Beyond 5G/6G
White Paper
ver. 2.0.1
KDDI Corporation
KDDI Research, Inc.
Oct. 2021

How can technology help us realize a more exciting future?
To find an answer to this question, KDDI has been working toward the goal of "creating a
reliable information-oriented society.”
The safe and secure information-oriented society will be realized by seven technologies,
Security as a core technology, as well as Network, IoT, Platform, AI, XR, and Robotics.
People will feel comfortable and secure. Such a reassuring feeling increases the possible
things in the world.
The massive data conveyed over ultra-high speed networks between physical and cyber
spaces, will connect people, economic mechanisms, and social systems together. This willlead to the realization of optimal lifestyles and a new world full of vitality.
KDDI will flexibly build a communication infrastructure to meet the needs of individual users.
KDDI will also understand their personality and situation, and propose the optimal actions
accordingly.
Such schemes will lead to the self-realization of individuals and the resolution of social issues.
Rather than people needing to be compatible with technology, we will develop technologies
compatible with them.
Toward the goal of creating the next exciting moments, we will continue to refine our reliable
technologies.

Creating an exuberant future with reliable technologies.

1

Table of Contents
Table of Contents .......................................................................................................... 2
1.

Introduction ............................................................................................................ 52.

Lifestyles in 2030 and B5G/6G Use Cases ............................................................ 7
2.1.

"FUTURE GATEWAY" Co-Creation Scheme with Progressive Consumers ..... 7

2.2.

Realizing the Fulfillment of Material Needs ...................................................... 9

2.2.1.

Changes in Food ...................................................................................... 9

2.2.2.

Changes in Shopping ............................................................................. 10

2.2.3.

Changes in Healthcare ........................................................................... 11

2.2.4.

Changes in Lifestyle ............................................................................... 12

2.3.

3.

4.

5.

Realizing the Fulfillment of Psychological Needs ........................................... 14

2.3.1.

Changes in Workstyle ............................................................................. 14

2.3.2.

Changes in Learning............................................................................... 15

2.3.3.

Changes in Hobbies and Entertainment.................................................. 16

2.3.4.

Changes in Rest ..................................................................................... 18

2.3.5.

Changes in Communication .................................................................... 19

A Concept for B5G/6G ......................................................................................... 21
3.1.

A Strategy for the Advancement of Society 5.0 and Beyond 5G .................... 21

3.2.

A Concept that Envisions the Future in 2030: KDDI Accelerate 5.0 ............... 23

3.2.1.

Upgrading the Environment in Three Layers ........................................... 24

3.2.2.

Seven Technologies and Their Orchestration.......................................... 24

The Seven Technologies ...................................................................................... 26
4.1.

An Overview of the Seven Technologies........................................................ 26

4.2.

Technologies Support B5G/6G Necessary to Realize the Lifestyles of 2030.. 27"1. Network" ......................................................................................................... 34
5.1.

The Role of Network Technology in B5G/6G ................................................. 34

5.2.

Network Technology Goals ............................................................................ 34

5.3.

Global Situation ............................................................................................. 38

5.4.

Technologies Required for 2030 .................................................................... 41

5.4.1.

User centric Architecture......................................................................... 41

5.4.2.

An Ultra-high-capacity Optical Fiber Network ......................................... 51

5.4.3.

Mobile Core Network .............................................................................. 56

5.4.4.

Operation................................................................................................ 61

5.4.5.

The White-boxing of Systems and Technology to Separate Hardware from

Software .............................................................................................................. 64
5.4.6.

Communications in space ....................................................................... 662

6.

"2. Security" ......................................................................................................... 69
6.1.

The Role of Security Technology in B5G/6G .................................................. 69

6.2.

Security Technology Goals ............................................................................ 70

6.2.1.

Security Technologies Embedded in Next-generation Communications

Networks.............................................................................................................. 70

7.

6.2.2.

Protection of Next-generation Communication Networks ........................ 70

6.2.3.

Encryption Technology Supports Next-generation Communications ....... 70

6.3.

Global Situation ............................................................................................. 71

6.4.

Security Technologies Required for 2030 ...................................................... 71

6.4.1.

Security Functions for Communications Networks .................................. 71

6.4.2.

Robust AI-based Security Measures ....................................................... 72

6.4.3.

A New Generation of Encryption ............................................................. 73"3. IoT" ................................................................................................................. 75
7.1.

The role of IoT technologies in B5G/6G ......................................................... 75

7.2.

IoT Technology Goals .................................................................................... 75

7.3.

Global Situation ............................................................................................. 77

7.4.

Technologies Required for 2030 .................................................................... 79

7.4.1.

Technologies Related to Sensory Data from Physical Space .................. 80

7.4.2.

Technology

RelatedtoCommunications

forTransferring

SensedInformation to Cyberspace ................................................................................... 80
7.4.3.

Technology Related to Data Processing for Processing Information in

Cyberspace ......................................................................................................... 81
7.4.4.

Technologies for the Design and Operation of IoT Devices with

Consideration for Maintenance ............................................................................ 81
8.

9.

"4. Platform" ......................................................................................................... 82
8.1.

The Role of Platform Technologies in B5G/6G............................................... 82

8.2.

Platform Technology Goals ............................................................................ 82

8.3.

Global Situation ............................................................................................. 83

8.4.

Technologies Required for 2030 .................................................................... 84

8.4.1.

Key Technologies for Operating a City OS .............................................. 84

8.4.2.

Opening up MaaS (Mobility as a Service) Using Automated Driving ....... 85

8.4.3.

Changes in Behaviors Resulting from MaaS........................................... 87

8.4.4.

Automated Driving .................................................................................. 88

8.4.5.

Quantum Computing............................................................................... 89"5. AI" ................................................................................................................... 92
9.1.

The Role of AI Technology in B5G/6G ........................................................... 92
3

9.2.

AI Technology Goals...................................................................................... 92

9.3.

Global Situation ............................................................................................. 93

9.4.

Technologies Required for 2030 .................................................................... 94

9.4.1.

Real-world AI .......................................................................................... 95

9.4.2.

AI for Behavior Change ........................................................................ 100

9.4.3.

Trustworthy AI ....................................................................................... 103

9.4.4.

Using Combined Multiple AI Technologies ............................................ 104

10. "6. XR" ............................................................................................................... 107
10.1.

The Role of XR in B5G/6G ....................................................................... 107

10.2.

XR Technology Goals............................................................................... 107

10.3.

Global Situation........................................................................................ 108

10.4.

Technologies Required for 2030............................................................... 110

10.4.1.

Physical Space Reconstruction and Augmentation .............................111

10.4.2.

Multimodal Interaction ....................................................................... 112

10.4.3.

High-efficiency, Ultra-low-latency 3D Space Data Transmission ........ 113

11. "7. Robotics" ...................................................................................................... 116
11.1.

The Role of Robotics in B5G/6G .............................................................. 116

11.2.

Robotics Technology Goals ...................................................................... 116

11.3.

Global Situation........................................................................................ 117

11.4.

Technologies Required for 2030............................................................... 121

11.4.1.

RaaS (Robotics as a Service) ........................................................... 121

11.4.2.

Community Support........................................................................... 122

11.4.3.

Autonomous Driving and Smart Drones ............................................ 124

12. Future Development........................................................................................... 125
13. Conclusion ......................................................................................................... 129
References ............................................................................................................... 130
Revision history ........................................................................................................ 139

4

1. Introduction
In Japan, the number of people infected with COVID-19 began to increase around thesame time that 5G became commercially available in March 2020, changing people’s
lives, economic activities, and the nature of society in a moment. Even before the spreadof COVID-19, people were aware of the need for digital transformation (“DX”), and the“with-Corona” (coexisting with COVID-19) and “post-Corona” (post COVID-19) eras,
also known as “the new normal,” there is an urgent need to accelerate DX to support
people’s lives and economic activities. On the other hand, to protect our earth from global
warming caused by the growing economic activity, there is a need to reduce greenhousegases and aim to promptly achieve a “decarbonized, carbon-neutral society.” In otherwords, there is a need to achieve a new social infrastructure that strikes a good balance
between enriching people’s lives, stimulating the economy, and protecting theenvironment.
There are various definitions of DX, but in a nutshell, based on the Ministry of Economy,
Trade and Industry’s document [1-1] , and taking into account the issues set out above,it can be defined as “the creation of new businesses and the transformation oforganizations and social infrastructure by utilizing data obtained using information andcommunications technology (ICT).” Data obtained using ICT is acquired from thephysical space in which we exist, with safety ensured through various devices, before
being sent to cyberspace via wired and wireless networks, where it is then processed.
Using the various data gathered in cyberspace, information and data useful in physicalspace can be created in cyberspace, then conveyed to people and the objects related
to them within physical space via networks. The information and data obtained fromphysical space can be used to improve daily life, economic activities, and social activities,
and then, data newly acquired in this process is sent to cyberspace again. Within thiscycle, new businesses are created, and organizations and social infrastructure aretransformed.
This is precisely the “Society 5.0” that Japan has set its sights on, and we believe thatthe prompt realization of Society 5.0 will lead to the advance of DX. Based on this, inAugust 2020, KDDI and KDDI Research Inc. (“KDDI”) announced “KDDI Accelerate 5.0,”
a “next-generation concept for society,” and set out the need to realize a Society 5.0
world promptly [1-2].
4G and 5G are essential to realizing Society 5.0, but as mentioned above, moreadvanced versions of 5G, or “Beyond 5G and 6G” (“B5G/6G”), will be required for thecreation of new businesses, promoting the transformation of society and social
infrastructure. ICT is required for the advancement of DX due to COVID-19, and5

achieving carbon neutrality. It is further essential to collaborate with partner companies,
universities, and research institutes—both in Japan and overseas—in various aspects,
such as the creation of new business models, R&D of new technologies, and theconstruction and operation of systems.
As to mobile communication systems, much attention has been paid to the progressof mobile communications technology, beginning with 1G and now reaching 5G. This isbecause the advance of mobile communications technology has had a significant impact
on people’s lives and economic activities. Going forward, it will be increasingly necessary
to integrate not only mobile communications technology, but also various othertechnologies to support new, environmentally conscious social infrastructure, improve
people’s living standards, and stimulate the economy. Rather, we may be approachingan era in which it is other technologies that enable us to make the most of mobile
communications technology.
Based on these assumptions, KDDI views “B5G/6G,” the next generation of 5G, as asystem encompassing mobile communications technology and other technologies.
Therefore, KDDI Accelerate 5.0 sets forth technologies in seven fields (“seventechnologies”), including network technologies such as mobile communications
technology, to accelerate the realization of Society 5.0.
As set out above, we believe that the world of 2030, around about the time when
B5G/6G will be implemented, will have taken a very different direction from the world of
the past. In this white paper, we present lifestyles and use cases for the year 2030, andintroduce the constituent technologies that constitute the seven technologies required torealize those lifestyles and use cases.

6

2. Lifestyles in 2030 and B5G/6G Use Cases
When Society 5.0 comes into being, all people and things will be connected throughthe development of IoT and AI (the Internet of Things and artificial intelligence), all kindsof knowledge will be shared, and unprecedented new value created. This is expected toovercome various issues in which we are currently embroiled. In September 2015, theGeneral Assembly of the United Nations adopted “Transforming Our World: The 2030
Agenda for Sustainable Development” as a definite course of action to overcome current
challenges. In response to this trend, KDDI has established "FUTURE GATEWAY," a cocreation scheme for a collaboration with progressive consumers who maintainprogressive lifestyles that have yet to become popular. This plan also entails theutilization of the knowledge that KDDI obtains through researching how people live, as
well as how their environments and attitudes change as ICT advances over the years.
2.1. "FUTURE GATEWAY" Co-Creation Scheme with Progressive Consumers
In December 2020, KDDI established the KDDI research atelier, a research base that
proposes "future lifestyles." Then, in August 2021, KDDI began initiating the FUTUREGATEWAY plan, a collaborative scheme with a variety of partners, most of whom include
progressive consumers who lead advanced lifestyles. The goal of FUTURE GATEWAY
is to contribute to the development of medium- and long-term solutions to societal
problems. To this end, people who lead progressive/advanced lifestyles, i.e., who livewith the intention to contribute to solving societal problems, were targeted for inclusion
as references. A more comprehensive description of this plan is as follows.
First, a discussion with progressive consumers is initiated to obtain feedback about
advanced lifestyle practices that are purposed to lead to solutions to societal problems;
the discussion also covers technologies and/or projects with applied research subjects
that may pose an obstacle to the generalization of such advanced lifestyle practices.
Next, together with various partners who have a desire to solve the target problem, theoptimal solutions are socially implemented by utilizing advanced technology. Then, thepopularization activity for the purpose of the extension of the habitant who practices the
new lifestyle is carried out in the form in which the advanced habitant leads.
This plan was also purposed to continue with the expansion of social infrastructure to
significantly contribute to the realization of the lifestyle modification of many consumers
by 2030, as well as to contribute to the development of solutions to societal problems. In
summary, FUTURE GATEWAY constitutes a plan to collaborate with progressive
consumers to minimize the harmful effects of societal problems in the future as well as
to co-create future social infrastructure with partners.
7

The progressive habitants who facilitate FUTURE GATEWAY implementation arereferred to as “transboundary runners,” because they are people who actively andintentionally transcend the constraints and boundaries of the world. Once thisdesignation was established, community development began. Discussion with thecollaborative progressive consumers has led to the establishment of the new lifestyle of2030, which has been projected to encompass security, safety, and comfort for thepeople. In this chapter, we divide these into two categories: those involving material
needs that form the basis of our lives, and those satisfying psychological needs basedon our relationships with others, and then separate scenes of life corresponding to thefulfillment of those needs into several themes.
It is expected that by 2030, with the acceleration of the identification and “data-fication”
of things and actions across all aspects of society, we will live in a multilayered
environment consisting of real physical space and digital cyberspace. Based on thispremise, we extracted “food,” “purchasing,” “health,” and “lifestyle” from among material
needs, and “learning,” “play,” “communication,” “work styles,” and “rest” frompsychological needs, as the things that are expected to bring about significant changes
to our lives through the evolution of ICT, and described each lifestyle and life scene [21].
The development of use cases is also extremely important to the commercialization of
B5G/6G. KDDI has developed use cases for B5G/6G, and has launched a project tocreate a “social good” lifestyle for the 2030s. In this project, we co-created use cases forthe era in which B5G/6G becomes widespread with cooperation from parties outside ofKDDI. By gathering signs of change in the various lifestyles and life scenarios, anddeepening the questions, we drew up use cases from aspects of physical needs, suchas “meals,” “shopping,” “payments,” “body,” “home,” and “living,” together with use cases
from psychological needs, “construction work,” “cyberspace,” “mobility,” “primaryindustry,” “watching sports,” and “walking.” KDDI has formulated “KDDI Sustainable
Action: Our power to make connections will help create a brighter future for all” to express
its determination to continue working to solve various social issues through its business.
The main purpose of each lifestyle and use case presented here is to achieve each of
the 17 Sustainable Development Goals (SDGs) established by the United Nations(Figure 2-2), which underlies the “Sustainable Action” described above [2-2].

8

Figure 2-1: The 17 SDGs (source: [2-2] )Going forward, we plan to study lifestyles other than those shown above, whileobserving changes in conditions, both in Japan and overseas, and consumer trends.
2.2. Realizing the Fulfillment of Material Needs
The following are situations in which we aim to satisfy the material needs that form thebasis of our lives: “changes in food,” “changes in shopping,” “changes in healthcare,”and “changes in lifestyle.” The economic growth that enabled us to develop comfortable,rich, civilized lives in 20th-century style has led directly to burdening the global
environment, and has destroyed both environments and ecosystems. However, the newsocial system for 2030 for which we are aiming should bring about a sustainable worldin which the global environment and economic growth can coexist without causing
environmental destruction or resource depletion. The various technologies describedhere that facilitate “food,” “shopping,” “healthcare,” and “lifestyle,” which satisfy people’smaterial needs, are developed not simply by achieving physical satisfaction, but throughthe coexistence of both the environment and the economy.
2.2.1. Changes in FoodAt present, families often eat meals from the same menu, but with the development of
AI and IoT, it is expected that by 2030 each individual in a family will be able to eat
9according to their preferences and health conditions. For example, it will be possible toprovide meals that match the physical condition of the person or family member on thatday, what they had for other meals, their degree of hunger at the time of their meal, etc.,
so food waste is expected to be reduced.
In addition to taking into account the family’s health status, an AI system will be able
to consider the refrigerator’s inventory, which it monitors constantly, as well as past meals,
to automatically suggest the most suitable meals at the time. In addition, it is expectedthat even if all family members share the same menu, they will be able to enjoy optimal
meals because the recipes will take into account any reductions in salt or sugar to suiteach family member’s own health situation.
The concept of meals will change dramatically in the B5G/6G era. By sharing cooking
data over a network, for example, family members who do not live nearby, such as
grandparents, can enjoy the same meal.
In addition, virtual restaurants using food printers will open, and physically separated
people will be able to enjoy popular three-star restaurants together, without making areservation, in cyberspace. When looking at a menu, you will be able to smell the food,and an AI system will be able to suggest meals you have never experienced before
based on your dietary history. It will also be possible to use food printers to recreate
foods that have become extinct, or are not available locally. People who cannot enjoycertain foods because of food allergies will be able to enjoy those meals in cyberspaceby reproducing the taste of those foods without reproducing the allergen using a foodprinter.
To achieve these new kinds of foods, it will be necessary to develop high-speed, lowlatency networks able to send and receive meal image data without delay, image
compression technologies to enable smooth data communications, and AI technologies
to facilitate optimal personal recommendations.
2.2.2. Changes in Shopping
At present, people check their supplies before purchasing products, but in 2030, withthe development of AI, IoT and robotics, it can be expected that domestic inventory
management for convenience products and daily necessities will be automated, andproducts will be ordered automatically based on users’ rate of consumption and inventory.
For example, sensors installed in refrigerators and pantries in the home willautomatically reorder products, foods, and daily necessities according to stock levels,
the rates of past consumption, and prices at stores.
Furthermore, automatically ordered products will be delivered to homes using delivery
10

robots, able to deliver products 24 hours a day; you will be able to choose how and when
you receive the products, according to your convenience.
The concept of stores will change dramatically in the B5G/6G era. For example, theclothing stores of the future will offer virtual store experiences, such as the on-the-spot,
customized trying-on of clothing. For example, users will be able to try on clothes virtuallywhich checking the feel and texture, and will be able to call friends for consultation using
holographic communication. An AI system will quantify the functionality of clothing for
purposes such as sleep or exercise, and will suggest clothes that suit the individual;
users will be able to try on samples, produced on the spot by a 3D printer, test thefunctionality of that clothing, and make changes depending on the price. An AI system
will judge the lifespan of pieces of clothing, and will refurbish clothing in your home using
other clothes in your possession. The clothes you buy will be delivered to your home bya delivery robot. You will be able to receive products that suit you anytime, anywhere.
The concept of payment will also change dramatically in the B5G/6G era. You will beable to directly transmit biometric data to automatically purchase products offered as
personal recommendations by an AI system. For example, when using car-sharing
services, your driver’s license information will be authenticated using biometric data, andthe seat height will be automatically adjusted to suit the individual. An AI system will porethrough vast amounts of past purchase data, together with your mood and emotions at
the time, to recommend needed products at the optimal time. By the time you wish topurchase something, payment will already have been made, freeing you from the hassleof making a payment or forgetting to buy something.
Realizing these new modes of purchasing will require a system to monitor inventory inreal time, AI technologies to calculate the appropriate timing for orders, and a high-speed,
low-latency network to facilitate the smooth movement of robots. The FUTUREGATEWAY and Life Delivery projects were designed with the aim of minimizing theburden on the consumer. To this end, these projects were designed to evaluate and apply
low-involvement solutions to processes such as inventory management, and orderplacement, delivery, and receipt of the products. Once the demonstration for the KDDI
employees is completed, the solution will be applied in the field with the relevant partner(see Section 11.3 for details).
2.2.3. Changes in Healthcare
With the evolution of wearable devices, it is now possible to monitor metrics such asheart rate in daily life. With the development of IoT and AI, in 2030, it will be possible tounderstand various data in real time, and as a result, it will be possible to provide
11optimized healthcare to each individual, enabling them to maintain their health easily andwith peace of mind, resulting in enduring independent living.
For example, previously cumbersome measurement of vital data and dietary nutrition
will be automated using wearable devices and sensors installed in the home, providing
real-time information about the health status of individuals 24 hours a day. As a result, it
is expected that it will become possible to propose exercise and dietary habits tailored
to individuals’ health and physical condition based on that data.
With the development of advanced networks and cross-reality (XR) technology, it will
become possible to consult physicians from home, and to take necessary steps before
a disease becomes serious, enabling people to continue living independently indefinitely.
The concept of physical disability will change dramatically in the B5G/6G era. It will
become possible to expand possibilities using powered suits to strengthen the body. For
example, these suits will be able to copy physical functions from the vital data of others,
making it possible to enjoy sports and activities. It will become possible for the physicallydisabled to freely cook, drive, practice calligraphy, paint, photograph, dance, etc. By
reducing limitations of physical functions, and adding vitality to life, people will be able toenjoy the “era of the 100-year life.”
Realizing this new healthy lifestyle will require a monitoring system that can obtain vital
data in real time, AI technologies that can give advice based on accumulated data, anda high-speed, low-latency network to facilitate smooth online medical treatment. Hoppin
Sauna, which is a mobile sauna FUTURE GATEWAY subproject, entails the use of
automation and vital sensors. The latest 5G technology will be applied to enabledispersion via water circulation to yield an automated sauna that can optimize the stateof the mind and body of the user.
2.2.4. Changes in Lifestyle
In recent years there has been a migration to rural areas and a growing interest in“workations” (working vacations), and it is expected that by 2030 it will be possible to live
in a place that best suits one’s personality, values, health, and situation at the time
without being limited by location. When visiting a city for the first time, it is expected thatAI systems will support serendipitous enjoyment and comfortable, trouble-free living,enriching everything from work to private life.
It will be possible to work and study comfortably without feeling dissatisfaction or asense of lack, regardless of location, and there will be no distinction in location between
home, work, and school. When traveling by car, automated driving will make vehicle
interiors to be like private rooms where we can develop our ideas and rest. They will also
12

provide information during the journey that will allow the user to move about with easeonce they arrive at the destination.
In realizing such a lifestyle, trends toward the enjoyment of nature and an emphasis
on aspects of our private lives, such as self-fulfillment and hobbies, will strengthen, and
it is expected that spending time in unusual ways will become the norm.
The concept of place of residence will also change dramatically in the B5G/6G era.
For example, it will be possible to transmit touch, scent, texture, and taste, enabling us
to connect with faraway family members anytime in cyberspace. A father posted
overseas for work can enjoy lunch with his distant family in cyberspace by wearing a suitthat can send and receive inputs corresponding to all five senses. Without moving somuch as a finger in physical space, we can move ourselves within cyberspace to connect
with faraway family members using all five senses, as though we were spending time
together in the same room, strengthening family ties despite being far apart.
The concept of living will also change dramatically in the B5G/6G era. You will be able
to work in your favorite place, surrounded by your favorite scents, without a fixed placeof residence. For example, by directly transmitting to the brain the sensory data
corresponding to the five senses, we can create a world without any cognitive
discrepancy. A physical space can be transformed into your ideal space using realistic
information; you can hold meetings with remote colleagues via hologram, whilebrainwave communication, in which what you imagine is transmitted directly to yourcolleagues, will produce the best performance (See Figure 2-2).

Figure 2-2: Remote conference via hologram transmission
13Realizing these new lifestyles will require the recommendation of AI technologies thatmake it possible for people to travel around a city they are visiting for the first time without
getting lost, together with behavioral adjustment AI technologies to enable people torespond optimally to situations in that city.
2.3. Realizing the Fulfillment of Psychological Needs
The following are situations in which psychological needs—those based on our
relationships with other people—are satisfied: “changes in workstyle,” “changes inlearning,” “changes in hobbies and entertainment,” “changes in rest,” and “changes incommunication.” Despite creating abundant wealth, the development of the global
economy in the latter half of the twentieth century also led to the widening of economic
disparity and social fragmentation due to the entrenchment of social hierarchies. The
world of 2030 that we should aim for must be an inclusive society that recognizes
individuals’ diversity and allows everyone to live autonomously, freely, and with dignity.
The various technologies that facilitate “working,” “learning,” “hobbies and entertainment,”“rest,” and “communication,” which will bring about the fulfillment of the psychologicalneeds discussed here, will be developed by honoring the diversity among individuals andachieving freedom.
2.3.1. Changes in WorkstyleJob-based employment has been becoming prevalent in Japan in recent years, andby 2030, it is expected that individuals will create their own job opportunities that match
their dreams and values by utilizing their skills, abilities, and experiences. To this end,
AI-based matching will be used to provide venues to meet with business partners that
promote optimal projects and co-creation ventures. Moreover, with the spread ofmachine translation, language barriers will disappear, and global working styles will
become more prevalent among individuals.
On the other hand, labor-intensive, low-productivity jobs will be replaced by AI systems
and robots, and it is expected that many people will become able to engage in rewarding
work, leading to the realization of their self-worth. At the same time, administrative tasks
such as data collection, document creation, scheduling, and information support will be
automated using AI systems, resulting in free time that can be spent on creative activities
such as developing ideas. The AI systems will also suggest ways to supplement
technology and professional skills to support these creative activities. It will also provide
opportunities to connect with professionals and peers who can provide appropriate
14advice.
The concept of construction work will change dramatically in the B5G/6G era. Heavy
manual tasks such as clearing snow will be automated. It will be possible to carry out
construction work requiring advanced technology, such as lunar surface terraforming andconstruction, remotely from a safe place from the remote by extending the range offeasible mobile communications to the space. Sensory data from a construction robot
will be analyzed using its digital twin, and administrative applications for changing
construction plans will be filed automatically. Users can learn the skills of expert robot
teleoperation through direct physical experience using a specialized suit. Skills learned
through this physical experience will be learned synchronously by the robot in real time.
The training data from one robot can be automatically linked to hundreds of other robots,
eliminating the need for hard manual labor and labor shortages in regions where thephysical environment is hard (See Figure 2-3).

Figure 2-3: Simulation of construction on the moon
These new modes of working require AI technologies that are able to match individuals
to the most suitable jobs, high-speed; low-latency networks that facilitate the smoothtransmission and reception of data anywhere; and technologies that replicate physicalspace to produce realistic online meetings (see Chapter 10).
2.3.2. Changes in Learning
The spread of COVID-19 has led to the rapid uptake of online classes. In 2030, the15development of XR technology will create a new era for education by optimizing
individuals’ learning so they can improve not only their academic performance in school,
but also efficiently acquire the skills required to achieve their goals and dreams.
Moreover, the expansion of online communities will allow students to connect totrustworthy teachers who can provide pertinent advice and connect them to peers formutual development, resulting in a deeper learning experience. In this context, in addition
to learning from others, by sharing one’s own skills and knowledge, anyone can teachand anyone can be taught, and the choices for learning will expand.
The learning content will include the skills required to realize students’ dreams andgoals, such as ICT knowledge and languages, as well as the learning methods that are
most suitable for each student. In addition, the most appropriate program will be applied
according to each individual student’s progress.
The concept of time in our daily lives will change dramatically in the B5G/6G era. We
may be living in an age where microchips are implanted in our brains before we are evenborn, through which our brain’s data is constantly uploaded to cyberspace. Day and night
do not matter in cyberspace, and you can enjoy a second, different life, and gain twice
as much life experience or more. For example, you can pursue your dream of being a
musician while working as a sushi chef, and by synchronizing your experiences incyberspace with those in physical space, you will be able to live as though there were 48
hours in a day.
In addition to a high-speed, low-latency network facilitating the exchange of large
amounts of data, and better image compression technology, a monitoring system thatmakes it possible to gauge levels of understanding and satisfaction among students inreal time will be needed for online learning to become even more valuable.
2.3.3. Changes in Hobbies and Entertainment
As the work-life balance concept spreads, more people in Japan are devoting time totheir hobbies and leisure activities. By 2030, it is expected that the spread of AI androbots will make it possible to work efficiently in less time, and the trend in pursuing one’s
own hobbies and leisure activities will strengthen.
The barriers of language and physical distance will disappear with the advancementof ICT, and people will be able to easily find instructors to learn the skills necessary to
improve their hobbies, and will be able to receive optimal instruction through two-way
communication. In addition, an AI system can also provide total professional support foreverything from training plans to health management and body care, allowing players to
improve their skills to the level they desire. It is expected that events and interactions
16

indistinguishable from those in the real world will be available in cyberspace, and thatpeople will be able to have fulfilling experiences in the comfort of their own home without
restrictions from weather or location.
Through AI matching, users can connect with friends whose skill levels and interests
match their own, and can have fun competing with one another and improving eachother’s skills. In addition, an AI system will provide new ways to enjoy daily hobbies andgames, and users will be able to discover new attractions and enjoy more fulfilling time.
As a result, it is expected that many people will be able to achieve self-fulfillment throughdevoting themselves to their hobbies, and that many will acquire professional-level skills.
The concept of mobility will change dramatically in the B5G/6G era (See Figure 2-4).
Purpose-driven journeys will be transformed into journeys that bring new learning along
the way. An AI system will suggest experiences, using VR 1 that fit into one’s travel plans.
For example, if you are travelling to Kyoto, the AI system will be able to recreate theconstruction of Kinkakuji Temple in a realistic manner. It will be possible to recreate aerial
spaces with virtual flying cars, enabling passengers to fly with the birds, and users will
be able to enjoy delicious meals prepared by food printers. Comfortable spaces will becreated using passengers’ biometric information, so passengers will be able to enjoy
their travel time without boredom.

