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

TL;DR

This paper introduces UCINet0, a machine learning-based receiver for 5G PUCCH Format 0 that improves decoding accuracy of uplink control information over traditional methods across various scenarios.

Contribution

The paper presents a novel neural network classifier, UCINet0, capable of decoding multiplexed UCI signals and detecting user activity in 5G PUCCH Format 0, outperforming conventional decoders.

Findings

UCINet0 outperforms correlation-based decoders across all SNRs.

The model effectively decodes multiplexed UCI from up to 12 users.

UCINet0 maintains high accuracy in simulated, lab, and field conditions.

Abstract

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.