# Residual-Aided End-to-End Learning of Communication System without Known Channel

arXiv: https://arxiv.org/abs/2102.10786 · HTML: https://mlsanity.com/paper/2102.10786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Model-free Training of End-to-end Communication Systems](https://mlsanity.com/paper/1812.05929/) (2018)
- [OFDM-Autoencoder for End-to-End Learning of Communications Systems](https://mlsanity.com/paper/1803.05815/) (2018)
- [Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks](https://mlsanity.com/paper/1805.06350/) (2018)
- [Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm](https://mlsanity.com/paper/2007.05952/) (2020)
- [End-to-end Deep Learning of Optical Fiber Communications](https://mlsanity.com/paper/1804.04097/) (2018)
- [Deep Learning for Channel Coding via Neural Mutual Information Estimation](https://mlsanity.com/paper/1903.02865/) (2019)
- [Physical Layer Communications System Design Over-the-Air Using Adversarial Networks](https://mlsanity.com/paper/1803.03145/) (2018)
- [WGAN-based Autoencoder Training Over-the-air](https://mlsanity.com/paper/2003.02744/) (2020)
- [Autoencoder-Based Error Correction Coding for One-Bit Quantization](https://mlsanity.com/paper/1909.12120/) (2019)
- [Capacity-Approaching Autoencoders for Communications](https://mlsanity.com/paper/2009.05273/) (2020)

## Built on by (most cited first, 1 of 1)

- [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)

## Abstract (arXiv)

Leveraging powerful deep learning techniques, the end-to-end (E2E) learning of communication system is able to outperform the classical communication system. Unfortunately, this communication system cannot be trained by deep learning without known channel. To deal with this problem, a generative adversarial network (GAN) based training scheme has been recently proposed to imitate the real channel. However, the gradient vanishing and overfitting problems of GAN will result in the serious performance degradation of E2E learning of communication system. To mitigate these two problems, we propose a residual aided GAN (RA-GAN) based training scheme in this paper. Particularly, inspired by the idea of residual learning, we propose a residual generator to mitigate the gradient vanishing problem by realizing a more robust gradient backpropagation. Moreover, to cope with the overfitting problem, we reconstruct the loss function for training by adding a regularizer, which limits the representation ability of RA-GAN. Simulation results show that the trained residual generator has better generation performance than the conventional generator, and the proposed RA-GAN based training scheme can achieve the near-optimal block error rate (BLER) performance with a negligible computational complexity increase in both the theoretical channel model and the ray-tracing based channel dataset.