Foundation Model Enhanced Joint Multi-Hop Task Offloading in Dynamic R2X/V2X-Based Edge Computing Networks

Recent popularization of the Internet of Vehicles (IoVs) and vehicles-to-everything (V2X) enables the emergence of real-time vehicular applications, posing challenges to resourcelimited vehicles. Toward this end, vehicle edge computing (VEC) has been proposed to alleviate the computational burden on vehicles by leveraging resources from roadside units (RSUs) and VEC servers. While existing works mainly focus on the task requirement for either vehicles or RSUs, the joint task offloading for both V2X and RSUs-to-everything (R2X) has not been fully studied. In this paper, we aim at optimizing the task offloading strategies for both vehicles and RSUs, and adopt a multi-hop task offloading manner to fully utilize the VEC network resources. This problem introduces a severe state-action space shift issue with varying dimensions and representation, which poses challenges for conventional DRL approaches. To address it, we propose a Bidirectional Encoder Representations from Transformers (Bert)-based matching Q-network (BMQN) algorithm. First, we design the BMQN model to efficiently capture correlations among all vehicles and RSUs through bidirectional attention. Then, we propose type-embedded grouped attention and available action embedding to mitigate the overfitting sequence length issue, thereby enhancing generalization capacity. Moreover, we propose to address the state-action space shift issue through a matching-based manner, which can significantly enhance the task offloading ability by matching the states among devices. Simulation results demonstrate that: 1) the BMQN can achieve much better performance than other approaches in scenarios comprising various numbers of vehicles and RSUs as well as diverse road lengths; 2) the BMQN has sufficient generalization capacity to adapt to inexperienced scenarios through matching-based architecture and available action embedding.