Hydra Radio Access Network (H-RAN): Accurate Estimation of Reflection Configurations (RCs) for Reconfigurable Intelligent Surfaces (RIS)

The necessity for a cohesive network that seamlessly integrates various networks, technologies, and services is becoming increasingly pressing. By adopting innovative networking solutions that unify these disparate networks, Hydra radio access networks (H-RANs) embody a forward-looking approach that is essential for navigating the complexities of the evolving digital landscape. By integrating advanced communication and sensing capabilities, as well as harnessing the power of artificial intelligence/machine learning (AI/ML), H-RANs are set to redefine how services are delivered and managed within modern telecommunications networks. This comprehensive strategy ensures that these networks are not only current but also poised for future innovations. Concurrently, reconfigurable intelligent surfaces (RISs) are envisioned as programmable surface structures that control electromagnetic wave reflection, thereby modifying the wireless communication environment. Despite their advantages, a major challenge for RIS technology lies in the high overhead associated with beam alignment and channel estimation for MMW systems. These conventional methods require extensive pilot signaling to estimate channel state information (CSI) accurately, leading to significant computational and operational burdens. This study proposes a novel approach to accurately estimate RIS's reflection configurations (RCs) using real-time sensor data extracted from sensor and radio units (SRUs) and AI/ML D-engine. This method aims to enhance RIS functionality by providing precise information that allows dynamic adjustments to RCs. Simulations demonstrate that our approach significantly boosts network performance, achieving high selection accuracy by achieving a remarkable 95% selection accuracy using only five RC index candidates.