# Learning Robust Representations for Communications over Noisy Channels

arXiv: https://arxiv.org/abs/2409.01129 · HTML: https://mlsanity.com/paper/2409.01129/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Learning to Decode Linear Codes Using Deep Learning](https://mlsanity.com/paper/1607.04793/) (2016)
- [Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention](https://mlsanity.com/paper/1608.06409/) (2016)
- [LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks](https://mlsanity.com/paper/1811.12707/) (2018)
- [Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment](https://mlsanity.com/paper/2001.09251/) (2020)

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

- [Learning Robust Representations for Communications over Interference-limited Channels](https://mlsanity.com/paper/2410.19767/) (2024)

## Abstract (arXiv)

We explore the use of FCNNs (Fully Connected Neural Networks) for designing end-to-end communication systems without taking any inspiration from existing classical communications models or error control coding. This work relies solely on the tools of information theory and machine learning. We investigate the impact of using various cost functions based on mutual information and pairwise distances between codewords to generate robust representations for transmission under strict power constraints. Additionally, we introduce a novel encoder structure inspired by the Barlow Twins framework. Our results show that iterative training with randomly chosen noise power levels while minimizing block error rate provides the best error performance.