论文标题
大型智能表面的深渠道学习帮助MM波巨大的MIMO系统
Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
论文作者
论文摘要
这封信介绍了第一批引入深度学习（DL）框架，以在大型智能表面（LIS）辅助大规模MIMO（多输入多输出）系统中进行通道估计。设计了双卷积神经网络（CNN）体系结构，并使用接收到的飞行员信号，以估算直接和级联渠道。在多用户方案中，每个用户都可以访问CNN来估算其自己的频道。评估了所提出的DL方法的性能，并将其与最先进的DL技术进行了比较，并证明了其出色的性能。
This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.
