Online Learning for Energy Saving and Interference Coordination in HetNets

In heterogeneous cellular networks (HetNets), switching OFF small cells under low user traffic periods has been proved to be an effective energy saving strategy. However, this strategy has strong interactions with interference coordination (IC) mechanisms, making it convenient to address both tasks simultaneously. The motivation of this paper is to develop a self-optimization algorithm capable of jointly controlling energy saving and IC mechanisms using an online learning approach. Our proposal is based on a contextual bandit formulation that, among other challenges, implies discovering the most energy-efficient control actions while satisfying a predefined level of Quality of Service (QoS) for the users. We propose a two-level framework comprising a global controller, in charge of a group of macro cells, and multiple local controllers, one per macro cell. The global controller implements a novel algorithm, referred to as the Bayesian Response Estimation and Threshold Search (BRETS), that is capable of learning, for each control action, its feasibility boundaries in terms of QoS and its energy consumption as a function of the aggregated user traffic. The algorithm comes with a bound on its expected convergence time. The local controllers translate the control actions learned by the global controller into local decisions. Our numerical results show that BRETS is only 1% less efficient than an ideal oracle policy, clearly outperforming other benchmark algorithms.