Figure 2-4: Example of VR-based purpose-driven experience

1

Virtual reality

17

The concept of primary industry will also change dramatically in the B5G/6G era. It will
become possible to choose optimal environments for crops, and manage the growth ofthe crop with all possible data. For example, mobile farms may automatically move toareas where crops can grow well based on weather forecast data of sunshine,
temperature, rain, and wind. The management AI will not only collect various data, butalso automatically control the operation of the agricultural machinery, manage soil
bacteria, filter rainwater, and manage crop watering, and will notify farmers of the next
crop that should be grown based on expected future market prices and weather forecasts.
In this way, it will become possible for anyone to become a primary industry player as
though enjoying game.
Making hobbies and games more fully realized, professional, and sophisticated will
require AI-powered matching technology; XR technology and body sensor technology to
evoke sensory experiences in cyberspace across all five senses; and high-speed, lowlatency networks to facilitate the transmission and reception of large amounts of data.
2.3.4. Changes in Rest
Due to the growing interest in work-life balance in recent years, the number of people
wanting to spend their time off in more fulfilling ways is increasing, and by 2030, it isexpected that people will be able to use technology to make the most of their time off.
By letting AI systems and robots replace household chores and menial tasks in the things
we enjoy, we can create ideal vacations where we can immerse ourselves in what wereally want to do.
If you wish to challenge yourself with something new, it will be possible to simulate itin advance by testing your preconceptions in virtual space, or superimposing them overreal space. In this way, it is expected that the range of possibilities available to us will
expand, and we can draw closer to our ideal selves every time we enjoy time off.
The concept of watching sports will change dramatically in the B5G/6G era. Throughcyberspace, we can share space with friends who are physically far away from us,
stimulating our five senses as though we are sitting in a stadium with our friends. In
addition, it will become possible to watch games from any point with 360-degree view,
and it will also be possible to check player information in real time. Moreover, by having
players teach sports directly in cyberspace, watching sports will be transformed from a
passive experience to an active one where spectators can experience the sport forthemselves (See Figure 2-5).

18

Figure 2-5: Example of real-time and engaging experience with active content
In addition to developments in robotics, the realization of these new modes of rest
using robots will require, high-capacity image analysis technology and high-speed, lowlatency networks that facilitate various simulations in cyberspace.
2.3.5. Changes in Communication
People’s interaction styles have changed dramatically with the increase in the numberof users of social networking services resulting from the spread of smartphones. By 2030,
technology is expected to eliminate hurdles that have historically prevented people from
community participation, resulting from differences in ability, including physicalcharacteristics, physical distance, and language, not to mention differences of race andgender. It is expected that an advanced virtual community will emerge, one that can beenjoyed by all. In such a place, we will be able to communicate with anyone however wewish, without being bound by time or place. While assisting in creating new encounters
suited to everyone by identifying individual tastes, values, and physical abilities, it isexpected that it will be possible to exclude malicious individuals from the community, andto express yourself as you are.
By optimizing each person’s interaction in this way, communication can be establishedwith safety and at scale, and it will be possible to create “ideal friendships” and business
relationships.
The concept of walking will change dramatically in the B5G/6G era. A three19

dimensional (3D) space will be reproduced with an ultra-high-resolution drawing from avast amount of sensor data from real cities, making it possible for users to take a stroll
through streets of the past. For example, it will become possible to explore history
together with our bedridden grandfathers through cyberspace. Information about
physical space is reflected in cyberspace, such that a granddaughter walking throughReiwa-era streets, and a grandfather walking through Meiji-era streets (streets in theearly 20th century) can enjoy a walk together while sharing different time periods. New
communications that transcend generations will emerge.
Realizing this new communication will require XR technology to achievecommunications within a virtual community, and AI technologies with matchingcapabilities that can introduce individuals to the most suitable people.

We have presented several lifestyles that are expected to emerge by 2030, but manyother new lifestyles are also likely to emerge in other aspects of life. For example, many
people are expected to pursue lifestyles that make valuable contributions to the global
environment and earth’s ecosystems, promoting ecology, zero waste, and carbon
neutrality. KDDI aims to build a culture and society in which everyone can effortlessly
participate and contribute to the world by introducing advanced ICT into the various
scenes of life involving these lifestyles.
The realization of such a society will require precise and real-time control using ICT,
for which the widespread adoption of B5G/6G is a prerequisite. Moreover, since it willhandle large volumes of personal data, a higher level of security will be required.
Therefore, in the next chapter, we set out a national-level concept for B5G/6G, andKDDI’s next-generation social concept, KDDI Accelerate 5.0.

20

3. A Concept for B5G/6G
In this chapter, we describe the national movement toward B5G/6G, and KDDI
Accelerate 5.0, a concept that envisions the future in 2030 which is expected to be when
B5G/6G is implemented, formulated by KDDI.
3.1. A Strategy for the Advancement of Society 5.0 and Beyond 5G
Society 5.0 was proposed in the Fifth Science and Technology Basic Plan as the modelfor the future society that Japan should aspire to. Following on from hunter-gatherer
society (Society 1.0), agricultural society (Society 2.0), industrial society (Society 3.0),
and the information society (Society 4.0), it is described as “a human-centered societythat balances economic advancement with the resolution of social problems by a systemthat highly integrates cyberspace and physical space” [3-1].
The society envisioned in Society 5.0 will overcome these challenges and difficulties
by connecting all people and things via IoT, sharing knowledge and information, andcreating new, never-before-seen value. Moreover, AI systems will provide necessary
information when it is required, and technologies such as robots and automated vehicles
will overcome issues such as declining birthrate and an aging population, thedepopulation of rural regions, and the disparities between rich and poor. Social reform(innovation) in Society 5.0 will achieve a forward-looking society that breaks down theexisting sense of stagnation, a society whose members have mutual respect for eachother, transcending the generations, and a society in which each and every person canlead an active and enjoyable life.
The 6th Science, Technology and Innovation Basic Plan [3-2] , which covers the fiveyear period from FY2021 to FY2025, inherits the concept of Society 5.0. This basic plan
was created to serve as a concrete strategy for overcoming rapidly changing
circumstances, both in Japan and overseas, such as the spread of COVID-19, andreceived approval from the Cabinet in March 2021. The plan summarizes Society 5.0,
the society that Japan should aspire to, as “a society that is sustainable and resilient in
the face of threats and uncertain circumstances, that ensures the safety and security of
its citizens, and that enables each and every individual to realize well-being on many
levels,” which is in line with the SDGs adopted at the UN Sustainable Development
Summit in 2015. The 6th Science, Technology and Innovation Basic Plan positions
Beyond 5G as requisite next-generation infrastructure and technology for the creation of
new value through the convergence of cyberspace and physical space, toward
transformation into a sustainable and resilient society that can ensure the safety andsecurity of Japanese nationals. The plan stipulates that the Ministry of Internal Affairs
21and Communications plays a central role in promoting its establishment, research, anddevelopment.
On the basis, in January 2020, the Ministry of Internal Affairs and Communications
held the “Beyond 5G Promotion Strategy Roundtable” to discuss expectations fortelecommunications infrastructure in the society of the 2030s, when Beyond 5G is
expected to be introduced, and the direction of policy to realize those expectations. The“Beyond 5G Promotion Strategy—Roadmap towards 6G” was published in June 2020[3-3] based on the “Proposals from the Beyond 5G Promotion Strategy Roundtable” [34], compiled by the roundtable meeting.
The “Beyond 5G Promotion Strategy” lists “an inclusive society where everyone canplay an active role,” “a sustainable society, growing sustainability and efficiency,” and “adependable society where safety and security are ensured,” as the expected image ofsociety in the 2030s, and holds that these will be achieved in the Society 5.0, in which
physical space and cyberspace converge (a cyber–physical system, or CPS).
To realize these, Beyond 5G must provide four new functions: “Autonomy,” “Scalability,”“Ultra Security and Resiliency,” and “Ultra-low power consumption,” in addition to thefurther refinement of 5G’s characteristic functions “Ultra fast and large capacity,” “Ultralow-latency” and “Ultra numerous connectivity” (Figure 3-1).

Figure 3-1: Key features for Beyond 5G (source: [3-4])
22

The Beyond 5G Promotion Strategy also describes an R&D strategy, an intellectual
property and standardization strategy, and a deployment strategy, taking (1) “global first,”(2) the creation of an ecosystem for generating innovation, and (3) intensive resource
investment as its basic policies. The R&D strategy is to realize an environment for theworld’s highest levels of R&D through intensive investment in the latest technologies,
and the bold opening up of the radio spectrum; and to establish constituent technologies
in turn, beginning around 2025. As to the intellectual property and standardization
strategy, the aim is to achieve at least a 10% share of standard-essential patents ofBeyond 5G by promoting strategic openness and de facto standardization, and to makegame-changing developments by collaborating with strategic partners overseas. The
deployment strategy aims to achieve a Beyond 5G-ready environment by deploying 5Gand optical fiber networks across society, and promoting industrial and public use
through demonstrations of 5G solutions.
To promote the strategy strongly and positively, the Beyond 5G Promotion ConsortiumwasestablishedinDecember2020

throughindustry-academia-government

collaboration. Moreover, the Beyond 5G New Management Strategy Center was
established in the same month to strategically acquire and standardize intellectual
property and standardization strategy for Beyond 5G [3-5]. Meetings will be held from2021, and activities are underway toward the Beyond 5G Strategy Promotion–Roadmap
toward 6G.
3.2. A Concept that Envisions the Future in 2030: KDDI Accelerate 5.0
As set out in Chapter Introduction, KDDI believes that the prompt realization of Society
5.0 is necessary to promote DX in Japan, and to accelerate this using 5G, has conceived
the KDDI Accelerate 5.0 concept for next-generation society, published in August 2020[3-6].
In “KDDI Accelerate 5.0”, KDDI pledges to work with a wide range of partners ranging
from large corporations to startups both Japanese and foreign, updating the environment
in three layers that will form the basis of new social infrastructure: networks, platforms,
and businesses. Moreover, we will promote the R&D of seven technologies that support
evolution, and will orchestrate them to achieve an advanced convergence of physicaland cyberspace that makes it possible for any and all consumers to enjoy the benefits oftechnology in safety and without conscious effort. In so doing, we aim to create a resilient
future society that balances the creation of new lifestyles for people through economic
development and solving social issues in Japan.
23

3.2.1. Upgrading the Environment in Three Layers(1) Network layer
We aim to establish and continuously develop advanced, robust domestic ICT
infrastructures through the quick rollout of 5G networks based on the latest worldclass advanced technologies.
Specifically, we plan to establish approximately 50,000 5G base stations by March
2022. We will also accelerate the prompt nationwide 5G area coverage by sharing
infrastructure for early expansion in local regions.
(2) Platform layer
We aim to create new value offerings, economic mechanisms, and social systems
by upgrading an open platform environment by introducing the latest technologies
from the collaboration with Japanese and overseas companies.
Specifically, we will launch a “personal agent,” which proposes optimal activities
and choices for consumers; an “industrial data platform” that realizes optimal
economic mechanisms; and a “city OS” that operates social systems to actualize
safety and security [3-7] together with various partners.
(3) Business layer
We aim to create new business models by promoting DX through open innovation
with a wide range of partnerships.
Specifically, the KDDI Digital Gate, a center for creating new businesses, the KDDI
Corporate Division at Toranomon Office, a center for promoting corporate working
style DX through the use of diverse work styles, and the KDDI research atelier, aresearch center for demonstrating lifestyles around 2030, described in Chapter 2,
will collaborate with one another to promote co-creation.
This new social infrastructure linking physical and cyberspace will bring real-time,
dynamic harmony among consumers, economic mechanisms, and social systems, andwill play a role in accelerating the construction of a vibrant society that makes lifestyles
optimized for each individual consumer.
3.2.2. Seven Technologies and Their Orchestration
KDDI promotes the R&D of advanced seven technologies to support the activities of
the three layers mentioned above. In addition, under the banner of “orchestration,” we24will focus on enhancing the linkage between R&D and business. This promotes thecirculation of data obtained from consumers, the economy, and society between physicalspace and cyberspace, and contributes to the realization of a sustainable society inwhich everyone can live comfortably.
With the expansion of 5G, the integration of physical space and cyberspace will
advance, and data from physical space will be collected in cyberspace for various
analyses and simulations. As a platform for various life-design businesses with
communications as their core, KDDI will strengthen R&D of technologies for secure andconvenient data utilization in cyberspace, as well as technologies for providing feedback
to physical space, such as AI, XR, and robotics, that encourage changes in behaviors,
and accelerate the convergence of the two spaces.
The seven technologies are described in detail in Chapter 4 onwards.

The sustainable, human-centered society that balances economic development withthe resolution of social issues that is envisioned in Society 5.0—the goal of KDDI
Accelerate 5.0—parallels the goals set out in the Ministry of Internal Affairs andCommunications’ Beyond 5G Promotion Strategy–Roadmap toward 6G. KDDI will
promote the realization of Society 5.0 while contributing to the activities of the national
government through the activities of the Beyond 5G Promotion Consortium and the
Beyond 5G New Management Strategy Center.

25

4. The Seven Technologies
Chapter 3 explained that KDDI is accelerating the realization of Society 5.0 sing theseven technologies. This chapter provides an overview of the seven technologies, thenintroduces use cases to demonstrate how the seven technologies will contribute torealizing the 2030 lifestyles described in Chapter 2.
4.1. An Overview of the Seven Technologies
Figure 4-1 shows the CPS, the Society 5.0 initiative, and a diagram describing theseven technologies involved, with arrows connecting the two spaces.
KDDI believes that collecting data (the left arrow in the figure) from physical space forcyberspace is now becoming a reality thanks to the evolution of networks to 5G as well
as evolution and spread of sensor devices. On the other hand, we believe that the
feedback (the arrow on the right-hand side of the figure) sent from cyberspace to physicalspace has yet to be fully realized. If sufficient feedback is provided to physical space,shown by the arrow on the right, and people in physical space perceive the efficacy ofsuch feedback, the volume of data collection, indicated by the arrow on the left, will
increase, and the frequency of data collection will increase, resulting in the creation of apositive spiral. Without this positive spiral, it seems unlikely that we will truly realize
Society 5.0. In Figure 4-1, the feedback (optimization) arrow on the right is drawn thicker
and brighter than the data (collection) arrow on the left to indicate the importance of thearrow on the right.

Figure 4-1: The seven technologies contributing to the realization of Society 5.026

On the arrow on the right, the technologies that directly affect people in the physicalspace are XR and Robotics. AI is responsible for determining what information and data
should be sent to these technologies, while Platform is necessary for safely managing
and manipulating the data to make decisions.
IoT is the technology required to send data from sensors to cyberspace. Security is atechnology required to securely manage various data that exist not only in cyberspace,but also in physical space. To transmit and receive large volumes of data reliably andwith low latency, Network is indispensable, and wireless communication is one of thetechnical fields that create such networks.
4.2. Technologies Support B5G/6G Necessary to Realize the Lifestyles of 2030
Chapter 2 classifies lifestyles into nine categories, and summarizes the lifestylechanges expected by 2030. The realization of these lifestyles requires a variety oftechnologies. In this section, we show specific examples of use cases and introduce thecomponent technologies required for B5G/6G to make them a reality. As an example use
case, we will discuss delivery services, which are easy to envisage given our familiarity
with their use in the “with-corona” era. We will then summarize the component
technologies required to realize “life delivery” (see Sections 2.2.2 and 11.3)—the futureof delivery services to accommodate the 2030 lifestyles set out in Chapter 2.
Let us say that a user wishes to order a variety of foods for a family birthday party. In
this scenario, only a limited number of people, including family members, can attend thereal party venue. First, appropriate menu options are selected according to the healthconditions of each participant at the party, per “Changes in Food” described in Section
2.2.1. It will therefore be necessary to track individuals’ health conditions using wearable
devices and health management sensors, as described in Section 2.2.3, “Changes in
Healthcare.” Moreover, as described in Section 2.2.2, “Change in Shopping,” sensors
like those shown in Figure 4-2 (a) will detect shortages of required beverages andfoodstuffs, and these can be purchased automatically. In making automatic purchases,
a virtual human (see Section 10.4.1) counsels individual family members based on theirhealth conditions, as shown in Figure 4-2 (b), and allows them to select food andbeverage options that consider their health and preferences. Furthermore, when an
unusual event such as a birthday party is held, the necessary food and beverages aredelivered automatically in advance by delivery robot (see Section 11.3) using sensors as
in Figure 4-2 (c). Pre-ordered food and birthday cake, etc., can also be delivered via
delivery robot. For those who are unable to attend the birthday party, food similar to thatat the party can be delivered by robot to their own homes upon request. In addition to27

3D-images based on point cloud data as shown in Figure 4-2 (d) (see Section 10.4.3),
tactile information is transmitted so that remote participants can get a sense of being at
the real venue (see Section 10.4.2).

(a) Automatic purchase of food and(b) Counseling by a virtual humandrink using sensors(c) Automatic delivery by robot(d) 3D animation using point cloud data

Figure 4-2: Example use-case for 2030: Birthday party
The technologies introduced thus far are technologies that can be directly perceived
by users through the five senses, but other technologies are also required.
First, to gather information using sensors, it is necessary to place sensors in various
locations. Sensors that use batteries face issues with recharging or having to be replaced.
In some cases, location data is necessary to send sensor data to cyberspace, but asensor will lack the functionality to receive GPS signals to obtain location data; even
where a sensor has such functionality, it may not be able to obtain a signal from GPS
28

satellites in the location where the sensor is placed. For these cases, energy harvesting
technology (see Section 7.4.4) and virtual positioning systems (VPS, see Section 10.3)
are needed to create maintenance-free sensors.
All possible measures must be taken to ensure the security of information sent by
sensors. However, it will be necessary to develop next-generation cryptography whose
information cannot be deciphered—even using quantum computers, which will see
widespread use in the future (see Section 6.4.3). Many people also have concerns about
all manner of data being sent to cyberspace by sensors. KDDI has developed atechnology called Privacy Policy/Preference Manager (PPM) to manage and control theinformation that users send to cyberspace (see Section 6.4.1). In the B5G/6G era, it will
be necessary to improve the PPM to cope with the increase in the types of information
handled and changes in user preferences.
Delivery robots are used in the use case set out above, but in the future, it will benecessary to manage and control various types of robots. For this reason, a new function
called Robotics as a Service (RaaS) will be required on the platform (see Section 11.4.1).
Mobility as a Service (MaaS) will also be required when robots move on public roads bythemselves and things are transported by automatic cars (see Section 8.4.2). In addition,a new platform will also require functions to control IoT devices and to link different typesof data (see Section 8.4.1). Indeed, the platform must take into consideration the balanceamong three elements: improving users’ quality of life, driving economic activity, andcreating active social systems that are conscious of environmental issues such as energy
consumption and global warming.
Using a variety of data, AI systems determine the next action in physical space byconsidering the balance of these three factors determined on the platform. Existing AI
technologies, as characterized by image recognition and games, accumulate a large
amount of historical data in cyberspace, and uses deep learning technologies to display
intelligent results and provide information in games (we will refer to such AI technologies
as “cyber-based AI”). On the other hand, because in physical space, the environment
around us changes over time, it is almost impossible to reproduce identical environments.
Thus, there are limits to the information that can be obtained from the same environment,
and this is even true for similar environments. This change in environment differs fromperson to person, and the data being sought differs depending on personal preferences
and one’s sentiment at the time. Thus, unlike conventional cyber-based AI, “real-world AI”
is required to solve issues particular to physical space, such as those with limited
relevant historical data (see Section 9.4.1). Applied to the example of a birthday party
above, because the external environment surrounding the birthday celebrant and29

accompanying well-wishers changes annually, it is necessary to predict the event basedon limited data. In addition, to encourage users in the physical space to change theireating habits, exercise habits, and means of transportation based on the results obtainedby real-world AI, it is necessary to send information tailored to the personality andhobbies of the target user. “Persuasive AI” is a technology that facilitates such an
approach (see Section 9.4.2.2). At the same time, whether a user takes actions such as
diet management or exercise to promote his or her health depends, at least in part, onwhether the user can trust the AI models or systems. We will therefore also need“trustworthy AI,” technology that provides reasons for the predictions and conclusions
made by the AI models or systems (see Section 9.4.3).
These new AI paradigms will determine what information should be transferred fromcyberspace to users in physical space through the arrows shown on the right side of
Figure 4-1. Here, XR is a technology to express information to be conveyed as sensory
information, including visual information using images and video. First, 3D imagetransmission is required because the 2D images currently used in telecommunications
lack realism and impact. One method to achieve 3D imagery is a technology used todisplay 3D images using a collection of points known as point cloud data, so this requires
much higher throughput and transmission capacity than 2D images. A new compression
technique is required to compress point cloud data without comprising the quality of 3Dimages (see Section 10.4.3). High-efficiency, Ultra-low-latency In addition, multimodal
interaction transmitting the senses of touch and smell is also necessary to make people
feel as if they are sharing the same space despite being in remote areas (see 10.4.2).
In addition to conveying electronic information to physical space using XR, looking to2030, robots are expected to serve as a means of providing feedback to physical space.
KDDI has been working together with TELEXISTENCE inc., which has developed aremote-operated robot, on R&D toward practical applications. One of the outcomes fromthe joint R&D is an encoding technology that reduces end-to-end delay from 100
milliseconds to 50 milliseconds using the international standard video codec
H.265/HEVC (see Section 10.4.3). As shown in the delivery robot example above, we
can see cases of having a single robot operate by itself, but in the future, it may benecessary for multiple robots to work in cooperation with one another. In this case,besides delays in video transmission between robots, and in addition to technology forcommunicating sensory data between robots, it will also be necessary to develop
technology enabling robots to determine what action to take based on the informationtransmitted. To operate multiple robots in a coordinated manner, it will be necessary to
standardize the interface for communicating information between robots, as well as the30mechanisms for controlling and managing robots used for RaaS as implemented on theplatform. Moreover, since the space in which a robot moves may not be a location fromwhich location data can be obtained, technology to obtain location data, such as VPS,
will also be required.
Since not only delivery robots but also domestic help robots and robots supporting
human activities will become widespread, human and robots must be able to coexist.
Thus far, humans have mainly given instructions to robots, but in the future, robots will
make suggestions to humans, and directions given by robots to humans when human
lives are at stake. As such, in addition to the AI technologies detailed above, technologies
relating to human-robot interaction (HRI) will be required. Furthermore, to advance HRI,
research into acceptability evaluations is also needed to measure whether people canor will accept robots, and to improve upon them (see Section 11.4.2).
As we have discussed, the contents of communication services will become
increasingly diverse, and will include not only large volumes of data such as 3D images,
but also data from all manner of devices, including sensors. The traffic characteristics ofeach service, and their network requirements, are expected to vary tremendously as aresult. It will be necessary to reduce delays—not only delays in the network layer, butalso delays in the application layer. Moreover, safety is essential not only to self-driving
cars, but also for the coming and going of goods, such as those carried by automateddelivery robots; a communication network is therefore required to support it.
Until 5G, communication network coverage areas had mainly focused on locations
where people are present. Mobile network operators have been adopting cellular-basedarchitectures to efficiently allocate coverage over wide areas. When using cellulararchitecture however, throughput becomes lower in so called cell edge area, where
locations are far away from base stations. It may not be possible to satisfy
communications requirements in such locations, even when using the end-to-end slicing
technology to be introduced in 5G, which is a mechanism to satisfy the communication
requirements of services and applications, such as throughput, delay, and quality. As
introduced in the use case above, coverage areas must be provided not only to locations
where people are present, but also to locations in which robots move about. KDDI
therefore believes that a new architecture named “user centric architecture” is necessary
to dynamically create coverage areas, as if multiple spotlights were to shine on places
where both people and robots exist (see Section 5.4.1). In user centric architecture, alarge number of distributed antennas cooperate with each other to create a coveragearea. To achieve this, we can use a technology called Cell-Free massive MIMO (seeSection 5.4.1.1).
31

In the future, in addition to people and sensors, large amounts of data will betransmitted from new devices such as robots. Until 5G, the main focus was on improving
throughput performance in downlink, which is the communication link from base stations
to user terminals. However, when a robot takes a video of its surrounding environment,
moves autonomously, and transmits the video data to a RaaS that exists in cyberspace
every time the environment changes, the amount of uplink traffic generated will be
extremely large. Until 5G, uplink communications are made by one user terminals andone or more base stations. The uplink throughput is limited by the upper limit oftransmission power imposed on user terminals. The idea of “virtualized terminals” will
therefore be necessary to achieve cooperative transmission with the multiple devices
around one person, such as wearable devices, to improve uplink throughput (see Section
5.4.1.4 ).
The throughput required for B5G/6G wireless access lines is expected to be evenhigher than for 5G, assuming that new devices such as robots will be added as subjects
for sending and receiving 3D images. Thus, the required capacity of optical fiber lines
known as “fronthaul,” which connect antennas to a central base station where radio
equipment is located, will increase. Until now, mobile fronthaul has used digitaltransmission interfaces called the common public radio interface (CPRI) and enhanced
CPRI (eCPRI). However, assuming a peak throughput of 100 gigabits per second for
B5G/6G, would require an unrealistic terabit-per-second optical fiber line. Thus, a newmechanism will be required to transmit radio signals from wireless devices to antennasites using analog transmission. KDDI has proposed intermediate frequency over fiber(IFoF) as an enhancement of radio over fiber (RoF) technology (see Section 5.4.2.2).
This solves not only the problem of fronthaul optical fiber capacity, it also has theadvantage of reducing the size and weight of equipment at antenna sites and reducing
power consumption, since analog transmission eliminates the need for digital-to-analog
and analog-to-digital conversions at antenna sites.
With regard to network-related technologies, we believe that new technologies will be
required not only for wireless and optical communications technologies, but also for flow
control technologies, to efficiently accommodate a wide variety of applications andservices; technologies to accommodate large numbers of devices; as well new systems
technologies with device movement management functionality—that is, a mobile core
network—and the ability to serve as a gateway for packet transmissions (see Section
5.4.3).
Thus, even when celebrating a birthday party in 2030, for example, it can only beachieved by making full use not only of networks, but also the various component
32technologies in the seven technologies. These technologies are introduced from Chapter
5 onwards, so please refer to those sections.
Of course, as we study further, we will find other component technologies not
introduced in this white paper. We hope for cooperation with companies, universities,
and research institutes, both Japanese and foreign, on these unexplored and as-yetundeveloped component technologies. Through the prompt realization of Society 5.0, we
hope to work together to construct a next-generation social infrastructure that brings tofruition “a social system that improves people’s lives, stimulates the economy, and isenvironmentally conscientious.”33

5. "1. Network"
5.1. The Role of Network Technology in B5G/6G
With the spread of the Internet, an online cyberspace has appeared and our lifestyles
and economic activities today have become increasingly dependent on this cyberspace
as well as the physical space of the real world. By 2030, CPS is expected to become
even more advanced through the cooperation of the seven technologies. KDDI calls thiscooperation “orchestration,” and network technologies will play an important role inconnecting physical space to cyberspace.
For example, in 2021, increasing numbers of users are using online services such as
streaming services to enjoy music rather than buying CDs or other media. This has beenachieved by linking the action of listening to music in physical space to cyberspace using
a network.
This trend will have further accelerated by 2030, and it is anticipated that many of theevents, objects, and data in physical space will be uploaded to cyberspace. Various
processing will be performed in cyberspace by leveraging AI technologies and Big Data.
The results of the processing will be fed back to users in physical space through XR,
robotics, etc.
It is expected that the flow of this feedback will be accelerated by improvements to thequality and capability of networks, and thus the barrier between physical space andcyberspace will be eliminated. It is expected that CPS will advance by forming a societythat transcends time, space, and physical constraints, with all objects and actions moving
back and forth between physical space and cyberspace as digital data.
5.2. Network Technology Goals
The most important networks are wireless communication networks. In March 2020,
5G services were launched in Japan. Thus far, wireless communication network
technologies have been evolved with an emphasis on transmission data rate. 5G mobile
technology offers higher performance through lower latency and larger numbers ofsimultaneous connections as well as a higher data rate.
We aim to further enhance these three features — “high data rate,” “low latency” and“large numbers of simultaneous connections”— in B5G/6G. Since creating CPS will
require the transfer of a large amount of data to reproduce physical space as-is incyberspace and then, to take the cyberspace information, generated and processed
based on the transferred data, and reproduce it in physical space using technologies
such as high-definition 3D images and holograms. There will therefore be a need totransfer vastly greater volumes of data than at present. In addition, transferring data from34

physical space to cyberspace and back with low latency improves real-time experience,and the simultaneous transfer of information by large numbers of users will create asense of unity, such that users existing in physical space will be able to experience thereality of information generated and processed in cyberspace.
Around 2030, 3D holography and full sensory communication, indistinguishable fromthe real thing, will be achieved through the real linkage of physical space and cyberspace,delivering excitement everywhere. However, implementing 3D holography will require
the ability to transmit one terabit-class data instantaneously [5-1]. This is because vastquantities of data will be exchanged between cyberspace and physical space, for which
ultra-high-capacity optical fiber communications offering high-capacity, high-speed (lowlatency) data transfer between the two spaces will be essential. With multi-core opticalfiber transmission technology that enables the construction of petabit-class ultra-highcapacity optical networks, and RoF transmission technology, which connects a huge
number of base station antennae and radio aggregation stations with high capacity andhigh efficiency, resilient fiber optic communication networks with higher capacity andlower power consumption than ever before become available, making it possible to havereal connections with distant family members, colleagues overseas, and friends on theother side of the world.
In addition to leapfrogging the requirements for high capacity, low latency, and large
numbers of simultaneous connections to be achieved by 5G, the mobile core network in
the B5G/6G era will be required to achieve power savings, autonomy, safety, reliability,and expandability [5-2]. The following describes the high-capacity, low-latency
communications that the mobile core network needs to equip.
Regarding high-capacity (broadband) communications, the mobile core network must
simultaneously achieve two goals: accommodating the high-capacity communications,and reducing the duration for which a mobile terminal reaches to the max-throughput.
Additionally, such high-capacity communications should be achieved for all the online
mobile terminals accommodated in the same base stations and/or gateways (theequipment delivering user data to the mobile terminals) in the mobile core network (seeFigure 5-1).

