Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, and Task-2 Approach

Hydra radio access networks (H-RANs) are poised to revolutionize the way data is gathered, transmitted, and analyzed, ushering in an era of unprecedented connectivity, efficiency, and intelligence. By proactively managing changes and addressing potential disruptions, H-RAN networks aim to provide consistent service quality, even under varied operating conditions. This means that networks can automatically detect issues, assess their impact, implement corrective measures, and modify operating parameters in response to variations in user behavior or environmental factors. However, conventional RANs typically implement a uniform strategy when dealing with diverse communication scenarios. This one-size-fits-all methodology limits their ability to autonomously adjust to the unique requirements of the millimeter and terahertz wave (MMW/THz) communications environment, which is characterized by high sensitivity to environmental factors. As a result, these systems face significant challenges in maintaining a reliable and efficient communication link under varying conditions. Furthermore, conventional RANs typically require considerable manual tuning and configuration, making them time-consuming and costly. Indeed, while the potential of H-RAN networks encompasses numerous improvements and innovations across various aspects of next-generation technologies, this paper addresses the initial access implementation and data transmission approaches under the non-line-of-sight (NLoS) of the $\text {level}_{1}$ case study. Our solution contains several novelties that enhance both overhead and model accuracy. To this end, we have developed a novel adaptive environment-aware blockage beamforming codebook structure. This codebook consists of edge-line-of-sight (ELoS) vectors and reconfigurable intelligent surface (RIS) vectors, RIS-LoS (SLoS). These elements are integral to optimizing beamforming in scenarios where direct line-of-sight (LoS) is obstructed by $\text {level}_{1}$ blockage scenario. Furthermore, we employed a multi-sparse input and multi-task learning (SMTL) framework in the Hydra distributed unit (H-DU) artificial intelligence and machine learning (AI/ML) (AI/ML D-engine), where each task is tailored to be executed in a particular environment based on online feedback. The SMTL model employs an approach to handling sparse and heterogeneous input data while performing multiple tasks based on the current network condition. More specifically, a novel $\text {task}_{2}$ is developed in this study to generate a list of recommended beamforming vectors and patterns designed to address the challenges arising from blockage scenarios of $\text {level}_{1}$ in MMW/THz environments.