🤖 AI Summary
This work addresses the UCI decoding challenge for 5G NR PUCCH Format 0 under multi-user multiplexing (up to 12 users) and idle-state coexistence—scenarios where conventional DFT-based demodulation fails due to its reliance on single-user assumptions and high SNR. We propose an end-to-end complex-domain neural receiver that jointly models phase-encoded features and user activity detection, enabling joint UCI demodulation without explicit transmitter detection—a first in the literature. By leveraging native complex-valued signal representation, end-to-end phase information learning, and joint training on both simulated and real-world over-the-air measurements, our method achieves consistent performance gains over the DFT baseline across the full SNR range. Experimental validation on hardware-collected data confirms strong robustness and generalization capability, even under realistic channel impairments and low-SNR 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. 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.