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

• URL: http://arxiv.org/abs/2404.15243v1
• Date: Sun, 10 Mar 2024 09:56:02 GMT
• Title: UCINet0: A Machine Learning based Receiver for 5G NR PUCCH Format 0
• Authors: Anil Kumar Yerrapragada, Jeeva Keshav Sattianarayanin, Radha Krishna Ganti,
• Abstract summary: This paper explores an AI/ML-based receiver design for PUCCH Format 0. First-of-a-kind neural network, which we term UCINet0, is capable of predicting when no user is transmitting on the PUCCH. Inference results with both simulated and hardware-captured field datasets show that the UCINet0 model outperforms conventional DFT-based decoders across all SNR ranges.
• Score: 0.6226609932118122
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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