# Physical Layer Communications System Design Over-the-Air Using Adversarial Networks

arXiv: https://arxiv.org/abs/1803.03145 · HTML: https://mlsanity.com/paper/1803.03145/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 12 of 12)

- [Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation](https://mlsanity.com/paper/2105.04184/) (2021)
- [Interference Suppression Using Deep Learning: Current Approaches and Open Challenges](https://mlsanity.com/paper/2112.08988/) (2021)
- [Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation](https://mlsanity.com/paper/2205.12445/) (2022)
- [Diffusion Models for Accurate Channel Distribution Generation](https://mlsanity.com/paper/2309.10505/) (2023)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Few-Shot Domain Adaptation For End-to-End Communication](https://mlsanity.com/paper/2108.00874/) (2021)
- [A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning](https://mlsanity.com/paper/2507.01976/) (2025)
- [Residual-Aided End-to-End Learning of Communication System without Known Channel](https://mlsanity.com/paper/2102.10786/) (2021)
- [Theoretical Analysis of Deep Neural Networks in Physical Layer Communication](https://mlsanity.com/paper/2202.09954/) (2022)
- [Deep Learning in Wireless Communication Receiver: A Survey](https://mlsanity.com/paper/2501.17184/) (2025)
- [Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding](https://mlsanity.com/paper/2106.00343/) (2021)
- [Machine Learning for Underwater Optical Wireless Communication Systems: A Comprehensive Survey](https://mlsanity.com/paper/2609.22294/) (2026)

## Abstract (arXiv)

This paper presents a novel method for synthesizing new physical layer modulation and coding schemes for communications systems using a learning-based approach which does not require an analytic model of the impairments in the channel. It extends prior work published on the channel autoencoder to consider the case where the channel response is not known or can not be easily modeled in a closed form analytic expression. By adopting an adversarial approach for channel response approximation and information encoding, we can jointly learn a good solution to both tasks over a wide range of channel environments. We describe the operation of the proposed adversarial system, share results for its training and validation over-the-air, and discuss implications and future work in the area.