Opening the Black Box of Deep Neural Networks in Physical Layer Communication
Abstract
Deep Neural Network (DNN)-based physical layer techniques are attracting considerable interest due to their potential to enhance communication systems. However, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques and their cost in terms of computational complexity. We further investigate and also experimentally validate how information is flown in a DNN-based communication system under the information theoretic concepts.
Keywords
Cite
@article{arxiv.2106.01124,
title = {Opening the Black Box of Deep Neural Networks in Physical Layer Communication},
author = {Jun Liu and Haitao Zhao and Dongtang Ma and Kai Mei and Jibo Wei},
journal= {arXiv preprint arXiv:2106.01124},
year = {2022}
}
Comments
6 pages, 5 figures, to be presented in the IEEE Wireless Communications and Networking Conference (WCNC) 2022 Workshop on Machine Learning for Communications: Future Large Scale MIMO and AI-Native Air-Interface