Dynamic Inter-Cell Interference Coordination in HetNets: A Reinforcement Learning Approach
In this paper, the authors investigate enhanced Inter-Cell Interference Coordination (e-ICIC) techniques for Heterogeneous Networks (HetNets), consisting of a mix of macro and picocells. They model this strategic coexistence as a multi-agent system in which decentralized interference management and cell association strategies inspired from Reinforcement Learning (RL) are devised. Specifically, they focus on time and frequency domain ICIC techniques in which picocells optimally learn their cell range bias and downlink transmit power allocation. In turn, the macrocell optimizes its transmission by serving its own users while adhering to the picocell interference constraint.