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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.
A Novel Deep Learning Approach to CSI Feedback Reporting for NR 5G Cellular Systems / Zimaglia, Elisa; Riviello, Daniel G.; Garello, Roberto; Fantini, Roberto. - ELETTRONICO. - (2020), pp. 47-52. ((Intervento presentato al convegno 2020 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW) tenutosi a Riga, Latvia nel October 2020 [10.1109/MTTW51045.2020.9245055].
| Titolo: | A Novel Deep Learning Approach to CSI Feedback Reporting for NR 5G Cellular Systems | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Abstract: | In this paper, we study 5G Channel State Information feedback reporting. We show that a Deep Lea...rning 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. | |
| ISBN: | 978-1-7281-9398-4 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| paper.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia | |
| paper2.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2854076
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