CsiNet-Plus Model with Truncation and Noise on CSI Feedback

被引：3
作者：
Liu, Feng [1 ]
He, Xuecheng [1 ]
Li, Conggai [1 ]
Xu, Yanli [1 ]
机构：
[1] Shanghai Maritime Univ, Coll Informat Engn, Shanghai, Peoples R China
来源：
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES | 2020年 / E103A卷 / 01期
基金：
中国国家自然科学基金;
关键词：
massive MIMO; CSI feedback; truncation; channel noise; deep learning;
D O I：
10.1587/transfun.2019EAL2123
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
For the frequency-division-duplex (FDD)-based massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) feedback plays a critical role. Although deep learning has been used to compress the CSI feedback, some issues like truncation and noise still need further investigation. Facing these practical concerns, we propose an improved model (called CsiNet-Plus), which includes a truncation process and a channel noise process. Simulation results demonstrate that the CsiNet-Plus outperforms the existing CsiNet. The performance interchangeability between truncated decimal digits and the signal-to-noise-ratio helps support flexible configuration.
引用
收藏
页码：376 / 381
页数：6
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