Improving handover of 5G networks by network function virtualization and fog computing

Qiu, Yu; Zhang, Haijun; Long, Keping; Sun, Hongjian; Li, Xuebin; Leung, Victor

Abstract

In Fifth Generation (5G) cellular networks, it is necessary to meet a number of requirements, such as high scalability, ultra-low latency, reduced energy consumption, and high energy efficiency. Particularly in the high mobility scenario, the optimization of handover through managing signalling overhead and delay is of primarily importance. In this paper, the idea of integrating Network Function Virtualization (NFV) and Fog Computing is explored. NFV has the advantage of improving network flexibility whilst reducing overall overhead. The Fog-Computing Access Points (F-APs) are then employed with certain caches in the edge of networks. Moreover, a direct-X2 based handover scheme is proposed. Taking advantages of both edge caching and Virtual Machines (VMs), this proposed handover scheme has superior performance: the signalling cost of handovers can be as little as 65% of that of a conventional LTE network.

Citation

Qiu, Y., Zhang, H., Long, K., Sun, H., Li, X., & Leung, V. (2017, October). Improving handover of 5G networks by network function virtualization and fog computing. Presented at 2017 6th IEEE/CIC International Conference on Communications in China (ICCC2017)., Qingdao, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2017 6th IEEE/CIC International Conference on Communications in China (ICCC2017).
• Start Date: Oct 22, 2017
• End Date: Oct 24, 2017
• Acceptance Date: Aug 21, 2017
• Online Publication Date: Apr 5, 2018
• Publication Date: Apr 5, 2018
• Deposit Date: Aug 28, 2017
• Publicly Available Date: Aug 29, 2017
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1-5
• Book Title: 2017 IEEE/CIC International Conference on Communications in China (ICCC) : 22-24 Oct. 2017, Qingdao, China ; proceedings.
• ISBN: 9781538645031
• DOI: https://doi.org/10.1109/iccchina.2017.8330444
• Public URL: https://durham-repository.worktribe.com/output/1147943