论文标题
基于RIS辅助通信的渠道外推的普通微分方程CNN
Ordinary Differential Equation-based CNN for Channel Extrapolation over RIS-assisted Communication
论文作者
论文摘要
可重新配置的智能表面（RIS）被认为是重新配置无线通信环境的有希望的新技术。为了准确有效地获取通道信息，我们只打开所有RIS元素的一部分，制定子采样的RIS通道，并设计基于深度学习的方案，以从部分中推断出完整的通道信息。具体而言，受到普通微分方程（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善其结构。提供了模拟结果，以证明我们提出的基于ODE的CNN结构可以比级联的CNN实现更快的收敛速度和更好的解决方案。
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 cascaded CNN.
