Data-Driven Design for IRS-Assisted Massive Multi-Modal Sensing in Industry 5.0

The transition into Industry 5.0 underscores the need for advanced communication frameworks capable of over-coming the challenges posed by complex industrial environments. This opens new possibilities for addressing industrial issues related to fading wireless channels and interference, potentially resulting in a significant leap forward in the capacity and reliability of wireless communications. This paper introduces a data-driven approach, utilizing a deep neural network (DNN) model to enhance cascaded channel estimation and mapping in Intelligent reflecting surface (IRS)-enabled industrial internet-of-things (IIoT) networks. By employing Remcom's Wireless InSite for realistic Ray tracing simulations, we have generated a robust dataset that accurately represents the spatial characteristics and varying noise conditions of an industrial setting. Our approach strategically divides the channel estimation process, directly estimating a subset of channels for a few users while leveraging the channel mapping model for the remainder. Simulation results demonstrated that the proposed DNN-aided scheme outperforms traditional estimation techniques in terms of reduced complexity and accuracy. Moreover, the proposed approach is particularly effective in both Gaussian and non-Gaussian noise scenarios, significantly contributing to developing reliable, scalable, and intelligent industrial wireless systems.