# Do not Interfere but Cooperate: A Fully Learnable Code Design for Multi-Access Channels with Feedback

arXiv: https://arxiv.org/abs/2306.00659 · HTML: https://mlsanity.com/paper/2306.00659/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2023.

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

- [DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning](https://mlsanity.com/paper/2402.08864/) (2024)

## Abstract (arXiv)

Data-driven deep learning based code designs, including low-complexity neural decoders for existing codes, or end-to-end trainable auto-encoders have exhibited impressive results, particularly in scenarios for which we do not have high-performing structured code designs. However, the vast majority of existing data-driven solutions for channel coding focus on a point-to-point scenario. In this work, we consider a multiple access channel (MAC) with feedback and try to understand whether deep learning-based designs are capable of enabling coordination and cooperation among the encoders as well as allowing error correction. Simulation results show that the proposed multi-access block attention feedback (MBAF) code improves the upper bound of the achievable rate of MAC without feedback in finite block length regime.