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.
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.
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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 |
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http://hdl.handle.net/11583/2854076