UCINet0: A Machine Learning based Receiver for 5G NR PUCCH Format 0
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917043339526144 |
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| author | Sattianarayanin, Jeeva Keshav Yerrapragada, Anil Kumar Ganti, Radha Krishna |
| author_facet | Sattianarayanin, Jeeva Keshav Yerrapragada, Anil Kumar Ganti, Radha Krishna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_15243 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UCINet0: A Machine Learning based Receiver for 5G NR PUCCH Format 0 Sattianarayanin, Jeeva Keshav Yerrapragada, Anil Kumar Ganti, Radha Krishna Networking and Internet Architecture Artificial Intelligence Machine Learning Signal Processing 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. |
| title | UCINet0: A Machine Learning based Receiver for 5G NR PUCCH Format 0 |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2404.15243 |