A Novel Deep Learning Approach to CSI Feedback Reporting for NR 5G Cellular Systems

被引：0

作者：

Zimaglia, Elisa ^{[1
]}

Riviello, Daniel G. ^{[2
]}

Garello, Roberto ^{[2
]}

Fantini, Roberto ^{[1
]}

机构：

[1] TIM SpA, Turin, Italy

[2] Politecn Torino, Dept Elect & Telecommun DET, Turin, Italy

来源：

PROCEEDINGS OF 2020 IEEE WORKSHOP ON MICROWAVE THEORY AND TECHNIQUES IN WIRELESS COMMUNICATIONS (MTTW'20) | 2020年

关键词：

5G; New Radio; Deep Learning; Convolutional Neural Network; CSI reporting;

D O I：

10.1109/mttw51045.2020.9245055

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：47 / 52

页数：6

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