35

Figure 5-1: Examples of issues with high-capacity (broadband)communications: Accommodating user communications
With respect to low-latency communication, in addition to low latency in the timerequired to transmit data, the key is to reduce the time required to begin communications
for user data transmissions. In most cases, the mobile communications system releases
radio resources when the mobile terminal has no communication, and it takes a certain
amount of time, corresponding to round trips of several signals, for making its
communication ready. By reducing this time, it is possible to achieve low-latency
communication from the beginning of the communication (Figure 5-2).

Figure 5-2: An example of issues in low-latency communication:
Reducing the time required to begin communication
36Moreover, automation of network operations is expected to become more advanced
toward the advent of B5G/6G, and as shown in Table 5-1: Definitions of Operational
Automation in Networking, we are aiming for its development in stages.
Table 5-5-1: Definitions of Operational Automation in Networking
Overview
Level 0Level 1

Decision-makerOperation by human operators
Human operators perform all tasks
Automation of a part of operation tasks
Automated execution of commands using scripts
Automation of the operation process flow

Level 2

Partial automation using rule-based decisionmaking (e.g., thresholds)
Automated

Level 3Operators

decision-making

inoperation

processes
Partial automation of decisions by the system

Operators (main)
System (sub)System (main)
Operators (sub)Full automation in specific environments
Level 4

Specialized, full automation for specific tasks andnetworking performed by the system
Full automation

Level 5

SystemAll operations and networking are automated by thesystemDefined in stages from Level 0, where all tasks are performed by human operators, toLevel 5, where all tasks and networks are automated by the system, we are aiming toachieve Level 4 to Level 5 automation of operations in the B5G/6G era. Dynamic
reconfigured slicing is anticipated for the BG/6G era, requiring flexible design changes
during service provision, and dynamic controls such as the reallocation of network
resources to suit a given situation. The complexity of network requirements is expected
to increase, and new technologies like multi-access edge computing (MEC) will beincorporated into services; IoT networks will also expand further, increasing the numberof items to take into consideration during design stages. The increase in the number of37

available choices, combined with existing design patterns, will result in a huge numberof design patterns.
5.3. Global Situation
Various efforts to implement B5G/6G have already begun in many countries. The
telecommunications division of the International Telecommunication Union (ITU), the
ITU-T, has launched the ITU-T Focus Group on Technologies for Network 2030 (FG NET2030), to examine existing network technologies, platforms, and standardization, and to
identify gaps and challenges in the requirements for the networks of 2030 [5-3]. FG NET2030 compiles issues from all aspects of Network 2030, such as the vision, requirements,
architecture, new use cases, methods of evaluation etc., publishing these issues as white
papers [5-4]. In response, the ITU-R, the wireless communications division of the ITU,
has begun to prepare technical reports listing the key technologies required for the
B5G/6G era. This technical report is expected to be released around 2023, and will be
used by 3GPP and other organizations to develop technical standards. ITU-T Study
Group 13 has established a focus group on autonomous networks to address theautomation of network operations [5-5], and it intends to develop technical reports andspecifications for autonomous networks, including considerations for future network
evolution. Moreover, the TM Forum, a leading standardization organization for network
technologies, has launched the Autonomous Networks Project [5-5], which proposes an
AI-based architecture that enables networks to perform operational tasks autonomously.
Specifically, in an architecture divided into four layers, network equipment, network,orchestrator, and customer portal, it aims at overall automation across network domains
with a total of four loops: three closed loops (a series of automated task cycles), and aclosed loop between the top and bottom layer.
In Europe, the 6Genesis project, supported by the University of Oulu, Finland, wasdesignated as a flagship program project of the Academy of Finland in June 2018, andhas begun early-stage research. The main goal of the project is to support future industry
by supporting the evolution of 5G, developing the basic technologies required to
implement B5G/6G, and accelerating the digitalization of society. The results of the
project were published in September 2019 as “Key Drivers and Research Challenges for
6G Ubiquitous Wireless Intelligence.”
In the US, New York University and the Defense Advanced Research Project Agency(DARPA) have launched ComSenTer, a research center for wireless and sensor
technology, to carry out R&D of wireless communications primarily in the terahertz band
(roughly 100–1,000 GHz). The University of California’s Santa Barbara, Berkeley, and38

San Diego campuses, together with Cornell University and the Massachusetts Instituteof Technology, are also participating in this project, for which R&D is conducted by four
teams: Systems, Integrated Circuits, Devices, and Demonstrations. The Alliance for
Telecommunications Industry Solutions (ATIS) has established the Next G Alliance,
which is working to formulate a roadmap for B5G/6G.
In Japan, B5G/6G is expected to further accelerate the development of CPS advanced
by 5G, and to serve a core function as a backbone to Society 5.0 [5-6]. The Beyond 5G
Promotion Strategy–6G Roadmap [5-7], published in June 2020, lists the seven major
requirements for B5G/6G as (1) Ultra Fast and Large Capacity, (2) Ultra Low Latency,(3) Ultra Numerous Connectivity, (4) Ultra low power consumption, (5) Ultra security andresiliency, (6) Autonomy, and (7) Scalability.
Optical fiber communications that support backbone and backhaul networks haveachieved a dramatic 30,000-times increase in capacity over the past 30 years or sothrough advances in optical signal multiplexing technologies such as time divisionmultiplexing, wavelength division multiplexing, and digital coherent optical transmission(for example, the capacity of optical submarine cables had increased from 560 megabits
per second in 1989 to 15 terabits per second/fiber by the late 2010s). However, the limitsof these multiplexes are approaching [5-8]. Because there is a limit to the amount of
optical power that can be input into existing single-mode fiber due to nonlinear
degradation and the fiber fuse 2 phenomenon that occur in optical fiber, and it is difficult
to increase capacity by more than 10 times current levels (about 100 terabits per second)using conventional multiplexing technology alone. Innovative technology that can realize
sustainable increases in capacity is therefore indispensable.
KDDI has been working on a new optical fiber communication technology, space
division multiplexing (SDM) to break through the conventional limitations describedabove. Spatial multiplexing technology was first discussed at the IEICE TechnicalCommittee on EXAT [5-9] in 2008, and KDDI has actively participated in the committee
as a key member since its creation, and has achieved many research results. Major
achievements are shown in Table 5-2. In particular, in 2017 we succeeded in building theworld’s first multi-core multimode fiber (19-core, six-mode fiber) transmission at 10
petabits per second, corresponding to 1,000 times the transmission capacity of current
optical fiber communication systems. This was opening the door to opticalcommunications in the ten quadrillion bit era [5-10]. This achievement received highpraise at one of the world’s leading international conferences, with the official press

2

A phenomenon in which an optical fiber is degraded by light.

39release from the conference offering praise: “In a heroic effort, researchers from Japan
have managed to break the current record by a factor of five.” This demonstrates thehigh scalability and potential offered by SDM technology. However, the transmission
distance remains short (about 10 km), and further studies are needed to extend thetransmission distance. In recent years, multicore fibers (MCFs) with a standard cladding
diameter of 125 micrometers have been gathering attention for their practical use
because of the manufacturability and mechanical durability of MCFs. Investigation of
their practical use has been accelerated by the creation of technical reports on SDM by
the Telecommunication Technology Committee (TTC) [5-11] and ITU-T SG15, and by thelaunch of NICT commissioned research “R&D for accelerating the practical use ofmulticore fiber” (2018–2022) [5-12].
Table 5-5-2: World’s First Achievements by KDDI in
Multicore Fiber Transmission Technologies
Year

World first results

Reference

2012

Success in a trans-oceanic multicore fiber transmission [5-13]
experiment

2013

Success in a 1 exabit x kilometer ultra-long-haul MCF [5-14]transmission experiment

2015

Successful MCF transmission experiment at 2 petabits per [5-15]
second2017

Successful MCF transmission experiment at 10 petabits [5-10]
per second

Technical development of mobile core networks includes the ongoing examination of
implementation architectures to reduce processing, or to secure new processing. For
example, an approach to reduce signaling traffic and the signaling process by an
integrated implementation of the functions specified in the mobile core (3GPP Standards
TS23.501 [5-16], TS23.502 [5-17], TS23.503 [5-18]), and an approach to deploy mobile
core network functions on peer-to-peer (P2P) infrastructure that utilizes on-device
processing has been proposed [5-19]. In addition to reducing delays in the transmissionof user data (communications data from applications running on the terminal) using MEC,
the number of studies into reducing delays in signal processing has been increasing. The
main approach used thus far has been to reduce the number of signals by integrating
functions [5-20][5-21], but approaches such as a fast signal message conversion process(serialization) [5-22] have also been proposed. Note that none of these proposals
40address the capability requirements of the mobile core network itself for B5G/6G.
Moreover, since it is essential to deal with multiple simultaneous connections—a resultof the increasing numbers of devices—there is a need to carefully scrutinize thecapability requirements of the mobile core network.
5.4. Technologies Required for 2030
In this section, we summarize the technologies that will support networks in the
B5G/6G era, i.e., from 2030 onward, from wireless, optical, network, and operations
perspectives. We will also touch on white-boxing (general-purpose equipment) andhardware/software separation, which are expected to widely spread in all network
devices in the future.
5.4.1. User centric Architecture
To implement B5G/6G, it is important to implement user centric networks in which a
communication area is formed for each user according to the communication
environment and individual communication requirements. In wireless communications,mobile communication devices, such as smartphones, communicate with base stations
by wireless connection.
Conventional wireless communication is built on the concept of providing
communication services with high performance requirements over as wide an area as
possible by deploying base stations across a plane. As shown on the left side of Figure
5-3, the area to which each base station provides communication services can be likened
to a cell, and this kind of network configuration is known as “cellular architecture.”

Figure 5-3: Cellular architecture (left) and user centric architecture (right)
41

In cellular architecture, base stations are deployed according to the distribution ofusers and traffic, but the radio signals from base stations are attenuated by distancearound the base station, especially at the cell boundary. Moreover, at the cell boundary,radio signal quality is greatly degraded by interference from neighboring base stations.
In 5G, high-frequency bands such as the millimeter-wave bands are exploited toachieve ultra-high speed and high-capacity communications. This trend is expected to
become even more pronounced in B5G/6G, but as frequencies become higher, this will
give rise to issues such as communication loss due to blocking, resulting in unstablecommunications.
KDDI believes that the introduction of “user centric architecture” like that shown on theright of Figure 5-3 will be an effective technology to solve this issue. In user centricarchitecture, instead of a specific base station providing services to users in a specificarea, multiple base stations work together to provide services for each user. This will
provide an optimal communication environment to meet the diversifying communication
requirements of the B5G/6G era. For example, for users who wish to send large amountsof data such as high-quality 3D movies, the data transmission rate is an important
indicator of communication quality. For such users, it is possible to provide high-capacity
communication services without interference between base stations by having nearbybase stations cooperate to send and receive radio signals. On the other hand, if we wish
to monitor the trajectory of movement in real time, we do not need high speed
communications, since the volume of data involved in GPS positioning data is small
compared to that of 3D movies—but we must be able to send and receive data with low
latency to avoid disruption of communication. It becomes possible to provide stable,
uninterrupted communications to such users, despite their movements and changes inthe surrounding environment, by implementing redundant connections with multiple basestations simultaneously providing communication services to devices. This ensures thesame level of connectivity in the mobile wireless environment as in a fixed-line
environment.
Thus, in user centric architecture, every user can enjoy stable, optimized
communications services in a variety of environments, and in doing so, it is expectedthat physical space and cyberspace will become linked to the extent that we are no longer
aware of the boundary between the two spaces.
In the following, we show the component technologies required to implement thisarchitecture (Figure 5-4).

42

Figure 5-4: User centric architecture and its constituent technologies
5.4.1.1. Cell-Free massive MIMO
KDDI believes that Cell-Free massive MIMO will be an effective component technology
for implementing user centric architecture. Cell-Free massive MIMO has been studied
since late 2010s, and is positioned as a combination of distributed MIMO and massive
MIMO technologies [5-23]. As shown in Figure 5-5, large numbers of base station
antennas are densely deployed and each base station antenna cooperates with theothers to provide service to individual users. Applying massive MIMO technology ataggregation stations, interference between users can be suppressed, and radio quality
degradation at cell boundaries—an issue in conventional cellular architecture—can be
reduced. This can increase the number of concurrent connections and can improvethroughput, reliability, and energy efficiency per user.

Figure 5-5: Cell-Free massive MIMO

43

The effects of Cell-Free massive MIMO are strongly dependent on the distribution of thebase station antennas that are to be operated by aggregation stations [5-24][5-25]. For
example, as shown in Figure 5-6, the spectrum efficiency is dependent on the numberof base station sites (L) and the number of antennas installed at each site (N) when the
total number of base station antennas is fixed. The results of computer simulations
indicated that the spectrum efficiency in the semi-distributed case (center of the figure)is significantly higher than that in the centralized case (left of the figure) at the same levelof one of the fully-distributed case (right of the figure) [5-26].

Figure 5-6: Spectrum efficiency (centralized / semi-distributed / fully distributed)
L : the number of base station sites
N : the number of antennas at each site
The deployment of mobile fronthaul to a large number of distributed antenna sites isalso very important for the deployment of Cell-Free massive MIMO. In the case of CellFree massive MIMO, a large number of high-capacity mobile fronthaul systems isrequired to transmit a large amount of traffic, e.g., radio waveform information, between
antenna sites and an aggregation station. KDDI believes that optical fiber wirelessmultiplex transmission that employs radio-over-fiber (RoF) technology, as shown in
Section 5.4.2.2,

has the potential to reduce the high-capacity mobile fronthaul

installation costs. To verify the feasibility of Cell-Free massive MIMO deployment using
RoF technology, a millimeter-wave radio communication environment was created byconnecting a 5G base station simulator and two millimeter-wave band (28 GHz)
antennas with multicore optical fibers. The following three conclusions were made [527]:(1) Inter-antenna site interference can be eliminated, and high throughput can beobtained anywhere.
44

(2) Stable throughput can be obtained to eliminate adverse effects by obstacles.
(3) High radio quality can be maintained even when the placement of the distributed
antenna sites is changed.

5.4.1.2.

User centric RAN

We believe that the user centric network implementation will require an unprecedented
architecture of radio access network (RAN) to realize flexible networks per users. Figure
5-7 shows the historical evolution of RAN architecture. Figure 5-7 (a) illustrates theconfiguration of a conventional base station, and the base station functions are definedfor each base station site. Base station functions have long been implemented using
hardware, but the use of software and virtualization for base station functions has
increased in recent years.
Figure 5-7 (b) shows an assumed architecture for 5G advanced on the 2025s [5-28].
In this architecture, signal processing functions (distributed unit, DU) and packet
processing functions (central unit, CU) of a base station will be virtualized. The
placement of the DU and CU, and the assignments of radio and computer resources will
be dynamically controlled according to user service quality requirements, such as ultralow latency and ultra-massive connections.
Figure 5-7 (c) shows a user centric RAN architecture [5-29]. To provide a cell-free radio
environment per users with a Cell-Free massive MIMO, the RAN architecture requires
more flexible network control using virtualization technology. The significant difference
between this architecture and that of 5G advanced is the expansion of the virtualized
target to include the backhaul of the mobile communication network and the radio link
between a base station and terminals, because logical networks per users are provided.
A radio processing unit (central processing unit, CPU) and a radio unit (access point, AP)
are placed for each user by using a virtualized method, and these units perform
distributed processing of the radio signals between APs. Those provides mobile
communication networks per users by the evolved RAN architecture, because it canplace and control base station functions for adaptation both the radio environments andcommunication services that are different per users. To implement this RAN architecture,
a construct method of physical networks is required. The method needs to consider how
to place computers for CPUs, transport links and APs. A control method to make logicalnetworks per users from the constructed networks is also required. The method includes
an assignment procedure of physical network resources such as computers and APs tological networks. The procedure is required to perform on the minimum order of45

milliseconds, because the logical networks per users will satisfy required communication
quality to adapt both the radio environments and communication services that are
different for each user. In addition, the constructed and control methods are required ascalability for a mobile operator network.

Figure 5-7: Changes in RAN architecture46

5.4.1.3.

Intelligent Reflecting Surface

To implement user centric architecture, i.e., to satisfy the required service quality foreach user, it is necessary to deploy base stations densely, and to have multiple basestations providing seamless coverage to each user. Because 5G makes full use of themillimeter-wavelength spectrum, radio signals with short wavelengths make it difficult toprovide wide service areas due to the strong linearity of the radio waves. This results inareas with poor radio signal quality, known as coverage holes, produced by shielding
from buildings, trees, vehicles, and people. This issue is likely to become one of thechallenges in implementing user centric architecture as the use of higher frequency
bands increases moving toward B5G/6G. The use of reflectors has been drawing
attention as a means to solve this issue. In particular, investigations into meta-surface
reflectors, which reflect radio waves in non-specular directions, have been carried out
making use of meta-surface technology, which can generate unnatural electromagnetic
properties of radio waves and light by means of fine artificial surface structures, as shownin Figure 5-8.

Figure 5-8: Meta-surface reflector
For example, we present an optically transparent meta-surface reflector consisting of ameta-surface reflective element, arranged for each of multiple frequency bands, using a
conductive thin film and supported by a transparent resin plate [5-30]. Figure 5-9 and
Figure 5-10 show the appearance of the optically transparent meta-surface reflector andthe structure of the reflective element, respectively. Reflectors using transparent andinvisible materials can be installed on the walls of buildings or urban structures without
spoiling the scenery, and are able to provide wireless connectivity from various locations
around users.
47

Figure 5-9: Optically transparent meta-surface reflector appearance

Figure 5-10: Reflector Element Structure
The directivity characteristics of the reflected wave created by the optically transparent
meta-surface reflector are uniquely determined by the shape of the reflective element

corresponding to each frequency band. The direction of reflection therefore cannot bechanged according to a user’s location or their movements. On the other hand, users
need communication services at various locations within physical space, and theirlocation and surrounding environments change over time. It will therefore be necessary
in the B5G/6G era to establish intelligent reflecting surface (IRS) technology, a reflector
technology that makes it possible to freely change the directivity characteristics ofreflection according to a user’s location and movement, while maintaining transparency
and permeability to keep their placement from spoiling the scenery.
For example, we present a direction-variable liquid crystal meta-surface reflector thatcan electrically induce reflected radio wave directional change by applying the liquid
crystal used in displays to control radio waves [5-32]. Figures 5-11 and 5-12 respectively
show the appearance of the prototype sample and structure of the reflector element of
the direction-variable liquid crystal meta-surface reflector. By incorporating a liquid
crystal layer between the reflecting element and the ground and applying a voltage using
the reflecting element as an electrode, the electrical characteristics of the liquid crystal
can be controlled. This makes it possible to electrically induce reflected radio wave
directional change. Furthermore, by using this reflector, it is possible to provide a wireless48

connection regardless of the user's location or environment. This reflector is expected toenable the development of IRS technology that can achieve high-quality wirelessconnection via high-speed tracking of user movement.

Figure 5-11: Direction-variable liquid crystal meta-surface reflector prototype

Figure 5-12: Components of the direction-variable liquid crystal meta-surface
reflector
5.4.1.4. Virtualized Terminals and Radio over THz Technology
In the wireless communication networks of the 2030s, user devices themselves will
also need to evolve to meet individual users’ high-communications performancerequirements. For example, while mobile phones and smartphones, the most popularuser devices today, have evolved in terms of weight and shape, their capabilities as radio
devices have not significantly changed since mobile phones first appeared 40 years ago.
49

In achieving the exchange of information between cyberspace and physical space with
the quality and quantity high enough to meet individual users’ diverse requirements, user
devices present significant limitations in terms of their size, which limits the number ofintegrated antennae and their maximum transmission power. It is not practical to increase
the size of smartphones to alleviate constraints such as the number of antennae, andthe performance of uplink communication from user devices to base stations is vastlyinferior to downlink communications from base stations.
We are introducing the concept of user centric architecture to the environment aroundusers to resolve these user device limitations. Specifically, through cooperation between
various devices that communicate with user devices, it becomes possible to solve issues
arising from the constraints caused by a single user device, such as power transmission
and the number of integrated antennae.
Figure 5-13 presents a concept for a virtualized terminal. For examples, devices
around the user, such as PCs, watches, glasses (smart glasses), or self-driving cars, canbecome wireless devices and cooperate with one another, making it possible toovercome transmission power constraints in a single user terminal, and to virtually
overcome limitations in the number of antennae. When riding in a car with a smartphone,
the antenna on the car can also be used virtually as the smartphone’s antenna to improve
communications performance.
Here, communication between a user terminal such as a user’s smartphone and aperipheral device such as a watch or pair of glasses requires a short-range but extremely
wideband signal transmission. Since the capabilities required for wireless signal
processing are limited in small devices such as watches and glasses, complex wirelesssignal processing should be avoided in such devices. Thus, in the virtualized device
concept, the user’s smartphone is responsible for wireless signal processing as aterminal; the smartphone and peripheral devices connect via terahertz broadband radio,
while for peripheral devices simply convert the terahertz radio into frequencies forconnecting to base stations (e.g., the millimeter wave bands and the sub-6 gigahertzband). This concept can be referred to as “radio over THz” technology, which is an issue
for future study. The use of terahertz radio has long been investigated for use in fixedand long-range radio applications such as wireless backhaul [5-33]. However, it isexpected that the terahertz radio application could also be disseminated in the form of
these short-range use cases.

50

Figure 5-13: A virtualized terminal concept
5.4.2. An Ultra-high-capacity Optical Fiber Network
The capacity required for networks in the 2030s will be more than 100 times the current
capacity, i.e., terabit-class capacity for access networks, and petabit-class capacity forcore networks. However, to realize the three “links” based on KDDI Sustainable Action[5-34], it is not enough to simply increase the capacity of the network by increasing thenumber of facilities: an ultra-high-capacity optical fiber network is required that is bothsustainable going forward, and fully satisfies energy and space saving characteristics. In
this section, we introduce “multi-core fiber transmission technology” and “radio over fiber(RoF) technologies”, which will be necessary for constructing networks in the B5G/6G
era.
5.4.2.1. Multi-core Fiber Transmission Technology
Advances in the R&D of multi-core fiber (MCF) transmission technologies will facilitate
the construction of petabit-class optical fiber networks that would be difficult to achievewith only conventional technologies, enabling ultra-high-capacity data transmission
between physical space and cyberspace. This also enables to achieve realistic spatial
reproductions such as holographic communication and full sensory communication.
Between now and 2030, the traffic demand of hyperscalers such as Google andFacebook will have further increased; additionally, by 2030, optical submarine cables
51

and interconnections between data centers that support global communications will
require significantly higher-capacity optical networks, and research and development is
expected to be conducted worldwide. MCFs and their transmission technologies are
expected to be a fundamental technology for ultra-large capacity optical networks in the
beyond-5G/6G (B5G/6G) era. For example, if a single optical fiber has four light paths(i.e., four cores), the space utilization efficiency can be increased by a factor of four,making it possible to increase the transmission capacity of the entire cable by a factor offour under the conditions of using the existing optical cable. KDDI, in collaboration witha fiber manufacturer, has succeeded in developing an ultra-low-loss, ultra-low-crosstalk,standard-cladding, uncoupled four-core fiber (4CF) with the same characteristics as thefibers used in conventional optical submarine cables; this was achieved by optimizing
the fiber structure and manufacturing method. In addition, KDDI successfully performed
the world's first standard-cladding 4CF transmission experiment by applying wavelengthdivision-multiplexed optical signals at a bitrate of approximately 100 Gbit/s over atransmission distance of 12,000 km, which is longer than the 9,000 km that is required
for trans-Pacific transmissions (see Figures 5-14 and 5-15 for images of the opticalsubmarine cable and cable structure, respectively) [5-35].
To further increase the transmission capacity through the use of spatial-divisionmultiplexing (SDM) technologies, it is necessary to increase the spatial multiplicity
according to the number of cores and modes. However, MIMO digital signal processing(MIMO-DSP) is necessary to achieve ultra-dense SDM processes, such as coupled MCF
transmission and mode-division multiplexing (MDM) fiber transmission, as MIMO-DSP
will eliminate the inter-core coupling and mode coupling that occurs between cores andmodes. Table 5-3 summarizes the pros and cons of SDM transmission technologies.
Although uncoupled MCF transmission does not require MIMO-DSP, coupled MCF
transmission and MDM fiber transmission should be considered because they have thepotential to significantly increase capacity of spatial multiplicity in the future. To realize
real-time MIMO-DSP, it is necessary to 1) understand the behavior of spatial inter-core
and mode coupling, and 2) create real-time MIMO signal-processing algorithms with highnoise tolerance. Thus, KDDI has been investigating real-time MIMO-DSP technology,
and has recently developed a real-time MIMO signal processing system using FPGAs[5-36]]. KDDI has consequently successfully conducted the world's first transatlanticclass, ultra-long-haul coupled MCF transmission experiment using real-time MIMO-DSP
circuits [5-37]
Additionally, to realize the development of SDM transmission systems and subsequent
deployment as a network, it is necessary to research and develop various peripheral
52technologies, such as compact and power-saving optical repeaters (optical amplifiers),
optical switches, and monitoring devices for SDM networks. KDDI is also in the processof developing 1) a cladding-pumped multicore erbium-doped fiber amplifier (MC-EDFA),
which is purposed to allow a single laser to simultaneously pump multiple cores, and 2)
novel optical devices that can pump a multicore erbium-doped fiber (EDF) core by coreand monitor the optical signal power core by core without a fan-in or fan-out devices [538]. In July 2021, KDDI began the research and development of SDM optical network
node technologies (e.g., SDM optical switching nodes and optical repeaters) as
fundamental technologies for ultra-high-capacity backhaul and backbone optical
networks. This research is being performed in collaboration with other research institutes
under the name "Research and Development of Space-Division Multiplexed Optical
Network Node Technologies to Support Beyond 5G Ultra-high Capacity WirelessCommunications" , which has been commissioned by the National Institute of Information
and Communications Technology (NICT) as part of the Beyond 5G R&D Promotion
Project [5-39].
Toward the B5G/6G era, in addition to the research and development dedicated toincrease the capacity of existing optical fiber networks [5-40][5-41], KDDI willcontinuously accelerate the research and development of MCF transmission
technologies in order to ensure the reality of ultra-high-capacity data transmission
between cyber and physical spaces in the future.

