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.
Zimaglia, E., Riviello, D.G., Garello, R., Fantini, R. (2020). A Novel Deep Learning Approach to CSI Feedback Reporting for NR 5G Cellular Systems. Institute of Electrical and Electronics Engineers Inc. [10.1109/MTTW51045.2020.9245055].
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
