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

Elisa Zimaglia, D. Riviello, R. Garello, R. Fantini
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引用次数: 6

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.
NR 5G蜂窝系统CSI反馈报告的一种新的深度学习方法
本文主要研究5G信道状态信息反馈报告。我们证明了基于卷积神经网络的深度学习方法可以用来学习高效的编码和解码算法。我们建立了一个完全兼容的链路级5G-New Radio模拟器,采用集群延迟线信道模型,并考虑了一个具有多种发射/接收天线方案和噪声下行信道估计的现实场景。结果表明,我们的深度学习方法可以达到与传统方法相当的结果,并且在某些条件下也可以优于传统方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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