AI-Enabled Connectivity in RIS-Assisted NOMA and RSMA-based Low-Mobility Networks

Objective

The evolution of 6G wireless networks relies on advanced multiple-access technologies that improve efficiency and scalability. Reconfigurable Intelligent Surfaces (RIS) are set to transform 6G by enabling dynamic control of signal propagation channels. This project focuses on optimizing User Equipment (UE) pairings with Resource Blocks (RBs) and RISs in RIS-assisted Non-Orthogonal Multiple Access (NOMA) environments, exploring various configurations to maximize system throughput, fairness, and energy efficiency. By integrating active and passive beamforming, our approach not only enhances communication performance but also reduces the carbon footprint by minimizing the need for additional base stations through efficient frequency reuse. With Munster Technological University (MTU) affiliated with ADAPT Centre, and secondment at Dell Technologies, this research is strategically positioned for high-impact outcomes. The project introduces a novel technique for intelligent role switching in low mobility networks (LMNs), enabling dynamic shifts between near and far users to enhance connectivity time. The methodology incorporates optimization algorithms such as Simulated Annealing, Hill Climbing, Genetic Algorithms, and Random Walk for heuristic solutions. For LMNs, we will develop and evaluate models based on stochastic geometry and deep reinforcement learning (DRL) (asynchronous advantage actor-critic) to optimize the angle of reflection (AoR) for RIS elements. Four-month secondment at Dell, Ireland, is a key aspect where industry-grade validation will assess the performance of RO3 and refine the interplay between the edge-cloud continuum for the DRL. These models aim to address challenges in RIS-assisted networks, supporting seamless 6G connectivity in smart and sustainable cities. Ultimately, the research will advance green communications by optimizing network designs for energy-efficient infrastructure, aligning with EU goals for sustainable smarter cities.