AI and Machine Learning in 5G - Lessons from the ITU Challenge No. 5, 2020
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No. 5, 2020 AI and Machine Learning in 5G Lessons from the ITU Challenge
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Editorial ITU News MAGAZINE No. 05, 2020 1
AI and machine learning in 5G
— the ITU Challenge 2020
By Houlin Zhao, ITU Secretary‑General
J In February this year, the we announced the approval
International Telecommunication by our 193 Member States of
Union (ITU) set the first ITU AI/ML in an ITU Radiocommunication
5G Challenge in motion — a global Sector (ITU–R) Recommendation:
competition that will culminate in “Detailed specifications of the
an online prize-winning event on radio interfaces of IMT‑2020.”
15–17 December, 2020.
IMT‑2020 specifications for the fifth
Through the Challenge, ITU generation of mobile communica‑
Through the
encourages and supports the grow‑ tions (5G) will be the backbone of
ing community driving the integra‑ tomorrow’s digital economy, lead‑ Challenge, ITU
tion of artificial intelligence (AI) and ing industry and society into the encourages
machine learning (ML) in networks automated and intelligent world and supports
and at the same time enhances the
community driving ITU standardiza‑
and promising to improve people’s
lives on a scale never seen before.
the growing
tion work for AI/ML. community
In this edition of the ITU Magazine driving the
The ITU Challenge enables the you will learn all about the ITU AI/ integration
collaborative culture necessary for ML in 5G Challenge and also find
success in emerging and future ample insight articles from industry
of artificial
networks such as 5G and creates and academia. intelligence and
new opportunities for industry and machine learning
academia to influence the evolution The Grand Challenge Finale will
in networks.
of ITU standards. feature keynotes by Professor
Vincent Poor of Princeton University,
As the UN specialized agency United States, Chih‑Lin I of the Houlin Zhao
for ICTs, ITU plays a central China Mobile Research Institute,
role in ensuring that these net‑ and Wojciech Samek of Fraunhofer
works are rolled out widely HHI, Germany. It will also launch the
and follow the highest qual‑ Challenge 2021. Enjoy!
ity standards. Most recently,
Contents ITU News MAGAZINE No. 05, 2020 2
AI and Machine Learning in 5G
Lessons from the ITU Challenge
Editorial
1 AI and machine learning in 5G — the ITU Challenge 2020
By Houlin Zhao, ITU Secretary‑General Cover photo: Shutterstock
5 ITU thanks the sponsors of the 2020 AI/Machine Learning
in 5G Challenge
ITU AI/ML in 5G Challenge
6 Building community and trust on the ITU platform
ITU News spoke with Chaesub Lee, Director of the ITU
Telecommunication Standardization Bureau, to learn more about
the context for the ITU Challenge on AI and Machine Learning ISSN 1020–4148
in 5G and its connection with the strategic priorities of ITU. itunews.itu.int
Six issues per year
Copyright: © ITU 2020
9 Message from the organizers
By Thomas Basikolo, AI/ML Consultant Editorial Coordinator & Copywriter: Nicole Harper
Art Editor: Christine Vanoli
Editorial Assistant: Angela Smith
12 Follow the ITU AI/ML in 5G Challenge
13 Problem statements Editorial office:
Tel.: +41 22 730 5723/5683
14 The Grand Challenge Finale — Tuesday, 15 December 2020 E‑mail: itunews@itu.int
15 The Grand Challenge Finale — Wednesday, 16 December 2020
Mailing address:
16 The Grand Challenge Finale — Thursday, 17 December 2020 International Telecommunication Union
Place des Nations
17 Winning prizes and certificates CH–1211 Geneva 20 (Switzerland)
18 A guide to AI/ML challenges for the next-generation CTx Disclaimer:
Opinions expressed in this publication are those
By Vishnu Ram OV, Independent Research Consultant of the authors and do not engage ITU. The des‑
ignations employed and presentation of mate‑
23 A standards round-up on autonomous networks rial in this publication, including maps, do not
imply the expression of any opinion whatsoever
By Xiaojia Song, Researcher, Xi Cao, Senior Researcher, Lingli on the part of ITU concerning the legal status of
Deng, Technical Manager, Li Yu, Chief Researcher, and Junlan any country, territory, city or area, or concerning
Feng, Chief Scientist, China Mobile Research Institute the delimitations of its frontiers or boundaries.
The mention of specific companies or of certain
30 ITU AI/Machine Learning in 5G Challenge webinars products does not imply that they are endorsed
or recommended by ITU in preference to others
of a similar nature that are not mentioned.
All photos are by ITU unless specified otherwise.
Contents ITU News MAGAZINE No. 05, 2020 3
Insights from industry
32 Capability evaluation and AI accumulation in future networks
By Jun Liao, Artificial Intelligence Director, Tengfei
Liu, Yameng Li, and Jiaxin Wei, Artificial Intelligence
Engineers, China Unicom Research Institute
35 Accelerating deep learning inference with Adlik open-source
toolkit
By Liya Yuan, Open Source and Standardization Engineer, ZTE
38 Challenges and opportunities for communication service
providers in applying AI/ML
By Salih Ergüt, 5G R&D Senior Expert, Turkcell
42 Autonomous networks: Adapting to the unknown
By Paul Harvey, Research Lead, and Prakaiwan Vajrabhaya, Research
Outreach and Promotion Lead, Innovation Studio, Rakuten Mobile
46 Quality of Experience testing in mobile networks
By Arnd Sibila, Technology Marketing Manager,
Mobile Network Testing, Rohde & Schwarz
50 A network operator’s view of the role of AI in future radio
access networks
By Chih‑Lin I, Chief Scientist, and Qi Sun, Senior Researcher,
Wireless Technologies, China Mobile Research Institute
55 AI and open interfaces: Key enablers for campus networks
By Günther Bräutigam, Managing Director, Airpuls; Renato L.G.
