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

Riviello, Daniel G.
;
2020

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.
2020
2020 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW)
47
52
Zimaglia, Elisa; Riviello, Daniel G.; Garello, Roberto; Fantini, Roberto
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/870595
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