Deep Learning-based Phase Reconfiguration for Intelligent Reflecting Surfaces
This work addresses a domain-specific problem for wireless communication systems by providing an incremental method to optimize IRS configurations, potentially enhancing signal power gains.
The paper tackles the challenge of configuring phase matrices in Intelligent Reflecting Surfaces (IRSs) for wireless communications, which lack active components and face high training overhead, by proposing a deep learning approach that uses reflected pilot signals to learn the propagation environment, resulting in improved performance as shown in numerical results.
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.