Cross-User Dependent Task Offloading and Resource Allocation in Spatial-Temporal Dynamic MEC Networks

The emergence of mobile edge computing (MEC) technology opens up new opportunities for the realization of demanding applications proximate to the user. This work aims to capitalize on the constrained resources of MEC networks to efficiently fulfill service requests by jointly optimizing task offloading and resource allocation. However, the growing complexity of applications such as intelligent transportation and autonomous driving introduces new challenges in modeling and problem-solving due to the causal correlation of service logic among various users, which adds to the intricate spatial-temporal dynamics of the network. To effectively capture the essence of cross-user task dependency in these scenarios while also accommodating dynamic factors like the variability of wireless channels and the mobility of terminals, we formulate a spatial-temporal correlative problem, aiming to minimize the weighted cost of task completion delay and energy consumption. To resolve this problem, a Double Deep Q Network (DDQN) is developed for task offloading while integrating subchannel allocation and transmit power control into it to interact with the dynamic environment to generate a reward signal, thereby optimizing the long-term performance of the system. Comprehensive simulations verify that the proposed algorithm surpasses the comparative methods in reducing the cost. Additionally, adaptability in different scenarios with different parameters and task structures has also been validated and analyzed.