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.

中文翻译：

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基于普通微分方程的 CNN 用于 RIS 辅助通信的信道外推

可重构智能表面（RIS）被认为是一种用于重构无线通信环境的有前途的新技术。为了准确有效地获取通道信息，我们只打开了所有 RIS 元素的一部分，制定了一个下采样的 RIS 通道，并设计了一种基于深度学习的方案，从部分信息中推断出完整的通道信息。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连接并改进其结构。仿真结果表明，我们提出的基于 ODE 的 CNN 结构可以实现比标准 CNN 更快的收敛速度和更好的解决方案。