Task Offloading for Vehicular Edge Computing: A Learning-Based Intent-Aware Approach

Abstract

Air-ground integrated vehicular edge computing (AGI-VEC) has emerged as an effective solution for task processing in vehicular networks. However, due to vehicle mobility, the network topology and available computing resources vary rapidly and are difficult to predict. In this paper, we develop a novel task offloading framework for AGI-VEC, which is called the learning-based Intent-aware Upper Confidence Bound (IUCB) algorithm. IUCB enables a UV to learn the long-term optimal task offloading strategy while satisfying the long-term ultra-reliable low-latency communication (URLLC) constraints in a best effort way under information uncertainty. Simulation results confirm that the proposed algorithm can approach the optimal performance.