论文标题
可重新配置的智能表面和无线指纹本地化的机器学习
Reconfigurable Intelligent Surfaces and Machine Learning for Wireless Fingerprinting Localization
论文作者
论文摘要
可重新配置的智能表面（RISS）有望改善，安全，更有效的无线通信。我们建议并演示如何利用RISS提供的多样性来生成并选择易于区分的无线电图，以用于无线指纹本地化应用程序。此外，我们采用机器学习特征选择方法来修剪RI的较大状态空间，从而降低复杂性并提高定位精度和位置获取时间。我们通过新颖的无线电传播建模和模拟来评估我们提出的方法。
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.