Cavalcante, Research Fellow, and Martin Kasparick, Research
Associate, Fraunhofer HHI; Alexander Keller, Director of Research,
NVIDIA; and Slawomir Stanczak, Head of Wireless Communications
and Networks Department, Fraunhofer HHI, Germany
58 Quotes from hosts of the ITU AI/ML in 5G Challenge problem
statements
61 Quotes from the ITU AI/ML in 5G Challenge participants
Contents ITU News MAGAZINE No. 05, 2020 4
Insights from academia
62 AI/machine learning for ultra-reliable low-latency
communication
By Andrey Koucheryavy, Chaired Professor, Telecommunication
Networks and Data Transmission Department, The Bonch-Bruevich
Saint Petersburg State University of Telecommunications (SPbSUT),
Chief Researcher, NIIR, and Chairman, ITU–T SG11; Ammar Muthanna,
Deputy Head, Science, Telecommunication Networks and Data
Transmission Department, SPbSUT, and Head, SDN Laboratory;
Artem Volkov, Researcher and PhD Student, Telecommunication
Networks and Data Transmission Department, SPbSUT, Russia
66 AI/ML integration for autonomous networking —
a future direction for next‑generation telecommunications
By Akihiro Nakao, Professor, The University of Tokyo, Japan
70 Realistic simulations in Raymobtime to design the physical
layer of AI‑based wireless systems
By Aldebaro Klautau, Professor, Federal University of Pará,
Brazil; and Nuria González‑Prelcic, Associate Professor,
North Carolina State University, United States
74 Building reliability and trust with network simulators and
standards
By Francesc Wilhelmi, Post-Doctoral Researcher, Centre
Tecnològic de Telecomunicacions de Catalunya (CTTC), Spain
78 Research projects in Nigeria advancing education and speech
recognition
By James Agajo, Associate Professor and Head of WINEST
Research Group, Department of Computer Engineering,
Abdullahi Sani Shuaibu and Blessed Guda, Students,
Federal University of Technology, Minna, Nigeria
82 Why we need new partnerships for new data
By Ignacio Rodriguez Larrad, PostDoc, Wireless
Communication Networks, Aalborg University, Denmark
86 Machine learning function orchestration for future generation
communication networks
By Shagufta Henna, Lecturer in Computing,
Letterkenny Institute of Technology, Ireland
88 Sponsorship opportunities for 2021
ITU thanks the sponsors
of the 2020 AI/Machine
Learning in 5G Challenge
Gold Sponsor
TRA, United Arab Emirates
TRA
Bronze Sponsors
Cisco Systems and ZTE
CISCO ZTE
ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 6
Building community and
trust on the ITU platform
ITU News spoke with Chaesub Lee, Director of the ITU
Telecommunication Standardization Bureau, to learn more about
the context for the ITU Challenge on AI and Machine Learning
in 5G and its connection with the strategic priorities of ITU.
This edition shares experiences How does the ITU Challenge
from the ITU Challenge. How factor into the strategic priorities
would you describe the aims of of ITU?
the ITU Challenge?
^ Building community and
The ITU
^ The ITU Challenge provided a trust is at the heart of all that Challenge
platform for participants to apply we do at ITU. We are a global provided a
ITU’s Machine Learning Toolkit
in solving practical problem
membership of 193 Member
States and over 900 companies,
platform for
statements. The ITU Challenge universities, and international participants
allowed participants to connect and regional organizations. ITU to apply ITU’s
with new partners in the ITU standards are developed in a Machine
community — and new tools and
data resources — to achieve goals
community, building the mutual
understanding that enables the
Learning
set out by problem statements community to advance together. Toolkit.
contributed by industry and ITU standards are significant feats
academia in Brazil, China, India, of international collaboration.
Chaesub Lee
Ireland, Japan, Russia, Spain, They represent voluntary commit‑
Turkey and the United States. It ments to common approaches to Director, ITU
Telecommunication
offered participants an opportu‑ technology development, appli‑
Standardization Bureau
nity to showcase their talent, test cation and supporting business
their concepts on real data and relationships. The value of ITU
real-world problems, and com‑ standardization, just like the value
pete for global recognition. of the ITU Challenge, lies in the
community that it creates.
ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 7
How do ITU standards connect Standard “toolsets”, built to be
with the ITU Challenge and how 5G represents major adapted to evolving user require‑
might this connection evolve? advances in networking ments and a broad scope of use
to meet the needs of cases, are also found in ITU stand‑
^ New ITU standards for AI/ML a very diverse set of ards in fields such as multimedia,
provide toolsets to enable AI/ML applications, across security, blockchain and quantum
integration in 5G and future net‑ industry sectors. information technology.
works as these networks evolve.
The ITU Telecommunication The ICT industry evolves very
Standardization Sector (ITU–T) Chaesub Lee rapidly. How have recent
Y.3172 architecture — derived years’ evolutions impacted ITU
from the study of use cases pub‑ standardization?
lished in ITU–T Y.Supplement55
— introduced the basic toolsets ^ ITU’s standardization arm
in relation to the underlying (ITU–T) has seen a very strong
network: ML Pipeline for model increase in new members in the
optimization and serving; ML Why are AI/ML and supporting past four years, topping over
Sandbox to trial models before standards important to 5G and 50 last year. We are addressing
deployment; and ML Function future networks? exciting new subjects, but the role
Orchestrator (MLFO) to control of the ITU platform has remained
AI/ML integration. ITU–T Y.3173 ^ Companies in the network‑ unchanged for over 150 years —
(intelligence evaluation), ITU–T ing business are introducing AI/ we build community and trust to
Y.3174 (data handling) and ITU–T ML as part of their innovations enable information and communi‑
Y.3176 (marketplace integration) to optimize network operations cation technology (ICT) advances
all build on the ITU–T Y.3172 and increase energy and cost on a global scale. The ITU stand‑
architecture. The ITU Challenge efficiency. 5G represents major ardization platform — for many
aimed to demonstrate and advances in networking to meet years central to building mutual
validate these ITU standards and the needs of a very diverse set understanding within the ICT sec‑
create new opportunities for of applications, across industry tor — is now helping the ICT sector
industry and academia to influ‑ sectors. Networks are growing to build mutual understanding
ence their evolution. in sophistication and complexity. with its many new partners. We
AI/ML will be key in managing see new partners collectively
this complexity. The ITU–T Y.317x advancing ITU standardization
standards provide versatile tool‑ work in fields such as smart cities,
sets to support AI/ML integration energy, health care, finance, auto‑
in tune with network evolution. motive, and AI/ML.
ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 8
How has ITU approached this Where is the influence of AI/
need to support a more diverse ML most pronounced in ITU The concept of a truly
set of ICT applications? standardization work and autonomous network —
what are the opportunities enabled by the Level 5
^ Although ITU’s role in build‑ to participate? intelligence described
ing community and trust remains by ITU–T Y.3173 — has
unchanged, we have entered ^ AI/ML is playing a key part in sparked considerable
a new era of standardization ITU standardization work in fields discussion in ITU.
in need of new approaches to such as network orchestration and
continue building this community management, multimedia coding,
and trust. We have spent many service quality assessment, digital Chaesub Lee
years bringing ICT decision-mak‑ health, environmental efficiency,
ers together with decision-makers and autonomous driving. And the
in other sectors. This inclusive concept of a truly autonomous
dialogue has helped us to create network — enabled by the Level 5
the conditions necessary to intelligence described by ITU–T
deliver influential standards in Y.3173 — has sparked considera‑
fields of innovation given life by ble discussion in ITU. We wel‑
new partnerships; fields such come you to join us.
