IP Flow Mobility based Offload in LTE Wi-Fi Interworking Scenario

Kamath, N (2014) IP Flow Mobility based Offload in LTE Wi-Fi Interworking Scenario. Masters thesis, Indian Institute of Technology.

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Abstract

Mobile data traffic has seen an exponential growth in the past few years with the trend expected to continue. LTE as a standalone cellular network is unable to keep pace with the increasing traffic demands. In the meanwhile, wireless LAN has proven itself as an economical wireless access technology. 3GPP has thus been encouraged to standardize the integration of Wi-Fi networks with LTE. This opens up numerous opportunities to study data offloading and mobility management protocols. One of the newer offloading technique is known as IP Flow Mobility, where individual IP flows are migrated from one network to the other without affecting other flows belonging to the same IP session. In this thesis work, a framework has been developed on ns-3 which supports flow mobility between LTE and Wi-Fi. This framework is based on PMIPv6. This flow mobility framework provides an opportunity to implement various algorithms to decide which network is used to serve which flows while trying maintain a balance between bandwidth utilization and user satisfaction. One such algorithm has been proposed here for a network consisting of LTE and Wi-Fi. This algorithm calculates a quality value for each flow on the network using parameters like flow type, SNR, velocity of the user, etc and tries to offload these flows onto either network based on the flow’s quality value. A simple simulation is carried out which validates the implementation of the framework, where a TCP flow is migrated to a Wi-Fi network from the LTE network based on the SNR of the Wi-Fi network. It also shows how the velocity of a UE affects the percentage of offload which can be achieved and how the flow’s performance is affected by the offload.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Thesis (Masters)
Uncontrolled Keywords: TD178
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 25 Nov 2014 10:23
Last Modified: 08 Jul 2015 07:10
URI: http://raiith.iith.ac.in/id/eprint/971
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