论文标题
基于深度学习的最佳RIS相互作用利用先前采样的通道相关性
Deep Learning-Based Optimal RIS Interaction Exploiting Previously Sampled Channel Correlations
论文作者
论文摘要
可重新配置的智能表面（RIS）技术因其有希望的覆盖范围和光谱效率功能而引起了人们的兴趣。但是，需要解决一些挑战，以实现实践中的这项技术。主要挑战之一是反映系数的配置，而无需横梁训练开销或大规模通道估计。早期的作品使用带有深度学习算法的估计通道信息来设计RIS反射矩阵。尽管这些作品可以减少横梁训练开销，但它们仍然忽略了先前采样的通道中的现有相关性。在本文中，与现有作品不同，我们建议利用先前采样通道中的相关性，以更可靠地估计RIS相互作用。为此，我们使用深层多层感知器。仿真结果表明，提出的算法取得了改善。
The reconfigurable intelligent surface (RIS) technology has attracted interest due to its promising coverage and spectral efficiency features. However, some challenges need to be addressed to realize this technology in practice. One of the main challenges is the configuration of reflecting coefficients without the need for beam training overhead or massive channel estimation. Earlier works used estimated channel information with deep learning algorithms to design RIS reflection matrices. Although these works can reduce the beam training overhead, still they overlook existing correlations in the previously sampled channels. In this paper, different from existing works, we propose to exploit the correlation in the previously sampled channels to estimate RIS interaction more reliably. We use a deep multi-layer perceptron for this purpose. Simulation results reveal performance improvements achieved by the proposed algorithm.
