|Mr. Abdul Aziz Abdul Ghaffar, Full-Time COE MS Student, will defend his MS Thesis on Monday, November 30, 2020, at 01:00 p.m. Online (By Using Microsoft Teams). His MS thesis title is "SOFTWARE DEFINED NETWORKING APPROACH FOR 5G CORE NETWORK". His thesis advisor is "Dr. Ashraf Mahmoud, Associate Professor, COE Department". You are cordially invited to attend by clicking the following link "Click here to join the meeting."|
Nowadays, the increase in the number of mobile users and cellular traffic leads to new challenges in the fifth-generation (5G) of cellular networks. The increase in the demand for high data rates brings challenges like scalability and flexibility in the 5G network. Software-defined networking (SDN) is a network paradigm that separates the control plane and data plane in the network and eases the management of the network. In this work, an SDN based 5G core architecture is proposed, in order to introduce flexibility and ease of management in the network. Another benefit of using SDN is to make the network vendor-independent. Furthermore, the explanation of initial attachment and handover procedures in the proposed architecture is provided. The signaling analysis of traditional 5G architecture and the proposed architecture is also provided. The numerical results of the analytical model show that the signaling load in the proposed architecture is less as compared to 5G architecture. A network simulator is built to evaluate the performance of proposed architecture, in terms of end-to-end delay, throughput, and resource utilization of controller, under different network factors. A performance comparison, in terms of end to end delay, between proposed SDN based 5G architecture and traditional 5G architecture is provided. Results show that the proposed architecture provides 18% to 62% less end-to-end delay, under different factors for different procedures, compared to the traditional 5G architecture. A comparison with previous works is also provided, which indicates similar trends in delay between our work and previous studies.