Bidding-Enabled Resource Pricing for Computation Offloading in 6G Vehicle-to-Edge Networks
Ming Tao, Lingling Liao, Renping Xie, Shuyue Chen, Dapeng Lan, Lei Liu, Yin Zhang⋆, Dong Li, Celimuge Wu · IEEE Transactions on Intelligent Transportation Systems · 2025
In 6G-enabled vehicle-to-edge networks, through deploying computing power resources closer to mobile vehicles for providing low latency and highly reliable services, Mobile Edge Computing (MEC) as an emerging paradigm has promoting mobile vehicles with limited capacities to come with diversified artificial intelligence (AI) applications. Nevertheless, the computing power of MEC server is still finite, seeking the optimal resource pricing and allocation strategies for MEC servers, and determining the optimal computation offloading for intelligent vehicle applications, still remain challenging issues that are necessary to be reasonably solved to improve the service experience. To address this issue, a scenario that multiple intelligent vehicle applications cooperatively initiate computation offloading requests in 6G-enabled multi-server multi-access vehicle-to-edge computing systems is considered in this paper, and with the defined reasonable utilities for MEC servers and intelligent vehicle applications, a solution of bidding-enabled dynamic resource pricing for computation offloading is proposed. Concretely, through considering the relationship of resource supply and demand, and the bidding among MEC servers, a dynamic resource pricing scheme is designed for MEC servers, meanwhile, with the complete consideration of dynamic resource pricing and time-varying wireless channel interference in multi-cell networks, a Q-learning based offloading decision algorithm is proposed for intelligent vehicle applications. Simulation experiments finally are conducted to demonstrate the efficiency in achieving the win-win situation with guaranteed utilities for both MEC servers and intelligent vehicle applications.