Development of hybrid artificial intelligent based handover decision algorithm

Engineering Science and Technology, an International Journal. 2017;20(2):381-390 DOI 10.1016/j.jestch.2017.01.005

 

Journal Homepage

Journal Title: Engineering Science and Technology, an International Journal

ISSN: 2215-0986 (Online)

Publisher: Elsevier

Society/Institution: Karabuk University

LCC Subject Category: Technology: Engineering (General). Civil engineering (General)

Country of publisher: Netherlands

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

A.M. Aibinu (Department of Mechatronics Engineering (DOME), Federal University of Technology (FUT) Minna, P. O. Box 63, Minna, Nigeria)
A.J. Onumanyi (Telecommunication Engineering Department, Federal University of Technology (FUT) Minna, P. O. Box 63, Minna, Nigeria)
A.P. Adedigba (Department of Mechatronics Engineering (DOME), Federal University of Technology (FUT) Minna, P. O. Box 63, Minna, Nigeria)
M. Ipinyomi (Department of Mechatronics Engineering (DOME), Federal University of Technology (FUT) Minna, P. O. Box 63, Minna, Nigeria)
T.A. Folorunso (Department of Mechatronics Engineering (DOME), Federal University of Technology (FUT) Minna, P. O. Box 63, Minna, Nigeria)
M.J.E. Salami (Mechatronics Engineering Department, International Islamic University Malaysia (IIUM), Malaysia)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 20 weeks

 

Abstract | Full Text

The possibility of seamless handover remains a mirage despite the plethora of existing handover algorithms. The underlying factor responsible for this has been traced to the Handover decision module in the Handover process. Hence, in this paper, the development of novel hybrid artificial intelligent handover decision algorithm has been developed. The developed model is made up of hybrid of Artificial Neural Network (ANN) based prediction model and Fuzzy Logic. On accessing the network, the Received Signal Strength (RSS) was acquired over a period of time to form a time series data. The data was then fed to the newly proposed k-step ahead ANN-based RSS prediction system for estimation of prediction model coefficients. The synaptic weights and adaptive coefficients of the trained ANN was then used to compute the k-step ahead ANN based RSS prediction model coefficients. The predicted RSS value was later codified as Fuzzy sets and in conjunction with other measured network parameters were fed into the Fuzzy logic controller in order to finalize handover decision process. The performance of the newly developed k-step ahead ANN based RSS prediction algorithm was evaluated using simulated and real data acquired from available mobile communication networks. Results obtained in both cases shows that the proposed algorithm is capable of predicting ahead the RSS value to about ±0.0002 dB. Also, the cascaded effect of the complete handover decision module was also evaluated. Results obtained show that the newly proposed hybrid approach was able to reduce ping-pong effect associated with other handover techniques.