as digital health, digital finance,
intelligent transport systems and ITU continues to grow in inclu‑
AI/ML. Here we see the value of sivity. This year we introduced a
open platforms such as ITU focus reduced membership fee option
groups or the AI for Good Global for start-ups and SMEs. Academia
Summit. These open platforms have benefitted from reduced
help to build community and fees since 2011. Companies of
trust. They help to clarify the con‑ all sizes in “low income” develop‑
tributions expected of different ing countries also benefit from
stakeholders, including the contri‑ reduced fees.
bution of ITU standardization.ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 9
ITU
AI/ML in 5G
Challenge
Applying machine learning in
communication networks
ai5gchallenge@itu.int
Message from the organizers
By Thomas Basikolo, AI/ML Consultant
J The ITU AI/ML in 5G Challenge rallied like-minded students and
professionals from around the globe to study the practical application
of artificial intelligence (AI) and machine learning (ML) in emerging The ITU AI/ML in 5G
and future networks. The Challenge was a first for ITU, but with many Challenge rallied like-
valuable lessons learnt, it looks to be the first of many. minded students and
professionals from
The Challenge welcomed over 1300 participants from 62 coun‑ around the globe.
tries, forming 911 teams, and we are looking forward to the Grand
Challenge Finale, 15–17 December online, where outstanding teams
Thomas Basikolo
will compete for a share in a prize fund totalling 20 000 CHF and a
range of other prizes offering global recognition.
Partnerships made the ITU Challenge possible, and partnerships
were also the name of the game.ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 10
and our Gold sponsor, the The problem statements of this
The Challenge Telecommunications Regulatory first ITU Challenge offered a
welcomed over Authority (TRA) of the United variety of opportunities to apply
1300 participants Arab Emirates; and Bronze spon‑ the ITU–T Y.317x techniques, and
from 62 countries, sors Cisco and ZTE. one problem statement demon‑
forming 911 teams. strated ML function orchestra‑
tor capabilities via reference
Mapping solutions to ITU implementations.
Thomas Basikolo standards
In future editions of the ITU
New ITU standards for AI/ML Challenge, we aim to provide
provide toolsets that, when a reference implementation of
integrated, form an end-to-end an end-to-end ML pipeline as
pipeline for AI/ML integration defined by ITU–T Y.3172. Such
in networks. The ITU Challenge reference implementations could
The ITU Challenge enabled aimed to demonstrate and vali‑ include notebooks for ML cod‑
participants to connect with date these ITU standards. In map‑ ing and integration; tools for
new partners in industry and ping solutions to ITU standards, data processing and manage‑
academia — and new tools and the ITU Challenge contributes to ment; and tools for ML model
data resources — to solve real- the growth of the community able selection, training, optimization
world problems with AI/ML, to support the iterative evolution and verification.
showcase their talent and share of these ITU standards.
new experiences. Twenty-three We also aim to enable access to
problem statements were contrib‑ The ITU–T Y.3172 architecture ITU–standard toolsets for initia‑
uted by industry and academia — derived from the study of tives such as plugfests and hack‑
in Brazil, China, India, Ireland, use cases published in ITU–T athons and to set the stage for
Japan, Russia, Spain, Turkey and Y.Supplement55 — introduced the collaboration in open-source pro‑
the United States, and these basic toolsets in relation to the jects and standardization work.
“regional hosts” offered resources underlying network: ML Pipeline
and expert guidance to sup‑ for model optimization and serv‑
port participants in addressing ing; ML Sandbox to trial models A learning experience
their challenges. before deployment; and ML for all
Function Orchestrator (MLFO) to
We would like to thank the control AI/ML integration. ITU–T Data availability is a key challenge
community that gave life to Y.3173 (intelligence evaluation), to be navigated when bringing
the Challenge, our partici‑ ITU–T Y.3174 (data handling) together a global community to
pants and regional hosts; our and ITU–T Y.3176 (marketplace innovate with AI/ML.
promotion partners LF AI & integration) all build on the ITU–T
Data, NGMN and SGInnovate; Y.3172 architecture.ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 11
Fifteen problem statements In our work to offer partici‑
were open to all participants. pants a level playing field, ITU Preparations for the
Eight were limited to partici‑ and our partners developed ITU Challenge 2.0 are
pation under conditions set by tailored workflows delivering in motion, driven by a
their hosts. And fourteen remain participants a unique, customized core team of challenge
“under development” without the Challenge experience. management board
necessary tools or data resources members, judges,
for this first ITU Challenge. We ITU engaged participants in tech‑ promotion partners
hope to see new partners coming nical roundtables and webinars and sponsors.
together to address these four‑ to provide expert guidance in
teen problem statements in future addressing problem statements
editions of the ITU Challenge. and the value of new ITU stand‑ Thomas Basikolo
ards in support. Together with
The data sharing guidelines of the our regional hosts, we reached
ITU Challenge incorporate a wide out in local languages, connected
range of perspectives from indus‑ participants with mentors and
try and academia on access to maintained interactive discussions
real network data, synthetic data on our Slack channel.
and open data. The guidelines problem statements, and new
describe measures to enable data tools and data resources. We are
sharing in view of different classifi‑ Up to the challenge creating new opportunities for
cations of datasets, pre-process‑ in 2021? industry and academia to solve
ing steps (including anonymizing) problems together, and new
and secure hosting of data. Preparations for the ITU opportunities to influence the
Challenge 2.0 are in motion, direction of ITU standards devel‑
We also saw the best outcomes driven by a core team of chal‑ opment and application. Contact
achieved in close collaboration. lenge management board mem‑ us to participate in the prob‑
The Challenge highlighted that bers, judges, promotion partners lem-solving, judge some of the
problem statements are best and sponsors. interesting submissions, promote
positioned for success when sup‑ the challenge, sponsor a prize, or
ported not only by the necessary We will continue to encourage mentor a few students.
tools and data resources, but also new partnerships in AI/ML and
by close collaboration between establish guiding principles for We thank you for your support
participants and regional hosts. the sharing of tools and data and look forward to seeing you
resources necessary to enact soon in Challenge 2.0.
