User association and resource allocation in green mobile edge networks using deep reinforcement learning

In order to meet the emerging requirements for high computational complexity, low delay and energy consumption of the 5 th generation wireless systems（5 G） network, ultra-dense networks（UDNs） combined with multi-access edge computing（MEC） can further improve network capacity and computing capability. In addition, the integration of green energy can effectively reduce the on-grid energy consumption of system and realize green computation. This paper studies the joint optimization of user association（UA） and resource allocation（RA） in MEC enabled UDNs under the green energy supply pattern, users need to perceive the green energy status of base stations（BSs） and choose the one with abundant resources to associate. To minimize the computation cost for all users, the optimization problem is formulated as a mixed integer nonlinear programming（MINLP） which is NP-hard. In order to solve the problem, a deep reinforcement learning（DRL）-based association and optimized allocation（DAOA） scheme is designed to solve it in two stages. The simulation results show that the proposed scheme has good performance in terms of computation cost and time out ratio, as well achieve load balancing potentially.