Figure 5-14: Image of submarine
optical cable system

53

Figure5-15:

Structuresubmarine optical cableofTable 5-3: Advantages and disadvantages of space-division multiplexing (SDM)
transmission technologies

FiberMulti-core fiber (MCF)

MDM fibertransmission

transmission

Uncoupled MCF

Coupled MCF

MMF, Stronglycoupled FMF

Multiplexing

Core

Core

Mode

Low inter-core

Large inter-core

Large modecrosstalkcoupling

coupling

MIMO DSP◎△△@ Spatial channels

Not required

Required

RequiredHigh capacity

△

〇

〇

＝Large spatial

Standard-claddingStandard-claddingStandard-cladding

multiplicity (SM)MCF, maxSM：～4,5

MCF, maxSM：～12

MMF, maxSM:～45

Long-haul〇〇〇

Low SMD

Large DMD

transmission

MDM:Mode-division multiplexing, MMF：Multimode fiber, FMF：Few-mode fiber, DSP:Digital
signal processing, SMD：Spatial-mode dispersion, DMD：Differential mode delay

5.4.2.2. Radio over Fiber (RoF) Technology
In the B5G/6G era, the data rate of wireless communication system is expected toreach the sub-terabit level, which is 10 times faster than 5G. It is assumed that thefrequency bands higher than the millimeter wave band will be utilized in B5G/6G torealize such a high-speed communication. However, propagation loss becomes muchhigher compared to the frequency bands used before 5G, so the installation of vast
numbers of base stations and antennas will be required. Therefore, it is essential for
networks accommodating these wireless systems to be low in power consumption,
space-saving, and cost effective, in addition to having the ability of conveying large
volumes of data.
As in the user centric architecture concept set out in Section 5.4.1, close cooperation
between large numbers of distributed antenna ports is a precondition for realizing highcapacity, high-quality wireless communications, and it will be necessary to transmit radio
signal waveform between central stations and antenna sites. Digital transmission
methods such as CPRI [5-42] and eCPRI [5-43] are widely used in conventional
54

centralized radio access networks (C-RAN) for the mobile fronthaul interface between
central stations and antenna sites. In this scheme, the temporal waveform or physicallayer information for modulating and demodulating radio signals is transferred as digital
signals, and a transmission speed of about five to 16 times larger than the user
throughput is required for mobile fronthaul network [5-44]. If we assume that the user
throughput will increase to more than 100 gigabits per second in the B5G/6G era, terabitclass communication will be required for mobile fronthaul, and it will be extremely difficult
to achieve such high speeds. Thus, novel mobile fronthaul technology that is not boundto conventional technologies will be required.
Based on the above, KDDI is working on the research and development of radio overfiber (RoF) technology, which can achieve both accommodation of large-capacity
wireless communication systems and a configuration of C-RAN architecture at the sametime. In the RoF system, the modulation and demodulation of wireless signals are
performed at the central station, and the radio signal waveform is transmitted “asis” overoptical fiber in the mobile fronthaul section. This obviates the need for modulation anddemodulation of radio signals at the antenna sites, which resulting in significant
improvement of the space and energy efficiency. Furthermore, as shown in Figure 5-16,
KDDI is investigating intermediate frequency-over-fiber (IFoF) as one form of RoF
technology which, rather than sending a single radio channel at a time, multiplexes
wireless signals in frequency domain for vast numbers of antenna sites and transmits
them over a small number of optical fibers and wavelengths. In 2017, in anticipation of
the 5G era, the world’s highest capacity signal transmission experiment was successfully
conducted [5-45], and completed feasibility studies from the large-capacity and highsignal quality points of view. Recently, in a joint study with other organizations, we havesucceeded in the world’s first RoF transmission experiment with large capacity andsuperior installability for a use case bringing millimeter-wave wireless signals into
enclosed spaces such as indoors [5-46]. In addition, by exploiting the four-wave mixing
phenomenon that occurs in optical fibers, KDDI was able to develop and successfully
test a new transmission method that was purposed to increase the signal-to-noise ratio(SNR) of optical signals that are input to an optical fiber by nine times before it reaches
the optical receiver . This method is expected to be adopted as a technological basis for
RoF transmission quality improvement [5-47].
The creation of new mobile fronthaul technology is essential for realizing B5G/6G, andfurther development of RoF-based mobile fronthaul is expected in the future for cost
reduction, operation and maintenance. Furthermore, configuration of antenna sites canbe simplified, i.e., “optical/electric (O/E) conversion + antenna,” which is expected to55

save power and space of antenna sites in the B5G/6G wireless systems utilizing
millimeter-waves or higher. With the aim of further developing such mobile fronthaul
technology, KDDI has also began the research and development of ultra-high-capacity
wireless fronthaul technologies that converge THz waves and optical wireless signals.
This research is being performed in collaboration with other research institutes under thename "Research and Development of Radio and Optical Converged WirelessCommunication Systems for Beyond 5G Ultra-high Capacity Wireless Networks" [5-48];
this research project has been commissioned by NICT as part of the Beyond 5G R&D
Promotion Project.

Figure 5-16: An example IFoF transmission system
5.4.3. Mobile Core Network
This section discusses the technologies that are expected to be utilized to implement
the various requirements of mobile core network, including the requirements describedin Section Network Technology Goals.
5.4.3.1. Technology to Realize High-capacity Communication
For high-capacity communications, in addition to supporting higher speeds in thewireless section, it is expected that multi-session communications using multiple
communication connections simultaneously, and explicit congestion notifications will be
used in accelerating communication throughput and controlling communication flows,
respectively (Figure 5-17). In using these technologies, the mobile core will be
responsible for controlling the number of flows and maximizing the communication speedof each flow by sufficient interworking with terminals and the radio access networks.
56

Figure 5-17: Multi-session communication (upper left) and
explicit congestion notification (lower right)
5.4.3.2. Technology to Achieve Low-latency Communication
The key to low latency communication is reducing the time to begin communication.
As shown in Figure 5-2, there is a need to establish a method of device registration andbearer management that can achieve user data delivery without waiting for signal
processing to establish a communication bearer.
5.4.3.3. Technology to Achieve Massive Multiple Simultaneous Connections
For massive multiple simultaneous connections, it is necessary to simplify the
management of terminals without registering the location of the terminals in the mobile
communications system (Figure 5-18). In the conventional mobile communication
systems, once a mobile terminal is powered on, the mobile core network registers themobile terminal. When the mobile terminal does not communicate, the connection to thewireless section is released, but the mobile core network track the position of all themobile terminals 3 . Because the number of IoT device connections is expected toincrease dramatically in the B5G/6G era, following the current location management ofmobile terminals leads to bringing bloated facilities.
The challenge when omitting the location management is that the mobile core network

3

To be precise, tracking area data is defined by the carrier.

57

enables the mobile terminals to begin their communications despite that they are notunregistered, and it is further necessary to provide a means to begin the registration
process originated by the communications begun by the mobile terminal more quickly
than before. Note that there are many round-trip signals in conventional mobile
communication systems, and the registration process takes a certain amount of time. If
we can resolve these two problems and achieve a means of connecting terminals without
location management, it will be possible to reduce the processing load of mobile
communication systems, and to avoid the bloating of facilities.

Figure 5-18: Responding to a dramatic increase in devices:
Simplifying device location management
5.4.3.4. Technology to Achieve Autonomy in Mobile Communications Systems
The key to autonomy is operational automation using AI technologies, as described in
Section 5.4.4. Achieving the autonomy of mobile communication systems will enable
them to predict and avoid network failures in advance. In addition, it can be expected tomaximize the effects of high-speed communication, with low-latency, multiple
simultaneous connections by adjusting the mobile terminals accommodation andnetwork configuration.
In predicting network failures, it is important to understand trivial changes from various
network states. Since various slices are provided in the B5G/6G era, various
communications characteristics should be achieved accordingly. It is therefore important
to be able to appropriately extract information concerning the network state, which resultsin the predictions of network failures, depending on the slice.

58

5.4.3.5. Technology to Achieve Safe and Reliable Communication
Secure, end-to-end secrecy of communications and continuity of connectivity are
required to improve the security and reliability of communications (Figure 5-19). Securityof communications requires the realization of end-to-end confidentiality for IoT devices
that require low power consumption. Continuity of connectivity will require a means tocompletely eliminate the conventional wireless communications characteristic of“occasionally being interrupted because it is wireless.” It is therefore necessary to utilize
multi-session control using multiple base stations simultaneously, and to develop ameans of monitoring the state of the network to identify areas that can continue to beconnected, and those that cannot.

Figure 5-19: Delegation of secret processing of communication andcommunication session redundancy
5.4.3.6. Technology to Achieve Scalability in Mobile Communications Systems
For scalability, it is necessary for the mobile core network to handle multi-hop
communications resulting from cooperation between the mobile terminals, and to
manage them efficiently in environments with vast numbers of base stations. Multi-hop
communications are a complementary measure to ensure continued connectivity, which
may be considered together with methods to ensure reliability (see Section 5.4.3.3). In
this multi-hop technology, one terminal carries the communications of another, and so a
power-saving method of communication is important for its widespread use. A flooding
time synchronization protocol has been developed as a technology to achieve this [5-49].
Although further technical innovation is required to apply this to high-speed
59communication, it is expected that a low-power method of multi-hop communications canbe implemented.
A very large number of base stations will be used. It is of significant importance to
avoid location management following conventional mobile communication systems. This
will dramatically increase the frequency the location registrations of mobile terminals are
updated, and leads to raising the processing load of the entire mobile communication
system. It will be necessary to re-design the mobile core network with more sophisticatedlocation management or without the location management as discussed in Section
5.4.3.3.
5.4.3.7. The Direction of Mobile Core Network Architecture
Based on the requirements in Sections 5.2 and 5.4.3, it is expected that the mobile
core network will handle diverse communications in the B5G/6G era. In the conventional
mobile core network, which deals with various communication requirements uniformly,
the expansion of management data is expected to cause the mobile cores themselves
to become bloated, resulting in a deterioration of their operating efficiency. For example,
in the transformation from 4G to 5G systems, the component functions of mobile core
networks have almost doubled, from six or seven functions to twelve or fourteen. Iffunctions expand by the same factor in B5G/6G mobile communication systems,
processing power consumption will need to increase dramatically, together with anincrease in connected terminals. It is therefore essential to revamp mobile core networkarchitecture, including re-designing the location management (see Section 5.4.3.3).
The architecture of conventional mobile core network systems has been designed witha focus on improving treating management data (information elements), and almost allfunctions (accessing or refreshing managed data) are used in response to changes inthe state of mobile terminal communications. In reforming the system architecture, it isdesirable to reduce signal processing, i.e., to keep processing to the minimum necessary
when the communication state of mobile terminal changes.
Considering the mobile core network architecture should also incorporate the adoptionof software implementation techniques. In terms of software implementation methods,
there have been various studies on methods to improve reusability and methods to make
rapid modifications. In the latter context, the concept of micro-service architecture has
been proposed as a means to treat the diversity [5-50]. In this micro-service architecture,various permutations of micro-services (components) are prepared, and the components
maintain a weak relationship while they cooperate with each other (Figure 5-20).
It is necessary to carefully consider how to define components when introducing a60

micro-service architecture in the mobile core network. The important question isunderstanding processing relationship between designed components from the behaviorof the mobile core network in order to keep the components to have a sparse relationship.
Once the components are properly defined, the processing load on the entire mobile
core network can be reduced. Because exchanges of management data become sparse,
the processing load on them may be kept at a certain amount of variation, and this may
bring that processing capacity can easily be estimated based on the number of terminals
and their communication requirements.

Figure 5-20: Provision via microservice architecture to satisfy necessarycommunication requirements: An example of mobile core network architecture
5.4.4. Operation
KDDI is carrying out various R&D efforts toward implementing operational automation.
5.4.4.1. AI-based Operational Automation
The use of “5. AI”, one of the seven technologies of KDDI Accelerate 5.0, is being
promoted in various fields, and its use is also being considered in the network operations
field. With the further development of network virtualization and dynamic changes in
network segmentation, known as slicing, in the B5G/6G era the task of constructing andoperating networks will become more complex than before. Thus, since operational tasks
are also increasing, efficiency is required.
AI technologies are used to analyze information obtained from networks to makenecessary decisions in network monitoring, such as detecting failures, determining
causes, and selecting the recovery procedure. As stated above, networks consisting of61

virtualized hardware and microservice-based applications have a huge number of targets
to monitor, and the data obtained from that monitoring is also vast. Moreover, in the
B5G/6G era in which network configurations will change dynamically, there will be limits
to the number of rule-based decisions that can be made based on that vast and changing
information. Overlooked failures or incorrect implementation of recovery measures
beyond those limits may hinder communication between physical space and cyberspace.
For this reason, KDDI is carrying out R&D of technology to automate network operation
monitoring tasks by using AI technologies to repeatedly learn data such as logs andalarms generated from faults.
Furthermore, it is expected that the technology used for network virtualization will shift
from virtual machine-based technology to container-based, cloud-native technology. The
software quality of cloud-native technologies, which have thus far mainly been used in
the ICT field, has been enhanced in the online service field by DevOps and canaryreleases. In carrier networks, it is necessary to satisfy service level agreements (SLA)
for all traffic. KDDI has therefore been carrying out R&D to ensure that cloud-native
infrastructure satisfies carrier quality requirements. Specifically, as described in Section
5.4.3.4 , we are defining datasets that enable the predictive detection of network faults,
and we engaged in the R&D of technology to obtain metric data from container data [551].
5.4.4.2. Operations Robots“7. Robotics,” one of the seven technologies in KDDI Accelerate 5.0, is also being
considered for use in network operations. Hardware operations are not eliminated evenin virtualized networks, and physical tasks, such as periodic patrols and responding tofaults, are still required. It will be of benefit to use robots to complete these physical tasks,
which are currently performed by people.
Floor surfaces in data centers are flatter than those in outdoor environments, and it iseasy to operate robots in them because they contain few uneven surfaces such as gravel
paths. In conjunction with security functions to manage entrance and exit, theenvironments are designed to facilitate the movement of robots while maintaining
security. Considering these advantages, it is possible for robots to automatically patrolrooms in a data center and to automatically notify users of any detected abnormalities.
It is also possible for a human to remotely operate a robot to open rack doors and replacea failed unit within a server. By utilizing robots to support this kind of physical work, it ispossible to provide stable hardware resources for network function virtualization (NFV).

62

5.4.4.3. Operational Automation using Open-source Software
There are also various open-source software initiatives for network operations. There
are several advantages to using open-source software. Although it depends on the policyof the community managing the open-source software for network operations—theinterface with the virtual network function (VNF) to be controlled and monitored, together
with its management system—the software is often compliant with typical standard
specifications such as 3GPP and ETSI, which is advantageous for multi-vendor
configurations. Moreover, open-source projects often include not only formulating
specifications but also software implementation in their scope, making it easy to proceedwith operational validation, and where it features a desired specification or function, thiscan be reflected by participating in the community and providing source code.
Within open-source software for network operations, the Open Network Automation
Platform (ONAP) is one of the most reliable from the perspective of the companies
participating the project, which is reflected in its presence and the frequency of releases.
ONAP is a platform that automates the operation of carrier-grade virtualized networks,comprised of DESIGN-TIME (the part for designing networks) and RUN-TIME (the partfor monitoring networks). ONAP is promoting the creation of an ecosystem throughsmooth linkage with external systems. For example, the project jointly with O-RAN
ALLIANCE, a community making an Open RAN specification, is formulating an interface
with a non-real-time controller to support network slicing feature providing SLA
assurance service and flexible function deployment.
In this way, open communities and standardization organizations are actively
collaborating and complying with one another’s specifications. By strategically adopting
examined standards and software, it will be possible to efficiently advance theautomation of operations using software that applies expertise from its various
stakeholders in a global manner.
5.4.4.4. Autonomous Networks
Autonomous networks are a concept in which the network itself operates automatically,
and is able to perform necessary configuration and fault handling automatically. The ITUT, ETSI, and TM Forum have been working on their respective projects under this name,and there is growing interest in expanding the possibilities for operational automation in
the future.
KDDI is working on the R&D of autonomous networks in the form of cooperative AI
technologies for inter-domain collaboration, extending the AI-driven network operations
described above, and to broaden the scope of application of operational automation.
63

Figure 5-21: Automation using autonomous networks
Figure 5-21 shows an example of automation by autonomous networks. The AI
automation in Section AI-based Operational applies to a single network domain suchas RAN or mobile core, and responds to faults within the networks. In actual practice,
however, the effects of operations or faults may extend beyond the domain, or thelocation in which a fault occurs may differ from the location of a recovery action, so it isnecessary to coordinate and address failures across domains. By learning the results ofworkflow execution and optimizing these adjustments, the impact of events such as
failures and planned work can be minimized and operational tasks can continue. This
cooperative AI technologies can be used to control events spanning multiple domains,such as RANs and mobile core networks, to accommodate end-to-end automation.
5.4.5. The White-boxing of Systems and Technology to Separate Hardware from
Software
The white-box development approach, which separates hardware from software(which are historically integrated), is expected to make further progress incommunication systems going forward to accelerate innovation and improveupgradeability, respectively. The expansion of the ecosystem is important to promoting
the development of white-box technology. To contribute this expansion, KDDIestablished the Telecom Infra Project (TIP) Community Lab (TIP-CL) in Tokyo in 2020,and continues to be active through the creation of projects to conduct basic validationand interoperability testing of base station aggregation gateways, to open up highcapacity routers (DOR: Disaggregated Open Router), and to promote study of RAN
64

slicing in O-RAN ALLIANCE.
Cloud-native networks are currently receiving attention as a new trend for white-boxing
communication systems. The original white-box approach uses a virtual machine (VM)
to implement network equipment on non-specific computers. However, such simple
white-boxing will not maximize the benefits of virtualization, because the costs ofdevelopment, operation, and management will remain the same. In contrast, cloudnative networks have been proposed as a tool to extract the benefits of hardwaresoftware separation to optimize development, operation, management, and scalability.
Cloud-native networks use container technology instead of a VM to separate network
functions into small units, called microservices, for flexible control. Such cloud-native
frameworks based on microservices are widely applied in the development andconstruction of modern large-scale web services; these frameworks ultimately enhancethe efficiency and scalability of development, operation, and management processes.
Such frameworks do not constitute a simple replacement of VMs with containers, as theyentail the re-design of network functions, such as those related to resilience,
manageability, and observability. Furthermore, the application of a cloud-native
framework to a communications system requires that the problems specific to cloudnative networks, such as the need to improve the throughput of inter-container
communication, be solved in advance.
Power consumption is a key concern for future technologies, because it increases withincreasing traffic. It should also be taken into consideration that it will be difficult tomaintain the conventional growth rules for processing technologies that haveunderpinned innovations in virtualization technologies to date [5-52]. In order to copewith this difficulty, it is necessary to utilize general purpose and energy-efficient chips
such as GPUs, FPGAs, and smart NICs as communication accelerators, rather thanrelying only on chips and processors such as ASICs and CPUs (Figure 5-22). Additionally,
we study a network domain-specific architecture (DSA) which optimizes chip
configurations, including accelerators with promoting clustered and networked
architectures [5-53].

65

Figure 5-22: Conventional virtualization infrastructure and virtualization
software configuration (left) andthe use of accelerators in future virtualization infrastructure (right)

5.4.6. Communications in space
To expand communication coverages for seas and mountainous areas that are
typically not covered by mobile communication services, the 5G Non-Terrestrial Network(5G NTN) is developed to provide communication services without terrestrial basestations. Figure 5-23 shows a realization form of 5G NTN, and it is through base stationsin the space/sky secure communication coverages, such as low-earth orbit (LEO),
geosynchronous (GEO) satellites, and high-altitude platform stations (HAPS). KDDI has
also begun to utilize LEO satellites, and a business collaboration with SpaceX to use
Starlink for backhauls of the au network is underway [5-54]. KDDI are also consideringvarious aspects of 5G NTN initiatives.

Figure 5-23: Applications for 5G NTN

66

Assuming around the 2030s, B5G/6G communications are expected to be prevalent, andthe scope of mobile communications is expected to be extended not only across theearth, but also to space. One of the prospective goals for space is to establish mobile
communications on the lunar surface. Achieving this goal will require lunar explorationand surface development, and those support by both the government and private sectors
has been steadily increasing in recent years. The Artemis program, which is led by theUnited States and has Japan as a participant, includes a plan for a lunar base by the2030s [5-55]. In addition, NASA has been studying LunaNet [5-56] to provide
interconnection networks between the earth and moon, such as the Internet. Regarding
the roles in LunaNet, NASA will handle the standardization, and the private sectors will
construct and operate the communication networks. In NASA's Lunar Surface Innovation
Initiative Technology Demonstration, a demonstration of LTE construction on the moonis being promoted by NOKIA [5-57]. In Japan, lunar communications are also discussed
at the strategic program for accelerating space development and utilize (called asstardust program), and at the Space ICT Promotion Initiative Forum [5-58] which waslaunched by NICT and JAXA and was participated by KDDI and KDDI Research [5-59].
Regarding the private sectors, a lunar transportation service by iSpace planned in 2023,
and SKY Perfect JSAT is studying communications between the moon and earth via
GEO satellites.
To establish B5G/6G communications on the lunar surface, three technical challenges
are addressed in Figure 5-24. The first challenge is the area-planning technology on the
lunar surface. Mobile communications area planning on the earth is achieved by applying
various conventional methods by mobile operators. However, a radio propagation on themoon may be different from that on the earth. Because the lunar surface has a metallic
component called regolith, it may have different reflection characteristics. In addition, it
is necessary to consider the lunar topography and the fact that radio propagation cannot
be measured as easily on the moon as it is on the earth. To overcome the first challenge,
the characteristics of radio reflection which are influenced by regolith must be understood.
Then, wide-area radio propagation estimation methods from sky-based images and asmall amount of radio propagation measurement will be developed [5-60]. The second
challenge is the network-control technology between the moon and earth. Because thedistance between the moon and earth is over 380,000 km, there is a communication
delay on the order of minutes. This is a different order from that for satellite
communication links, which has a delay of approximately 500 milliseconds. The satellite
communication links are often used as mobile communications backhaul links for basestations on the earth’s surface. To manage delays that exist on the order of minutes, flow67

control and congestion-control technologies based on store and forward communication.
In addition, construction method of the mobile communication backhaul with the store
and forward communication links will be required. The third challenge is optical wirelesstechnology that connects the moon and earth, and the earth surface and satellites. The
technology is expected to be applied to space communications, because it does not
require a frequency band, and it is suitable for large capacities. Utilization of thetechnology requires a digital signal-processing method to stabilize the communication
links, an interference control method for the lasers when there are many optical wirelesscommunication links, and a QoS control method. One-to-one communication is aprerequisite for current space communications technology, but the optical wirelesstechnology enable simultaneous n-to-n communications.

Figure 5-24: Technical challenges to establish a B5G/6G communication in the lunar
surface
KDDI has been collaborating on the HAKUTO project. As a part of the project, westudied the application of antennas and radio propagation on the lunar surface [5-61]. By
utilizing the knowledge in the HAKUTO project and efforts to overcome the three abovementioned challenges in Figure 5-24, KDDI will realize mobile communications on the
lunar surface with the same level of quality as that on the earth surface in 2030s, when
B5G/6G is expected to be applied.

68

6. "2. Security"
6.1. The Role of Security Technology in B5G/6G
The communication infrastructure that will support DX society is not limited to network
facilities in physical space such as base stations and core networks, but also includes
functions that allow data acquired from physical space to be stored and processed incyberspace, including systems such as authentication systems for various services, andBig Data processing systems for industrial data platforms. It will further develop as afoundation for CPS that feeds analyzed results from cyberspace into physical space.
Various functions required in the B5G/6G era will be added to communication networks
that incorporate virtualization technology enabling the flexible configuration of networks,
simultaneous access by large numbers of IoT devices, and new technologies that enableultra-low-latency communication. New features and increased network complexity will
create new security concerns, however. Ensuring the security of the ultra-high speed andlow-latency communication specified by B5G/6G, will require cryptographic techniquesthat can operate on a variety of devices at high speed and with low computational
complexity. If future technological advances succeed in creating a general-purpose
quantum computer, existing cryptographic schemes will be vulnerable to attacks by thequantum computer, potentially creating a foundational threat to communication secrecy.
It is therefore necessary to implement communication technologies that remain securein the face of quantum computers. Cyberattack targets will increase even further, due tothe massive connectivity and diversification of use cases that are expected in the course
developing B5G/6G as well. It is therefore essential to adopt and operate security
technologies appropriately to maintain a highly reliable communication network. The
implementation of appropriate security technologies in communication networks is a
critical issue to implementing highly reliable, ultra-secure communication networks.
Furthermore, the reliability of the systems used in the communication network must be
ensured by verifying the hardware and software. Although there is much clamor over ashortage of security technicians, and the training of security technicians is an urgent
issue so far, it will take even more time to train security personnel who understand thediversified and complex technologies of the B5G/6G era. Thus, there is a potential needto simultaneously consider security measures premised on reducing amounts of humanresources, and the importance of AI-driven automation will increase in the cybersecurity
field.

69

6.2. Security Technology Goals
6.2.1. Security Technologies Embedded in Next-generation Communications
Networks
Advanced security technologies are required to ensure the stable operation ofcommunication networks and protect network services from all manner of cyberattacks
in the B5G/6G era. In addition to configuring networks with secure hardware andsoftware, security verification and privacy protection mechanisms should be provided as
basic functions on the network. Moreover, while security has conventionally focused
primarily on boundary measures, either at end points or at gateways, in the B5G/6G era,functions will be distributed across the network, and since the creation of networks using
virtualization technologies will expand the range of aspects requiring security measures,
it will be necessary to implement security measures that can be applied efficiently to thevirtualized systems. In which communication networks will be more closely integrated
into various social activities, it will become more important for people to take appropriate
security measures, and efforts to support those actions will be required.
6.2.2. Protection of Next-generation Communication Networks
There are various technical challenges to the use of AI learning in the cybersecurity
field, including learning to attack events—which occur far less frequently than normal
events—effectively while amassing vast quantities of learning data; training AI models in
the knowledge of advanced security personnel, such as analysts working at the Security
Operations Center (SOC); and ensuring that AI models are able to keep pace withchanging attack surfaces and methods. In particular, the efficient creation/collection oftraining data, and the improvement in the quality and quantity of training data, areessential requirements for performance improvement, and so collaboration with multiple
organizations will be of benefit. The key to the security measures in B5G/6G is therefore
to promote broad collaboration in the cybersecurity field, and to accelerate thedevelopment of AI-driven security technologies by uniting an abundance of data withknowledge from a variety of people and organizations. It is also important to apply
homomorphic encryption, which enables arbitrary operations (addition and multiplication)
while remaining encrypted, so that data from multiple institutions can be aggregated in
an encrypted state, and analyzed in combination without disclosure to other parties.
6.2.3. Encryption Technology Supports Next-generation Communications
B5G/6G communication speeds exceed 100 gigabits per second [6-1], and fast andlightweight encryption schemes that can meet this speed requirement on a variety of70

devices will be required in B5G/6G era. The Shor algorithm

breaks current RSA public

key encryption and elliptic curve cryptography, and mutual authentication and end-toend encrypted communication will require the replacement of public key cryptographic
algorithms such as key exchange schemes and digital signatures with post-quantum
encryption.
6.3. Global Situation
Today’s 5G networks feature a mechanism to provide network-side functions toexternal users provided by network exposure function (NEF). Scenarios in which security
functions are provided by communications carriers, and in which an external user
incorporates security functions, have been proposed as an application [6-3]. At present,
firewalls, IDS/IPS 4, etc., are installed in the network, but there is also a need to develop
new security features for the advanced functionality of B5G/6G. Moreover, while the skills
required for security are becoming more sophisticated, the technology for implementing
people-adjacent security measure support has yet to be sufficiently studied.
On the other hand, the application of AI technologies to cybersecurity measures has
been progressing rapidly in recent years. AI technologies are being studied for use in avariety of cybersecurity products and services, including intrusion detection, attack risk
auditing, and threat information analysis , but this has produced many false positives
when compared to signature-based methods of detection, issues with explaining thereason for detection, and reliability issues with the AI itself, so it is being used in
combination with traditional methods.
Efforts to establish next-generation standard public key algorithms have also begun.
In 2017, anticipating the possibility that existing public-key cryptography will be broken
by quantum computers around 2030, the US National Institute of Standards andTechnology (NIST) launched a project, NIST-PQC, to define US standards for public-key
cryptography that will be secure against quantum computers [6-5]. In this project,cryptographic schemes in two categories—encryption/key exchange, and digital
signature—have been selected, and as of March 2021, seven final candidate schemes
have been released. The selection of a US standard is scheduled to be completed
around 2023, and the migration of cryptographic schemes is expected to proceed in turn.
6.4. Security Technologies Required for 2030
6.4.1. Security Functions for Communications Networks
The following technologies are being researched and developed as advanced security
4

Intrusion Detection System/Intrusion Prevention System.

71

functions required in future communication networks. There is a project on design of amechanism to prevent intrusion by unauthorized users by quantifying the trustworthinessof people and devices using the service, and controlling connection to the network basedon a trust relationship. Another project fucuses on development of a mechanism toaccurately detect abnormal or unauthorized communication attempts, even against new
types of devices, by using advanced traffic analysis technology, and to enableappropriate controls such as blocking access. A trusted information-sharing platform for
supply chains has been developed by utilizing security verification techniques for
hardware chips; the PoC is conducted to demonstrate the trustworthiness of the network
equipment. In addition, technologies enabling users to control the use of data such as
PPM , standardized by ITU-T, serve an important function in protecting privacy, and it isdesirable to use the technologies on communication networks as common functions fromthe perspective of privacy-by-design. By making a default function on communication
networks, it will be possible to apply appropriate privacy protection measures based on
the user’s consent and privacy preferences for each service to achieve appropriate data
usage. As shown in Figure 6-1, we aim to provide more extensive security and privacy
measures by making these technologies seamlessly applicable to each service on thecommunication network.

Figure 6-1: Security infrastructure with embedded security
and privacy functions
6.4.2. Robust AI-based Security Measures
As shown in Figure 6-2, KDDI has been conducting research and development of
attack detection and defense technologies using AI. In the WarpDrive project , whichaims for the advancement of online security countermeasure technologies, a large72amount of online access data is collected and accumulated, and seven domestic security
research organizations are cooperating to build and operate infrastructure for analysis
and research, and several innovative results have been produced 5. KDDI will continue
to both accumulate security Big Data and collaborate with other organizations to promote
the R&D of cybersecurity measures using AI in B5G/6G. AI technologies will also be used
to efficiently verify a vast amount of hardware and software. On the other hand, measures
to prevent attacks against AI models are also an important topic in preventing thecreation of new vulnerabilities through the adoption of AI technologies. Attacks against
an AI model itself have rapidly progressed in recent years, and methods for creating
malware that cleverly hide the malware from an AI’s detection are becoming increasingly
definite. As the use of AI technologies increases, threats from attacks are expected to
become more apparent, and KDDI is proceeding with R&D of countermeasuretechnologies. KDDI is also improving high speed, high performance, next-generation
homomorphic encryption for use in integrated analysis to make effective use of AI
technologies.

