An Efficient Neural Network-Based Prediction Scheme for Heterogeneous Networks

An Efficient Neural Network-Based Prediction Scheme for Heterogeneous Networks

Khalid M. Hosny (Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt), Marwa M. Khashaba (Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt), Walid I. Khedr (Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt) and Fathy A. Amer (Department of Information Technology, Faculty of Computers and Informatics, Cairo University Giza, Egypt)
Copyright: © 2020 |Pages: 14
DOI: 10.4018/IJSKD.2020040104

Abstract

In mobile wireless networks, the challenge of providing full mobility without affecting the quality of service (QoS) is becoming essential. These challenges can be overcome using handover prediction. The process of determining the next station which mobile user desires to transfer its data connection can be termed as handover prediction. A new proposed prediction scheme is presented in this article dependent on scanning all signal quality between the mobile user and all neighboring stations in the surrounding areas. Additionally, the proposed scheme efficiency is enhanced essentially for minimizing the redundant handover (unnecessary handovers) numbers. Both WLAN and long term evolution (LTE) networks are used in the proposed scheme which is evaluated using various scenarios with several numbers and locations of mobile users and with different numbers and locations of WLAN access point and LTE base station, all randomly. The proposed prediction scheme achieves a success rate of up to 99% in several scenarios consistent with LTE-WLAN architecture. To understand the network characteristics for enhancing efficiency and increasing the handover successful percentage especially with mobile station high speeds, a neural network model is used. Using the trained network, it can predict the next target station for heterogeneous network handover points. The proposed neural network-based scheme added a significant improvement in the accuracy ratio compared to the existing schemes using only the received signal strength (RSS) as a parameter in predicting the next station. It achieves a remarkable improvement in successful percentage ratio up to 5% compared with using only RSS.
Article Preview
Top

1. Introduction

Reducing the interruption in handover is the main handover prediction advantages. Redundant handovers numbers (unnecessary handovers) can be reduced when designing an expert and a proper handover prediction. While mobile stations (MS’s) moving nearby the edge of cell borders, it may switch between two adjacent stations continuously, this is called the ping-pong impact which is the fundamental reason behind redundant handover (Becvar & Mach, 2013). Delay in handover is a vital problem in wireless and mobile networks connections. Considerable attempts have been made by researchers to design an appropriate solution to reduce handover delays. Hysteresis Margin (HM) (Yusof et al., 2013) and Time-To-Trigger (TTT) (Khan & Han, 2014) are examples of techniques which can be used for limiting the redundant handovers (Bao et al., 2011; Luo 2013).

Handover process is divided into two types; horizontal and vertical handover. Handover is termed as horizontal when the MS moved between two stations with the same network form. Otherwise, the handover is called vertical as illustrated in Figure 1. The process of managing the vertical handover in an efficient and a perfect way is considered a strong challenge. Therefore, current handover procedures need to be enhanced to avoid the non-noticeable conflicts, to ensure a stable connection when the MS moves between different networks, and to reduce the number of redundant handovers (Grech, 2017).

Figure 1.

Handover types

IJSKD.2020040104.f01

There are differences between WLANs and mobile networks, in terms of several protocols stacks, mobility tools, network access structures, QoS and so on. Hence, conventional interconnecting between these types of networks is not straight forward, even though both are IP-based structures. I-WLAN (3GPP 2017) is considered the primary strategy described basically for the integration of WLAN networks. It is proposed with 3GPP mobile structure. This structure describes the misunderstanding between both networks with data, access protocols, control behaviors and authentication procedure.

Therefore, designing a prediction scheme is a vital task, which is utilized to predict the desired handover station before handover occurs. Executing the handover procedure more smoothly and efficiently and guarantying the continuous connectivity through handover are considered the most benefit of handover prediction scheme (Hosny et al., 2019). There are many handover decision methodologies which have been proposed lately for both horizontal and vertical handover for different mobile networks, for example: Long Term Evolution (LTE) (Han & Wu, 2010) and 3G (Javed et al., 2011) as examples of horizontal handover. Handover between 3rd Generation Partnership Project 3GPP and Wireless Local Area Networks (WLANs) (Qamar et al., 2017), LTE and the Worldwide Interoperability for Microwave Access (WiMAX) (Miyim et al., 2012) are examples of vertical handover.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 13: 4 Issues (2021): Forthcoming, Available for Pre-Order
Volume 12: 4 Issues (2020): 3 Released, 1 Forthcoming
Volume 11: 4 Issues (2019)
Volume 10: 4 Issues (2018)
Volume 9: 4 Issues (2017)
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing