Graph-based altruistic games for interference mitigation in femtocell networks

Femtocell networks are a common means of offloading in current mobile networks. Game theoretic and learning-based self-organization schemes are known as two viable approaches for interference control in co-existing macro/femtocell networks. Nevertheless, extensions of these schemes to concurrent multi-agent scenarios often face several challenges including high computational complexity, cyclic behaviors, and lack of convergence. This paper addresses such challenges by introducing local games based on a graph representation of the network. The goal of the games is to optimize the aggregate network capacity through power control. Fairness among femtocells and quality of service (QoS) for macrocell users are also addressed. Finally, we prove that our algorithm converges to an equilibrium point.