.png){kind=icon}
返回
Data Augmentation Empowered Neural Precoding for Multiuser MIMO With MMSE Model
.png){kind=icon}
OA
AI
DOI：10.1109/LCOMM.2022.3156946.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Precoding design exploiting deep learning methods has been widely studied for multiuser multiple-input multiple-output (MU-MIMO) systems. However, conventional neural precoding design applies black-box-based neural networks which are less interpretable. In this letter, we propose a deep learning-based precoding method based on an interpretable design of a neural precoding network, namely iPNet. In particular, the iPNet mimics the classic minimum mean-squared error (MMSE) precoding and approximates the matrix inversion in the design of the neural network architecture. Specifically, the proposed iPNet consists of a model-driven component network, responsible for augmenting the input channel state information (CSI), and a data-driven sub-network, responsible for precoding calculation from this augmented CSI. The latter data-driven module is explicitly interpreted as an unsupervised learner of the MMSE precoder. Simulation results show that by exploiting the augmented CSI, the proposed iPNet achieves noticeable performance gain over existing black-box designs and also exhibits enhanced generalizability against CSI mismatches.
Keyword:
Precoding
Artificial neural networks
Training
Matrix converters
MIMO communication
Deep learning
Array signal processing
Precoding
deep learning
MU-MIMO
interpretable design
augmented CSI
generalization ability
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
Deep Learning-Based Downlink Channel Prediction for FDD Massive MIMO System基于深度学习的FDD大规模MIMO系统下行信道预测
OAAI
.png){kind=icon}
OAAI分享收藏分享收藏Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMOFDD大规模MIMO中分布式信道反馈和多用户预编码的深度学习OAAI
OAAI分享收藏分享收藏
查看更多内容