Joint access point selection and resource allocation in MEC-assisted network: A reinforcement learning based approach

A distributed reinforcement learning (RL) based resource management framework is proposed for a mobile edge computing (MEC) system with both latency-sensitive and latency-insensitive services. We investigate joint optimization of both computing and radio resources to achieve efficient on-demand matches of multi-dimensional resources and diverse requirements of users. A multi-objective integer programming problem is formulated by two subproblems, i.e., access point (AP) selection and subcarrier allocation, which can be solved jointly by our proposed distributed RL-based approach with a heuristic iteration algorithm. The proposed algorithm allows for the reduction in complexity since each user needs to consider only its own selection of AP without knowing full global information. Simulation results show that our algorithm can achieve near-optimal performance while reducing computational complexity significantly. Compared with other algorithms that only optimize either of the two sub-problems, the proposed algorithm can serve more users with much less power consumption and content delivery latency.