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Comparison Data Traffic Scheduling Techniques for Classifying QoS over 5G Mobile Networks

Lee, GM (2017) Comparison Data Traffic Scheduling Techniques for Classifying QoS over 5G Mobile Networks. In: The 31st IEEE International Conference on Advanced Information Networking and Applications (AINA-2017), 27 March 2017 - 29 March 2017, Taipei, Taiwan.

Comparison Data Traffic Scheduling Disciplines for Classifying QoS over 5G Mobile Network .pdf - Accepted Version

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Enhancing Quality of Service (QoS) in mobile networks is the key aim for mobile operators. Mobile networks transport several forms of data traffic for real-time applications (i.e., video monitoring). These applications need to get the advantage of QoS adaptation. Numerous scheduling techniques are utilized at the router to assure the QoS of the mobile networks. Upcoming 5G mobile networks will be launched; hence, Human-Type-Communication (HTC) and Machine-to-Machine (M2M) data traffic are expected to increase dramatically over mobile networks, which results in growing the capacity and raising high data rates. These networks are expected to face challenges in cases of Radio Access Network (RAN) overload and congestion due to the massive smart devices data traffic with various QoS requirements. This paper presents a comparison for data traffic scheduling techniques, which are Priority Queuing (PQ), First-In-First-Out (FIFO) and Weighted Fair Queuing (WFQ). We consider to select a suitable data traffic scheduling technique in terms of QoS provisioning and helping 5G network, also we propose models and algorithms for efficiently utilized the smallest unit of a RAN in a relay node by aggregating and slicing the data traffic of several M2M devices.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: 5G Network; Network slicing; Machine to Machine
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: IEEE
Date Deposited: 01 Feb 2017 15:40
Last Modified: 13 Apr 2022 15:15
URI: https://researchonline.ljmu.ac.uk/id/eprint/5413
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