论文标题
基于深度学习的阶段重新配置，用于智能反射表面
Deep Learning-based Phase Reconfiguration for Intelligent Reflecting Surfaces
论文作者
论文摘要
智能反射表面（IRSS），包括可重新配置的超材料，最近引起了人们的关注，作为一种有希望的成本效益技术，可以为无线通信带来新功能。这些表面可用于部分控制传播环境，并有可能提供与以适当方式配置时与IRS元素数量成正比的功率增益。但是，IRSS上本地相位矩阵的配置可能是一项艰巨的任务，因为它们故意设计为没有任何活动组件，因此，它们无法处理任何试验信号。此外，美国国税局的大量要素可能会创造出巨大的培训开销。在本文中，我们提出了一种深度学习（DL）的方法，以在IRS上进行阶段重新配置，以学习和利用当地传播环境。提出的方法使用通过IRS反射的接收到的飞行员信号来训练深馈网络。评估了建议的方法的性能，并提供了数值结果。
Intelligent reflecting surfaces (IRSs), consisting of reconfigurable metamaterials, have recently attracted attention as a promising cost-effective technology that can bring new features to wireless communications. These surfaces can be used to partially control the propagation environment and can potentially provide a power gain that is proportional to the square of the number of IRS elements when configured in a proper way. However, the configuration of the local phase matrix at the IRSs can be quite a challenging task since they are purposely designed to not have any active components, therefore, they are not able to process any pilot signal. In addition, a large number of elements at the IRS may create a huge training overhead. In this paper, we present a deep learning (DL) approach for phase reconfiguration at an IRS in order to learn and make use of the local propagation environment. The proposed method uses the received pilot signals reflected through the IRS to train the deep feedforward network. The performance of the proposed approach is evaluated and the numerical results are presented.