Our priority was to create com‑ these partnerships. We are wel‑
munity value in the field of AI/ML. coming new partners and newFollow the ITU AI/ML
in 5G Challenge
26 partners See Challenge
(telecom operators, vendors,
and academia) hosted 23 problem website
statements
1300+ participants from
60+ countries from 6 regions
45% industry — 55% academia
26 webinars Don’t miss the
Grand Challenge
4 technical tracks: Networks, Enablers, Finale winner
Verticals, Social Good
announcements
20 000 CHF in cash prizes 15–17
Certificates — 5 categories December 2020
online here
Timeline
Global Global Grand
call for round Best teams Challenge
challenge begins advance Finale —
entries online
Problem statement selection Keynotes
Dataset release Winners’ presentations
Registration Prize awards
Warm-up Global
phase round ends
February 2020 to June 2020 July 2020 November 2020 15–17 December 2020Problem statements
Title Host entity
ML5G-PHY-beam-selection: Machine learning applied to the physical layer of
Federal University of Pará (UFPA), Brazil
millimeter-wave MIMO systems
Improving the capacity of IEEE 802.11 WLANs through machine learning Pompeu Fabra University (UPF), Spain
Graph Neural Networking Challenge 2020 Barcelona Neural Networking Center (BNN‑UPC), Spain
Compression of deep learning models ZTE
5G+AI (smart transportation) Jawaharlal Nehru University (JNU), India
Improving experience and enhancing immersiveness of video conferencing
Dview
and collaboration
5G+ML/AI (dynamic spectrum access) Indian Institute of Technology Delhi (IITD)
Privacy preserving AI/ML in 5G networks for healthcare applications Centre for Development of Telematics (C‑DOT)
Shared experience using 5G+AI (3D augmented + virtual reality) Hike, India
Demonstration of Machine Learning Function Orchestrator (MLFO) capabilities
Letterkenny Institute of Technology (LYIT), Ireland
via reference implementations
ML5G-PHY-channel estimation: Machine learning applied to the physical layer
North Carolina State University, United States
of millimeter-wave MIMO systems
NEC, RISING Committee, Telecommunication
Network state estimation by analysing raw video data
Technology Committee (TTC)
Analysis on route information failure in IP core networks by NFV-based KDDI, RISING Committee, Telecommunication
test environment Technology Committee (TTC)
Using weather info for radio link failure (RLF) prediction Turkcell
Traffic recognition and Long-term traffic forecasting based on AI algorithms St. Petersburg State University of Telecommunications
and metadata for 5G/IMT‑2020 and beyond (SPbSUT)
5G+AI+AR China Unicom (Zhejiang Division)
Fault localization of loop network devices based on MEC platform China Unicom (Guangdong Division)
Configuration knowledge graph construction of loop network devices based
China Unicom (Guangdong Division)
on MEC architecture
Alarm and prevention for public health emergency based on telecom data China Unicom (Beijing Division)
Energy-saving prediction of base station cells in mobile communication network China Unicom (Shanghai Division)
Core network KPI index anomaly detection China Unicom (Shanghai Division)
Network topology optimization China Mobile
Out of service (OoS) alarm prediction of 4/5G network base station China MobileThe Grand Challenge Finale —
Tuesday, 15 December 2020
Time
Challenge Title Team Members Affiliation
(CET)
12:15 5G+AI+AR Jiawang Liu Jiaping Jiang CITC and China Unicom
Analysis on route information failure in IP core
12:30 Fei Xia Aerman Tuerxun Jiaxing Lu Ping Du The University of Tokyo
networks by NFV-based test environment
Analysis on route information failure in IP core Nara Institute of Science and
12:45 Takanori Hara Kentaro Fujita
networks by NFV-based test environment Technology, Japan
Analysis on route information failure in IP core Ryoma Kondo Takashi Ubukata Kentaro Matsuura
13:00 The University of Tokyo
networks by NFV-based test environment Hirofumi Ohzeki
Fault localization of network devices based on MEC Guochuang Software
13:15 Zhang Qi Lin Xueqin
Platform Co. Ltd
Han Zengfu Wang Zhiguo Zhang Yiwei
13:30 Network topology optimization China Mobile Shandong
Wu Desheng Li Sicong
Gang Zhouwei Rao Qianyin Feng Zezhong
13:45 Network topology optimization China Mobile Guizhou
Xi Lin Guo Lin
14:00 Break Break Break
Energy-Saving Prediction of Base Station Cells in
14:15 Wei Jiang Shiyi Zhu Xu Xu AsiaInfo Technologies Ltd
Mobile Communication Network
Out of Service (OoS) alarm prediction of 4/5G
14:30 Zhou Chao Zheng Tianyu Jiang Meijun Nankai University
network base station
Demonstration of Machine Learning Function
14:45 Orchestrator (MLFO) capabilities via reference Abhishek Dandekar Technical University Berlin
implementations
ML5G-PHY-beam-selection: Machine learning
Mahdi Boloursaz Mashhadi Tze‑Yang Tung
15:00 applied to the physical layer of millimeter-wave Imperial College London
Mikolaj Jankowski Szymon Kobus
MIMO systems
ML5G-PHY-beam-selection: Machine learning
Batool Salehihikouei Debashri Roy Northeastern University,
15:15 applied to the physical layer of millimeter-wave
Guillem Reus Muns Zifeng Wang Tong Jian Brazil
MIMO systems
ML5G-PHY-beam-selection: Machine learning
15:30 applied to the physical layer of millimeter-wave Zecchin Matteo Eurecom, Brazil
MIMO systems
Improving the capacity of IEEE 802.11 WLANs Pompeu Fabra University,
15:45 Ramon Vallès
through machine learning Spain
Improving the capacity of IEEE 802.11 WLANs Paola Soto David Goez Miguel Camelo University of Antwerp,
16:00
through machine learning Natalia Gaviria Belgium
Mohammad Abid Ayman M. Aloshan
Improving the capacity of IEEE 802.11 WLANs
16:15 Faisal Alomar Mohammad Alfaifi Saudi Telecom
through machine learning
Abdulrahman Algunayyah Khaled M. Sahari
Note: The above teams have been selected to make presentations at the Grand Challenge Finale (Finale Conference).
(Each team has 8 minutes for its presentation, followed by a 7‑minute Q&A with the judges and the audience).
Don’t miss the Final Conference!
Take a look at the list of best teams.