Figure 6-2: AI-driven technologies for attack detection and prevention
6.4.3. A New Generation of Encryption
KDDI is continuing to research and develop a next generation of cryptographic
algorithms for the B5G/6G era. In 2020, we succeeded in designing a next-generation
digital signature scheme as a post-quantum cryptographic algorithm. This is the first5

In particular, WarpDrive (Web-based Attack Response with Practical and Deployable Research InitiatiVE).

73

scheme in the world to be based on the LWR problem 6. It has already been proven to be
secure in a quantum random oracle model; it cannot be solved efficiently even with aquantum computer, and is about 10 times faster than conventional RSA encryption. We
are also designing ultrafast, next-generation encryption algorithms. These feature afocus on assessing the security of post-quantum encryption. The challenge when
adopting new cryptographic algorithms is to establish a consensus on the security. In
order to contributing to this global challenge, KDDI have been participating ininternational cryptanalysis contests targeting post-quantum encryption. We have
performed parallel optimization of lattice and symbolic cryptosystem decryption
algorithms for multi-core environments, and have achieved world records in four different
contests , which have contributed to building a matured consensus around secureparameter design. We will continue new efforts to prepare for further paradigm shifts incryptography to protect the security of communications from advancement ofcryptanalysis techniques.

6

The “learning with rounding" problem: Solving simultaneous equations with rounding errors.

74

7.

"3. IoT"

7.1. The role of IoT technologies in B5G/6G
In recent years, a wide variety of devices, including smartphones, have been equippedwith communication functions, and 30 billion devices are said to be in operation as of2021 [7-1]. These devices are referred to as IoT devices. In the world of 2030, IoT will
permeate all aspects of life more than they do today, and all aspects of cities, including
homes, cars, buildings, roads, factories, shops, and fields will be “connected,” i.e.,
physical space and cyberspace are expected to become more closely connected. The"vast number of simultaneous connections" feature of B5G/6G, which is discussed in
Section 3.1, also supports this expectation. The series of flows that KDDI Accelerate 5.0
aims to achieve is one in which data acquired in physical space is given value incyberspace before being fed back to physical space. IoT technology, which makes allthings connected, will be important in accelerating this trend.
7.2. IoT Technology Goals
In this chapter, IoT technologies are classified into the following four categories forfurther discussion:(1) Technology related to sensing information in physical space(2) Technology related to communications for transferring sensed information to
cyberspace(3) Technology related to data processing for processing information in cyberspace(4) Technologies for the design and operation of IoT devices, with consideration formaintenanceKDDI has been utilizing IoT in a variety of use cases [7-2]. In one, we take theautomotive field in this section, because the benefits of high capacity and low latency,
which are prominent feature of B5G/6G, can be enjoyed to the greatest extent. The
automotive field is predicted to become a vast market worth $166.3 billion globally by2027 [7-3], and discuss the forms of (1) to (3) for which we are aiming. In the near future,vehicles will equip with many sensors such as cameras and LIDAR 7 to monitor road andtraffic conditions, and vehicle sensors will acquire sufficient data to reproduce traffic
conditions around the vehicle in cyberspace. With the evolution of device technology, theperformance and cost of sensors has improved at a rapid pace, but each type of sensor
has its own characteristic. Cooperation between multiple sensors, and the consolidationof sensed information, are therefore important. Furthermore, technology is required to
implement fast, low-power cooperation between sensors and the consolidation of data.
7

Light detection and ranging (LiDAR), or laser imaging detection and ranging (LIDAR).

75

The vast quantities of varied data acquired by these sensors are first processed by thecomputer in each vehicle, before being transmitted to cyberspace via mobile network. A
new network for the B5G/6G era, one capable of handling large quantities of data andlarge numbers of simultaneous connections, will be essential to the transfer of hugevolumes of information from physical space, such as images and point clouds,
simultaneously from vast numbers of vehicles. In addition, since the volumes of
information from physical space to be handled are expected to increase with thisexpansion in data volumes, media compression technology will be as important as everto reducing the volume of bits transferred. Moreover, the cyberspace described above is
not constructed within a single server, but in a cloud, or a large number of distributededge servers. Technology is therefore required to dynamically select appropriate servers
and transfer data according to user requests, data type, vehicle location, and service
subscriptions. This data transfer includes cases in which processing that cannot be
handled by a vehicle’s on-board computer due to computational resource constraints is
offloaded to a server on the network. In this way, vast amounts of data generated bylarge numbers of vehicles reach cyberspace, and these data may require ultra-fastprocessing. This might include, for example, a traffic safety application. Looking to thebreakdown of the time delay moving from physical space to cyberspace and back tophysical space again, the time required for computation tends to be longer than the timerequired for communication, although this depends on the content and quantity to beprocessed. In other words, high-speed data processing technology is extremely
important from the viewpoint of reducing end-to-end delay. The establishment of thesetechnologies will bring us closer to the realization of scenarios in which, for example,
traffic conditions are completely reproduced in cyberspace in real time; traffic accident
risks are instantaneously calculated based on those traffic conditions; and vehicles thatare judged to be at high risk of causing an accident are remotely controlled to preventthe traffic accident.
Next, we look at a case applying IoT technology to primary industry, which has
requirements that are the opposite of those in the automotive sector—requiring neither
high capacity nor low latency—and consider the appropriate form for this technology. In
primary industry, large numbers of IoT devices are expected to be installed in forests,
fields, and maritime regions, and so power supply and maintainability are extremely
important. While it is easy to ensure a power supply when implementing IoT technology
in vehicles, it is very difficult in primary industries. Since many devices are installed inlocations where physical access is difficult, progress in technologies that enhancemaintainability, including battery replacement, will prove key to utilizing IoT in primary76

industry.
IoT is expected to be a catalyst to increase productivity and revitalize primary industry.
In agriculture, the system senses information in physical space, such as temperature,humidity, illumination, crop growth, and soil conditions, and uses this information forpredictions in cyberspace about the amount of pesticide to be applied, the planting time,
harvesting time, yield, and shipping time, and then feeds the information back to the
producer. In the fishing industry, physical environmental information—such as watertemperature, speed, direction of tidal currents, and salinity—is sensed, appropriate
fishing grounds and bait amounts are calculated in cyberspace and then fed back to thefishing industry workers. In these cases, IoT devices will be battery-powered, so ultralow power technology for sensing in physical space will be required. Power consumption
for communications when sending the sensed data to cyberspace should also be
minimized. Furthermore, energy harvesting technology is also important in the use of
ubiquitous energy, such as sunlight and vibrations, as a power source for IoT devices. In
the absence of these technologies, replacing batteries in IoT devices that are installed
deep in the mountains or offshore will incur significant costs. In addition to battery
replacement, daily maintenance and tuning are often difficult. The effort required torespond when a problem occurs, such as a decrease in sensitivity or data not beingtransferred, is also very large. Technologies for the design and operation of IoT devices
are therefore required to reduce maintenance costs as much as possible. If
maintenance-free devices can be achieved using these technologies, primary industry
productivity could be dramatically increased, as set out above, making it a very attractive
industry.
In this section, we have discussed an ideal state of IoT technology, taking automobiles
and primary industry as examples. As discussed above, the areas of application of IoTtechnology are extremely broad, and another characteristic of IoT is that there are few
technologies that can be used universally across all use cases. Constructing a common
platform to increase versatility, and optimizing it for each use case and application
through validation in the field, will be an important issue going forward.
7.3. Global Situation
Taking the automobile and primary industries discussed in the previous section as
examples, this section provides a bird’s-eye view of IoT technologies, as classified into
groups (1) to (4) set out in the previous section.
IoT-enabled vehicles are widely known as “connected cars.” At present, most selfdriving vehicles rely on sensor data from physical space to drive autonomously; however,77since Level 3 and greater vehicles will be equipped with remote monitoring and control
functions, and will need to both download high-resolution maps and upload sensor data
[7-4], these can be regarded as connected cars 8. With the entry of companies from manydifferent industries, including IT companies in the US and China, business in theconnected car field is expected to expand greatly. An ecosystem is being constructedconsisting of a variety of players, including not only existing automakers, on-board
equipment manufacturers, and semiconductor manufacturers, but also IT companies,
ICT infrastructure vendors, mobile service providers, insurers, map providers, and so on.
The application of LiDAR to self-driving vehicle sensors is drawing attention [7-5]. Most
self-driving cars being testing in Japan and overseas are autonomous vehicles that
operate using point cloud data of surrounding traffic sensed using a LIDAR installed in
the vehicle. Prices are expected to fall further in the coming years. A global consortium
led by players in the telecommunications and self-driving vehicle industries has been
actively discussing communications for self-driving vehicles. KDDI has joined the AECC
[7-6] and the 5GAA [7-7] to participate in these discussions 9 , 10 . Two communication
standards, cellular V2X (C-V2X) [7-8], based on cellular communications, and dedicatedshort-range communication (DSRC), based on 802.11 technology, have been proposed.
In Japan ETC2.0 11 is adopting DSRC.
On the other hand, China has decided to adopt C-V2X in the ITS frequency band (5.9
gigahertz band), and the sale of compatible vehicles is expected to begin soon. While
US IT companies such as Google, which proposed MapReduce in 2004, have a longlead in the foundations of data processing for self-driving vehicles, the open-source
community is also highly active in the field, and the technology is evolving day by day. In
both R&D and the business field, the use of open-source software, and the relationshipwith the community, are becoming very important.
IoT technologies for use in primary industry are known by names such as “smart
fisheries” and “smart agriculture,” and some of these are already being put into practicaluse. Some municipalities are actively supporting the introduction of these systems. For
example, KDDI is promoting the R&D of smart fisheries in cooperation with
Higashimatsushima City, Miyagi, and has developed a buoy equipped with sensors andcommunication modules called a “smart buoy.” In a proof-of-concept experiment, thesewere floated offshore, sensing air temperature, air pressure, water temperature, water
Levels of automated driving are defined by the US Society of Automotive Engineers (SAE), which
defines Level 3 as “conditional automated driving.” The driver of Level 3 and greater vehicles considered tobe the system, rather than a person.
9 Automotive Edge Computing Consortium.
10 5G Automotive Association.
11 Electronic Toll Collection 2.0
878pressure, salinity, acceleration, etc. This data was sent to a server for analysis via cellular
network, and analyzed in combination with past catch performance and weather data toquantitatively predict the catch. Compared with conventional predictions based on
intuition and experience, the new predictions based on IoT technology were highly
accurate. As discussed above, issues included power supply and maintenance. Since
the battery life of the buoy at the time of initial development was one month, it wasnecessary to replace the battery every month. The buoys also weigh more than 20 kg,
creating heavy manual work, and their structural complexity and need for frequentcleaning have laid bare maintenance-related issues. Field experiments are being
conducted in various locations, but since use cases and issues differ depending on theindustry and people involved, optimal solutions are being developed for each use case.
7.4. Technologies Required for 2030
As described above, the IoT field has a wide range of use cases and issues, and thedomain of required technologies is also broad. KDDI is aiming to create technologies tosolve social issues by actively promoting collaboration with partners (universities,
companies, etc.) and communities (standards developing organizations, open-source
communities, etc.) in peripheral fields, while keeping communications at its core. Below,
as shown in Figure 7-1, we introduce IoT technologies according to the categories
defined in Section 7.2.

Figure 7-1: IoT technologies

797.4.1. Technologies Related to Sensory Data from Physical Space
When sensing location information in physical space, different sensors will be suitablefor different conditions. GNSS 12 is suitable for outdoor use but not for indoor use; video
is suitable for clear, daytime conditions but not for bad weather or at night; and fixed
sensors such as cameras or millimeter-wave radars can sense only areas with line-ofsight. We are therefore considering approaches to improve sensor accuracy through theuse of sensor-fusion technologies, which integrate data gathered by various types of
sensor. KDDI has been conducting field experiments to demonstrate sensor fusion
technology and advanced location information sensing. As described above, sensor datacompression technology is also important. KDDI is actively advancing R&D into video
compression and point cloud data compression technology, and has been promoting
activities to standardize these technologies. The video compression and point cloud datacompression technologies are described in detail in Chapter 10.
7.4.2. Technology

RelatedtoCommunications

forTransferring

SensedInformation to Cyberspace
To satisfy diversifying requirements, it is important for packet processing nodes tounderstand the requirements of IoT devices with a very fine granularity, and process
them optimally using data transfer technologies, together with the cloud and large
numbers of MEC hosts. KDDI has been actively promoting the R&D of these.
Since current IP-based networks are based on best-effort packet forwarding, a newarchitecture is required for packet processing nodes to understand application and user
requirements in detail. KDDI has therefore been conducting R&D into application-aware
networking, a framework enabling appropriate control and resource allocation on the
network side by explicitly notifying the network of application quality requirements [7-9].
KDDI anticipates that various IoT devices will be connected to networks, such as selfdriving vehicles and dedicated devices for XR, and is investigating how MECs canensure versatility and interoperability of applications in the face of differences inrequirements between applications, and differences in functionality and performance
between devices. In environments where mobility and resource utilization are constantly
changing, there is a need for each application to select an appropriate MEC host toconnect to so that all devices can effectively utilize MEC on an ad-hoc basis to maximize
application quality. In addition, KDDI is working on standardization to realizeinteroperability between MEC systems installed on each operators’ network domain. This
12

Global Navigation Satellite System

80technology is necessary when each device connects to each MEC located in different
operator's network [7-10].
Furthermore, communication systems with ultra-low power consumption are desirable,
particularly for applications in primary industry. We are also studying communication
methods that can extend battery life several times longer than under the existing lowpower, wide-area (LPWA). Since there is generally a trade-off between communication
quality and device power consumption reduction, we will also study methods to flexibly
update design and operation according to application and use case.
7.4.3. Technology Related to Data Processing for Processing Information in
Cyberspace
Infrastructure for high-speed, efficient Big Data processing is more important than ever.
Drawing on the wisdom of the open-source community, KDDI will actively participate inecosystems that return results to their communities. We have already proposed ascheduling scheme based on Apache Spark that improves resource utilization efficiency[7-11], and a method that reduces latency when loading applications [7-12], and willcontinue our research going forward.
7.4.4. Technologies for the Design and Operation of IoT Devices with
Consideration for MaintenanceWe aim to improve maintainability and eventually achieve maintenance-free operation.
Achieving this will require not only reducing power consumption as much as possible,
but also energy harvesting technology to convert the ubiquitous energy around us into a
power source. In the smart buoy example above, the system initially operated for only
about a month, but by developing a new communication method and using solar power,
it became possible to operate the smart buoys for about a year. Going forward, we willcontinue to explore the best methods to reduce power consumption, and methods ofenergy harvesting, that are appropriate for each use case. In addition, hardware,
including sensors, must be maintained, monitored, and replaced, limiting improvement
to maintainability. We will therefore research technologies to improve maintenancecharacteristics by increasing the number of functions that can be defined using software.

81

8. "4. Platform"
8.1. The Role of Platform Technologies in B5G/6G
With the spread of the Internet, cyberspace has appeared as an addition to thephysical space of the real world, and now it plays an important role in our lives andeconomic activities. For example, rather than buying CDs or other media, people are
increasingly using online services such as streaming services to access music, and thishas been achieved by linking the action of listening to music in physical space tocyberspace using a network.
KDDI predicts that by 2030, CPS will be advanced by the linking of the seventechnologies set out above. KDDI calls this linkage “orchestration,” and the platform is at
the center.
Platforms play an important role even today. In streaming music services, for example,
one cannot provide such services simply by preparing music data. Many functions are
required, including digital space for storing data, a server for managing user access, anda billing service. Having a single company develop and provide all functions required forthe service would thus require tremendous cost and research, and it would takeconsiderable time to provide the service.
This is where platforms come into play. By using services such as data storage andelectronic payment on a platform, music streaming companies can focus on development
efforts such as recommending songs to users, and shorten the time to service launch.
8.2. Platform Technology Goals
KDDI is engaged in research on a wide range of platforms (Figure 8-1). These include
not only Internet access and payment services, but also social infrastructure such as
electricity. By combining many of these platforms, it will be possible to use them for evenlarger social infrastructure. One example of which is a “city OS,” the Japanese version
of smart cities. The various controls provided by a city OS required consideration of threefactors: improvements in users’ standards of living; economic activity as a driver; andenvironmentally conscious activities as a social system, such as those involving energy
and global warming.
Today it is often necessary to visit the municipal office to access public services. In
other words, this is a system that often centers on physical space. While there are someorganizations and fields that are moving toward online systems, in many cases thesehave adopted specialized systems, making it difficult to reuse them in other regions or tocollaborate in horizontal development.
City OSes are being studied as a solution to these problems. A city OS provides
82

service APIs (application programming interfaces) for education, healthcare, energy,
mobility, urban planning, and disaster prevention. These can be used to simplify reusein other regions, and can be developed and introduced inexpensively.
City OSes further provide functions for managing and analyzing data collected fromsmartphones, IoT devices, and various sensors, as well as functions for personalauthentication, payments, and personalization to provide services to users. In so doing,
it is possible to construct a system in which the services provided by the city OS are
created in cyberspace available for access by residents living in physical space.
City OSes will also introduce IDs and payment functions that will be shared between
all services. Since there is no need to create a login ID for each service, services can beaccessed seamlessly. Moreover, by using the mobile payment function and associatedbiometric authentication, payments can be automatically completed at the time a service
is used, eliminating the action of making a payment. When you receive a service inphysical space, the payment is completed automatically in cyberspace. We aim to realize
such a CPS by 2030.

Figure 8-1: A platform concept for 2030
8.3. Global Situation
The movement to solve social problems and design cities by leveraging AI
technologies and Big Data in cyberspace are gaining momentum around the world. For
example, in Singapore, “Smart Nation Singapore” has been promoted since 2014 as asmart city policy to solve social problems, generate innovation, and improve people’s
83

lives by using digital technology and data [8-1]. In 2014, Dubai in the United ArabEmirates announced “Smart Dubai 2021,” which presented a roadmap for conversion toa smart city by 2021. Many studies are underway, such as a virtual currency strategy
using blockchain; efficiency improvements for administrative services using egovernment; optimized energy use; etc. One of the themes of Smart Dubai 2021, “smart
mobility,” focuses on the development of automated driving to prevent traffic congestion
and achieve a safe travel environment [8-2]. In Japan, the “Super City Concept” has been
proposed to achieve the world’s most advanced Japanese-style super city by utilizing
the national strategic special zone system [8-3]. With regard to MaaS, a time-limited
proof-of-concept experiment is underway that includes next-generation mobility built on
on-demand and ridesharing, and a fixed-rate service that integrates multimodaltransportation, based on the characteristics of each region. Some of these proof-ofconcept experiments are being conducted without driver operations, using remotecontrolled vehicles in anticipation of the coming shift to automated driving under nextgeneration mobility. As exemplified by the lifting of the ban on Level 3 automated driving
on public roads in April 2020, the evolution of self-driving technology has beenaccompanied by deregulation in laws and regulations.
8.4. Technologies Required for 2030
8.4.1. Key Technologies for Operating a City OS
In addition to convenience for residents, city OSes have the advantage of operating
cities more efficiently. For example, it is possible to predict the movement of people
based on information from sensors positioned in each city, and to reduce unnecessary
power consumption by appropriately adjusting the amount of power supplied. Thus, bycollecting and utilizing user data, we can improve convenience and economy. The issue,
however, is how to handle user data gathered in such cases. Moreover, at present, eachservice has its own terms of service, and consent is required for the data to be collected.
PPM (see Section Security Functions for Communications ) is a technology that can beapplied to the issues above.Security Functions for Communications
PPM features a function to act as a proxy for the management of personal information.
It works this way: (1) A user gives a PPM his or her personal information such as an email address or residential address, and a telephone number that can be transferred toa service provider without issue. (2) When a user uses a service, the PPM presents the
personal information required to use the service to the user and obtains the user’s
consent. (3) The PPM then passes the necessary personal information onto the service
provider. By using a PPM, users do not need to provide data they do not wish to give to84

service providers, and can use the service in a secure manner.
Operating a city OS will require the management and analysis of an enormous amountof data. Moreover, high-speed, low-latency data transmission technology will beessential for sending and receiving that data. AI and quantum computing technologies
will be used to manage these vast amounts of data, and both 5G and B5G/6G will be
used for high-speed, low-latency data transmission. In other words, a platform alone
cannot operate a city OS: it is important that the seven technologies work together.
In so doing, it is important to utilize and manage large volumes of data collected invarious ways. This has led to the development of FIWARE, a data utilization platformthat can integrate and manage a wide variety of data models. In many cases, traditional
city-provided services do not allow for the cross-sectional use of data. The adoption of
FIWARE as a standard in the smart cities field will enable the interoperable use of datafrom various services and the construction of a more flexible system spanning a breadthof services (Figure 8-2).
Message queue telemetry transport (MQTT) has also been developed as a protocol
for the efficient transfer of data. This has been developed based on TCP/IP, and theprocessing involved has been made lightweight so it can work even when the network is
unstable or when the device’s performance is poor. While smartphones and PCs are
becoming more sophisticated, IoT devices often have limited performance, so using
MQTT enables stable and reliable data transmission.

Figure 8-2: City OS architecture and constituent technologies
8.4.2. Opening up MaaS (Mobility as a Service) Using Automated Driving
Besides city OSes, there are other fields where platforms are expected to be used.
One example is MaaS, a service that improves the efficiency of transportation by85

seamlessly connecting various modes of transportation such as public transportation
facilities (Figure 8-3).
At present, it is necessary to arrange modes of transportation to a certain place
individually. For example, if people travel from their home to the nearest station using
bicycle sharing, take a train to the nearest station in the region they wish to travel, andthen take a taxi to their destination, they would need to arrange for each individual service
separately. MaaS eliminates this hassle and makes it possible to use the services ofeach transportation system in unison. Platforms are important when making lateral useof services. For example, it is necessary to implement one-stop payments for bicycle
sharing, trains, and taxis. To solve this problem, the general transit feed specification(GTFS) format, which is used today for data related to timetables for public transportationand geographic information, can be used for applications and functions such as route
searches. In addition, payment services are becoming increasingly platform-based, and
it is becoming possible to make payments for multiple services with a single mobile
payment. By linking the payments for each service, it becomes possible to provide
services such as discounted rates.
MaaS-connected systems are not limited to public transportation; they can also
connect to services in cyberspace. For example, it may become possible to automaticallybook a taxi simply by searching for a destination on one’s smartphone and registering
the travel route in one’s itinerary.
Moreover, if user location data can be obtained, the taxi company can automaticallydispatch a vehicle when the user arrives at the station.
MaaS, like city OSes, consists of platform infrastructure and application infrastructure.
The application infrastructure has access to platform infrastructure such as
authentication, search, bookings, and payment, and it facilitates the rapid developmentof applications tailored to the needs of each region. In addition to the functions of a city
OS such as positioning, mapping, and data management, the platform infrastructurefeatures microservices with MaaS-specific functions, such as route searches and vehicle
dispatch calculations, which can be freely combined and used according to the needs ofeach region via a service API. It may be possible, for example, to use a service API for
urban planning and disaster prevention in the city OS, built for the purpose of handlingmovements of people, to adjust the numbers of taxis or shared bicycles dispatched.
MaaS is expected to spread rapidly alongside the development of automated driving
technology. Self-driving vehicles can operate more efficiently because the system canmanage vehicle arrival and departure locations, boarding and alighting of passengers,
travel routes, booking status, the arrangement of ridesharing and automation of route86

settings Japan Cabinet: National Strategic Special Zones, and connections to othermobility systems

Figure 8-3: MaaS and platforms
8.4.3. Changes in Behaviors Resulting from MaaS
MaaS is a new service to improve the efficiency of transportation, but its introduction
is expected to give rise to other, new demands. One of these is a change in user behavior.
Suppose, for example, that a user is using a MaaS to travel using a self-driving vehicle
service. When a traffic jam occurs, the MaaS (its automated vehicle service) suggests a
detour. In so doing, depending on the time of day, a service separate from the automateddriving service can provide a list of restaurants to stop by on the detour.
Thus, through the introduction of MaaS, users can act and move rationally and enjoy
further added value, but one of the issues that must be solved to achieve this is acombinatorial optimization problem. For example, when a salesperson tours several
cities, they can choose from various routes, but there are only a limited number of
efficient routes available. At present, however, it is necessary to consider an extremely
large number of conditions and patterns to search for the optimal route, and this takes
time to analyze even when using a supercomputer. KDDI is working on technology toprovide this processing at high speed (Figure 8-4).

87Figure 8-4: The combinatorial optimization problem
8.4.4. Automated Driving
Dynamic maps that reflect real-time information are also important for implementing
automated driving (Figure 8-5). Automated driving vehicles are equipped with various
sensors including not only camera imaging, but also LIDAR, sonar, etc. By utilizing data
gathered in real time using these sensors, seamless automated driving can be achieved.
Furthermore, if these data can be reflected on an actual map and used as a dynamic
map by other automated vehicles, this will make possible smoother automated driving.
R&D of automated driving technology is underway with a view to 2030. ”Autoware,”open-source software facilitating the development of automated driving, also continues
to evolve, making it possible to construct systems simply by using existing vehicles andsensors in the development of automated driving and testing.
AI technologies are also essential for automated driving, and unsupervised learning
for automated driving AI is also under development. Using unsupervised learning, an AI
engine can be created without drawing on vast quantities of data, which is expected tosignificantly reduce both cost and time.
Automated driving vehicles themselves have communication functions, and V2X has
been formed to enable vehicles to communicate with all manner of objects. However,communication functions are required not only by vehicles, but also by the infrastructure
required for driving. For example, if all the objects that come together to form roadways,
such as traffic signals, signs, surveillance cameras, etc., are equipped with
communication functions, it will become possible to provide more comfortable automateddriving.
88

Figure 8-5: Distribution of maps to self-driving vehicles
8.4.5. Quantum Computing
Quantum computers are expected to be able to solve problems that are difficult forclassical computers because of computational complexity. A classical computer performs
sequential calculations using bits that represent either 0 or 1 states explicitly. Alternatively,a quantum computer uses qubits, which represent probabilistic states of 0 and 1 byapplying the concept of quantum mechanics, to calculate superposed multiple
information in parallel. Thus, by increasing the number of qubits, a quantum computercan achieve massively parallel computations that are not feasible for classical computers.
The first commercially available quantum computer is a quantum annealing machinethat was developed by D-Wave Systems [8-5]. Although it cannot be used for generalpurpose calculations, unlike classical computers, its potential as a solver for
combinatorial optimization problems, i.e., its ability to quickly find good approximate
solutions, is being investigated. In addition, other systems that can be used in the same
way as a quantum annealing machine, such as high-performance simulation software[8-6], and dedicated machine implemented in electronic circuit [8-7], have also beendeveloped. These systems are not quantum computers, and are inferior in terms ofspeed, but they can be applied to solve larger combinatorial optimization problems
because of the limited number of qubits in the current quantum annealing machine.
Systems that are dedicated to combinatorial optimization problems are called Isingmachines, and they include quantum annealing machines.
Gate-based quantum computers are also being developed for more general-purpose
calculations. A number of companies, including major players such as IBM and Google,89

as well as start-ups such as IonQ and Rigetti Computing, have announced thedevelopment of gate-based quantum computers. Some of the systems are available via
cloud services such as IBM Quantum [8-8] and Amazon Braket [8-9]. Gate-based
quantum computers are currently being applied to solve optimization problems, and for
simulations, AI, and security, which involve calculation that are difficult or time-consuming
for classical computers [8-10].
The concept and use of quantum computing differ from those of classical computing.
Ising machines can be utilized by assuming that qubits are the decision variables of the
combinational optimization problem to be solved, and by formulating the problem into an
Ising model, which is a statistical mechanics model. In the case of gate-based quantum
computers, a dedicated algorithm that considers the behavior of qubits (i.e., a quantum
algorithm) must be designed for the process to be executed; this algorithm isimplemented by a quantum circuit, which is a sequence of qubit operations called aquantum gate. Thus, quantum computing technologies that differ from existing classicalcomputing technologies are required for the effective utilization of quantum computers.
Existing quantum computers have many practical problems that need to be overcome[8-11], such as a small number of available qubits and a high rate of qubit errors, which
serve to degrade the state of a qubit. However, some Ising machines have already been
applied to address these practical problems. It is expected that Ising machines, including
quantum annealing machines, will be available for application to various combinatorial
optimization problems by 2030. Gate-based quantum computers are also expected to
become practical for application in fields such as security by 2030 [8-12][8-13]; the extent
of practical applicability is expected to expand thereafter.
KDDI is also investigating the application of quantum computing in various domains,
including the seven technologies. We applied an Ising machine to the problem of the
dynamic allocation of shared frequencies to operators in order to confirm its utility in a
research project commissioned by the Ministry of Internal Affairs and Communicationsin Japan [8-14][8-15][8-16]. We also participated in a quantum computer demonstration
experiment to evaluate its practicability for industrial application; the experiment wasdemonstrated at the Mobile Computing Promotion Consortium; consequently, theeffectiveness of Ising machines was verified jointly with Hitachi, Ltd. and Jij Inc. byapplying one to an area management optimization problem that involved local 5G
operators [8-17] Furthermore, we evaluated the performance of Ising machines byapplying it to a parameter optimization problem involving LTE base stations [8-18], andto a generalized vehicle routing problem involving MaaS and logistics [8-19]. To ensurethat quantum computers will be able to be utilized in various domains in the future,90

including the seven technologies, we plan to validate the application of Ising machines
for more combinatorial optimization problems, and to establish quantum computing
technologies for the effective utilization of gate-based quantum computers.

