Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Networks

Abstract

Heterogeneous networks (HetNets) can offload the traffic and reduce the deployment cost, which is regarded as a promising technique in next-generation cellular networks. Because of the non-convex and combinatorial features of the joint issue of user association and resource allocation, it is challenging to achieve an optimal solution. In this paper, a novel method is proposed to maximize the long-term overall network utility while ensuring the user equipments' quality of service requirements in the downlink of HetNets. Multi-agent reinforcement learning approach is developed to obtain the distributed optimal strategy. To solve the computationally expensive issue with the large action space, the multi-user deep reinforcement learning is presented. Double deep Q-network (DDQN) approach is introduced to achieve an optimal policy. Simulation results clearly indicate the better performance of DDQN than that of other reinforcement learning methods.