Register here.The Grand Challenge Finale —
Wednesday, 16 December 2020
Time
Challenge Title Team Members Affiliation
(CET)
Network state estimation by analysing raw Osaka Prefecture University,
12:00 Yuusuke Hashimoto Yuya Seki Daishi Kondo
video data Japan
The Kyoto College of
Network state estimation by analysing raw
12:15 Yimeng Sun Badr Mochizuki Graduate Studies for
video data
Informatics, Japan
National Institute of
Network state estimation by analysing raw Fuyuki Higa Gen Utidomari Ryuma Kinjyo
12:30 Technology, Okinawa
video data Nao Uehara
College, Japan
Institute of Computing
12:45 Compression of deep learning models Yuwei Wang Sheng Sun Technology Chinese
Academy of Sciences
Satheesh Kumar Perepu Saravanan Mohan
13:00 Compression of deep learning models Ericsson Research India
Vidya G Thrivikram G L Sethuraman T V
Atheer K. Alsaif Nora M. Almuhanna
13:15 5G+AI (smart transportation) Saudi Telecom Company
Abdulrahman Alromaih Abdullah O. Alwashmi
Privacy preserving AI/ML in 5G networks for Mohammad Malekzadeh Mehmet Emre Ozfatura
13:30 Imperial College London
healthcare applications Kunal Katarya Mital Nitish
Shared experience using 5G+AI Easyrewardz Software
13:45 Nitish Kumar Singh
(3D augmented + virtual reality) Services
14:00 Break Break Break
Loïck Bonniot Christoph Neumann
14:15 Graph Neural Networking Challenge 2020 InterDigital; Inria/Irisa
François Schnitzler François Taiani
Nick Vincent Hainke Stefan Venz
14:30 Graph Neural Networking Challenge 2020 Fraunhofer HHI, Germany
Johannes Wegener Henrike Wissing
Martin Happ Christian Maier Jia Lei Du Salzburg Research
14:45 Graph Neural Networking Challenge 2020
Matthias Herlich Forschungsgesellschaft
Using weather info for radio link failure (RLF)
15:00 Dheeraj Kotagiri Anan Sawabe Takanora Iwai NEC Corporation
prediction
Using weather info for radio link failure (RLF) Juan Samuel Pérez Amín Deschamps Santo Domingo Institute of
15:15
prediction Willmer Quiñones Yobany Díaz Technology (INTEC)
Traffic recognition and long-term traffic forecasting Ufa State Aviation Technical
Ainaz Hamidulin Viktor Adadurov Denis Garaev
15:30 based on AI algorithms and metadata for 5G/ University (USATU)
Artem Andriesvky
IMT‑2020 and beyond University, Russia
ML5G-PHY-Channel Estimation: Machine Learning
15:45 Applied to the Physical Layer of Millimeter-Wave Dolores Garcia Joan Palacios Joerg Widmer IMDEA Networks
MIMO Systems
ML5G-PHY-Channel Estimation: Machine learning
Emil Björnson Pontus Giselsson Linköping University and
16:00 applied to the physical layer of millimeter-wave
Mustafa Cenk Yetis Özlem Tugfe Demir Lund University, Sweden
MIMO systems
Chandra Murthy Christo Kurisummoottil Thomas
ML5G-PHY-Channel Estimation: Machine learning Eurecom, France, Indian
Marios Kountouris Rakesh Mundlamuri
16:15 applied to the physical layer of millimeter-wave Institute of Science, India
Sai Subramanyam Thoota
MIMO systems Communications, Canada
Sameera Bharadwaja H
Note: The above teams have been selected to make presentations at the Grand Challenge Finale (Finale Conference).
(Each team has 8 minutes for its presentation, followed by a 7‑minute Q&A with the judges and the audience).
Don’t miss the Final Conference!
Take a look at the list of best teams.
Register here.The Grand Challenge Finale —
Thursday, 17 December 2020
Time
Programme
(CET)
11:30–12:00 Join session to test connection
12:00–12:30 Opening ceremony
Welcome remarks
Houlin Zhao, ITU Secretary-General
Chaesub Lee, Director, ITU Telecommunication Standardization Bureau
United Arab Emirates Telecommunications Regulatory Authority
Overview of the 2020 Challenge
Thomas Basikolo, ITU
12:30–12:55 Keynote — Recent advances in federated learning for communications
Wojciech Samek, Head of Machine Learning Group, Fraunhofer HHI
12:55–13:40 Special Session: Vision for the future — AI/ML in 5G roadmap
Regulator perspective
Telecommunications Regulatory Authority, United Arab Emirates
Industry perspective
Cisco
Industry perspective
Wei Meng, Director of Standard and Open Source Planning, ZTE Corporation
13:40–14:05 Keynote — The unfinished journey of network AI
Chih-Lin I, Chief Scientist, Wireless Technologies, China Mobile Research Institute
14:05–14:30 Keynote — Learning at the wireless edge
H. Vincent Poor, Professor of Electrical Engineering, Princeton University, United States
14:30–15:15 Winners’ presentations
15:15–15:30 Award announcements
Prizes and certificates
15:30–15:35 Call for papers to special issue of ITU Journal on Future and Evolving Technologies (ITU J-FET):
“AI/ML Solutions in 5G and Future Networks”
Ian Akyildiz, Editor-in-Chief, Georgia Institute of Technology, United States
15:35–15:45 2021 outlook for Challenge 2.0
Vishnu Ram, Independent Researcher
15:45–16:00 Closing ceremony
Closing remarks
Hosts of the ITU AI/ML in 5G Challenge 2020
Chaesub Lee, Director, ITU Telecommunication Standardization Bureau
Don’t miss the Final Conference!
Register here.Winning prizes and certificates
Teams from various problem statements will compete for
the ITU AI/ML in 5G Challenge Champion title, and several
awards will be presented to winning solutions at the Grand
Challenge Finale taking place 15–17 December 2020.
Winners’ certificate: Awarded to winning teams in the following categories:
1st prize: 2nd prize: 3rd prize:
ITU AI/ML in 5G ITU AI/ML in 5G ITU AI/ML in 5G
Challenge Gold Challenge Silver Challenge Bronze
Champion Champion Champion
Cash prize: 5000 CHF Cash prize: 3000 CHF Cash prize: 2000 CHF
Three Runners up will receive 1000 CHF each.
Judges Prize certificates: Awarded to winners of each problem statement
as recommended by the host (excluding those under Winners
certificate). Each winner receives 300 CHF.
Honorable mention certificate.
Encouragement/Community award certificate: Awarded to teams
that were active during the mentoring programme and successfully
submitted a solution.
Certificate of completion: Awarded to teams that completed the
challenge by submitting a solution.ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 18
Shutterstock
A guide to AI/ML challenges for the next-generation CTx
By Vishnu Ram OV, Independent Research Consultant
J The new CTx* at FutureXG repository that CTx was banking
analysed the reports on on was being pulled in a zillion
the screen. directions. And the buzz around
autonomous networks meant
(x+1)G specification delayed. xG that every part of the network And the buzz around
deployments yet to be justified. was working on its own brand of autonomous networks
Research & development lost in a autonomy. meant that every part
maze of acronyms, old and new. of the network was
New architecture diagrams every Will CTx survive this challenge? working on its own
few weeks. New use cases to brand of autonomy.
support in every market. Applying New ITU standards describe con‑
and integrating AI/machine cepts to enable AI/ML integration
learning (ML) in the network was in 5G and future networks as
nothing smooth. The open source these networks evolve.
*Any resemblances or similarities with real-life CTOs are purely futuristic.
