Robust user association for ultra dense networks

We study the user association problem in the context of dense networks, where standard adaptive algorithms become ineffective. The paper proposes a novel data-driven technique leveraging the theory of robust optimization. The main idea is to predict future traffic fluctuations, and use the predictions to design association maps before the actual arrival of traffic. Although the actual playout of the map is random due to prediction error, the maps are robustly designed to handle uncertainty, preventing constraint violations, and maximizing the expectation of a convex utility function, which allows to accurately balance base station loads. We propose a generic iterative algorithm, referred to as GRMA, which is shown to converge to the optimal robust map. The optimal maps have the intriguing property that they jointly optimize the predicted load and the variance of the prediction error. We validate our robust maps in Milano-area traces, with dense coverage and find that we can reduce violations from 25% (achieved by an adaptive algorithm) down to almost zero.