Joint MEC selection and wireless resource allocation in 5G RAN

With the vigorous development of the Internet of Things (IoT), the demand for user equipment (UE) computing capacity is increasing. Multiaccess edge computing (MEC) provides users with high-performance and low-latency services by offloading computational tasks to the nearest MEC server-configured 5G radio access network (RAN). However, these computationally intensive tasks may lead to a sharp increase in the energy consumption of UE and cause downtime. In this paper, to address this challenge, we design an intelligent scheduling and management system (ISMS) to jointly optimize the allocation of MEC resources and wireless communication resources. The resource allocation problem is a mixed-integer nonlinear programming problem (MINLP), an NP-hard problem. The ISMS models this problem as an MDP with a state, action, reward, and policy and adopts a modified deep deterministic policy gradient (mDDPG) algorithm to ensure the weighted minimization of the energy consumption, latency, and cost of users. The simulation results show that the ISMS can effectively reduce the system's energy consumption, latency, and cost. The proposed algorithm can provide more stable and efficient performance than other algorithms.