91

9. "5. AI"
9.1. The Role of AI Technology in B5G/6G
With the spread of the Internet, both the physical space of the real world andcyberspace have come to play important roles in our daily lives, economic activities, andsocial systems. In 2030 we will see the advance of CPS through the linking of the seventechnologies described above. In this linking, AI will analyze data and information fromthe real world accumulated on platforms in cyberspace, and deduce ways of providing
feedback to the physical space.
9.2. AI Technology Goals
The most commonplace form of AI technologies today is “cyber-based AI,” which has
been developed in cyberspace. Cyber-based AI has been driven by global OTT players
in Internet services, such as Google, Amazon, Microsoft, Baidu, and Alibaba. It hasdeveloped by specializing in tasks such as image and text classification using deeplearning, drawing on large volumes of image and text data, etc., held by these companies.
The creation of AI models using deep learning requires vast amounts of data, so thekey issue is how to collect such data. For instance, voice recognition requires voice datafrom a variety of people, and OTT, which has many users, can collect data more easily
than other companies. As such, AI is being used for many online services, such asmachine translation, image recognition, as well as targeted advertising and pricepredictions in the digital marketing field. On the other hand, future applications of AI are
expected to shift from cyberspace to physical space—one example being the use ofvoice recognition in smart speakers.
The challenge, in terms of the creation of AI models in physical space, lies in creating
the technology to generate AI models from small quantities of data obtained from diverse
environments. For example, when using AI for traffic and disaster response, thecharacteristics of each region and site as well as the available data vary greatly. As such,it is necessary to optimize the use of available data in performing tasks such as predicting
traffic congestion and demand, as well as predicting the scale, details, and incidence ofdisasters.
Moreover, we can assume that society will change in the future based on AI-derived
outcomes, and users themselves will change their behavior in response. To this end, AI
technology will be required to provide more than uniform information and qualitativeknowledge of human psychology as it has done in the past, and persuade people
effectively to change their behavior by using quantitative understanding of psychology
grounded in data and drawing on qualitative knowledge of psychology. There are, for92example, many cases where restaurant recommendations are standardized, showing asimple AI-selected list of restaurants that a user may like. However, by timing the results
or presenting them differently (e.g., using visuals) to make users want to visit a restaurant,
it will be possible to amplify their desire to go to that restaurant, and cause them tochange their behavior.
9.3. Global Situation
The need for “physical space-oriented AI” to generate AI models that solve various
tasks in a variety of environments with small amounts of data has been described in“Future Courses of Action Japan Should Take for Basic AI Technology” [9-1], publishedby the Artificial Intelligence Research Strategy Division of the National Institute ofAdvanced Industrial Science and Technology (AIST) in May 2018, and in “AI Strategy
2019” [9-2], published by the Japanese government in June 2019. In the corresponding
AIST literature, the issues are organized around three axes: AI that can cooperate withhumans, AI that can be trusted in the real world, and easily constructed AI, i.e., AI that is
not limited to cyberspace but that can be applied to data generated from services that
include physical settings, which is one of Japan’s strengths. Additionally, thegovernment’s AI Strategy 2019 states that the field of “real-world industry,” where value
is created through interactions with people, nature, and hardware and not solely incyberspace, contains vast amounts of information that have yet to be systematicallyacquired, and that it will be necessary to actively focus on this area. At international
conferences on AI, where the number of submissions has increased significantly inrecent years, there has been an increasing number of workshops and sessions related
to real-world AI, such as the “Workshop on Continual Learning” [9-3] and “Transfer,
Adaptation, Multi-task Learning” [9-4], a session conducted at IJCAI 2020.
Behavioral change techniques have long been addressed in the field of social
psychology in persuasion research [9-5], but such efforts have begun to use and apply
ICT in recent years. Fogg proposed CAPTOLOGY (“computer as persuasive
technologies”), a concept that uses computers to persuade [9-6]. Oinas-Kukkonen
proposed the persuasive system design (PSD) model, systematizing design proceduresand factors to be considered when designing systems to elicit user compliance [9-7];
examples of its applications in model interface design have been published [9-8].
Although personalizing persuasion based on these investigations remains a challenge
at present, efforts to use AI to elicit behavioral change are beginning to draw attention in
the academic community. For instance, a new workshop, “AI for Behavior Change,” wasconducted in February of 2021 at AAAI, one of the leading international conferences in93

AI [9-9], along with a new topic added to its scope that uses “persuasive technology”(PT), aptly named “AI for Persuasive Technology” [9-10]
KDDI has been using for some time AI that uses location-based Big Data in thephysical space. Demographic analysis/forecasting technology, which estimates andpredicts population dynamics in any given area in Japan on a quasi-real time basis, isbeing developed based on behavioral analysis technology that evaluates routes andmodes of transportation using location-based Big Data provided by consenting users [911]. This makes it possible to estimate and predict not only the population distribution ofa given area in real time, but also detailed information such as the number of people
travelling or staying in said area, and those travelling by rail or along roads. Providing
such information on road conditions and evacuation sites in times of disaster, or traffic
congestion and transportation facilities during sizable events, can support a safe andsecure society. On the other hand, since circumstances in the city—especially physicalspaces—are constantly changing, vast quantities of training data for the most up-to-datesituations are required to maintain a high predictive accuracy, and cost is consequently
an issue.
9.4. Technologies Required for 2030
KDDI aims to solve social issues by using “real-world AI” that resolves issues specificto the real world, namely, “AI for behavior change” that understands human psychology
and can individually tailor their solutions, and “trustworthy AI” that can be used withconfidence.
Real-world AI can be classified into four types:
“Reusable AI,” which uses pre-generated AI models and adapts them to multiple
tasks using limited quantities of data;“Multimodal AI,” which combines varied modes of data (e.g., spatial and temporal)
to generate highly accurate AI models;“Collaborative AI,” which leverages both implicit and explicit human knowledge; and“Self-improving AI,” which modifies itself to adapt to frequently changing real-world
environments.
As Figure 9-1 shows, KDDI launched a new joint research project with top-level

overseas universities in October 2020 to promote their and our research [9-12].

94

Figure 9-1: Real-world AI and collaborators
Additionally, we have defined two types of AI for behavior change:“Human psychology AI,” which models characteristics based on psychologicalresearch findings and seeks to understand users and their psychologicalcharacteristics through biological data and daily actions; and“Persuasive AI,” which predicts the most effective and appropriate means of
persuasion and its results for each user, based on research findings in social andcognitive psychology.

In addition to these, we are undertaking R&D in defining “trustworthy AI,” which shows
the user the basis on which the AI makes its decisions in an easily understood format.
9.4.1. Real-world AI
9.4.1.1. Reusable AI
Reusable AI is a type of technology that generates AI models which can solve a varietyof tasks using small quantities of data.
To use AI in real-world situations like predicting railway demand for Route A, or
forecasting disasters for District B, across areas like transportation, disaster response,and health or medical care, it is necessary to generate individual AI models for eachenvironment or area. Moreover, a certain amount of accurate data appropriate to theenvironment or task to be analyzed is required for learning. Accurate data for trainingrefers to accurately labeled feature data. For example, in the case of predicting railway
demand, an accurate label would refer to the number of passengers, while the feature95

data would be the daily number of passengers video footage from around the station,
weather forecasts, population demographics in the station vicinity, and so on.
It is easy to collect large amounts of accurate data in more technologically advanced
metropolitan areas and regions. However, low-population areas face problems such aslack of sensors and small number of users, which lead to an insufficient amount of data.
As such environments from which feature data can be collected is limited. There are also
many issues regarding quality, quantity, and cost of predicting accidents, disasters, andfailures—for example, the huge cost of manually assigning accurate labels and the rarity
with which relevant events occur.
As described above, “reusable AI” is effective in cases where only small amounts ofaccurate training data can be secured for analysis, as AI models generated from accurate
training data from other environments and tasks are used to generate highly accurate AI
models for the environment and task at hand for analysis.
Reusable AI generates models attuned to the target for analysis by further training
already trained models. In the railway demand forecasting example above, the model istrained by incorporating data collected from stations in low-population areas into thealready trained model created for stations in metropolitan areas. In marketing, where itis difficult to build an AI model for product recommendations using only data collectedfrom the company’s userbase, marketing that handles products across multiple
companies (known as “cross-domain marketing”) can be achieved using data from othercompanies’ userbases and product recommendation AI models. By reusing these AI
models instead of collecting vast quantities of data for each use case, the cost and time
involved in data collection can be greatly reduced.
In recent years, there has been much research on transfer learning as a key
technology for reusable AI. Briefly, transfer learning is a technique for improving theaccuracy of AI models in a particular domain by using data from other domains. This can
be classified into two types, homogenous transfer learning and heterogeneous transfer
learning, depending on whether the data types used are the same across domains.
A common method for homogenous transfer learning is to fine-tune a genericrepresentation model for images or text (ResNet50 for images [9-13], BERT for text t [914]) using homogenous data in one’s own domain 13 . There are also more general
methods that do not restrict the data type, such as using density ratio estimates toaccount for differences in data distribution across domains when training a model with

13

A method for relearning the weights of an entire model, taking the weights of a learned network as initialvalues.

96

training data [9-15], and using adversarial generative networks, which have gained
prominence in image generation, to learn how to make latent data representations moredifficult to distinguish [9-16].
When considering applications for the aforementioned cross-domain marketing,heterogeneous transfer is essential as the types of user behavior (e.g., browsing
websites, visiting physical stores, etc.) and product information (book synopses, fooddescriptions, etc.) vary. Many heterogenous transfer learning methods were proposed in
the 2010s [9-17][9-18], but these assume that user IDs are linked and that raw data isshared. On the other hand, recent years have seen a tightening of laws and regulations
due to growing privacy concerns, and data has been highlighted as an asset in business,making it difficult to share raw data as is in many situations. For these reasons, it isdifficult to apply these previously proposed methods as is, and the technical challenge isto build a method for heterogenous transfer learning that does not require coordinatinguser IDs or sharing data.
KDDI is working on heterogeneous transfer learning under the premise that data
sharing and user ID exchange is minimized or not performed. This research was
accepted in 2018 by JST CREST, “Creation and Integration of Artificial Intelligence
Fundamental Technologies for Innovation Creation” [9-19], under the title, “Persona
Model Transfer Technology for Heterogeneous Domain User Behavior Prediction” [9-20],
and was conducted in collaboration with Osaka University and Nagoya University.
Among these, we proposed methods for extending domain adversarial learning inhomogenous transfer learning to heterogenous transfer learning [9-21] , and applying
Word2Vec [9-22], which learns the latent representation of textual data, to user action
sequence data , confirming that they had higher accuracy than existing training methods,
as well as methods that did not use transfer learning in actual data. These methods stillrequire some form of user ID exchange, but going forward, we aim to establish
heterogeneous transfer learning techniques that can be used without sharing data or
exchanging user IDs. The research on heterogeneous transfer learning was accepted
as an acceleration phase research project by JST CREST’s "Development andIntegration of Artificial Intelligence Technologies for Innovation Acceleration" on March
18, 2021 [9-23]. The acceleration phase activities will include the establishment of aconsortium to proceed with the R&D and demonstration experiments in autumn of 2021.
The purpose of this consortium is to ensure that the research will be effectively utilized
to attract interest of potential customers and to perform customer marketing acrossvarious industrial fields related to cyber and physical spaces.

979.4.1.2. Multimodal AI“Multimodal AI” refers to AI technology that makes comprehensive decisions by
integrating various forms of data obtained from both cyberspace and physical space,such as video, images, voice, text, location data, and weather. Such a cross-domain
integration makes it possible to generate more accurate AI models compare to models
that would be using only one type of data.
Single-modal AI makes decisions with a single input, such as detecting a face from animage, recognizing speech and extracting text from it, or forecasting demand using timeseries data.
Within a product recommendation system, multimodal AI could help recommendations
be more interesting. In fact, and based on various inputs such as facial expression, voice
tone, temperature at the time of recommendation, time series data, etc., it is possible to
make a more comprehensive judgment of the individual in question and present reasons
behind recommendations.
In addition, multimodal AI has an advantage when dealing with missing data. Realworld data, such as sensor data, are not always available in an optimal form. Some may
have missing data, some (such as temperature) may suddenly become unavailable, andothers may have different frequency of acquisition. In such cases, it is difficult for singlemodal AI to produce highly accurate models. However, multimodal AI can extract
relationships among heterogeneous data types. Such knowledge enables multimodal AI
models to appropriately fill missing data and obtain high accuracy, even when multiple
data types are incomplete.
In recent years, there have been five technical challenges facing multimodal AI:
representation acquisition between modalities (representation), translation between
modalities (translation), association between modalities (alignment), prediction withintegrated modal information (fusion), and knowledge transfer between modalities (colearning) [9-24].
Of these technical issues, KDDI is focusing on prediction with integrated modal
information (fusion) because of its affinity with issues specific to the real world. Various
researches have tackled the issue of fusion-based prediction, such as speech
recognition using both speech and images [9-25] and emotion recognition frommultimodal data [9-26]. However, most of these studies have targeted time-series datawith temporal changes. And only few efforts have been focusing on spatiotemporal
multimodal data that include spatially changing information such as flows of people andweather. As mentioned in Section 9.3, KDDI has a strong experience with analysis oflocation-based information. Building from such strength introduction of spatiotemporal98

information such as population dynamics to domains that have traditionally been limited
to forecasts using only time-series data is an important part of KDDI on multimodal AI.
For example, when forecasting electricity consumption, it is possible to detect anomalies
by focusing on unusual movements of people during unexpected events such asabnormal weather conditions [9-27]; as well as, detect increased demand due to growth
in remote work and staying indoors, depending on where people are living [9-28]. We
believe that prediction technology that integrates all kinds of spatiotemporal multimodal
data will prove to be necessary in tackling real world problems.
9.4.1.3. Collaborative AI
As mentioned above, constructing AI models that can cope with a variety of tasks andenvironments generally requires a correspondingly large amount of accurate data, but it
is highly difficult to secure in advance accurate, well-organized data that covers all
possible events across a variety of tasks and environments. One way of solving thisproblem is to use explicit and tacit human knowledge, otherwise known as “collaborativeAI”.
In recent years, the concept of human-in-the-loop (HITL), where AI learns efficiently incooperation with humans [9-29], has been proposed. To create highly accurate AI models,
HITL learning requires these main factors: active learning, where data that is likely tocontribute to improving the AI’s performance is automatically extracted [9-30]; imitation
learning, in which AI learns through observation and imitation of human behavior [9-31],
and inverse reinforcement learning, in which AI learns optimal rewards from humanbehavior. Other related technologies include explainable artificial intelligence (XAI) [932], which clarifies the basis for AI decisions in cooperation between humans and AI.
KDDI’s R&D focuses on tasks and environments in the real world, such as in smart
cities and smart homes.
9.4.1.4. Self-improving AI“Self-improving AI” responds to frequently occurring real-world changes and additions
to its environment and tasks, by itself generating evolving a model.
It is desirable to re-generate the AI model after each environmental change to maintainthe accuracy of the initial model—for example, during the construction of new stations
and competing routes, or surrounding roads in the case of railway demand forecasting.
On the other hand, since there may be little data that can be collected in a newenvironment, it is difficult to generate a model suitable for the new environment that only
has a small amount of data.
99

We are therefore developing a “self-improving AI” that can generate an AI model thatcan be applied to new environments and tasks, using models trained on priorenvironments and tasks whenever changes or additions arise. The key is to enhance the
AI model by leveraging knowledge gained from existing environments and tasks that canbe used as references, to respond to successive changes and additions.
In recent years, lifelong learning has emerged as a key technology for self-improving
AI 14. It is well-known that in deep leaning, the AI model is optimized for the most recent
task when learning it, and previously learned knowledge is often forgotten (catastrophic
forgetting) [9-33]. Thus, lifelong learning represents a key challenge in overcoming thisproblem.
To date, there are three known approaches for overcoming catastrophic forgetting: (1)the replay-based approach, in which part of past data is stored and replayed whenlearning a new task [9-34]; (2) a regularization-based approach, in which a constraint(regularization) is applied so that important information in the past AI model is notforgotten when learning new tasks [9-35]; and (3) a parameter isolation-based approach,in which task-specific knowledge is secured by preparing task-specific parameters in the
AI model [9-36]. In computer vision, benchmark evaluation methods such as processing
and evaluating to increase the number of new recognition targets by using general
handwritten character recognition data are being established, and methods based on
approaches (1) to (3) above are being introduced and evaluated worldwide. Conversely,
it has been indicated that the development of general methodologies that can be applied
to fields other than computer vision, as well as methods of knowledge representation
and use within these processes, are issues that need to be addressed [9-37].
KDDI is tackling this research with an eye toward using self-improving AI in the realworld, envisioning a situation where not only new tasks are added, but environmental
changes also simultaneously occur. We are attempting to determine which of
approaches (1) to (3) is most suitable for this situation; we seek to develop one of them,and investigate methods of representing and using knowledge that will understandenvironmental changes.
9.4.2. AI for Behavior Change
9.4.2.1. Human Psychology AI“Human psychology AI” creates AI models for understanding the psychologicalcharacteristics of individuals when getting results derived from real world-oriented AI and
feeding them back to humans and society. People are born with personalities and values
14This is also known as “continual learning.”

100

that differ from those of others; these influence how much people are drawn to somethingor how much they will avoid it, and in general have a significant impact on the motivations,choices, judgments, and executions in their everyday behavior.
For example, the prevailing theory in contemporary psychology is that a person’s
personality is composed of five axes (the Big Five) [9-38], and it is known that people
with high extroversion (one of the Big Five) tend to seek out positive emotions such as
excitement, achievement, and praise, while people with high neuroticism tend to avoid
negative emotions such as anxiety, worry, and hardship.
In a study examining the relationship between the Big Five and the appeal ofadvertisements where there were (A) advertisements promoting an exciting aspect ofeveryday life brought about by smartphones, and (B) advertisements promoting the highlevel of security of smartphones, it was found that highly extroverted people were more
motivated toward purchasing by (A) rather than (B), while the exact opposite was true
for people with highly neurotic tendencies.
As these examples demonstrate, it is crucial to understand the personality traits ofeach individual to encourage people to change their behavior. To encourage real-worldchanges in behavior, especially where smoking, exercising, and staggered commuting
are concerned, it is necessary to understand the psychological characteristics of each
person more deeply and broadly. This is because they require stronger motivation than
the actions that are carried out in online spaces such as shopping or consuming content.
Thus far, approaches to understanding human psychological characteristics in the fieldof psychology have taken the form of questionnaire surveys and interviews by experts,
but these methods pose the following problems: (1) the range of psychologicalcharacteristics that can be investigated is limited due to restrictions on the number ofquestions and the length of the interview; (2) the interpretation of questionnaire contents
and interview results differ from person to person, resulting in the distortion of survey
results, and (3) the number of subjects that can be investigated is limited due to cost.
KDDI is engaged in R&D of AI to understand the psychological characteristics ofindividuals based on daily behavioral data and biometric data observed by the CPS,
while making use of research findings gathered to date in the field of psychology.
Specifically, we are working on researching and developing technology to assess
personality (the Big Five) and values (Schwartz’s Basic Value)[9-39] from daily webbrowsing patterns and driving logs [9-40][9-41], modeling the relationship between these
psychological characteristics and advertising design preferences [9-42][9-43], andpredicting responses to those advertisements [9-44]. We are also investigating AImediated human-to-human interaction, which involves the utilization of AI to mediate101

interactions between humans based on an understanding of their psychographic
compatibility. The study was designed to take into account the diversity of social activities
that involve various types of human relationships, such as husband-wife, friend, teacherstudent, boss-subordinate, and company agent-customer relationships. As an example,
we examined an approach that matches call center agents and customers in an
outbound telemarketing context; we confirmed that our approach resulted in significantly
better conversion lift than existing approaches [9-45].
The scope of our research also includes the application of the theory of mind (ToM) to
AI; ToM is the ability to understand the mental state, goals, intentions, etc. of other people.
ToM is known to be essential for humans to lead healthy social lives. This means that
ToM is also essential if AI is to be incorporated into the social communities of humans
and interact with humans in a natural way. With this as motivation, much effort has been
dedicated to equipping AI with a ToM [9-46][9-47][9-48]. Our research is distinguishable
from other existing studies because it focuses on including a sense of agency (SoA) [949], which is the subjective experience of control over and responsibility for one’s own
actions and their outcomes; it is known to affect self-awareness, the experience of
volition, the understanding of causal structures, the sense of social responsibility, etc.[950]. We envision that AI will become capable of helping and interacting with humans inmore comfortable and effective ways if it can be designed to understand the human SoA.
We will continue to pursue these studies with the goal of realizing AI systems that havea broader and deeper understanding of human psychology and are capable of naturaland seamless human-AI interaction.
9.4.2.2. Persuasive AI
Persuasion is a form of communication used to change people’s feelings and behavior.
To change behavior through persuasion, it is effective to make use of behavioral
characteristics, or “cognitive biases,” as they are known in the field of psychology, in
addition to providing motivation for the target behavior. However, the difficulty andmethod of persuasion varies depending on the target behavior, the individual, and theenvironment at the time.
For example, accidents caused by people walking while using their smartphone havebecome a social problem. To make pedestrians voluntarily stop using their smartphones
while walking, instead of forcibly interrupting them when spotted, we show pictures thatevoke negative emotions along with information about the possibility of imprisonment if
another party is injured in an accident caused by smartphone use while walking. The“emotional approach” is an example of persuasion [9-51].
102

KDDI is researching and developing “persuasive AI” that can accurately predict
effective, reasonable, and agreeable means of persuasion and their effects on an
individual, using human psychology-estimated user behavioral data in cyberspace andthe real world, derived from human psychology AI and psychological knowledge. For
example, as left of Figure 9-2 shows, in an experiment with an expressway operator who
encouraged taking detours to mitigate traffic congestion, the intent to detour and theactual detours were verified by displaying content at the top of a smartphone app thatencouraged taking detours, with multiple varying psychological effects. As a result, wewere able to confirm that the efficacy of psychological effects varied among users. For
instance, content using visual information to remind users that roads along the detour
were clear was effective for those who did not usually drive and were not used to it;
content that appealed to the emotions and had the effect of arousing empathy waseffective for those with children [9-52]. In addition, we are investigating anddemonstrating methods in several fields, such as tourism, health, and healthcare, with
our partners [9-53].

Figure 9-2: Experiments with expressway operators
9.4.3.

Trustworthy AI

There are some cases where there is a certain distrust and uneasiness in using results
derived from AI systems. This is because AI decisions are black-boxed, and the basis fortheir decisions is unknown, and there is a risk that biases related to human preferences
may potentially be reflected in the real world. Even if a highly reliable AI engine isdeveloped, users will not use this with confidence if the AI technology itself is consideredunreliable.
103Thus, we have formulated and published the "AI R&D and Utilization Principles forKDDI Group" to allow users to use AI with confidence and contribute to society in asustainable manner [9-54]．By promoting initiatives based on these principles, we aim
to achieve what we refer to as "Trustworthy AI."In recent years, there has been an increasing amount of research on “explainable AI”(XAI) as a technical solution to the black-boxing of decisions using machine learning anddeep learning techniques[9-55]. For example, there is active R&D of AI and with machinelearning engineers building models for image recognition, speech recognition, etc. [9-56].
KDDI has also been conducting research and development in this field; we have been
awarded the top prize in a competition on explainable AI [9-57].
KDDI will continue to develop and investigate technologies to achieve explainability of
AI for users in all positions (AI developers, users, etc.) in real-world services.
Additionally, as AI technologies evolve and become ever more complex, there is thepossibility of human rights violations occurring on a scale unimaginable in the past, suchas data being collected without individuals’ consent. As laws and regulations have not
kept pace with the technological evolution of AI, it has become necessary to develop AI
technologies in a way that anticipates regulations while striving for international harmony.
Therefore, we will conduct a wide range of research and analysis on AI regulations thatare being discussed in parallel around the world (especially from the perspective of ethics
and privacy protection), and will ensure that these latest developments are reflected in
the AI technologies developed in-house, as well as make recommendations for policies
in Japan. KDDI will continue developing AI technologies that are not only convenient anduser-friendly, but also safe and secure to use.
9.4.4. Using Combined Multiple AI Technologies
As Figure 9-3 shows, the seven new AI technologies to be researched and developed
by KDDI will be used to solve real-world problems, but they will not be used independentof each other; rather, they will have complementary roles, and will be combined with AI
technologies created through conventional machine learning and deep learning fordifferent purposes and use-cases.

104

Figure 9-3: Relationship between issues specific to the real world and theseven AI solutions
For example, in the case of creating a lively city by constructing a new station, a modelof population from before and after the opening of the new station generated by (2)multimodal AI using various data of a different city with similar characteristics, can be
combined with data specific to this city, and (1) reusable AI can be used to generate apredictive model of population change for this city. Should there be insufficient data, (3)collaborative AI will improve the accuracy of the model by supplementing it with realworld human behavior and knowledge. Based on the generated model for predictive
population changes after the opening of the new station, the system automaticallygenerates an announcement plan that encourages behavioral change––a plan thatbalances and harmonizes liveliness and congestion in the city, satisfaction levels forindividual residents, and the overall optimization of the city––by using (5) human
psychology AI and (6) persuasive AI, which consider the traits of residents in this city. Insuch cases, local stakeholders such as transportation companies and local governments
implementing these measures will feel a greater sense of conviction if the rationale forsuch AI-based decisions are presented, as in (7) trustworthy AI. When acquiring andusing various kinds of data, we will comply with data collection, use, and AI regulations
appropriate to each occasion. Although the city and its residents will change over timeafter the station begins operating, the predictive model for population change will be
continuously updated using (4) self-improving AI and used on an ongoing basis.
In the case of starting to cohabitate with one’s parents in a new house with a homerobot, and based on (3) collaborative AI, the home robot recognizes their preferences105

while sometimes asking them questions, and (1) reusable AI supports their lifestyle byreplacing the integrated support functions in the new house. Furthermore, it has alreadylearned parental behavioral data prior to cohabiting thanks to (2) multimodal AI, and thisbehavioral data is added to a model for the general behavior of people in their 60s using(1) reusable AI. It adapts to its circumstances and location using (4) self-improving AI,
and provides support in a way that satisfies the entire family.

10610. "6. XR"
10.1. The Role of XR in B5G/6G
XR is a generic term for technologies that take the results of the fusion of cyberspaceand physical space to and make them available for human perception, such as VR, AR,
etc. In the seven technologies of KDDI Accelerate 5.0, XR plays the role ofcommunicating the results of AI-derived analysis to humans and encouraging them tochange their behavior 15,16. Content using XR technology needs to be delivered to users
at high capacity and low latency compared to conventional video streaming, and this canbe achieved through B5G/6G optimized communication and highly efficient, ultra-lowlatency 3D space data transmission technology. The combination of B5G/6G and XR
technology can create an experience with an unprecedented sense of presence thatmixes the five senses in all sorts of life scenes.
10.2. XR Technology Goals
In 2030, IoT devices and sensors installed everywhere will be able to scan informationin the real world and reproduce it in cyberspace. Physical space reconstruction andaugmentation, which not only reproduces but also superimposes virtual landscapes andobjects on the real world, will be realized. With “multimodal interactions,” which combine
various perceptual expressions, this is not limited to the display of flat images, but canbe combined with a variety of perceptual expressions, such as stereoscopic images
through VR/AR glasses, holography, which displays 3D images indistinguishable fromthe real thing, spatial acoustics that can be felt even at the edges of the field, and force
feedback, which gives users the sensation of touching an object.
These advances in XR technology will bring about a tremendous change incommunication styles. Specifically, you can recreate places you have visited in the past,and remotely share those memories with your friends and family while in your own room.
You can touch the shoulders of someone close to you, and gently lay your hand on theirs.
It is possible to express delicate nuances that cannot be conveyed by words alone.
“Highly efficient, ultra-low-latency 3D space data transmission” is important as amechanism to support such a rich XR experience. Here, information in the real world and
XR contents expanded in cyberspace are transmitted back and forth without any timelag as highly efficiently compressed data. The result is a seamless XR experience in
CPS.

15

Augmented reality

16

Mixed reality10710.3. Global Situation
With the rapid spread of remote work and virtual events associated with the spread of
COVID-19, remote communication and collaboration using XR technology is becoming
more common. For example, virtual conferences and exhibitions that make full use of
the viewing experience through VR glasses and self-expression using avatars—such as
IEEE VR, a representative international conference on VR—are increasing [10-1]. Some
US universities and companies have gone all out and are using similar technologies tointroduce virtual campuses and virtual offices [10-2][10-3]. The use of VR/AR glasses is
expanding as major IT companies in the US, China, and other countries are steadily
improving their performance in terms of higher image quality, wider viewing angle, size,
and weight.
As such, although XR technology is steadily gaining traction, as traditionally only
certain goods and services have been digitized, the experience of receiving cyberspace
feedback has been fragmented. As an example, we would like to introduce KDDI’s virtual
Shibuya. In this virtual Shibuya, the streets of Shibuya are reproduced in cyberspace asa digital twin, and you can participate as an avatar 24 hours a day from anywhere in theworld [10-4]. As another example from Shibuya City, a demonstration experiment thatsuperimposes restaurant information and other items on the actual scenery of Shibuya
was conducted using VPS technology that recognizes space from images taken throughcameras mounted on smartphones and smart glasses (Figure 10-1) [10-5].