Disclaimer: This article contains some fictional information which may be referred to as forward-looking statements.ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 19
The ITU–T Y.3172 architec‑ evaluation), ITU–T Y.3174 (data in the underlying network. Using
ture, derived from the study of handling) and ITU–T Y.3176 (mar‑ the concepts described by the
use cases published in ITU–T ketplace integration) all build on ITU–T Y.317x standards, even as
Y.Supplement55, introduced the ITU–T Y.3172 architecture. the underlying network architec‑
basic toolsets including the ML ture changes from generation to
Pipeline, ML Sandbox and ML Together these ITU standards the next, it will remain possible to
Function Orchestrator (MLFO) in provide powerful toolsets — specify AI/ML integration using
relation with the underlying net‑ standard toolsets — for operators the common terminology pro‑
work. ITU–T Y.3173 (intelligence to monitor and adapt to changes vided by ITU.
High level architecture for integration of AI/ML in networks (ITU–T Y.3172)
Management ML sandbox subsystem
subsystem
6
Other SRC C PP M P D SINK
management 2 1
and Simulated ML underlay networks
orchestration
functions 3
ML pipeline subsystem
7
M P D SINK Level-N
MLFO 8 …
5
SRC C PP Level-2
9
ML intent SRC Level-1
ML = Machine learning
MLFO = Machine learning function orchestrator 4
SRC = Source of data
C = Collector ML underlay networks
PP = Pre-producer
M = Model
P = Policy NF 1 … NF n NF 1 … NF n
D = Distributor
SINK = Target of ML output Underlay Network 1 Underlay Network 2
NF = Network functionITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 20
Real network data adds to The ITU–T Y.317x concept of ML
New alerts pop up on the accuracy of the models. Pipeline and ML Sandbox man‑
the screen from the “[ML‑usecase-1xx::status::ready]” aged by MLFO enables operators
MLFO monitor. What? CTx inputs to the message-box. to decouple the underlying net‑
A network-update alert! work from the AI/ML integration.
The MLFO described by ITU–T
Y.3172 is a logical node that man‑ At reference point 7, the ITU–T
ages and orchestrates the nodes Y.3172 architecture allows the
in an ML pipeline. ITU–T Y.3173 tracking of changes in the under‑
(intelligence evaluation) describes lying network and the application
a key architecture scenario for the of optimizations and configura‑
evaluation of network intelligence tions in the ML pipeline by the
levels by the MLFO. ITU–T Y.3174 MLFO. The architecture scenario
The details of a new use case (data handling) describes the described by ITU–T Y.3173 (intel‑
arrive in the message-box. CTx sequence diagrams correspond‑ ligence evaluation) also includes
runs it through the Intent-parser ing to the instantiation of various monitoring the intelligence level
tool. Interesting, but how to components of the ITU–T Y.317x of each node of an ML pipeline
implement it? CTx finds an ITU toolsets, based on the incoming by the MLFO.
webinar on MLFO orchestration ML Intent from the operator.
for managed AI/ML integration. The draft ITU standard
A few API calls later, CTx is ready In combination with MLFO, ML Y.ML‑IMT2020‑MODEL-SERV aims
with a tentative ML pipeline. Sandbox provides a managed to provide an architectural frame‑
environment for operators to work supporting the efficient
CTx kicks off simulations in the train, test and validate ML models optimization of ML models for
ML Sandbox while waiting for before they are deployed in the heterogeneous hardware envi‑
approval to access real network live network. The data handling ronments, flexible deployment of
data. Digital “twins” rev into mechanism defined in ITU–T ML models for different use-case
action; data is generated based Y.3174 further allows the addi‑ scenarios, and effective interfaces
on previous patterns and models tion of new sources of data and in the ML pipeline when a serving
are trained in the ML Sandbox, other scenarios. model is deployed.
all while the Approval Authority
takes its time. CTx sends the New alerts pop up on the screen
results from the ML Sandbox from the MLFO monitor. What?
trial models. This has the desired A network-update alert! As usual,
effect. The approval arrives in the an unscheduled virtualized
message-box. network function upgrade by the
vendor. Do we need to rework the
whole ML pipeline?ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 21
CTx parses a new message in ITU–T Y.3176 supports the admin‑ Done! A new ML pipeline in place
the message-box “[ML‑usecase- istration of different types of ML in the ML Sandbox, tested and
1xx::Evaluate::partner.edu::‑ marketplaces, internal or external, verified for the new use case.
model.url]”. Pioneering algorithm and ML marketplace federation. “[status::ready]” CTx inputs to
work at a partner university had The APIs defined in ITU–T Y.3176 the message-box. “[status::ap‑
produced a wrapped model enable marketplaces to find and proved]” the Approval Authority
suitable for the use case. But the select ML models in other mar‑ responds. CTx schedules an
Approval Authority needs an eval‑ ketplaces and pull from federated update of the network.
uation of the model. Hopefully marketplaces. And they ena‑
the external ML marketplace com‑ ble marketplaces to exchange Meanwhile, unknown to CTx, a
plies to ITU–T Y.3176! CTx pulls updated ML models and interact CTx-software-update package
the model from the marketplace. with ML Sandboxes. had arrived in the message-box.
It was time for evolution and a
ML marketplace integration can new CTx-agent to take over.
help network operators to follow
the ML innovation curve.
The ML model metadata, ML
marketplace requirements and Architecture for ML marketplace integration
the architecture reference points in network (ITU–T Y.3176)
defined in ITU–T Y.3176 (mar‑
ketplace integration) enable the
External ML marketplace
efficient exchange and deploy‑ Management
ment of ML models using stand‑ subsystem
12 (Optional)
ard interfaces. Not only can this Other
method help solve networking management 15
and Internal ML marketplace
problems using ML techniques, orchestration
but also has the potential to share functions
13
and monetize ML techniques. 7
6
MLFO ML sandbox subsystem 14
3
5
ML intent ML pipeline subsystem
4
ML underlay networks
ML intent input to MLFO
Reused reference points (ITU–T Y.3172)
ML = Machine learning
New reference points MLFO = Machine learning function orchestratorITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 22
About ITU AI/ML in 5G Challenge
On top of the ability to
ITU AI/ML in 5G Challenge provided a platform for
adapt and improve network participants to apply the ITU–T Y.317x techniques in
management and control, an solving practical problem statements. A varied selection
autonomous network could of topics including beam selection, WLAN capacity
self-evolve through online analysis, network state analysis, network slicing and
traffic forecasting, radio link failure prediction, the
experimentation, enabling better
optimization of deep learning models, and MLFO
compositions of controllers reference implementations, were offered in the
and controller hierarchies. Challenge. Different types of data, including data
from real networks, were provided in some cases for
developing solutions to these problems.
Vishnu Ram OV
The concept of a truly autono‑ and improve network manage‑ Level 5 intelligence. The disaggre‑
mous network — enabled by the ment and control, an autonomous gation of network components,
Level 5 intelligence described by network could self-evolve through rapid DevOps and better and
ITU–T Y.3173 — sparked consider‑ online experimentation, enabling better AI/ML models meant more
able discussion in the ITU–T Focus better compositions of controllers work in the AI/ML integration.
