Ordinary Differential Equation-Based CNN for Channel Extrapolation Over RIS-Assisted Communication
Ordinary Differential Equation-Based CNN for Channel Extrapolation Over RIS-Assisted Communication
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基于常微分方程的 CNN，用于 RIS 辅助通信的信道外推
DOI：
10.1109/lcomm.2021.3064596
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Dobre Octavia A.
中科院分区：
文献类型：
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作者：
Xu Meng;Zhang Shun;Zhong Caijun;Ma Jianpeng;Dobre Octavia A.
可重构智能表面（RIS）被认为是一种很有前途的无线通信环境重构新技术。为了准确有效地获取信道信息，我们只打开所有RIS元件的一部分，制定子采样RIS信道，并设计基于深度学习的方案来从部分信道信息外推完整的信道信息。具体来说，受常微分方程（ODE）的启发，我们在卷积神经网络（CNN）中的不同数据层之间建立了连接，并改进了其结构。仿真结果表明，我们提出的基于ODE的CNN结构可以实现更快的收敛速度和更好的解决方案比标准CNN。
The reconfigurable intelligent surface (RIS) is considered as a promising new technology for reconfiguring wireless communication environments. To acquire the channel information accurately and efficiently, we only turn on a fraction of all the RIS elements, formulate a sub-sampled RIS channel, and design a deep learning based scheme to extrapolate the full channel information from the partial one. Specifically, inspired by the ordinary differential equation (ODE), we set up connections between different data layers in a convolutional neural network (CNN) and improve its structure. Simulation results are provided to demonstrate that our proposed ODE-based CNN structure can achieve faster convergence speed and better solution than the standard CNN.