Page 228 - Kaleidoscope Academic Conference Proceedings 2020

Session 2: Design principles, architecture and protocols for the digital transformation
S2.1 Lightweight and instant access technologies and protocols to boost digital transformations
Yihua Ma, Zhifeng Yuan, Weimin Li and Zhigang Li, ZTE Corporation and State Key Laboratory
of Mobile Network and Mobile Multimedia, China

The further integration of telecommunications and industry has been considerable and is expected
to bring significant benefits to society and economics. It also leads to some evolution trends for
next-generation communication systems, including further rises in machine-type communications
(MTC), uplink-dominated systems, and decentralized structures. However, the existing access
protocols are not friendly to these trends. This paper analyzes the problems of existing access
protocols and provides novel access technologies to solve them. These technologies include
contention-based NOMA, data features, enhanced pilot design and successive interference
cancellation (SIC) of diversity. With these key enablers, lightweight and instant access can be
realized, and some potential modifications of protocols are analyzed. Finally, this paper uses
massive and critical scenarios in digital transformations to show the great necessity of introducing
novel access technologies into future communication protocols.

S2.2 Automation of computational resource control of cyber-physical systems with machine learning*
Ved P. Kafle and Abu Hena Al Muktadir, National Institute of Information and Communications
Technology (NICT), Japan

Cyber-physical systems require the quality of service (QoS) guaranteed performance of service
functions and processes executed in cyberspace. Because of low-latency requirements, most of
such functions must be executed in edge computing infrastructure, where computational resources
are limited. For efficient management of limited available resources in edge cloud to meet very
low-latency requirements of services, this paper proposes a dynamic resource control scheme to
adjust computational resources allocated to virtual network functions (VNFs). The scheme
employs machine learning (ML) techniques composed of multiple regression models, which are
continuously retrained online by using performance data collected from the running system. We
demonstrate its effectiveness through experimental evaluation results obtained from an
implementation of an IoT-directory service function in a resource virtualization platform provided
by Docker containers in cyberspace. The IoT-directory service, whose architecture is based on
Recommendation ITU-T Y.3074, is a scalable system that can store a huge amount of control
information of a billion IoT devices in the form of name records and provides a very fast lookup
service with the latency of a few milliseconds. The proposed scheme is related to ML-based
network control and management methods currently being standardized in the ITU-T Study Group
13.

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