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Many real-time application scenarios are developed in 6G communications. Driven by the low-latency data processing requirements, multi-tier computing has become an important technology to improve user experience and reduce network overhead. In this paper, we consider a multi-tier computation offloading network structure for 6G applications, in which the cloud computing center and the nearby vehicle edge server (VES) are able to partially calculate the tasks offloaded from the user equipment (UE), and the remaining task is processed locally in the UE. By jointly optimizing user scheduling, cloud offloading ratio, VES offloading ratio, and VES mobility, the objective function is to minimize the delay of the system transmission and computation under the constraints of discrete variables and energy consumption. To solve the problem, a primal-dual deep deterministic policy gradient (PD-DDPG) algorithm based on multi-tier computation offloading is proposed. Simultaneously, compared with baseline algorithms, PD-DDPG algorithm has an obvious advantage in both the speed of convergence and the system delay.
Zhang et al. (Wed,) studied this question.