.png){kind=icon}
返回
A Deep Learning-Based Framework for Low Complexity Multiuser MIMO Precoding Design
.png){kind=icon}
OA
AI
DOI：10.1109/TWC.2022.3190435.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional algorithms including the zero-forcing (ZF) algorithm and the weighted minimum mean square error (WMMSE) algorithm failed to achieve a satisfactory trade-off between complexity and performance. In this paper, leveraging on the power of deep learning, we propose a low-complexity precoding design framework for MU-MIMO systems. The key idea is to transform the MIMO precoding problem into the multiple-input single-output precoding problem, where the optimal precoding structure can be obtained in closed-form. A customized deep neural network is designed to fit the mapping from the channels to the precoding matrix. In addition, the technique of input dimensionality reduction, network pruning, and recovery module compression are used to further improve the computational efficiency. Furthermore, the extension to the practical MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) system is studied. Simulation results show that the proposed low-complexity precoding scheme achieves similar performance as the WMMSE algorithm with very low computational complexity.
Keyword:
Multi-user MIMO
precoder design
deep learning
model compression
MIMO-OFDM
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
引用论文
.png){kind=icon}
On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming关于使用迫零波束成形的多天线广播调度的最优性
PREAI
分享收藏Optimal Multiuser Transmit Beamforming: A Difficult Problem with a Simple Solution Structure最佳多用户发射波束成形: 具有简单解决方案结构的难题
OAAI
OAAI分享收藏
查看更多内容