Distributed Learning Strategies for Interference Mitigation in Femtocell Networks

In this paper, the emph{strategic} coexistence between macro and femtocell tiers is studied using tools from evolutionary game theory and reinforcement learning. In the first case, femto base stations (FBSs) exchange information through a central controller, and adapt their strategies based on their instantaneous payoffs and average payoffs of the femtocell population. A fictitious play formulation is also examined where FBSs maximize their payoffs given the empirical frequency of other femtocells´ actions. In the second case, when information exchange among femtocells is no longer possible, each femtocell gradually learns by interacting with its local environment through trials-and-errors, and adapt its strategies. Variant of the evolutionary game approach (referred to as replication by imitation) is also investigated where femtocells probabilistically review their strategies and imitate other femtocells in the network. Finally, the overall performance of the network in terms of spectral efficiency and convergence is shown to be adamantly driven by the type of information available at femtocells.