UCINet0: A Machine Learning based Receiver for 5G NR PUCCH Format 0
This work addresses the need for reliable 5G wireless communication by improving receiver design for control channels, though it appears incremental as it applies existing ML methods to a specific domain problem.
The paper tackled the problem of accurately decoding Uplink Control Information in 5G PUCCH Format 0 by proposing a neural network classifier called UCINet0, which outperformed conventional decoders across all SNR ranges and fading scenarios in simulated, lab, and field tests.
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.