# UCINet0: A Machine Learning based Receiver for 5G NR PUCCH Format 0

arXiv: https://arxiv.org/abs/2404.15243 · HTML: https://mlsanity.com/paper/2404.15243/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 10 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Analysis of dropout learning regarded as ensemble learning](https://mlsanity.com/paper/1706.06859/) (2017)
- [Physical Uplink Control Channel Design for 5G New Radio](https://mlsanity.com/paper/1809.09671/) (2018)
- [Machine Learning Decoder for 5G NR PUCCH Format 0](https://mlsanity.com/paper/2209.07861/) (2022)

## Abstract (arXiv)

Accurate decoding of Uplink Control Information (UCI) on the Physical Uplink Control Channel (PUCCH) is essential for enabling 5G wireless links. This paper explores an AI/ML-based receiver design for PUCCH Format 0. Format 0 signaling encodes the UCI content within the phase of a known base waveform and even supports multiplexing of up to 12 users within the same time-frequency resources. The proposed neural network classifier, which we term UCINet0, is capable of predicting when no user is transmitting on the PUCCH, as well as decoding the UCI content for any number of multiplexed users (up to 12). The test results with simulated, hardware-captured (lab) and field datasets show that the UCINet0 model outperforms conventional correlation-based decoders across all SNR ranges and multiple fading scenarios.