Machine Learning Decoder for 5G NR PUCCH Format 0
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866918297185812480 |
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| author | Yerrapragada, Anil Kumar S, Jeeva Keshav Gautam, Ankit Ganti, Radha Krishna |
| author_facet | Yerrapragada, Anil Kumar S, Jeeva Keshav Gautam, Ankit Ganti, Radha Krishna |
| contents | 5G cellular systems depend on the timely exchange of feedback control information between the user equipment and the base station. Proper decoding of this control information is necessary to set up and sustain high throughput radio links. This paper makes the first attempt at using Machine Learning techniques to improve the decoding performance of the Physical Uplink Control Channel Format 0. We use fully connected neural networks to classify the received samples based on the uplink control information content embedded within them. The trained neural network, tested on real-time wireless captures, shows significant improvement in accuracy over conventional DFT-based decoders, even at low SNR. The obtained accuracy results also demonstrate conformance with 3GPP requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_07861 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Machine Learning Decoder for 5G NR PUCCH Format 0 Yerrapragada, Anil Kumar S, Jeeva Keshav Gautam, Ankit Ganti, Radha Krishna Networking and Internet Architecture Information Theory Machine Learning Signal Processing 5G cellular systems depend on the timely exchange of feedback control information between the user equipment and the base station. Proper decoding of this control information is necessary to set up and sustain high throughput radio links. This paper makes the first attempt at using Machine Learning techniques to improve the decoding performance of the Physical Uplink Control Channel Format 0. We use fully connected neural networks to classify the received samples based on the uplink control information content embedded within them. The trained neural network, tested on real-time wireless captures, shows significant improvement in accuracy over conventional DFT-based decoders, even at low SNR. The obtained accuracy results also demonstrate conformance with 3GPP requirements. |
| title | Machine Learning Decoder for 5G NR PUCCH Format 0 |
| topic | Networking and Internet Architecture Information Theory Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2209.07861 |