Opening the Black Box of Deep Neural Networks in Physical Layer Communication
This addresses the problem of interpretability in DNN-based communication systems for researchers and engineers, though it appears incremental as it builds on existing applications.
The paper tackled the lack of theoretical understanding of how deep neural networks (DNNs) work in physical layer communication systems, by quantitatively analyzing their performance and computational complexity compared to traditional techniques and investigating information flow using information-theoretic concepts.
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.