Joint Optimization of Clustering and Resource Allocation based on Game Theory for Ultra-Dense Networks

In this paper, we consider the amalgamation of clustering and resource allocation based on game theory in ultra-dense networks (UDNs) which consist of a vast number of randomly distributed small cells. In particular, to mitigate the inter-cell interference, we propose a coalition game (CG) for clustering small cells with the utility function to be the ratio of signal to interference. Then, resource allocation is divided into two sub-problems such as sub-channel allocation (SCA) and power allocation (PA). We use the Hungarian method, which is efficient for solving binary optimization problems, for assigning the sub-channels to users in each cluster of SBSs. Additionally, an iterative algorithm to solve the convex optimization is provided to maximize the network sum-rate. Numerical results prove that the game-based clustering method outperforms the traditional clustering method (with and without PA optimization) and random clustering method in terms of the sum-rate.