Generative AI-Assisted Mobile-Edge Computation Offloading in Digital-Twin-Enabled IIoT

As a key technology in the Industrial Internet of Things (IIoT), the combination of digital twins (DTs) and mobile-edge computing (MEC) facilitates edge intelligence in B5G, while the application of generative artificial intelligence (GenAI) further enhances edge intelligence in resource allocation. However, the DT-enabled MEC system in IIoT faces the challenges of the low latency and high reliability demands, especially with the massive increase in machine-type communication devices and limited wireless and computing resources. To reduce overall delay and ensure high-reliability communication among machine devices (MDs), we propose an optimization problem for joint wireless and MEC computation resource allocation (JWMC-RA), which is shown to be NP-hard and intractable. Thereafter, the original problem is decomposed into two stages, i.e., machine-to-machine (M2M) links clustering, and MEC computing resource management. In the step of M2M links clustering, a heuristic clustering scheme via spectrum radius (HCS-SR) is presented for reducing interference of MDs which uses GenAI and graph theory. In the step of MEC computing resource management, the initial optimization problem is transformed to a convex problem, then, the optimal task offloading ratios of MD and MEC computing resource allocation are obtained. Finally, simulations show that the JWMC-RA scheme can reduce the overall delay and ensure the communication reliability.