Group on “machine learning for and controller hierarchies.
future networks including 5G” CTx.v2 searches the context
and this discussion continues in CTx.v2 scanned the environment. for solutions.
the ITU’s standardization expert
group for “future networks and ML Pipelines, Sandboxes and ML Perhaps time for another
cloud”, ITU–T Study Group 13. marketplaces are in place and ITU AI/ML in 5G Challenge?
MLFO reports are green, but CTx.v2 logs into the Geneva
Autonomous networks would issues remain. Divergent data for‑ Sandbox of ITU and triggers
display the “self” properties: mats impacting latency between “[AI‑ML‑Challenge::v2::init]”, but
the ability to monitor, operate, the ML pipelines in the network. that’s another day, another story
recover, heal, protect, optimize A multitude of open-source tool‑ (for CTx.v3).
and reconfigure themselves. kits to integrate. More challenges
On top of the ability to adapt in the demand mapping towardsITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 23
Shutterstock
A standards round-up on autonomous networks
By Xiaojia Song, Researcher, Xi Cao, Senior Researcher, Lingli Deng, Technical Manager,
Li Yu, Chief Researcher, and Junlan Feng, Chief Scientist, China Mobile Research Institute
J Mobile networks are evolving into the intelligence era with multiple
application scenarios, features, services and operation requirements.
Technologies including artificial intelligence (AI) are expected to enable
autonomous networks in areas such as network planning, deployment, Mobile networks are
operation, optimization, service deployment, and quality assurance. evolving into the
intelligence era with
Most of the standards development organizations (SDOs), e.g. the ITU multiple application
Telecommunication Standardization Sector (ITU–T), 3GPP, ETSI, and scenarios, features,
CCSA, are actively developing standards for autonomous networks. services and operation
requirements.
Industry bodies such as GSMA, TM Forum, and the Global TD-LTE
Initiative (GTI) are working to promote autonomous networks. GSMA
stated that the automatic network operation capability will become the
indispensable 4th dimension of the 5G era together with enhanced
mobile broadband (eMBB), massive machine type communications
(mMTC) and ultra-reliable low-latency communications (URLLC), and
become one of the most important driving forces for 5G service innova‑
tion and development.ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 24
There are discussions among Since industrial convergence For example, a rule-based policy
SDOs about the level of autono‑ is the key for reducing the cost engine could be one of the
mous capabilities in networks (see for any single vendor or single common functional modules to
framework approach in Table 1). network operator, building an support both timed control tasks
open collaboration platform (see in Level 1, imperative closed
The study of autonomous net‑ Figure 1) for cohesively develop‑ loops in Level 2, and adding
work levels (ANL) can provide ing both a reference implementa‑ intent-to-rule translation modules
reference and guidance to tion for case-agnostic functional in Levels 3 and 4.
operators, vendors and other architecture and standardized
participants of the telecommuni‑ external or internal interfaces The main activities on autono‑
cation industry for autonomous would be the easy way for com‑ mous networks in the SDOs and
networks, standardization works munication service providers industry bodies are presented
and roadmap planning. (CSPs) to kick off and stay in the in Figure 2 and briefly intro‑
converged direction towards duced below.
network autonomy.
Table 1 — Framework approach for classification of autonomous network
intelligence level (source: ITU–T Y.3173)
Dimensions
Network intelligence level Action Data Demand
implemen- Analysis Decision
tation collection mapping
L0 Manual network operation Human Human Human Human Human
Human and Human and
L1 Assisted network operation Human Human Human
System System
Human and Human and
L2 Preliminary intelligence System Human Human
System System
Human and Human and
L3 Intermediate intelligence System System Human
System System
Human and
L4 Advanced intelligence System System System System
System
L5 Full intelligence System System System System System
NOTE 1 — For each network intelligence level, the decision process has to support intervention by human being, i.e., decisions
and execution instructions provided by a human being have the highest authority.
NOTE 2 — This table may be used to only determine the network intelligence level for each dimension (and not the overall
network intelligence level).ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 25
Figure 1 — Open industrial collaboration towards autonomous networks
Open implementation SLA intent Open standards
OPS intent RT control
Intent
RT data
Analysis Knowledge
Analysis Knowledge RT assistance
Training Data lake Rule
Offline control
RT collection RT control Training Knowledge
Offline data
Case-agnostic Data-lake Rule Offline assistance
Functional components
RT collection RT control
Case specific
RT = Real-time data/control/assistance
SLA = Service Level Agreement Managed objects
OPS = Operations
Figure 2 — Main activities of SDOs and industry bodies
ITU–T Y.3172
ITU–T Y.3173
ITU–T ITU–T Y.3174
ITU–T Y.3176
ITU–T Y.ML‑IMT2020‑RAFR
IDM, SON, MDT, COSLA
3GPP ANL
MDA, NWDA
Telecommunication network planning application based on AI
CCSA Grading method for intelligent capability
Technical requirements for ANL
AN Whitepaper 1.0
AN Whitepaper 2.0
TM Forum IG 1193 Vision & Roadmap v1.0
Autonomous Network catalyst
Business requirements & architecture v1.1
Global AI Challenge
GSMA
AI in Network use cases in China
2018 2019 2020 2021
SDO
General Management Architecture Data Use cases
Industry partiesITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 26
Table 2 — ITU–T standardization activities on AI/ML and
Activities in ITU AI-based autonomous networks
In ITU–T, Study Group 13 focuses on Reference number Title
future networks and network aspects Supplement 55 to Machine learning in future networks including IMT‑2020:
Y.3170 Series use cases
of mobile telecommunications. The
Focus Group on Machine Learning ITU–T Y.3172
Architectural framework for machine learning in future
networks including IMT‑2020
for Future Networks including 5G
(FG-ML5G), active from January 2018 Framework for evaluating intelligence level of future networks
ITU–T Y.3173
including IMT‑2020
until July 2020, had been set up to
Framework for data handling to enable machine learning in
study interfaces, network architec‑ ITU–T Y.3174
future networks including IMT‑2020
tures, protocols, algorithms and data
Machine learning marketplace integration in future networks
formats. Of FG ML5G’s ten technical ITU–T Y.3176
including IMT‑2020
specifications, four have already been
Requirements, architecture and design for machine learning
FG-ML5G spec
turned into ITU Recommendations function orchestrator
