Online learning for interference coordination in heterogeneous networks

This paper focuses on interference coordination between the small cell and macro cell tiers of a wireless access network. We propose a new perspective based on a modelfree learning strategy, not requiring any previous knowledge about the network (e.g., topology, interference graph, scheduling algorithms). Our approach is based on a stochastic optimization algorithm known as Response Surface Methodology, that we use to learn the optimal parameter configuration during network operation (online learning) adapting to changes on network conditions (e.g., traffic, user positions). The result is a simple, effective and flexible mechanism that outperforms previous proposals. As a case study we apply our scheme to the dynamic adjustment of LTE-A eICIC parameters (CRE bias and ABS ratio).