5G Mobile Edge Computing With Enriched Radio Network Information Services
Project Status: Finished
Start Date: November 2018
End Date: October 2020
Budget (total): 2974 K€
Effort: 37.4 PY
Project-ID: 2016/3-4
Name: Arda Akman
Company: ARGELA
Country: Turkey
E-mail: arda.akman@argela.com.tr
Argela, Turkey
JCP-Connect SAS, France
Saguna, Israel
Celfinet, Portugal
University of Aveiro, Portugal
Allbesmart, Portugal
Instituto Politecnico de Castelo Branco, Portugal
Abstract
5G has a number of challenges to solve. Higher data usage and processing power necessities have emerged from the increasing number of mobile users as well as mobile applications becoming more and more demanding. There is a significant need to reduce the latency of the mobile network while cutting down on energy consumption. Backhaul traffic needs to be optimized to avoid setting up costly backhaul connections. Many IoT scenarios have conflicting requirements; the need for cheap and low complexity devices vs the need for processing power. Operators need to come up with value added services to avoid being dumb-pipe operators. The answer to these challenges and more require cloud-computing capabilities within the Radio Access Network (RAN) as well as a platform for mobile operators and third-party application providers to utilize these computing capabilities. This is where Mobile Edge Computing (MEC), one of the key emerging technologies for 5G, comes into play. With advanced technologies and architectural concepts MEC provides computation and storage capabilities in close proximity to mobile subscribers and offer applications and services with context-related service capabilities to have direct access to real-time radio network information and a service environment with ultra-low latency and high bandwidth. The project proposes to not only develop and demonstrate a complete MEC solution, including the MEC framework, Base Station services and applications to run on top, but also offers innovative features like constraint based mobile edge selection, using data analytics to enrich and refine real-time radio network information and utilizing Software Defined Wireless Networks (SDWN) concepts to improve mobility and resource allocation services to enable operators and applications providers to come up with new and improved service capabilities.