Active Element Arrangement for Deep Learning-Based CSI Prediction in IRS-Assisted Systems

Recently, intelligent reflecting surface (IRS) components have garnered attention as a technology for advancing next-generation wireless communications. For effective IRS control with minimized overhead, a scheme is needed for estimating partial channels using active elements. Given that the number of active elements affects production costs and energy consumption, various methods have been proposed for predicting channels with the fewest elements. However, despite the significant impact of active element arrangement on prediction accuracy, as corroborated in this study, there has been limited discussion on this aspect, especially in deep learning (DL)-based prediction methods. In this study, we conducted experimental investigations to explore the conditions under which an active element arrangement resulting in accurate DL based predictions. Subsequently, we developed a novel active element arrangement based on three policies extracted from the simulation results. Through numerical experiments, we demonstrated that the proposed arrangement facilitates accurate prediction regardless of the number of active elements.