Deep Learning-Based Limited Feedback Designs for MIMO Systems

被引：28
|
作者：
Jang, Jeonghyeon [1 ]
Lee, Hoon [2 ]
Hwang, Sangwon [1 ]
Ren, Haibao [3 ]
Lee, Inkyu [1 ]
机构：
[1] Korea Univ, Sch Elect Engn, Seoul 02841, South Korea
[2] Pukyong Natl Univ, Dept Informat & Commun Engn, Busan 48513, South Korea
[3] Huawei Technol, Wireless Network Res Dept, Shanghai 201206, Peoples R China
来源：
IEEE WIRELESS COMMUNICATIONS LETTERS | 2020年 / 9卷 / 04期
基金：
新加坡国家研究基金会;
关键词：
MIMO; deep learning; limited feedback;
D O I：
10.1109/LWC.2019.2962114
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We study a deep learning (DL) based limited feedback methods for multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedure including pilot-aided channel training process, channel codebook design, and beamforming vector selection. The DNNs are trained to yield binary feedback information as well as an efficient beamforming vector which maximizes the effective channel gain. Compared to conventional limited feedback schemes, the proposed DL method shows an 1 dB symbol error rate (SER) gain with reduced computational complexity.
引用
收藏
页码：558 / 561
页数：4
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