Figure 10-1: Extension of physical space at Shibuya Scramble Crossing “With regard to vision, conventional cyberspace feedback experiences were mainly
limited to forms mediated by 2D displays. As an attempt to pseudo-extend this to 3D, an108

approach projecting 2D high-quality CG in 3D space using a transparent display or halfmirror is being studied [10-6]. Moreover, R&D of 3Ddisplay technology [10-7] using
holography (Figure 10-2), which records and reproduces light waves reflected from thesurface of real objects, is underway to realize image expressions that are, in principle,
impossible to distinguish from the real object; however, high image quality and wideviewing angles have not yet been achieved due to limitations of display devices.

Figure 10-2: Principles of holography
In terms of auditory perception, KDDI’s “OtonoVR” technology enables an interactive
viewing experience by synthesizing a sound field zoomed to an arbitrary range in space,allowing the user to freely focus on the part of the 360-degree video that he or she wants
to see or hear [10-8]. Figure 10-3 shows an example of a virtual concert using “OtonoVR”
technology.

109

Figure 10-3: Example of a virtual concert using “OtonoVR” technology
Finally, let us discuss the network transmission of XR content. Global data distribution
volume (IP traffic) is rapidly increasing, and is expected to reach 278 EB (exabytes: 1 EB
= 106 TB) per month in 2021 [10-9]. Of this, mobile data traffic is growing at a remarkablerate (from 6 EB per month in 2016 to 41 EB per month in 2021, an increase of about
seven times [10-9]), and most of that content in future will be video data, the ratio of
which is expected to reach about 80% in 2022 [10-10]. High efficiency video coding(HEVC) is the international standard of video compression technology in practical use
as of 2021. While HEVC has already achieved compression to the bandwidth of a few
hundredths, further advanced technologies in video coding to reduce data volume while
maintaining video quality have been promoted, such the advent of 360-degree video,
ultra-high definition (4K/8K) and high dynamic range video, as well as the diversificationof viewing devices such as smartphones, tablets, and head-mounted displays.
Specifically, by using versatile video coding (VVC) [10-11], which is the latest
international standard in 2020, the live broadcasting of 4K/8K high-definition video via
mobile network will be possible, and 4K/8K video services for smartphones and tablets
are expected to be developed in the future [10-12][10-13].
10.4. Technologies Required for 2030
This section summarizes the technologies supporting XR in the B5G/6G era from theperspectives of physical space reconstruction and augmentation, multimodal interaction,and highly efficient, ultra-low-latency 3D space data transmission.

11010.4.1.Physical Space Reconstruction and Augmentation
As the reproduction and augmentation of physical space progresses, every person
and object in the real world will be scanned in fidelity and transmitted to cyberspace.
After analysis in cyberspace, this cyber information will be superimposed on the entiretyof physical space, and we will be able to enjoy services that transcend time and spacein every aspect of our lives. B5G/6G will further accelerate the progress of CPS, enabling
every place to be connected to cyberspace and enabling us to come and go freely.
In a world that merges physical space and cyberspace, it is possible to recreate in its
entirety a place you have visited, in your own room, to be with your friends and family
from a distance, or to relive past memories. Or a foreign sporting event can be recreatedas a space where you are in a stadium in Japan, rendering possible the unprecedented
experience of watching this sporting event as if it were being played right in front of you.
In addition, virtual humans, which represent humans as elaborate 3DCG
(photorealistic avatars [10-14]), will be used daily for customer service and guidance instores, education, nursing care, etc., and will be socially accepted as a common
presence in our lives. The image of a virtual human is shown in Figure 10-4 [10-15].
Virtual humans will not only play a role as an interface between people, but will also be
widely used as a tool to digitally transform the supply chain; for example, cyberspace will
be able to exist as a space to create product samples for product planning and design[10-16]. Also, in cyberspace, one’s own agent exists as a digital twin, and one’s own
appearance, clothing, and even gestures and facial expressions can be optimallycontrolled according to the circumstances; in particular, this will be used in the businessworld on a daily basis as a form of communication going beyond face-to-facecommunication.

111

Figure 10-4: Image of a virtual humanIn the reproduction of physical space, information about any object or space needs tobe sensed and modeled in cyberspace. In the future, advanced signal processing
technology will be extremely important for constructing precise models from a vast
amount of information obtained from 3D sensors and high-definition cameras, thus
expanding targets to include not only people at close proximity but also in wider spaces
at the town or city level.
10.4.2.Multimodal Interaction
With the advancement of multimodal interaction through XR technology, the field ofexperience will be extended to 3D space, and the viewing experience with 3D images
and sound fields, the sensation of touching people and objects, and even the sense of
smell and taste will be provided, unrestricted by time or space, resulting in a rich, naturalexperience comparable to the real thing.
By 2030, holography technology will be able to reproduce light waves in physical spacethat are exactly the same as those reflected from the surface of a real object. This will
enable us to view ideal 3D images through a compact, lightweight transmissive headmounted display (HMD) without 3D sickness or visual fatigue, which will provide visualand intuitive AI assistance in all aspects of daily life.
In a world where CPS has advanced, physically distant spaces as well as the soundfield will be connected in real time, and an immersive experience that transcends live
viewing will become possible, as if you were there right now.
Furthermore, by supporting the sense of touch as a mode in addition to the senses of112sight and hearing, it is possible to reproduce the sensation of touching other people andobjects during remote communication in cyberspace. This enables face-to-facecommunication such as a tap on the shoulder, a gentle touch on the hand, or ahandshake to be realized across time and space. For example, in physicalcommunication such as “toasting” and “pouring” in remote gatherings, various tactile
experiences can be realized through the real-time synthesis technology of tactile
waveforms. As an example, Figure 10-5 shows an image of a system that reproduces
the tactile sensation of “toasting” and “pouring” from a remote location [10-17].

Figure 10-5: An image of a system that enables remote “toasting” and“pouring” with tactile sensation [10-17]
In interpersonal communication, it is more important to pursue cross-modality effects
that can be obtained by coordinating multiple senses with each other, in addition to thenatural functioning of each sense. In communication, physical and environmental
constraints placed on the other person vary from person to person. In other words,
working with all the senses is not always optimal. For this reason, it is vital to establish
cross-modality technologies that can identify which senses are effective in each situation,such as transmitting sound using light and vibration, or pointing to input using sound andvibration, and superimpose other senses in an extended manner.
10.4.3.High-efficiency, Ultra-low-latency 3D Space Data Transmission
As XR permeates our daily lives, the visual experience will be extended from 2D
displays to (physical) space itself, and the subject of the information handled there will
also change from 2D video information to 3D space information. Point cloud information
representing 3D space is much larger than conventional 2D video information. Therefore,113

point cloud compression (PCC) technology [10-18], which is a coding method for highly
efficient compression and transmission of point cloud data, and its successor
technologies, have been established to enable stable handling of 3D space informationeven on mobile network (Figure 10-7). Figure 10-8 shows the past and future roadmap
for 3D space data compression technology. This figure also shows the progress ofcompression technology for 2D video and 3D space reproduction technology.

Figure 10-6: Transmission of 3D spatial information

Figure 10-7: Roadmap for 3D spatial data compression technology114

In addition, the ability to reproduce and transmit 3D space will increase the need forhighly interactive applications, such as remote surgery and robot control. In suchapplications, ultra-low-latency transmission technology is utilized for instantaneoustransmission in 3D space while maintaining the compression performance. For example,
when connecting the physical space, where the remote robot exists, to the pilot’s space,an end-to-end display delay of 50 milliseconds or less (the world’s smallest class incurrent 4K video transmission) can be achieved, virtually eliminating the gap between
visual and physical sensations, enabling accurate and physically intuitive operation. In
addition, it alleviates the operator’s cybersickness (VR sickness), which is said to be oneof the causes of image transmission delay, and enables remote control for extended
periods of time [10-19].
Thus, a world with advanced CPS will be realized by both ultra-high-speed, highcapacity, ultra-low-latency mobile networks with B5G/6G and highly efficient, ultra-lowlatency volumetric–video transmission technologies.

11511. "7. Robotics"
11.1. The Role of Robotics in B5G/6G
In 2030, physical space and cyberspace will be connected by B5G/6G, and the various
data obtained from physical space will be transmitted to cyberspace for intelligent
analysis. One of the systems that uses such analyzed data to re-act to the physical world
is robotics. Here, robotics is not limited to physical robots (equipped with actuators), it
also includes virtual robots (or bots) such as virtual agents, and the mechanisms bywhich these robots act.
The superior network performance of B5G/6G is essential for the development ofrobotics. For example, ultra-fast, high-capacity communication is important for theplatform analyzing sensory data obtained from such sensors as cameras and LiDAR
mounted on robots. Ultra-low-latency communication is also required for thetransmission of sensory data to cyberspace and the transmission of control data (suchas motion planning obtained from analysis) from cyberspace back to robots. Moreover,communications with multiple simultaneous connections will be put to effective use
where large numbers of robots of various sizes are installed and operate in humansociety. Ultra-low power consumption and ultra-safe and reliable communications arealso essential for robots to operate safely and reliably for long periods of time.
Thus, the society in which CPS has been advanced by B5G/6G is regarded as a typicalscenario for the use of robotics. In the future, robotics will be implemented as part of thecity OS, and having entered our lives in a natural way and providing further enrichment.
11.2. Robotics Technology Goals
In 2030, robots that are integrated into human society will be used to perform various
tasks, such as patrolling streets and facilities, working at hazardous locations, anddelivering goods. It will be necessary to extend the robotics platform to further expand
the range of applications for robots. This platform is regarded as a foundation for RaaS,
Robotics as a service. The data collected from robotics in physical space are sent to acommon platform and analyzed by AI systems built in cyberspace. Instructions from AI
systems will act to control the behavior of robots in physical space, and in this situation,a variety of robots will be able to operate with one another via the platform to carry out
more complex tasks and share information required to carry out these tasks.
For robots to coexist with people, it is important for robots to be socially andeconomically accepted in terms of safety and service expense. From the perspective ofsocial acceptability, robots will be able to act autonomously by sensing the environments
in physical space by making advanced estimations in cyberspace. This is, for example,
116

expected to contribute to “community support” by understanding a user’s healthconditions and personality to casually encourage them to take a walk. By observing
robots’ behavior, assessing and analyzing their acceptability to people, and further
improving robots’ behaviors, robots will move beyond coexistence, becoming
indispensable partners to people.
Through the spread of the RaaS infrastructure described above, the scope of activities
performed by robots will expand moving toward 2030. On land and in the air, tests todemonstrate “autonomous driving and smart drones,” responsible for transporting people
and goods, are ongoing, but robots will also move into marine fields. In the future, therange of robots’ activities will expand into marine fields through the development of
optical communication technologies enabling wireless communications underwater,
together with the development of hydrogen fuel cells enabling drones to operate forextended periods of time. This can generate economic effects in a wide range ofindustries, such as underwater exploration, fishery monitoring, infrastructure inspection,and live undersea video communications using undersea drones.
11.3. Global Situation
The main field in which robots have been operated so far is industry. Industrial robotics
is a field in which Japan has shown its strength, and it is said that Japanese-made robots
occupy almost a 60% share of the global market. To take advantage of this strength andexpand both the applications of robots and global market share, the Japanese
government formulated its New Robotics Strategy [11-1] in 2015, and its Plan for the
Promotion of Social Transformation through Robotics [11-2] in 2019. This will further
promote the implementation of robots in society in step with the advancement of sensing
through IoT, AI analysis of sensory data, and the development of computing
environments in which AI technologies are implemented. With the participation of various
players, the government aims to accelerate the practical application of robots and to
make them a more familiar presence in people’s lives. Robotic vacuum cleaners are
already in widespread use in people’s homes, and in the business domain; proof-ofconcepts of automated deliveries using drones and delivery robots are underway. The
fields in which robots play an active role are expected to further expand in the future, andthere is a growing need for a robotics platform to efficiently operate and manage a wide
variety of robots, as well as to establish the technologies that robots should feature forcooperation with people.
With regard to robotics platforms, some robot manufacturers have constructed theirown platforms to create systems to manage their robots [11-3]. On the other hand, efforts117to operate a group of robots from various manufacturers and with different functions arealso underway [11-4].The main purpose of these existing platforms is the integrated
management of robots, monitoring robots’ status, and remote operation in the case of
unexpected situations. In response, KDDI has proposed its “life delivery” concept, and isconducting proof-of-concept using the robotics platform. This embodies the “changes in
shopping” described in Section 2.2.2, and aims to create a lifestyle where the purchaseof goods such as daily necessities becomes an effortless, unconscious action.
Specifically, proof-of-concepts are being carried out in the form of “order and delivery”(Figure 11-1), in which a small-scale convenience store is set up in an office, orders areplaced using a smartphone application, a remotely operated robot picks products up in
response to those orders, and the goods are loaded onto an autonomous delivery robot
to deliver them to the orderer. We are constructing a platform that links not only robots,
but in considering business development, also incorporates features such as payments.

Figure 11-1: The “life delivery” service
With regard to community support, for robots to fit into the human society of the 2030s,
they must be able to perform complicated tasks while understanding the situations of thepeople around them, and be able to cooperate with people. This will require further
advances in human-robot interaction (HRI) technologies. In current HRI technology, forexample, human poses can be estimated using an on-platform behavior recognition AI[11-5] using images taken by a camera mounted on the robot, and motion planning canbe developed according to those estimations. In a task handing objects to a person, therobot creates rule-based definitions, such as whether the person’s gaze is toward therobot, whether the direction of the body is appropriate, whether the distance between the
person and the robot is appropriate, etc., and then acts to satisfy those conditions. There118

has been progress in planning optimal motion for robots using reinforcement learningand other techniques in recent years, but incorporating actual human responses into
simulations remains a difficult task. In addition to human actions, facial expressions areunderstood from camera images, and these are integrated into estimated emotions andreflected in the content of the robot’s dialogue with people. Besides camera images,
various biometric data such as voice, heartbeat, and body temperature are used toestimate emotional states.
The transportation of goods and persons unconstrained by location and mode, i.e.,
land, sea, and air, is a function expected as the social system aimed towards 2030. Today,on land and in the air, self-driving proof-of-concepts are underway, and drones are being
introduced, though the marine field has yet to be sufficiently explored. Development andproof-of-concept testing of aquatic, hydrogen-powered drones has progressed in recent
years (Figure 11-2) [11-6]. Alternatively, a new type of drone, i.e., the aerial-andunderwater-assembled drone, has been developed; this drone includes a UAV that holds
an ROV that can fly to and land on the surface of a water body, and then be released to
cruise underwater （Figure 11-3）[11-7], but are still behind compared with developmentson land and in the air. This is a consequence of the communication issues. Since radio
waves are attenuated in water, communication becomes impossible when using radio
waves to communicate. For example, in the case of the above-mentioned aerial-andunderwater-assembled drone, a cable is used to connect the ROV and UAV to enable
underwater communications. Underwater video captured by the ROV can be transmitted
to the UAV via wired communications; the UAV operator is able to watch the underwater
video via LTE communications. Thus, the remote control of unmanned undersea vehicles
is still performed using forms of wired communication such as optical fiber, or wirelesscommunication using acoustic signals, which have low transmission capacity. In
response, efforts are underway to develop underwater communications using blue LED
optical wireless communications technology, which has a relatively low attenuation ratein water, to increase transmission capacity [11-8]. With a remote-operating robot, on theother hand, it is extremely important to shorten the end-to-end delay between informationacquisition from physical space, such as video, and its reproduction in cyberspace. In
July 2020, an end-to-end delay of 50 milliseconds was achieved for the TELEXISTENCE
robot, currently the world record for the shortest such delay (Figure 11-3) [11-9]. Prior tothis, 100 milliseconds had been the minimum end-to-end delay for 4K video transmission
by a drone [11-10].

119Figure 11-2: A hydrogen fuel cell-powered aquatic drone

Figure 11-3: Aerial-and-underwater-assembled drone

Figure 11-4: Remote control of robots120

In addition, several organizations have jointly established a place for co-creation tocollaborate on the development of robots and robotic services [11-11][11-12][11-13]. The
rate at which use cases and services employing robots are realized (e.g., theimprovement of robotic functionalities, implementation of smooth communications
between human and robot, and evaluations and experimental environments to facilitate
cooperation of robots and communications) is expected to accelerate.
11.4. Technologies Required for 2030
In this section, we compile the technologies supporting robotics in the B5G/6G era
from the perspective of RaaS, community support, and autonomous driving and drones.
11.4.1.RaaS (Robotics as a Service)
Figure 11-4 shows an illustration of a robotics platform for B5G/6G. As an example,
when providing a robotics service that patrols and cleans a facility, information about thefacility collected by the patrolling robot can be managed on a common platform so thatthe cleaning robot can make use of security information it could not detect on its own. In
this case, the cleaning robot does not need to be equipped with sensors and functions
for security, and should be equipped only with the minimum functionality necessary forcleaning. In other words, having the cognitive functionality for a robot in cyberspacefacilitates flexible operations, such as sharing information about a facility between
multiple robots. By constructing such kind of robotics platform, the cognitive and decisivefunctionalities that govern control over a robot can be upgraded, simplifying theimplementation of RaaS. Ultimately, a robot’s hardware needs only to be equipped with
the necessary sensors and actuators, and functionalities related to cognition andjudgment based on sensory data, and the generation of motion planning (or more
precisely, the AI technologies required for these functionalities) can be selected fromappropriate options provided on the platform. This will make it possible for eveninexpensive hardware to have a high level of intelligence, and such updates can boost
its conformity of changes to the robot’s environment and its growth of capabilities.

121

Figure 11-5: Robotics platform

11.4.2.Community Support
By 2030, the world will be one in which robots autonomously solve problems inphysical space using analysis performed in cyberspace, and without giving specificinstructions to robots from a human. This includes physical tasks such as transportation,
maintenance, and housekeeping, as well as solving problems for people. Taking an
example case of encouraging a person to exercise in physical space, the robot canunderstand the situation and atmosphere in physical space using analysis performed incyberspace, and can cause behavior modifications by changing the environment toencourage that person to exercise (providing information, encouragement. etc.).
Specifically, this is a world in which the person does not actively say to the robot, “I would
like to go for a walk, can you recommend a route?” but rather, the robot interacts withthe person, naturally and thoughtfully drawing on its knowledge of that person’s lifestyleand interests stored in cyberspace.
The evolution of HRI is indispensable for realizing such kind of world, and the elementsthat seem particularly important are (1) predictive detection, (2) formulating persuasive
strategies, and (3) natural and sensible dialogue. Figure 11-5 shows the positioning ofthese elements in the CPS. (1) Predictive detection is a technology in which a robot
observes a person and anticipates behaviors by predicting actions and emotions. This
makes it possible for the robot to behave according to person’s own actions, and toprevent unintended actions such as a person falling in advance. The system models
122human motion, robots, and surrounding environment, then learns and predicts important
correlations and causal factors affecting human behavior in complex interactions. (2)Persuasive strategies are required for robots making proposals to people. Persuasive AI(see Section 9.4.2.2) makes it possible to determine the most effective timings, and plan
the most effective approach based on each person’s personality, psychological state,
and environment which are estimated via data obtained from cyberspace. Regarding (3)dialogue technologies, together with understanding the other party’s emotional state byunderstanding facial expressions and gestures, robots and virtual humans will be able touse smooth and sensible expressions using a series of dialogue processes, such as
expressing gestures, producing communication that is filled with empathy and trust.

Figure 11-6: Constituent technologies for implementing HRI
Sense of Agency (SoA) and social acceptability are success indicators for the HRI
technology described above. SoA refers to a mindset in which a person proactively
decides what to do, and the robot plays a supporting role. For example, a one-sided
assertion from a robot, even when it is a reasonable action or a suggestion for a person,gives a sense that a person is being controlled by an AI system or a robot, which leads
to a decrease in SoA. Social acceptability, on the other hand, is a criterion that shows
how well the presence of robots is accepted in a society consisting of their users andothers who are not associated directly. Robots will be active in various places in thesociety of 2030, and will coexist with people in an open environment. At this point, it is123

important to improve the psychological assessments of robots, in terms of whether there
are concerns that a robot’s actions will result in harm, and whether robots can be trusted
to act on behalf of people. With the observation of robot and user behaviors, and the fulluse of analysis in cyberspace, SoA and social acceptability can be improved, which leads
to establishment of an ultimate HRI.
11.4.3.Autonomous Driving and Smart Drones
In anticipation of the B5G/6G era, KDDI is aiming to extend its wireless broadband
network into the sea using optical communication technology so that wirelesscommunication becomes possible even in marine and aquatic environments. This will
make it possible to deploy amphibious drones and remotely operate drones at sea.
Drones can be operated for remote underwater exploration, fishery monitoring, andinfrastructure inspection without the need for a person to travel to a specific area anddive. It will also become possible to deliver goods to people and to facilities under thesea.
Furthermore, if an undersea wireless communication environment can be createdthrough the technology shown in [11-8], the way of enjoying marine leisure activities willchange dramatically: in cyberspace we could reproduce and process live video
transmitted from beneath the waves, provide realistic XR experiences in physical space,and experience realistic undersea driving experiences using amphibious vehicles. It will
also be possible to provide remote support to underwater workers, and undersea
environments will become the new life and economic zone.
On the other hand, the scope of aerial and amphibious drone activities will further
expand in 2030. For example, these can be used to assess damage in the event of
natural disasters, to search for survivors in marine accidents and mountain rescues, andcloser to home, to monitor elderly people and school children on their way to and fromschool, and to deliver goods to marginal settlements and isolated islands. In 2021,
drones offer continuous cruising times in the tens of minutes, but this will have expanded
to several hours by 2030, greatly improving the range of actions and capabilities. The
development and widespread use of hydrogen fuel cells, produced from renewableenergy sources, is an extremely important energy technology, which will replace today’slithium-ion batteries. The use of hydrogen fuel cells for all kinds of transport and logistics
by land, sea, and air will serve to accelerate achieving a decarbonized society.

12412. Future Development
In addition to presenting a roadmap for the future of the seven technologies set out inthe previous chapters, this chapter shows KDDI’s future activities in standardization andopen communities as well as joint research projects with outstanding researchers
overseas, particularly in policy for global deployment.
Overall Roadmap
Table 12-1 summarizes the roadmap for efforts for each of the seven technologies
discussed in Chapter 5 through 11.
Table 12-1: Roadmap for the seven technologies
B5G/6G

５G
Seven Technologies–2024

2025–2029

2030–

Network

User centric

Developing essential

Standardization, PoC

Commercial

networktechnologies

OpticalDeveloping essential

transmission

technologies, standardization

Open

Input to the open communities

Ecosystem formation,community

Cloudification

PoC

Communicatio

PPM for city OS

Trustworthy distribution

Security-enabled

P/F

network

Security

deployment
PoC, partial deploymentns
infrastructure
Encryption

Post-quantum encryption

Advanced feature
cryptographic

Detection andAI-based cybersecurity

protection
IoT

Security to protect AI
models

Maintenance-

Automated configuration

Automated fault

Automated fault

freemanagement

detection

recoverySensorModular API

Energy harvesting

Reduced burden on
the environment

Platform
AI

PhysicalOSes for specific cities

General purpose OSes for cities

Reusable/multimodal AI

Collaborative/self-

Co-evolutionary

improving AI(with people) AI

orientation
Behavior

Human psychology/persuasive(Continuous) persuasivechange

AI

AI

125

XR

Transmission

PCC

Beyond PCC/VCC

Ultra-low-latency 3D
space datatransmission

Expressive

Holography

Unassisted holography

Large-scale
holography

Robotics

RaaS

HRI

Technologies for cooperation

Cooperative actions with

between different robots

peopleRecognition of each human

Value-based feedback

Versatility

value

Activities in Global Standardization and Open Communities
KDDI intends to advance the R&D of the seven technologies described in thepreceding chapters according to the roadmap above, hence standardization efforts ofthe seven technologies are essential toward commercial services and deployments. In
addition, activities in the open communities have become important initiatives in recent
years. Since these discussions are often conducted on a global scale, we feel that thesewill be important to global deployment.
As to the future of these standardization activities with an eye to the B5G/6G era, it isimportant to achieve inter- and intra- coordination and collaboration between the threelayers described in KDDI Accelerate 5.0 concept, i.e. network, platform and businesslayers. In other words, the coordination and collaboration between networks as well as
platforms is being crucial.
Standardization of northbound/southbound APIs, which facilitate coordination between
layers, will create a foundation to implement XaaS using a platform integrated with
networks, and drive a new era of business. At the same time, in addition to thecooperation between networks that have already been discussed by various
standardization

bodies—that

is,

the

standardizationofinterconnection—thestandardization of eastbound/westbound APIs enabling cooperation between platforms
will promote both competition and cooperation between players.
KDDI will achieve organic linkage both between and within layers, accelerating
collaboration and mergers between businesses, and leading to the creation of new value.
While some of the above overlaps with the discussion in earlier chapters, let us touchon them again:The technologies on the user centric architecture set out in Section 5.4.1 will be126studied with the aim of reflecting them in technical standards to be specified from2025 onwards such as 3GPP.


Each of the technologies set out in Section 5.4.2, such as MCF transmission and
RoF, will be studied to reflect them in ITU-T technical specifications.



End-to-end network slicing is one of 5G’s key enablers, and indispensable
technologies for telecom operators to meet the demands of diverse use cases andto provide network services optimized to those use cases in the B5G/6G era. While
the application of virtualization technology is a prerequisite for constructing andoperating network slices, the use of operation automation platforms and interdomain collaboration is essential for the mobile operators to operate multiple logicalnetworks concurrently to satisfy complex and fragmented requirements. As
described in Section 5.4.4, ONAP is one of the promising upcoming platforms, andimplementations based on the specifications provided by various standardization
bodies and communities such as 3GPP, IETF (Internet Engineering Task Force), ORAN ALLIANCE, and TM Forum are underway. Going forward, the leverage of open
communities such as ONAP will be important when considering end-to-end service
provision that goes beyond operations management platforms to include
collaboration with IoT application marketplaces and edge application management
platforms.

Conventional communication networks often consist of proprietary software andhardware, but it is important to be able to construct these more flexibly in the future.
The need for edge computing is expected to be particularly pronounced, since its
applications are expected to expand, particularly from wide areas to near-local areas.
In these efforts, it is necessary to respond to various needs both flexibly and quickly;
it is therefore important to enable the edge computing platform to be applied tovarious applications through discussions with various global partners to form an
ecosystem. Thus, to facilitate these efforts, we prioritize to standardization activities
in the bodies such as 3GPP, ETSI MEC etc., as well as open communities such as
OCP (the Open Compute Project) and TIP. In particular, for TIP, we will utilize the
TIP Community Lab, established by KDDI, to facilitate the disaggregated softwareand hardware solutions (see Section 5.4.5).

Security measures for next-generation communication systems, and ensuring thesecurity of software and hardware used in infrastructure, as well as new businessfields and functions, are vital initiatives to standardize security for communications
system in the B5G/6G era [12-1] As part of infrastructure-facing security measures,KDDI is leading standardization activities for Privacy Policy Management technology
127

(PPM), as discussed in Section 6.4.1, in oneM2M, ITU-T, and ISO/IEC, to contributeto promote security and privacy by design. We are also investigating thestandardization of cryptographic technology for the quantum computer era, as
described in Section 6.4.3, as part of our effort to ensure superiority in new businessfields and functions. In other fields, we plan to deploy the results of our R&D globallyby standardization.
KDDI has been working on the standardization of transmission technologies for XR
content, and will continue to do so going forward. In particular, the 2D video encoding
technology (VVC, H.266) being studied by the JVET (Joint Video Experts Team, ajoint team consisting of ISO/IEC JTC1/SC29 and ITU-T SG16 WP3), described in
Section 10.3, and PCC being developed by ISO/IEC JTC1/SC29 WG7, described in
Section 10.4.3, will be studied and deployed globally as technical standards
supporting the high capacity, low latency, and scalability expected from B5G/6G fromthe application layer.

Joint Research Projects with Outstanding Researchers Overseas
To implement KDDI Accelerate 5.0, as of October 2020, KDDI has begun joint researchwith researchers overseas with proven track records in various fields of research [12-2].
KDDI will steadily expand this initiative going forward. In each project, several
researchers from the Advanced Technology Laboratory of KDDI Research Inc. andvarious universities engage and collaborate to achieve state-of-art research results. To
this end, KDDI intends to make our advanced technology research activities further
global and strengthen the system of R&D by developing the human resources andinviting/recruiting researchers through participation in world-class communities andexchange and collaboration with researchers.

