ETRI Journal Special Issue on
5G & B5G Enabling Edge Computing, Big Data & Deep Learning Technologies

Fifth Generation (5G) mobile systems are designed to support voice, data, video, games, smart grids, smart factories, intelligent building systems (IBS), internet of things (IoT), cyber physical systems (CPSs), autonomous vehicles, connected vehicles, intelligent transportation systems (ITS), and much more. In addition, various novel designs for Beyond 5G (B5G) technologies are now being proposed. Two major issues arise when trying to enable these new services, where one issue is that almost all interactive applications need cloud support; however, conventional mobile cloud computing approaches cannot satisfy the 5G/B5G performance requirements. The second issue is that the increase in volume of networking support units (which can go up to a million in a km2 area) radically increases the complexity, time delay, and cost of networking and storing application data. In addition, the level of required performance and complexity is expected to increase significantly more for B5G systems. In order to solve these issues while supporting extremely high peak data rates and connection densities with minimized end-to-end (E2E) latency, edge computing, big data, and deep learning technology is needed. Edge computing enables the cloud resources to come closer to user equipment (UE) to enable minimized delay. In addition, big data analytics are needed to proactively allocate network resources in real-time, and deep learning technology is needed to produce real-time accurate solutions for these very complex situations that have a tendency to change. Based on this focus, the following topics of interest include, but are not limited to:

• Management of resource, functionality, services chaining, and network for edge computing in 5G & B5G
• Guaranteed quality of service (QoS) control schemes for wireless edge computing in 5G & B5G
• 5G interoperability between heterogeneous access networks with edge computing support
• Network slicing and energy-aware schemes for 5G & B5G networks
• Network measurements and characterization for edge computing 5G data traffic
• Cross layer design and optimization of edge computing in 5G networks
• Security and privacy concerns of edge computing communication for 5G networks
• Big data analytics and deep learning based control to support reliable QoS in 5G & B5G networks
• Big data analytics and deep learning based performance optimization of 5G & B5G networks
• Big data analytics and deep learning based minimized operational management in 5G & B5G networks
• Big data analytics and deep learning to optimize 5G RANs for target service applications
• Big data analytics and deep learning based social media data analysis and security schemes for 5G
• Deep learning based physical layer design/developments for B5G
• Vertical applications of 5G & B5G: smart grid, vehicular communications (e.g., V2X, HST)
• Multi-RAT load balancing algorithms for 5G
• Performance evaluation of testbeds and trials for key enabling technologies of 5G & B5G
• Standardization activities of 5G & B5G by IEEE, 3GPP and ETSI

Important Dates (extended)
Paper submission due: April 11, 2020
First Decision: June 17, 2020
Final paper due: July 28, 2020
Tentative Publication Date: October 10, 2020

Guest Editors
Dr. Jong-Moon Chung, Yonsei University, Rep. of Korea, jmc@yonsei.ac.kr
Dr. Taesang Choi, ETRI, Rep. of Korea, choits@etri.re.kr
Dr. Sejun Song, University of Missouri–Kansas City, United States, songsej@umkc.edu
Dr. Emilio Calvanese Strinati, CEA‐LETI, France, emilio.calvanese-strinati@cea.fr
Dr. Sang-Chul Kim, Kookmin University, Rep. of Korea, sckim7@kookmin.ac.kr

Paper Submission
Papers should be submitted at https://mc.manuscriptcentral.com/etrij, and should adhere to the journal’s Author Guidelines.

The Editorial Office can be contacted at etrij@etri.re.kr.