基于神经网络的超颖表面辐射方向图预测
Radiation pattern prediction for Metasurfaces: A Neural Network based approach
摘要：
随着当前5G网络的标准化接近完成，了解6 G无线网络潜在技术的工作已经在进行中。用于6 G网络的这些潜在技术之一是可重构智能表面（RIS）。它们为设计无线信道提供了前所未有的自由度，即，无论何时和以何种方式需要都能够修改信道的特性。然而，这些属性要求在所有可能的操作条件下很好地理解相关联的元表面（MSF）的响应。虽然可以通过分析模型或全波模拟来理解辐射图案特性，但它们分别在某些条件下和极高的计算复杂性下具有不准确性。因此，在本文中，我们提出了一种新的基于神经网络的方法，使MSF响应的快速和准确的表征。我们分析了多个场景，并展示了所提出的方法的能力和实用性。具体地说，我们表明，这种方法是能够学习和预测的反射波辐射模式的准确性的全波模拟（98.8%-99.8%）和时间和计算复杂性的分析模型的参数。上述结果和方法对于将部署在6 G网络环境中的数千个RIS的设计、容错和维护具有特别重要的意义。
英文摘要：
As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks are Reconfigurable Intelligent Surfaces (RISs). They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface (MSF) is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full wave simulations, they suffer from inaccuracy under certain conditions and extremely high computational complexity, respectively. Hence, in this paper we propose a novel neural networks based approach that enables a fast and accurate characterization of the MSF response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method is able to learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full wave simulation (98.8%-99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance and maintenance of the thousands of RISs that will be deployed in the 6G network environment.
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