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EnglishIn this paper, we study 5G Channel State Information feedback reporting. We show that a Deep Learning approach based on Convolutional Neural Networks can be used to learn efficient encoding and decoding algorithms. We set up a fully compliant link level 5G-New Radio simulator with clustered delay line channel model and we consider a realistic scenario with multiple transmitting/receiving antenna schemes and noisy downlink channel estimation. Results show that our Deep Learning approach achieves results comparable with traditional methods and can also outperform them in some conditions.
| Titolo: | A Novel Deep Learning Approach to CSI Feedback Reporting for NR 5G Cellular Systems | |
| Autore/i: | Zimaglia, Elisa; Riviello, Daniel G.; Garello, Roberto; Fantini, Roberto | |
| Autore/i Unibo: | ||
| Anno: | 2020 | |
| Titolo del libro: | 2020 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW) | |
| Pagina iniziale: | 47 | |
| Pagina finale: | 52 | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/MTTW51045.2020.9245055 | |
| Abstract: | In this paper, we study 5G Channel State Information feedback reporting. We show that a Deep Learning approach based on Convolutional Neural Networks can be used to learn efficient encoding and decoding algorithms. We set up a fully compliant link level 5G-New Radio simulator with clustered delay line channel model and we consider a realistic scenario with multiple transmitting/receiving antenna schemes and noisy downlink channel estimation. Results show that our Deep Learning approach achieves results comparable with traditional methods and can also outperform them in some conditions. | |
| Data stato definitivo: | 27-feb-2022 | |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
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