Dynamic Min-Cut Clustering for Energy Savings in Ultra-Dense Networks

Abstract

Femtocells are envisioned as a key solution to embrace the ever-increasing high data rate and thus are extensively deployed. Given that numerous femtocell access points (FAPs) deployed in ultra- dense networks (UDNs) lead to significant energy consumption, boosting their energy efficiency (EE) becomes an important issue to be addressed. However, most existing works either focus on homogeneous networks or assume that FAPs are equally distributed, which is not realistic in a dense network as random deployments cause severe interference. This paper explores the realistic scenario of randomly distributed FAPs in heterogeneous networks and proposes a clustering approach combined with an active FAP selection algorithm to boost both spectral and energy efficiency without manual configuration. Taking into account traffic load and interference, the paper reduces the complexity from the Bell Number to polynomial time by exploiting a graph-based Min-Cut strategy to cluster FAPs and allocate orthogonal resources in one cluster to mitigate interference that in turn improves EE. Simulation results confirm the effectiveness of the framework.