# Channel model for end-to-end learning of communications systems: A survey

arXiv: https://arxiv.org/abs/2204.03944 · HTML: https://mlsanity.com/paper/2204.03944/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Learning to Decode Linear Codes Using Deep Learning](https://mlsanity.com/paper/1607.04793/) (2016)
- [Model-free Training of End-to-end Communication Systems](https://mlsanity.com/paper/1812.05929/) (2018)
- [Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks](https://mlsanity.com/paper/1805.06350/) (2018)
- [End-to-End Learning of Communications Systems Without a Channel Model](https://mlsanity.com/paper/1804.02276/) (2018)
- [Learning to Protect Communications with Adversarial Neural Cryptography](https://mlsanity.com/paper/1610.06918/) (2016)
- [Unsupervised Representation Learning of Structured Radio Communication Signals](https://mlsanity.com/paper/1604.07078/) (2016)
- [RNN Decoding of Linear Block Codes](https://mlsanity.com/paper/1702.07560/) (2017)

## Abstract (arXiv)

The traditional communication model based on chain of multiple independent processing blocks is constraint to efficiency and introduces artificial barriers. Thus, each individually optimized block does not guarantee end-to-end performance of the system. Recently, end-to-end learning of communications systems through machine learning (ML) have been proposed to optimize the system metrics jointly over all components. These methods show performance improvements but has a limitation that it requires a differentiable channel model. In this study, we have summarized the existing approaches that alleviates this problem. We believe that this study will provide better understanding of the topic and an insight into future research in this field.