Robust optimization framework for proactive user association in UDNs: A data-driven approach

Liakopoulos, Nikolaos; Paschos, Georgios S; Spyropoulos, Thrasyvoulos
IEEE/ACM Transactions on Networking, Vol. 27, N°4, August 2019

We study the user association problem in the context of dense networks, where standard adaptive algorithms become ineffective. This 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 is used to accurately balance base station loads. We propose a generalized 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% (inflicted by a baseline adaptive algorithm) down to almost zero.

Systèmes de Communication
Eurecom Ref:
© 2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.