IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 9, No 2: June 2020

Features detection based blind handover using kullback leibler distance for 5G HetNets systems

Adnane El Hanjri (Hassan 1st University Settat)
Aawatif Hayar (University Hassan II Casablanca)
Abdelkrim Haqiq (Hassan 1st University Settat)



Article Info

Publish Date
01 Jun 2020

Abstract

The Fifth Generation of Mobile Networks (5G) is changing the cellular network infrastructure paradigm, and Small Cells are a key piece of this shift. But the high number of Small Cells and their low coverage involve more Handovers to provide continuous connectivity, and the selection, quickly and at low energy cost, of the appropriate one in the vicinity of thousands is also a key problem. In this paper, we propose a new method, to have an efficient, blind and rapid handover just by analysing Received Signal probability density function instead of demodulating and analysing Received Signal itself as in classical handover. The proposed method exploits KL Distance, Akaike Information Criterion (AIC) and Akaike weights, in order to decide blindly the best handover and the best Base Station (BS) for each user

Copyrights © 2020






Journal Info

Abbrev

IJAI

Publisher

Subject

Computer Science & IT Engineering

Description

IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like ...