IIUM Repository

Development of hybrid artificial intelligent based handover decision algorithm

Aibinu, Abiodun Musa and Onumanyi, Adeiza J. and Adedigba, A. P. and Ipinyomi, M. and Folorunso, T. A. and Salami, Momoh Jimoh Eyiomika (2017) Development of hybrid artificial intelligent based handover decision algorithm. Engineering Science and Technology, an International Journal, 20 (2). pp. 381-390. E-ISSN 2215-0986

[img] PDF - Published Version
Restricted to Repository staff only

Download (1MB) | Request a copy
[img] PDF (SCOPUS) - Supplemental Material
Restricted to Repository staff only

Download (526kB) | Request a copy


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

Item Type: Article (Journal)
Additional Information: 2470/57253
Uncontrolled Keywords: Artificial Neural Network; Base Transceiver Station Fuzzy logic;Handover ;Prediction;Received signal strength
Subjects: Q Science > QA Mathematics
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User: Prof Momoh-Jimoh Salami
Date Deposited: 13 Jun 2017 09:40
Last Modified: 18 Apr 2018 10:30
URI: http://irep.iium.edu.my/id/eprint/57253

Actions (login required)

View Item View Item


Downloads per month over past year