Reconfigurable Intelligent Surfaces (RISs) promise improved, secure and more efficient wireless communications. We propose and demonstrate how to exploit the diversity offered by RISs to generate and select easily differentiable radio maps for use in wireless fingerprinting localization applications. Further, we apply machine learning feature selection methods to prune the large state space of the RIS, thus reducing complexity and enhancing localization accuracy and position acquisition time. We evaluate our proposed approach by generation of radio maps with a novel radio propagation modelling and simulations.

中文翻译：

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用于无线指纹定位的可重构智能表面和机器学习

可重构智能表面 (RIS) 承诺改进、安全和更高效的无线通信。我们提出并演示了如何利用 RIS 提供的多样性来生成和选择易于区分的无线电地图，以用于无线指纹定位应用程序。此外，我们应用机器学习特征选择方法来修剪 RIS 的大状态空间，从而降低复杂性并提高定位精度和位置获取时间。我们通过使用新颖的无线电传播建模和模拟生成无线电地图来评估我们提出的方法。