A Novel Stochastic Learning Automata Based SON Interference Mitigation Framework for 5G HetNets

Radioengineering. 2016;25(4):763-773

 

Journal Homepage

Journal Title: Radioengineering

ISSN: 1210-2512 (Print); 1805-9600 (Online)

Publisher: Spolecnost pro radioelektronicke inzenyrstvi

LCC Subject Category: Technology: Electrical engineering. Electronics. Nuclear engineering

Country of publisher: Czech Republic

Language of fulltext: English

Full-text formats available: PDF

 

AUTHORS

M. N. Qureshi
M. I. Tiwana

EDITORIAL INFORMATION

Peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 12 weeks

 

Abstract | Full Text

Long Term Evolution Advanced (LTE-A) Heterogeneous Networks (HetNet) are an important aspect of 5th generation mobile communication systems. They consists of high power macrocells along with low power cells i.e. picocells and femtocells to fill up macrocell coverage gaps. HetNet permit deployment of femtocells by users for added flexibility, but then interference issues between neighbouring cells have to be addressed as all femtocells use the same frequency channels for transmission. To mitigate this problem, LTE-A standard offers two new features, one is carrier aggregation in which Component Carriers (CC) form the basic aggregate units shared among cells and the other is enhanced Inter-Cell Interference Co-ordination (eICIC) through X2 interface. The physical implementation of these features is left open to research. This paper investigates two distinct techniques for orthogonal CC selection through Stochastic Cellular Learning Automata (SCLA) to improve the QoS performance of a femtocell. The first, technique uses SCLA with user feedback, and the second technique uses SCLA with a central publishing server where all cells upload their past used CC vectors. SCLA methods are better suited for Self Organizing Network (SON) as they do not require synchronized cell coordination, have low complexity and have good optimization characteristics. The simulation results show that the techniques enhance the cell edge performance considerably.