(standards), one into a Supplement, Machine Learning Sandbox for future networks including
FG-ML5G spec
and the other five are in the process IMT‑2020 requirements and architecture framework
of being turned into ITU standards. Machine learning based end-to-end network slice
FG-ML5G spec
Recommendations on the AI-based management and orchestration
autonomous network, e.g. ITU–T FG-ML5G spec
Vertical-assisted Network Slicing Based on a Cognitive
Framework
Y.ML‑IMT2020‑RAFR, are currently in
draft stage (see Table 2). Architecture framework for AI-based network automation of
Draft ITU–T
resource adaptation and failure recovery for future networks
Y.ML‑IMT2020‑RAFR
including IMT‑2020
Activities in 3GPP
Autonomous networks came in sight
of 3GPP in the 4G era. The topics Table 3 — Standardization activities in 3GPP RAN
mainly focused on Self-Organizing
Networks (SON) and Minimization of TS/TR Title
Drive Tests (MDT). In the 5G era, 3GPP Study on RAN-centric data collection and utilization for LTE and
3GPP TR 37.816
NR
undertakes standardization efforts to
enable autonomous networks: 3GPP TS 38.314 New Radio (NR); Layer 2 measurements
3GPP TS 38.300 NR; Overall description; Stage 2
3GPP RAN: RAN data collection
3GPP TS 37.320 Minimization of Drive Tests (MDT); Overall description; Stage 2
(TR 37.816), SON/MDT (TS 38.314,
3GPP TS 38.306 NR; User Equipment (UE) radio access capabilities
TS 38.300, TS 37.320, TS 38.306,
TS 38.331, etc.) (see Table 3). 3GPP TS 38.331 NR; Radio Resource Control (RRC); Protocol specification
TS=Technical Specification, TR=Technical Report.ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 27
3GPP SA2: Network Data Analytics Table 4 — Standardization activities in 3GPP SA2
(NWDA) (TR 23.791, TR 23.288,
TR 23.700‑91) (see Table 4). TS/TR Title
3GPP TR 23.791 Study of Enablers for Network Automation for 5G
3GPP SA5: Management Data Architecture enhancements for 5G System to support network
3GPP TS 23.288
Analytics (MDA) (TR 28.809), data analytics services
autonomous networks levels 3GPP TR 23.700‑91 Study on Enablers for Network Automation for 5G — Phase 2
(TR 28.810, TS 28.100, intent
TS=Technical Specification, TR=Technical Report.
driven management (TR 28.812,
TS 28.312), closed loop SLS
assurance (TR 28.805, TR 28.535,
TR 28.536, etc.), SON (TR 28.861, Table 5 — Standardization activities in 3GPP SA5
TS 28.313) and MDT (TS 28.313,
TS 32.42X series) (see Table 5). TS/TR Title
3GPP TR 28.809 Study on enhancement of Management Data Analytics (MDA)
Study on concept, requirements and solutions for levels of
3GPP TR 28.810
Activities in ETSI autonomous network
3GPP TS 28.100 Management and orchestration; Levels of autonomous network
ETSI is actively studying autonomous
Telecommunication management; Study on scenarios for Intent
networks and has several groups 3GPP TR 28.812
driven management services for mobile networks
working on the following rele‑ 3GPP TS 28.312 Intent driven management services for mobile networks
vant topics:
Telecommunication management; Study on management
3GPP TR 28.805
aspects of communication services
ENI (Experiential
Management and orchestration; Management services for
3GPP TS 28.535
Networked Intelligence). communication service assurance; Requirements
Management and orchestration; Management services for
3GPP TS 28.536
NFV (Network communication service assurance; Stage 2 and Stage 3
Functions Virtualization). 3GPP TR 28.861 Study on the Self-Organizing Networks (SON) for 5G networks
3GPP TS 28.313 Self-Organizing Networks (SON) for 5G networks
OSM (Open Source MANO
3GPP TS 32.42X series
(Management and Orchestration).
Telecommunication management; Subscriber and equipment
3GPP TS 32.421
trace; Trace concepts and requirements
MEC (Multi-access
Telecommunication management; Subscriber and equipment
Edge Computing). 3GPP TS 32.422
trace; Trace control and configuration management
Telecommunication management; Subscriber and equipment
F5G (Fifth Generation 3GPP TS 32.423
trace; Trace data definition and management
Fixed Network).
Telecommunication management; Performance Management
3GPP TS 32.425 (PM); Performance measurements Evolved Universal Terrestrial
Radio Access Network (E-UTRAN)
Telecommunication management; Performance Management
3GPP TS 32.426 (PM); Performance measurements Evolved Packet Core (EPC)
network
TS=Technical Specification, TR=Technical Report.ITU AI/ML in 5G Challenge ITU News MAGAZINE No. 05, 2020 28
In November 2019, ETSI pub‑ Activities in industry
SDOs should continue lished the report “Experiential
their relevant Networked Intelligence (ENI): ENI Industry bodies such as GSMA,
standardization work Definition of Categories for AI TM Forum and GTI are exploring
and play a leading Application to Networks” (ETSI GR and promoting the collaboration
role in enabling ENI 007), which defines various of autonomous network topics
autonomous categories for the level of applica‑ among SDOs, operators, vendors
networks. tion of AI techniques to the man‑ and other industry participants.
agement of the network, going
from basic limited aspects to the In GSMA, AI and Automation is
full use of AI techniques for per‑ one of the topics of the “Future
forming network management. Network”. In June 2019, the first
GSMA Global AI Challenge was
ENI is developing a general-pur‑ held, investigating three specific
pose architecture for enhanced areas: connectivity in rural areas,
network intelligence, and a map‑ mobile energy efficiency and
TC INT AFI (Technical ping on NFV policy management enhanced services in urban areas.
Committee Core (TC) is under discussion as NFV started
Network and Interoperability policy modelling work for auto‑ At its workshop in June, at the AI
Testing (INT) working group mating NFV management and in Network Seminar of the Mobile
Autonomic Management and VNF CI/CD. World Congress Shanghai 2019,
Control Intelligence for Self- GSMA called on the entire indus‑
Managed Fixed and Mobile try to focus on and contribute to
Integrated Networks). Activities in CCSA the key applications of AI in the
mobile networks, and jointly build
ZSM (Zero-touch network & As one of the most influen‑ the 5G era for intelligent autono‑
Service Management). tial SDOs in the field of com‑ mous networks. In October 2019,
munication in China, China GSMA published AI in Network
TC INT AFI is studying Generic Communications Standards Use Cases in China”.
Autonomic Networking Association (CCSA) began stand‑
Architecture (GANA), and ZSM is ards work on autonomous net‑ TM Forum has held several work‑
discussing closed-loop automa‑ works from 2010, and the items shops on autonomous networks
tion in the ZSM framework, opti‑ are mainly set up in Technical since 2019, and the Autonomous
mized for data-driven machine Committee TC 1, TC5 and TC7, Networks Project (ANP) was
learning and AI algorithms. including use cases, architecture, established in August 2019.
data handling, levels of auton‑
omous network, management
requirements, etc.You can also read
