Optimization of Intelligent Surfaces for Communication and Sensing: Addressing Overhead and Complexity Constraints

In an era marked by an unprecedented surge in wireless communication demands, our study pioneers a transformative approach to harnessing the potential of reconfigurable intelligent surfaces (RISs) to revolutionize network performance while tackling the critical challenges of computational complexity and channel overhead. This work weaves together three innovative projects, each targeting critical challenges in real-time user tracking, wireless systems energy efficiency, and coverage enhancement. Our first case study tackles user mobility in near field multiple input multiple output (MIMO) systems through a novel two-timescale joint precoding and RIS optimization that allows the self-tracking of a single multi-antenna User Equipment (UE). This adaptive strategy ensures that channel resources are used effectively while minimizing the overhead associated with frequent reconfigurations. In a second study, we introduce an optimization framework for RIS-assisted systems, where we strategically minimize power usage while ensuring that users meet essential data rate requirements. By intelligently reducing the problem’s dimensionality, we streamline the optimization process, making it feasible to deploy large-scale RIS networks. This innovative approach significantly alleviates the computational burden traditionally associated with RIS reconfigurability. Finally, in a third study, we delve into the realm of metaprisms (MTPs), a groundbreaking technology leveraging frequency-dependent reflection to provide an almost completely passive solution for enhanced coverage in industrial IoT applications. MTPs reduces the necessity of frequent reconfigurations, drastically reducing channel overhead but still making it easier to manage numerous devices simultaneously. Together, these advances mark a significant leap toward establishing intelligent radio environments that are not only resource-efficient but also scalable and adaptable. Our findings highlight a promising future for wireless communications, offering a unified framework that effectively addresses the challenges of computational complexity and channel overhead in RIS technology.