On-demand offloading collaboration framework based on LTE network virtualisation

Recently, there has been a significant increase in data traffic on mobile networks, due to the growth in the numbers of users and the average data volume per user. In a context of traffic surge and reduced revenues, operators face the challenge of finding costless solutions to increase capacity and coverage. Such a solution should necessarily rule out any physical expansion, and mainly conceive real-time strategies to utilise the spectrum more efficiently, such as network offload and Long-term Evolution (LTE) network virtualisation. Virtualisation is playing a significant role in shaping the way of networking now and in future, since it is being devised as one of the available technologies heading towards the upcoming 5G mobile broadband. Now, the successful utilisation of such innovative techniques relies critically on an efficient call admission control (CAC) algorithm. In this work, framework is proposed to manage the operation of a system in which CAC, virtualisation and Local break out (LBO) strategies are collaboratively implemented to avoid congestion in a mobile network, while simultaneously guaranteeing that measures of quality of service (QoS) are kept above desired thresholds. In order to evaluate the proposed framework, two simulation stages were carried out. In the first stage, MATLAB was used to run a numerical example, with the purpose of verifying the mathematical model of the proposed framework in air interface level. The second stage involved of using open source applications such as, Emulated Virtual Environment (EVE) and Wireshark, for emulating the traffic in the network for different scenarios inside the core network. The results confirm the effectiveness of the proposed framework.

Level dc:type.qualificationlevel

Doctoral (Level 8)

Year dc:date.issued

2026

Author dc:creator

• Albinali, SA

Language dc:language

en

Identifier

oai:salford-repository.worktribe.com:1369081

OAI identifier oai:identifier

oai:salford-repository.worktribe.com:1369081