Graph Attention Network-based DRL for Network Slicing Management in Dense Cellular Networks

Abstract

Network slicing (NS) devotes to provisioning various services with distinct requirements over the same physical communication infrastructure. Considering a dense cellular network scenario that contains several NS over multiple base stations (BSs), it remains challenging to design a proper resource management strategy in real time, so as to cope with frequent BS handover and meet distinct service requirements. In this paper, we propose to formulate this challenge as a multiagent reinforcement learning (MARL) problem and leverage graph attention network (GAT) to strengthen the cooperation between agents. Furthermore, we incorporate GAT into deep Q network (DQN) and correspondingly design an intelligent resource management strategy for NS. Finally, we verify the superiority of the GAT-based DQN algorithm through extensive simulations.