UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 6 Issue 5
May-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIRCU06112


Registration ID:
218144

Page Number

575-579

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Title

NETWORK TRAFFIC PREDICTION IN WIRELESS NETWORKS FOR IMPROVING QoS USING DEEP LEARNING TECHNIQUES

Abstract

Many mechanisms lying on bandwidth reservation, handoff mechanism is proposed in the literature to reduce the connection dropping probability for handoffs in wireless communications networks. The handoff actions occur at higher rate in a packet-switched wireless communication(5G) networks than in a traditional communication systems. Hence, an well-organized bandwidth reservation mechanism for a neighboring cells is critical in the process of handoff through the connection of multimedia calls. This mechanism avoids the undesired force termination and waste of limited bandwidth in fourth and fifth generation mobile communication networks, particularly when the mobility of the mobility device is high. In this paper, a phased solution of priority detection, mobility scheduling using deep learning techniques have been proposed. The motivation of the phases is to provide the Quality of Service (QoS) at regular communication and high mobility condition by considering the physical parameters. Meanwhile, a deep learning based service model is integrated to accommodate novel metrics used in handing out handoffs and task scheduling in wireless communication networks. This mechanism provides the more advantages in terms of choosing the tasks in a priority based scenario and providing un-interrupted service at the point of handoffs as well as an efficient way of utilizing the bandwidth.

Key Words

— Neural Network, Iterative Process, Bandwidth Estimation, Deep learning.

Cite This Article

"NETWORK TRAFFIC PREDICTION IN WIRELESS NETWORKS FOR IMPROVING QoS USING DEEP LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.575-579, May 2019, Available :http://www.jetir.org/papers/JETIRCU06112.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"NETWORK TRAFFIC PREDICTION IN WIRELESS NETWORKS FOR IMPROVING QoS USING DEEP LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp575-579, May 2019, Available at : http://www.jetir.org/papers/JETIRCU06112.pdf

Publication Details

Published Paper ID: JETIRCU06112
Registration ID: 218144
Published In: Volume 6 | Issue 5 | Year May-2019
DOI (Digital Object Identifier):
Page No: 575-579
Country: Chennai, Tamil Nadu, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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