Evolutionary game theoretical model for stable femtocells’ clusters formation in HetNets

Abstract Femtocell deployment is one of the key solutions to achieve the high data rate of the fifth generation mobile communication. Nevertheless, dense femtocell networks face several challenging tasks such as interference control and resource management. In this paper, we address the problem of resource allocation for heterogeneous networks (HetNets), namely dense femtocell networks, by forming stable clusters using an evolutionary game where femtocells learn from the environment and make their decisions considering the achieved payoff. In the literature, clustering has been proposed to organize network topologies by joining nodes (e.g. femtocells) with similar behaviors into logical groups. We focus on cluster stability that is important to obtain good network performance but can be difficult to achieve especially in ultra-dense and heterogeneous networks. In order to guarantee the cluster stability, we use the replicator dynamics that find the evolutionary equilibrium of the evolutionary game. Thus, by guaranteeing cluster stability the network performance is improved and the computational complexity is reduced. In addition, Particle Swarm Optimization (PSO) is used for the resource allocation algorithm that runs locally within each cluster owing to the fact that PSO has been proved to find a satisfying near-optimal solution while having the advantage of speeding up the optimization process. We run simulations for non-dense and dense femtocell networks taking into account two scenarios: fixed public users and public users that keep mobility such as pedestrians or cyclists. Simulation results show that the proposed solution is able to enhance the network throughput, to provide higher subscribers satisfaction, and to reduce the co-tier interference in dense femtocell networks.