Multi-Dimensional Resource Management for 6G IoT Networks with Generative Artificial Intelligence

The Internet of Things (IoT) has fundamentally changed our interaction with the physical world, bringing a paradigm shift that has transitioned physical objects into digital and interconnected devices. This digitalization revolution largely hinges on to take a significant leap forward with the deployment of emerging 6G networks. While 6G holds great promise, it also poses severe challenges, especially in dynamically managing its vast multi-dimension resources amid heightened demands for latency, rates, reliability etc. Traditional approaches, particularly those relying on Deep Reinforcement Learning (DRL), have obvious limitations in various and complex 6G IoT networks. In this context, we will explore Generative Artificial Intelligence (GAI) to solve these challenges. First, we will establish a hypergraph-based framework for multi-dimensional resource representation and modeling, targeting the optimization of service capabilities. Subsequently, we will use the Generative Diffusion Model (GDM) to streamline communication intent extraction, facilitating swift decision-making and improving the adaptability of the network. Finally, we will leverage GDM in crafting resilient incentive structures, aiming to ensure the fairness of resource distribution and enhance user experience in the 6G IoT environment. The project has three work packages (WPs): WP1 aims at designing measurements for multi-dimensional resources, WP2 investigates the utilization of GDM for adaptive resource management, and in WP3, we seek to use GDM to ensure fairness in resource sharing. By integrating GAI as a solution to the multifaceted challenges of 6G IoT, we seek to expand and redefine the capabilities and applications of 6G IoT in real-world contexts.