  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. Our first-of-a-kind 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 of any number of multiplexed users, up to 12.
Inference results with both simulated and hardware-captured field datasets show
that the UCINet0 model outperforms conventional DFT-based decoders across all
SNR ranges.
