# Opening the Black Box of Deep Neural Networks in Physical Layer Communication

arXiv: https://arxiv.org/abs/2106.01124 · HTML: https://mlsanity.com/paper/2106.01124/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [OFDM-Autoencoder for End-to-End Learning of Communications Systems](https://mlsanity.com/paper/1803.05815/) (2018)
- [Understanding Autoencoders with Information Theoretic Concepts](https://mlsanity.com/paper/1804.00057/) (2018)
- [On the Information Bottleneck Problems: Models, Connections, Applications and Information Theoretic Views](https://mlsanity.com/paper/2002.00008/) (2020)

## Built on by (most cited first, 2 of 2)

- [Theoretical Analysis of Deep Neural Networks in Physical Layer Communication](https://mlsanity.com/paper/2202.09954/) (2022)
- [On the Adversarial Robustness of Generative Autoencoders in the Latent Space](https://mlsanity.com/paper/2307.02202/) (2023)

## Abstract (arXiv)

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.