# End-to-End Learning of Communications Systems Without a Channel Model

arXiv: https://arxiv.org/abs/1804.02276 · HTML: https://mlsanity.com/paper/1804.02276/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2018.

## Built on by (most cited first, 21 of 21)

- [A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning](https://mlsanity.com/paper/2505.09160/) (2025)
- [Reinforcement Learning-powered Semantic Communication via Semantic Similarity](https://mlsanity.com/paper/2108.12121/) (2021)
- [OFDM-Based Digital Semantic Communication with Importance Awareness](https://mlsanity.com/paper/2401.02178/) (2024)
- [MIMO-GAN: Generative MIMO Channel Modeling](https://mlsanity.com/paper/2203.08588/) (2022)
- [Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications](https://mlsanity.com/paper/2207.00300/) (2022)
- [Learning Linear Block Error Correction Codes](https://mlsanity.com/paper/2405.04050/) (2024)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [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)
- [Semantics Alignment via Split Learning for Resilient Multi-User Semantic Communication](https://mlsanity.com/paper/2310.09394/) (2023)
- [DeepIC: Coding for Interference Channels via Deep Learning](https://mlsanity.com/paper/2108.06028/) (2021)
- [Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning](https://mlsanity.com/paper/2211.04311/) (2022)
- [Short Blocklength Wiretap Channel Codes via Deep Learning: Design and Performance Evaluation](https://mlsanity.com/paper/2206.03477/) (2022)
- [Deep Deterministic Policy Gradient for End-to-End Communication Systems without Prior Channel Knowledge](https://mlsanity.com/paper/2305.07448/) (2023)
- [On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge](https://mlsanity.com/paper/2401.12645/) (2024)
- [DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reduction](https://mlsanity.com/paper/2603.23544/) (2026)
- [Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding](https://mlsanity.com/paper/2106.00343/) (2021)
- [Channel model for end-to-end learning of communications systems: A survey](https://mlsanity.com/paper/2204.03944/) (2022)
- [Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach](https://mlsanity.com/paper/2609.33605/) (2026)
- [End-to-End Optical Semantic Communication over a Nonlinear WDM Fiber Link](https://mlsanity.com/paper/2609.37551/) (2026)

## Abstract (arXiv)

The idea of end-to-end learning of communications systems through neural network -based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm iterates between supervised training of the receiver and reinforcement learning -based training of the transmitter. We demonstrate that this approach works as well as fully supervised methods on additive white Gaussian noise (AWGN) and Rayleigh block-fading (RBF) channels. Surprisingly, while our method converges slower on AWGN channels than supervised training, it converges faster on RBF channels. Our results are a first step towards learning of communications systems over any type of channel without prior assumptions.