作者
Chao Lu, Wei Xu, Hong Shen, Jun Zhu, Kezhi Wang
发表日期
2019/1
期刊
IEEE Communications Letters
卷号
23
期号
1
页码范围
188-191
出版商
IEEE
简介
In a multiple-input multiple-output (MIMO) system, the availability of channel state information (CSI) at the transmitter is essential for performance improvement. Recent convolutional neural network (NN)-based techniques show competitive ability in realizing CSI compression and feedback. By introducing a new NN architecture, we enhance the accuracy of quantized CSI feedback in MIMO communications. The proposed NN architecture invokes a module named long short-term memory that admits the NN to benefit from exploiting temporal and frequency correlations of wireless channels. Compromising performance with complexity, we further modify the NN architecture with a significantly reduced number of parameters to be trained. Finally, experiments show that the proposed NN architectures achieve better performance in terms of both CSI compression and recovery accuracy.
学术搜索中的文章
C Lu, W Xu, H Shen, J Zhu, K Wang - IEEE Communications Letters, 2018