NeurDORA: Neural-Aided Decentralized Offloading Based on Resource Auction
Rentian Wei, Wenli Zheng · 2025
Mobile Edge Computing (MEC) is a key solution to overcome vehicles' limited on-board computation capabilities when dealing with data-intensive tasks.Existing studies usually assume guaranteed resource availability at base stations (BSes), or restrict a user to a single BS, overlooking the resource-competition failures due to multiple BS accesses.This paper proposes a decentralized offloading algorithm (NeurDORA) for multi-user-multi-BS (MUMB) scenarios, ensuring efficient and fair allocation of BS resources.With Neur-DORA, each vehicle predicts its chance of successfully securing communication and computation resources at each candidate BS, and then competes for offloading opportunities in an iterative auction process that converges to Nash Equilibrium.Simulation results show that NeurDORA reduces offloading failures by 35-53% and achieves a 12-26% reduction in average offloading latency.