Inter-user Dependent Task Offloading and Resource Allocation in Dynamic MEC Networks

The advent of mobile edge computing (MEC) technology offers new prospects for executing demanding applications close to the user. However, complex applications like intelligent transportation and autonomous driving pose modeling and problem-solving challenges due to inter-user service logic correlations. Therefore, we construct a model that considers the terminal's mobility, time-varying channel status, and inter-user task dependencies and formulate a problem aiming to optimize the task completion delay and the energy consumption weighted cost in a dynamic MEC scenario. To resolve this problem, a Double Deep Q Network (DDQN)-based algorithm is developed for task offloading, while integrated sub channel allocation and transmit power control constitute part of the interaction with the dynamic environment to generate the reward signal, optimizing the long-term system performance. Comprehensive simulations verify that the proposed algorithm outperforms the comparative methods in terms of reducing the cost, and its adaptability in different scenarios has also been validated and analyzed.