Stochastic learning automata based resource allocation for LTE-advanced heterogeneous networks

Abstract

A heterogeneous network (HetNet) contains macrocells and different small cells, such as picocells, femtocells, and relay nodes. In HetNets, small cells are used to increase network capacity. Femtocells are usually used to increase coverage range in indoor environment, and can be deployed by users arbitrary. Besides, carrier aggregation is a major improvement in Long-Term-Evolution-Advanced (LTE-Advanced), and component carriers (CCs) are the basic aggregated units which are shared among cells. Therefore, cells use the same CCs lead inter-cell interference among them. In this work, we propose a resource allocation algorithm in LTE-Advanced HetNet called ”Stochastic Automata component Carrier Selection” (SACS) based on stochastic learning automata. SACS is a fully-distributed and self-optimizing algorithm, and it doesn't any information exchange among cells. SACS has some good properties:(i) energy saving, (ii) no information exchange, and (iii) low complexity. From the simulation results, SACS has fast convergent time and saves more energy than others.