coding. However, the existing works adopted neural network architectures from
applications such as computer vision and applied them as black boxes. Consequently,
they often require huge amounts of training samples, yield poor performance in large-scale
networks, and generalize poorly to different network settings. As 5G and beyond
networks typically have densely deployed access points, massive clients, and dynamically
changing client numbers and SNRs, it will be highly ineffective to apply these learning-based
methods.
The main theme of this thesis is to open the black box by integrating the prior knowledge
of wireless communications into the structures of neural networks. It consists of
four parts. In Part I, we exploit the topology of wireless networks and investigate the
application of graph neural networks in wireless communications. In Part II, we calibrate
the input of classic linear algorithms in wireless communications with neural networks.
In Part III, we replace the modules in conventional iterative algorithms with deep neural
networks. We will demonstrate the advantage of the proposed methods in terms of
scalability, computational complexity, sample complexity, and generalization errors both
theoretically and empirically. Our techniques and insights go beyond wireless communication
and have broad applications, which shall be presented in Part IV.
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