Spatio-Temporal EV Task Offloading, Energy, and Traffic Management for 6G Communication-Power-Transportation Coupling Network

The integration among 6G communication networks, power grids, and transportation systems is emerging as a promising paradigm to achieve mutual benefits among autonomous-driving electric vehicle (EV) users, communication operators, and power grids. Task offloading strategies for autonomous driving and the traveling patterns of EVs can induce communication load fluctuation within 6G network, which subsequently influences energy flow in power grid. Conversely, electricity price from the power grid affects EV charging/discharging strategies, impacting traffic flow and autonomous driving task offloading within the 6G network. Based on the interdependencies among the three networks, this paper constructs a communication-power-transportation coupling network with 6G base stations (BSs) and fast charge stations (FCSs) acting as coupling hubs. Besides, a spatio-temporal electricity price model considering spatial traffic distribution and temporal load fluctuation is developed. Moreover, the optimization problem is formulated to jointly coordinate FCS selection, bidirectional charging/discharging power regulation, task offloading decisions, and route selection strategies to maximize demand response quality of experience (QoE), grid stability and balance under the constraint of autonomous driving quality of service (QoS). Then, a knowledge transfer collaboration-based spatio-temporal EV task offloading, energy, and traffic management joint optimization algorithm is proposed, which improves the optimization performance through knowledge transfer collaboration among EV. Finally, simulation results validate the performance improvement of the proposed algorithm in demand response QoE, grid stability and balance, and autonomous driving QoS.