# Machine Learning Decoder for 5G NR PUCCH Format 0

arXiv: https://arxiv.org/abs/2209.07861 · HTML: https://mlsanity.com/paper/2209.07861/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [A Survey of Complex-Valued Neural Networks](https://mlsanity.com/paper/2101.12249/) (2021)
- [5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View](https://mlsanity.com/paper/1804.05057/) (2018)
- [Physical Uplink Control Channel Design for 5G New Radio](https://mlsanity.com/paper/1809.09671/) (2018)

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

- [UCINet0: A Machine Learning based Receiver for 5G NR PUCCH Format 0](https://mlsanity.com/paper/2404.15243/) (2024)

## Abstract (arXiv)

5G cellular systems depend on the timely exchange of feedback control information between the user equipment and the base station. Proper decoding of this control information is necessary to set up and sustain high throughput radio links. This paper makes the first attempt at using Machine Learning techniques to improve the decoding performance of the Physical Uplink Control Channel Format 0. We use fully connected neural networks to classify the received samples based on the uplink control information content embedded within them. The trained neural network, tested on real-time wireless captures, shows significant improvement in accuracy over conventional DFT-based decoders, even at low SNR. The obtained accuracy results also demonstrate conformance with 3GPP requirements.