Clustering and Reinforcement Learning based Multi-Access Edge Computing in Ultra Dense Networks

Multi-access edge computing (MEC) and Ultra-dense networks (UDN) are a special case of 5G cellular networks where the density of base stations is higher compared to that of the end users (UE). Hence, a UE is likely to be present in the coverage of multiple base stations at any given time instant. This paper deals with providing scheduling algorithm for multiaccess edge computing in UDN. Unlike existing works, where the transmission scheduling (i.e., assigning the base stations for each client) and the computation resource scheduling are jointly considered. Due to the uncertainties in the task generation and path losses, we model the scheduling problem as deep reinforcement learning (DRL) problem which maximizes the total utility of the clients. The DRL model (based on actor-critic neural networks) is trained using the deep deterministic policy gradient (DDPG) algorithm. The results show the convergence of the total utility and better performance compared to a greedy policy and a priority based scheduling policy.