Title
Interference Mitigation in Next Generation Networks Using Clustering and Intelligence Techniques.
Abstract
This work is intended to analyze an interference mitigation model between Small Cells in a dense scenario by using computational intelligence techniques and study the behavior of the mobile users batteries. This is of great importance in the planning and implementation phases of Small Cells networks, since many parameters must be taken into account, about which little or even no information is initially available. Furthermore, we are concerned with the allocation of equipment in such a way to provide the best performance. We observed a problem related to the overall optimization of the system, in which case the use of Genetic Algorithms (GAs) proved to be very effective. In order to address this problem, we first developed an analytical model, in which we could compare the SINR (Signal-to-Interference-Plus-Noise Ratio) values before and after the application of the clustering model and, later, in order to validate the model, we executed simulations and evaluated the Quality of Service (QoS) parameters. We noticed significant improvements in the SINR, achieving about 80% of the Small Cells and keeping the battery consumption behavior stable. Through the simulations, we observed improvements in the quality of the service offered to the users, such as the reduction in the lag above 33%, as well as a drop in the number of lost packets.
Year
DOI
Venue
2016
10.1016/j.procs.2016.08.042
Procedia Computer Science
Keywords
Field
DocType
Small Cells,Next Generation Networks,Interference
Data mining,Next-generation network,Computational intelligence,Computer science,Network packet,Quality of service,Interference (wave propagation),Artificial intelligence,Cluster analysis,Lag,Machine learning,Genetic algorithm
Conference
Volume
ISSN
Citations 
94
1877-0509
0
PageRank 
References 
Authors
0.34
0
3