Cross layer architecture with integrated MIH in heterogeneous wireless networks

Mansouri, Wahida; Zarai, Faouzi; Mnif, Kais; Kamoun, Lotfı

doi:10.1016/j.comnet.2017.08.007

Cited by 7 publications

(3 citation statements)

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“…However, in this method delay was not considered. In [22], Media Independent Handover (MIH) using a scheduling algorithm was introduced for achieving effective seamless data transmission. But, the packet delivery rate was not sufficient.…”

Section: Related Workmentioning

confidence: 99%

Generalized Light Gradient Boost Classifier for Traffic Aware Seamless Mobility Management in Heterogeneous Network

D.¹,

Dr.²

2020

INDJCSE

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Seamless mobility management is an ability to provide the various services during the communication in wireless heterogeneous networks. Due to the random mobility of the mobile terminals, the connectivity between different mobile devices gets lost. In order to provide the lossless connectivity between the mobile devices, the handover from the point of current attachment to another point is necessary. To improve the Seamless mobility management and traffic control, an efficient model called Generalized Light Gradient Boost Decision Tree-based Traffic-Aware Seamless Mobility (GLGBDT-TASM) model is introduced in the heterogeneous network. When a mobile node in the network moves out of its communication range, the signal strength of the nodes is calculated. Based on the signal strength estimation, the Generalized Light Gradient Boost Decision Tree classifier categorizes the mobile nodes into the weak and strong signal strength with the threshold value. The boosting algorithm initially constructs' weak learners i.e. binary decision tree to identify the weak signal strength of the mobile node. Then the ensemble classifier combines the results of weak learners and minimizes the generalization error. This helps to perform the handover only with the weak signal strength of the node resulting in minimizes the redundant handover. In addition, the weak signal strength of the mobile node from the current attachment point handover towards the nearest available attachment point to improve the continuous data delivery. Followed by, bandwidth availability is measured for reducing the packet loss due to thenetwork traffic resulting in improves the seamless data delivery between the nodes. The simulation is carried out to evaluate the performance of the GLGBDT-TASM model with two related approaches. The results show that the GLGBDT-TASM model effectively improved traffic-aware seamless mobility in a heterogeneous network with minimum delay and packet loss as well as a higher data delivery rate as compared to state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Generalized Light Gradient Boost Classifier for Traffic Aware Seamless Mobility Management in Heterogeneous Network

D.¹,

Dr.²

2020

INDJCSE

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“…The SINR is defined as the power of a certain signal of interest divided by the sum of the average interference power and the power of some background noise. The SINR of LTE and WLAN is calculated as follows:Here, G j , m is the channel gain from user j to its eNB, G (E) is the antenna gain, P N is the power AWGN, and I is the power of interference. In this study, the MN is equipped with Global Positioning System (GPS).…”

Section: Sdn/nfv‐based Handover Management Approachmentioning

confidence: 99%

Section: Sdn/nfv‐based Handover Management Approachmentioning

confidence: 99%

SDN/NFV‐based handover management approach for ultradense 5G mobile networks

Gharsallah

Zarai

Néji

2018

Int J Communication

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With the great increase of connected devices and new types of applications, mobile networks are witnessing exponential growth of traffic volume. To meet emerging requirements, it is widely agreed that the fifth-generation mobile network will be ultradense and heterogeneous. However, the deployment of a high number of small cells in such networks poses challenges for the mobility management, including frequent, undesired, and ping-pong handovers, not to mention issues related to increased delay and failure of the handover process.The adoption of software-defined networking (SDN) and network function virtualization (NFV) technologies into 5G networks offers a new way to address the above-mentioned challenges. These technologies offer tools and mechanisms to make networks flexible, programmable, and more manageable. The SDN has global network control ability so that various functions such as the handover control can be implemented in the SDN architecture to manage the handover efficiently. In this article, we propose a Software-Defined Handover (SDHO) solution to optimize the handover in future 5G networks. In particular, we design a Software-Defined Handover Management Engine (SDHME) to handle the handover control mechanism in 5G ultradense networks. The SDHME is defined in the application plane of the SDN architecture, executed by the control plane to orchestrate the data plane. Simulation results demonstrate that, compared with the conventional LTE handover strategy, the proposed approach significantly reduces the handover failure ratio and handover delay. KEYWORDS 5G ultradense networks, fast handover, network function virtualization, software-defined networking, virtual cell Int J Commun Syst. 2019;32:e3831. wileyonlinelibrary.com/journal/dac FIGURE 1 Topology of ultradense network (UDN) toward 5G. SDN, software-defined networkingexperience. The topology of UDN toward 5G is illustrated in Figure 1. In UDN, a high number of point of attachment (PoA) are distributed randomly without network planning. 3 The next-generation network is characterized by heterogeneous networks having different coverage ranges such as picocell, femtocell, attocell, and multiple radio access technologies (RATs) such as 4G, 5G, and WLAN. In this way, a greater number of simultaneous device connections are enabled by the ultradense heterogeneous small cells.Although UDN helps to solve coverage, capacity, spectrum efficiency, and data rates problems on the access side, it issues many challenges to the mobility management such as handover problems faced by mobile users and poor performance like long handover delay, failures, and increased signaling overhead. This causes disconnection and quality of service (QoS) degradation. Accordingly, ultradense 5G mobile networks require innovations in the management of the network and necessitate a novel and accurate handover management mechanism to deal with these challenges. Recently, the 3rd Generation Partnership Project (3GPP) organization has focused on software-defined networking (SDN) and netwo...

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