12813. Conclusion
This white paper has introduced KDDI’s concept for B5G/6G, which is expected to
become commercially available around 2030, and for the technologies involving B5G/6G.
In terms of lifestyle changes, we summarized new lifestyles from nine perspectives.
We described the various technologies that will be required to realize these new lifestyles.
We will continue to study new lifestyles with external partners.
Next, as a concept for B5G/6G, we introduced the Society 5.0 concept, presented by
the Japan Cabinet office, and the Beyond 5G Promotion Strategy, by the Ministry ofInternal Affairs and Communications, and gave an overview of KDDI Accelerate 5.0,
which KDDI announced in August 2020.
We cite “life delivery” as an example of a use case based on the new lifestyles, anddiscuss how the component technologies in the seven technologies set out in KDDI
Accelerate 5.0 can contribute to achieving that use case. We further introduced the roles
and objectives of each of the seven respective technologies in B5G/6G, their situation
globally, as well as the technologies that will be needed heading into 2030.
As you can see from the “life delivery” example, it will be necessary for these seventechnologies to work together to realize Society 5.0. A cooperative operation is referred
to as “orchestration” in this white paper, and will be key to realizing Society 5.0. Thus,
orchestration will be studied in parallel to the R&D of the seven technologies.
In this white paper, we have summarized KDDI’s concept for B5G/6G, lifestyles aroundthe year 2030, and the component technologies they will require. However, we do notbelieve the contents set out here will satisfy the necessary and sufficient conditions for
B5G/6G. We would therefore be grateful for any opinions, suggestions, or comments
received from the readers of this white paper. We at KDDI are determined to contributeto the prompt realization of Society 5.0 by eliciting opinions from outside the company,
and by actively incorporating the thoughts, ideas, and technologies of our partners. With
the help of our partners, we shall continue our studies, R&D, and in turn revise this white
paper.

129

References
Chapter 1
[1-1]
[1-2]

Ministry of Economy, Trade and Industry, Japan: “Guidelines for Promotion of Digital
Transformations (DX Promotion Guidelines) Ver. 1.0,”
https://www.meti.go.jp/english/press/2018/1212_003.html, December 12, 2018.
“KDDI Accelerate 5.0,” KDDI Research, Inc.,
http://www.kddi-research.jp/kddi_accelerate5_0/ (in Japanese).

Chapter 2
[2-1]

[2-2]

"KDDI and KDDI Research Inc. open the research base ‘KDDI research atelier’ to offer new
lifestyles taking 2030 into account on Dec. 17, 2020, - Promoting ‘KDDI Accelerate 5.0’ with theirdomestic and foreign partners -", KDDI Corp./KDDI Research Inc.,https://www.kddi-research.jp/newsrelease/2020/121701.html, Dec. 17, 2020 (in Japanese).
The United Nations: “Sustainable Development Goals,”
https://www.un.org/sustainabledevelopment/ .

Chapter 3
[3-1]
[3-2]
[3-3]

[3-4]
[3-5]

[3-6]
[3-7]

Japan Cabinet: “Society 5.0,”
https://www8.cao.go.jp/cstp/english/society5_0/index.html .
Japan Cabinet: “the 6th Science, Technology and Innovation Basic Plan,”
https://www8.cao.go.jp/cstp/kihonkeikaku/index6.html (in Japanese).
Ministry of Internal Affairs and Communications, Japan:“Beyond 5G Promotion Roadmap -Roadmap to 6G-,
”https://www.soumu.go.jp/main_sosiki/joho_tsusin/eng/presentation/pdf/Beyond_5G_Promotion_S
trategy-Roadmap_towards_6G-.pdf, Jul. 30, 2020.
Ministry of Internal Affairs and Communications, Japan: “Recommendations from the Beyond 5G
Promotion Strategy Roundtable,” https://www.soumu.go.jp/main_content/000696612.pdf ,Jun. 30, 2020 (in Japanese).
Ministry of Internal Affairs and Communications, Japan: “Establishment of Beyond 5G Promotion
Consortium, and Holding of General Meeting, and Establishment of Beyond 5G New
Management Strategy Center,”
https://www.soumu.go.jp/main_sosiki/joho_tsusin/eng/pressrelease/2020/12/11_02.html,
December 11, 2020.
“KDDI Accelerate 5.0,” KDDI Research, Inc.,
http://www.kddi-research.jp/kddi_accelerate5_0/ (in Japanese) (redisplaying).
For example, “JR East and KDDI sign basic agreement for aiming joint commercialization todevelop new decentralized city planning centered on Shinagawa Development Project”,KDDI etc.,
https://news.kddi.com/kddi/corporate/newsrelease/2020/12/15/4865.html, Dec. 15, 2020 (inJapanese).

Chapter 4[4-1]

M. Nakazato, “Efforts to realize 5G in 2020,”
https://www.soumu.go.jp/main_content/000593247.pdf, Dec. 18, 2018 (in Japanese).

Chapter 5
[5-1]
[5-2]

[5-3]
[5-4]
[5-5]

R. Li (Chairman of ITU-T SG15 FG-NET2030), "Towards a New Internet for the Year 2030 and
Beyond," Third Annual ITU IMT-2020/5G Workshop and Demo day, Jul 2018.
Ministry of Internal Affairs and Communications, Japan: Release of “Beyond 5G Promotion
Strategy -Roadmap towards 6G-,”
https://www.soumu.go.jp/main_sosiki/joho_tsusin/eng/pressrelease/2020/6/30_7.html,
Jun. 30, 2020.
ITU-T Focus Group on Technologies for Network 2030,
https://www.itu.int/en/ITU-T/focusgroups/net2030/Pages/default.aspx
ITU-T FG-NET-2030, "Network 2030 A Blueprint of Technology, Applications and Market Drivers
Towards the Year 2030 and Beyond",
https://www.itu.int/en/ITU-T/focusgroups/net2030/Documents/White_Paper.pdf
ITU Focus Group on Autonomous Networks (FG-AN),https://www.tmforum.org/resources/standard/autonomous-networks-empowering-digitaltransformation-telecoms-industry/#130[5-6]
[5-7]

[5-8]
[5-9]
[5-10][5-11][5-12][5-13][5-14][5-15][5-16][5-17][5-18][5-19][5-20][5-21][5-22][5-23][5-24][5-25][5-26][5-27][5-28][5-29]

Japan Cabinet, “Society 5.0”, https://www8.cao.go.jp/cstp/english/society5_0/index.html
Ministry of Internal Affairs and Communications, Japan: “Beyond 5G Promotion Roadmap Roadmap to 6G-,
”https://www.soumu.go.jp/main_sosiki/joho_tsusin/eng/presentation/pdf/Beyond_5G_Promotion_S
trategy-Roadmap_towards_6G-.pdf, Jul. 30, 2020 (redisplaying).
“Innovative photonic devices for Peta bit/s-class optical fiber transmission,” O plus E, vol.41, No.5,
Sep. 2019 (in Japanese).
Technical Committee on Extremely Advanced Optical Transmission Technologies (EXAT),
https://www.ieice.org/~exat/?en/TopPage"Success of ultra-high capacity optical fiber transmission breaking the world record by a factor offive and reaching a 10 Petabits per Second -Transmission Rates of Ten Quadrillion, Contributing
to the Technology that Supports Post-5G Mobile Communications-," KDDI Research, Inc.,https://www.kddi-research.jp/english/newsrelease/2017/092201.html, Sep. 22, 2017.
"TR-1077-Technical Report of Space Division Multiplexing (SDM) technologies ",Telecommunication Technology Committee (TTC),
https://www.ttc.or.jp/document_db/information/view_express_entity/1238 (in Japanese).
National Institute of Information and Communication Technology (NICT): “R&D on innovative
optical fiber and communication technologies toward standardization (i-FAST),”
https://www.nict.go.jp/collabo/commission/k_20301.html (in Japanese).
“World’s first demonstration of transoceanic multi-core fiber transmission,” KDDI R&DLaboratories Inc., https://www.kddi-research.jp/newsrelease/2012/092001.html, Sep. 20, 2012 (inJapanese).
“Success of largest transmission capacity on transoceanic multi-core fiber transmission
experiments -World-record capacity-distance product of 1 Exa (bit/s x km) -,” KDDI R&DLaboratories, Inc., https://www.kddi-research.jp/newsrelease/2013/092501.html, Sep. 25, 2013 (inJapanese).
"KDDI R&D Laboratories Achieves Ultra-High-Speed Data Transmission of 2 Pbps over OpticalFiber Approximately Double the World’s Previous Maximum Data Transmission Capacity," KDDI
R&D Laboratories, Inc.,https://www.kddi-research.jp/english/newsrelease/2015/100101.html, Oct. 1, 2015.
“3rd Generation Partnership Project; Technical Specification Group Services and System Aspects;
System architecture for the 5G System (5GS); Stage 2 (Release 16),” 3GPP TS23.501, v16.7.0(2020-12).
“3rd Generation Partnership Project; Technical Specification Group Services and System Aspects;
Procedures for the 5G System (5GS); Stage 2 (Release 16),” 3GPP TS23.502, v16.7.0 (2020-12).
“3rd Generation Partnership Project; Technical Specification Group Services and System Aspects;
Policy and charging control framework for the 5G System (5GS); Stage 2 (Release 16),” 3GPP
TS23.503, v16.7.0 (2020-12).
A. G. Forte, W. Wang, L. Veltri and G. Ferrari, “A Next-Generation Core Network Architecture for
Mobile Networks,” Future Internet, Vol. 11, No. 7, Jul 2019.
R. J. Cho and Z. J. Van., “A Scalable, Programmable and Evolvable Mobile Core Control Plane
Platform,”. Proc. MobiCom, Oct. 2018.
A. Mohammadkhan, KK Ramakrishnan, and V. A. Jain, “CleanG - Improving the Architecture andProtocols for Future Cellular Networks With NFV,” IEEE/ACM Trans. on Networking, Vol.: 28,
Issue: 6, Dec. 2020.
M. Ahmad, S. U. Jafri, A. Ikram, W. N-A Qasmi, M. A. Nawazish, Z. A. Uzmi, Z. A. Qazi, “A Low
Latency and Consistent Cellular Control Plane,” Proc. SIGCOMM 2020, Aug. 2020.
H.Q. Ngo, A. Ashikhmin, H. Yang, E.G. Larsson, and T.L. Marzetta, “Cell-free massive MIMO
versus small cells,” IEEE Trans. Wireless Commun., vol.16, no.3, pp.1834–1850, March 2017.
E. Björnson and L. Sanguinetti, “Making cell-free massive MIMO competitive with MMSE
processing and centralized implementation,” IEEE Trans. Wireless Commun., vol.19, no.1, pp.77–
90, Jan. 2020.
T. Choi et al., “Co-located vs distributed vs semi-distributed MIMO: measurement-based
evaluation,” Proc. of Asilomar Conf. Signals Syst. Comput., Nov. 2020.
M. Ito et al., “On Spectral and Energy Efficiency of Semi-Distributed Cell-Free massive MIMO,”
2021 IEICE general conference, B-5-19, 2021.
“World first successful demonstration of wireless network deployment methodology for Beyond
5G, KDDI Research Inc., https://www.kddi-research.jp/english/newsrelease/2021/100701.html,
Oct 7, 2021.
T. Murakami et al., “Research Project to Realize Various High-reliability Communications in
Advanced 5G Network,” 2020 IEEE Wireless Communications and Networking Conference(WCNC), 2020, pp. 1-8.
Naoki AIHARA, Akio IKAMI, Takahide MURAKAMI, Yu TSUKAMOTO, Hiroyuki SHINBO, ゛A131[5-30][5-31][5-32][5-33][5-34][5-35][5-36][5-37][5-38][5-39][5-40][5-41][5-42][5-43][5-44][5-45][5-46][5-47][5-48][5-49][5-50][5-51][5-52]

User-Centric RAN Architecture using Cell Free massive-MIMO for 6G System, " 2021 IEICE
Society conference BS-3-9, Sep. 2021. (In Japanese).
“World's First Successful Development of Dual-band Light-visible Metasurface Reflector in 28 and39 bands in 5G era” KDDI Research Inc.,https://www.kddi-research.jp/newsrelease/2021/012001.html, Jan. 20, 2021 (in Japanese).
Q. Wu, S. Zhang, B. Zheng, C. You and R. Zhang, "Intelligent Reflecting Surface Aided WirelessCommunications: A Tutorial," IEEE Trans. on Communications, doi:
10.1109/TCOMM.2021.3051897.
“Development of the world’s first direction-variable liquid crystal meta-surface reflector”, KDDI
Research Inc. and Japan Display Inc., https://www.kddiresearch.jp/english/newsrelease/2021/100702.html, Oct 7, 2021.
T. Kawanishi, K. Inaga, A. Kanno, N. Yamamoto, T. Aiba, H. Yasuda, T. Wakabayashi, “Terahertz
and Photonics Seamless Short-Distance Links for Future Mobile Networks,” Earth and Space
Science Open Archive, doi: 10.1002/essoar.10503612.1.
KDDI’s SDGs: https://www.kddi.com/english/corporate/csr/sdgs/"Realization of multicore fiber with low crosstalk and ultralow attenuation loss - Demonstration ofultralong-distance transmission with current submarine cable size -," KDDI Research, Inc.,https://www.kddi-research.jp/english/newsrelease/2020/113001.html, Nov. 30, 2020.
Shohei Beppu, et al., “Real-Time Strongly-Coupled 4-Core Fiber Transmission,” Proc. OpticalFiber Communication (OFC) 2020, Th3H.2, San Diego, CA, USA, March 2020.
“World's first successful 7,200-km coupled multicore fiber transmission experiment by real-time
MIMO signal processing method,” KDDI Research, https://www.kddiresearch.jp/english/newsrelease/2021/062401.html, June 24, 2021.
Yuta Wakayama, et al., “Pure-Silica Single-Core to Multi-Core Fiber Coupler with Side-Polishing
Approach,” Proc. Opto-Electronics and Communications Conference (OECC) 2020, T3-3, Taipei,
Taiwan, Oct. 2020.
NICT “Beyond 5G R&D Promotion Project: Research and Development of Space-Division
Multiplexed Optical Network Node Technologies to Support Beyond 5G Ultra-high Capacity
Wireless Communications (002),” https://www.nict.go.jp/info/topics/2021/07/05-1.html“A New Record of 2048-QAM Transmission over Optical Fiber with COTS Devices,” KDDI
Research, https://www.kddi-research.jp/english/topics/2021/062901.html, June 29, 2021.
Ministry of Economy, Trade and Industry (METI) “Post 5G Information and Communication
System Infrastructure Enhancement R&D Project, Research Item 3) Cutting‐edge Research (b)Transmission line technology “High-capacity optical transmission system technology for metro
and long-haul network”, https://www.meti.go.jp/press/2020/10/20201027001/20201027001.html
CPRI Specification V7.0 (2015-10-09),
http://www.cpri.info/downloads/CPRI_v_7_0_2015-10-09.pdf
eCPRI Specification V2.0 (2019-05-10),
http://www.cpri.info/downloads/eCPRI_v_2.0_2019_05_10c.pdf
3GPP TR 38.801 V14.0.0 (2017-03),
https://www.3gpp.org/ftp//Specs/archive/38_series/38.801/38801-e00.zip"Success of optical fiber transmission breaking the world record for mobile wireless signal
capacity -Contribution to the rapid deployment of infrastructure for 5G and beyond mobile
communication-," KDDI Research Inc.,https://www.kddi-research.jp/english/newsrelease/2017/101901.html, Oct. 19, 2017.
"World’s First Demonstration of Radio over Fiber Technology for Mobile Networks with Large
Capacity Transmission and Superior Installability – Effectively covers indoor dead spot andenables transition to Beyond 5G/6G –," KDDI Research Inc.,https://www.kddi-research.jp/english/newsrelease/2020/120701.html, Dec. 7, 2020.
“New transmission method for nine-fold improvement of signal-to-noise ratio at an optical fiber,”KDDI Research, https://www.kddi-research.jp/newsrelease/2021/072601.html, July 26, 2021.
NICT ”Beyond 5G R&D Promotion Project: Research and Development of Radio and Optical
Converged Wireless Communication Systems for Beyond 5G Ultra-high Capacity Wireless
Networks (005),” https://www.nict.go.jp/info/topics/2021/07/05-1.html
F. Ferrari, M. Zimmerling, L. Thiele, O. Saukh, “Efficient network flooding and timesynchronization with Glossy,” Proc. 10th ACM/IEEE International Conference on Information
Processing in Sensor Networks, Chicago, IL, USA, 2011, pp. 73-84.
J. Lewis and M. Fowler, “Microservices,”
http://martinfowler.com/articles/microservices.html, 2014.
“KDDI, NEC, and NICT announced research and development activities for advanced virtualized
network infrastructure technology towards the 5G and Beyond-5G era”, KDDI,https://news.kddi.com/kddi/corporate/newsrelease/2021/09/07/5379.html, 2021 年 9 月 7 日.
John L. Hennessy and David Andrew Patterson, “A new golden age for computer132[5-53][5-54][5-55][5-56][5-57][5-58][5-59][5-60][5-61]

architecture,” Vol. 62, No. 2, Communications of the ACM, Jan. 2019.
“KDDI and Tokyo University adopted by NEDO, to research power-saving and AI-driven
operational technologies for communications infrastructure in the post-5G era.”, KDDI,https://news.kddi.com/kddi/corporate/newsrelease/2020/12/04/4848.html, 2020 年 12 月 4 日.
KDDI、“KDDI selects SpaceX's Starlink for cellular backhaul,” KDDI Press Release,https://news.kddi.com/kddi/corporate/english/newsrelease/2021/09/13/5400.html, Sep. 2021.
NASA, “What is Artemis?”, https://www.nasa.gov/specials/artemis/
D. J. Israel et al., "LunaNet: a Flexible and Extensible Lunar Exploration Communications andNavigation Infrastructure," 2020 IEEE Aerospace Conference, 2020, pp. 1-14, doi:
10.1109/AERO47225.2020.9172509.
NOKIA, “Nokia aims for the Moon with LTE/4G,” https://www.nokia.com/aboutus/newsroom/articles/nokia-aims-for-the-moon-with-lte4g/, Nov. 2020.
Space ICT Promotion Initiative Forum, https://spif.nict.go.jp/
Cabinet Office in Japan, “About selection of strategy project in the strategy program ofaccelerating space development and usage,” Committee on National Space policy SubCommittee on Satellite development and evaluation 8th meeting Document 1,
https://www8.cao.go.jp/space/comittee/02-jissyou/jissyou-dai8/siryou1.pdf, Jul. 2021. (InJapanese).
T. Nagao and T. Hayashi, “A Study on Urban Structure Map Extraction for Radio Propagation
Prediction using XGBoost”, 2021 15th European Conference on Antennas and Propagation(EuCAP), 2021.
Masayuki Nakano, Hiroaki Honma, Yoji Kishi, Toshiki Tanaka, Toshiro Shimizu, “Efforts of
Antennas and Propagation in au×HAKUTO MOON CHALLENGE Project,” IEICE technicalreport, vol. 117, no. 436, AP2017-190, pp. 77-80, Feb. 2018. (In Japanese).

Chapter 6
[6-1]
[6-2]
[6-3]
[6-4]
[6-5]
[6-6]
[6-7]
[6-8]

6G Flagship, “Key Drivers and Research Challenges for 6G Ubiquitous Wireless Intelligence,”
University of Oulu,
http://jultika.oulu.fi/files/isbn9789526223544.pdf Sept. 2019.
P.W. Shor, "Algorithms for quantum computation: Discrete log and factoring," Proc. 35th AnnualIEEE Symp. on Foundations of Computer Science, pp.124-134, Santa Fe, NM, USA, Nov. 1990.
Security Aspects of Network Capabilities Exposure in 5G, NGMN: next generation mobile
networks, 2018.
Deloitte, Smarter Cyber, How AI can help manage cyber risk,
https://www2.deloitte.com/content/dam/Deloitte/ca/Documents/risk/ca-en-smart-cyber-povaoda.pdf
National Institute of Standards and Technology, "Post-Quantum Cryptography Standardization,"
https://csrc.nist.gov/projects/post-quantum-cryptography/post-quantum-cryptographystandardization, 2016.
ITU-T, X.1363: Technical framework of personally identifiable information handling in Internet ofthings environment, 2020.
WarpDrive Project, https://warpdrive-project.jp/
N. Aragon, J. Lavauzelle, and M. Lequesne,"decodingchallenge.org: Syndrome Decoding in the
Goppa-McEliece Setting," Inria, https://decodingchallenge.org/goppa, 2019.

Chapter 7
[7-1]

[7-2]
[7-3]
[7-4]
[7-5]
[7-6]
[7-7]
[7-8]

Ministry of Internal Affairs and Communication, Japan: “2020 White Paper on Information andCommunications in Japan,”https://www.soumu.go.jp/johotsusintokei/whitepaper/eng/WP2020/2020-index.html“IoT Case Study,” KDDI, https://iot.kddi.com/cases (in Japanese).
Report Ocean, "Global Automotive IoT Market 2020-2027 by Offering, Communication Type (InVehicle, V2V, V2I), Connecting Form (Embedded, Tethered, Integrated), Application, Distribution(OEM, Aftermarket), and Region: Trend Outlook and Growth Opportunity".
Liu, R., Wang, J., and Zhang, B., "High Definition Map for Automated Driving: Overview andAnalysis," Journal of Navigation, vol.73, no.2, Aug. 2019.
Y. Li and J. Ibanez-Guzman, "Lidar for Autonomous Driving: The Principles, Challenges, andTrends for Automotive Lidar and Perception Systems," IEEE Signal Processing Magazine, vol.37,
no.4, July 2020.
AECC, https://aecc.org/
5GAA, https://5gaa.org/"3GPP, TR21.905 Vocabulary for 3GPP Specifications V15.0.0,", Dec. 2018.

133

[7-9][7-10][7-11][7-12]

Miyasaka, T., Hei, Y., and Kitahara, T., "NetworkAPI: An In-band Signalling Application-aware
Traffic Engineering using SRv6 and IP anycast," ACM NAI'20, Aug. 2020.
M. Suzuki et al., "Enhanced DNS Support towards Distributed MEC Environment," ETSI ISG MECWhitepaper, Sep. 2020.
Y. Morisawa, M. Suzuki and T. Kitahara, "Resource Efficient Stream Processing Platform with
Latency-Aware Scheduling Algorithms," USENIX HotCloud'20, Jul. 2020.
Y. Morisawa, M. Suzuki and T. Kitahara, "Flexible Executor Allocation without Latency Increase for
Stream Processing in Apache Spark," IEEE Workshop on Real-time & Stream Analytics in Big
Data, Dec. 2020.

Chapter 8

[8-1]
[8-2]
[8-3]
[8-4]

[8-5]
[8-6]
[8-7]
[8-8]
[8-9]
[8-10][8-11][8-12][8-13][8-14][8-15][8-16][8-17][8-18][8-19]“Smart Nation Singapore”, https://www.smartnation.gov.sg/“Smart Dubai 2021”, https://2021.smartdubai.ae/
Japan Cabinet: National Strategic Special Zones,
https://www.chisou.go.jp/tiiki/kokusentoc/english/index.html“Launch of Demonstration Experiment of Service Route and Ridesharing Management System for
Self-driving Vehicles - First automatic setting of multiple reservations in Japan for solving the
problem of transportation in the Kozoji New Town-”,KDDI,https://www.kddi.com/corporate/csr/regional-initiative/pressrelease/20210618/, Jun 18,
2021.
M. W. Johnson, et al.,“Quantum annealing with manufactured spins,” Nature, vol. 473, no. 7346,
pp. 194–198, 2011.
T. Okuyama, T. Sonobe, K. Kawarabayashi, and M. Yamaoka, “Binary optimization by momentum
annealing,” Phys. Rev. E 100, 012111, 2019.
Tsukamoto, S., Takatsu, M., Matsubara, S. and Tamura, H.: An accelerator architecture for
combinatorial optimization problems, Fujitsu Scientific and Technical J., Vol.53, No.5, pp.8–13,
2017.
IBM Quantum, https://quantum-computing.ibm.com/
Amazon Braket, https://aws.amazon.com/jp/braket/“Quantum Computer Science for All – Towards novel quantum applications”,
https://www.jst.go.jp/crds/pdf/2018/SP/CRDS-FY2018-SP-04.pdf (in Japanese)
J. Preskill, “Quantum computing in the NISQ era and beyond,” Quantum J., vol. 2, p. 79, 2018.
M. Grassl, B. Langenberg, M. Roetteler, and R. Steinwandt, “Applying Grover’s algorithm to AES:
Quantum resource estimates,” Lecture Notes in Computer Science, vol. 9606, pp. 29–43, 2016.
“IBM plans a huge leap in superfast quantum computing by 2023”,
https://fortune.com/2020/09/15/ibm-quantum-computer-1-million-qubits-by-2030/, Sept. 15, 2020.
“World’s First Development of New Dynamic Spectrum Allocation Method to Effectively Utilize
Radio Resources and Successful Demonstration of Application of Quantum Computing
Technology,” KDDI Research, Inc., https://www.kddi-research.jp/newsrelease/2021/031701.html,
Mar. 17, 2021 (in Japanese).
K. Saito, A. Ikami, C. Ono, “An Evaluation of Dynamic Spectrum Allocation using Ising Machine,”
IPSJ SIG Technical Report, 2021-QS-2, no. 4, pp. 1–8, 2021 (in Japanese).
K. Saito, A. Ikami, C. Ono, “Evaluating dynamic spectrum allocation using quantum annealing,”
IEICE ComEX, vol. 10, no. 9, pp. 726–732, 2021.
“Report on the Results of the Mobile Computing Promotion Consortium’s Project ”Demonstration
Experiment for Industrial Application of Quantum Computers”,” KDDI Research, Inc.,https://www.kddi-research.jp/topics/2020/060501.html, June 5, 2020 (in Japanese).
K. Satio, M. Tanaka, H. Okamoto, “Parameter optimization of LTE Using Annealing Machine,”
MCPC technical book “Expanding Quantum Computing, Its Potential for Social Implementation”,
pp. 34-37, 2020 (in Japanese).
K. Saito, S. Oyama, T. Umeki, M. Kurokawa, C. Ono, “Evaluation of Quantum Annealing for
Vehicle Routing Problem,” IPSJ Transactions of Databases, vol. 14, no. 1, pp. 8–17, 2021 (InJapanese).

Chapter 9
[9-1]
[9-2]
[9-3]

”Future direction of AI fundamental technologies in Japan,” Intelligence Research Strategy
Department, National Institute of Advanced Industrial Science and Technology (AIST), May 2018
(in Japanese).
Integrated Innovation Strategy Promotion Council Decision, “AI Strategy 2019,”
https://www.kantei.go.jp/jp/singi/ai_senryaku/pdf/aistratagy2019en.pdf, Jun. 2019.
Workshop on Continual Learning at ICML 2020, https://icml.cc/virtual/2020/workshop/5743

134[9-4]
[9-5]
[9-6]
[9-7]
[9-8]

[9-9]
[9-10][9-11][9-12][9-13][9-14][9-15][9-16][9-17][9-18][9-19][9-20][9-21][9-22][9-23][9-24][9-25][9-26][9-27][9-28][9-29]IJCAI 2021 Session on Transfer, Adaptation, Multi-task Learning, https://static.ijcai.org/ijcai-pricai2020-schedule/technical_sessions/#machine-learning-transfer-adaptation-multi-task-learning
Hovland, C. L., Janis, I. L., and Kelley, H. H., "Communication and persuasion: Psychologicalstudies of opinion change," Yale University Press, 1953.
Fogg, B. J., "Persuasive computers: perspectives and research directions," Proc. CHI 1998, ACM
Press, 225-232, 1998.
Oinas-Kukkonen H. & Harjumaa M., "Persuasive Systems Design: Key Issues, Process Model,
and System Features," Commun. Association for Information, Systems, Vol. 24, Article 28, pp.
485-500, March 2009.
Paraschivoiu I., Meschtscherjakov A., Gärtner M., Sypniewski J., "Persuading the Driver: A
Framework for Persuasive Interface Design in the Automotive Domain," In 14th International
Conference on Persuasive Technology (PERSUASIVE 2019), pp. 128-140, Springer, Cham,
2019.
AAAI 2021 workshop AI for Behavior Change, https://ai4bc.github.io/ai4bc21/
Persuasive 2021, https://persuasive2021.bournemouth.ac.uk/“Forecasting Population Flow for New Visibility: KDDI has Developed Demographic Movement
Analysis/Forecasting Technology,” KDDI Research Inc.,https://www.kddi-research.jp/newsrelease/2018/042401.html, Apr. 24, 2018.
"Launch of Joint Research Projects with Outstanding Researchers Overseas -Toward realizing
KDDI Accelerate 5.0, our concept of the future society-," KDDI Research Inc., https://www.kddiresearch.jp/english/newsrelease/2020/102601.html, Oct. 26, 2020.
K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, In
Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778,
2016.
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, Bert: Pre-training of deep bidirectionaltransformers for language understanding, arXiv preprint arXiv:1810.04805.
M. Yamada, T. Suzuki, T. Kanamori, H. Hachiya, and M. Sugiyama, Relative density-ratio
estimation for robust distribution comparison. Neural computation, 25(5), pp.1324-1370, 2013.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V.
Lempitsky, Domain-adversarial training of neural networks, The journal of machine learning
research, 17(1), pp. 2096-2030, 2016.
O. Day, and T. M. Khoshgoftaar, A survey on heterogeneous transfer learning, Journal of Big
Data, 4(1), 2017.
M. Kurokawa and K. Yonekawa, "Introduction to Research on Heterogeneous Transfer Learning,"
Journal of IEICE, Mar. 2019.
"[Artificial Intelligence] Development and Integration of Artificial Intelligence Technologies forInnovation Acceleration", JST CREST,
https://www.jst.go.jp/kisoken/crest/en/project/1111094/1111094_2018.html
K. Yonekawa, H. Niu, M. Kurokawa, A. Kobayashi, D. Amagata, T. Maekawa, and T. Hara,
Advertiser-Assisted Behavioral Ad-Targeting via Denoised Distribution Induction, In 2019 IEEE
International Conference on Big Data (Workshop on Big Data Transfer Learning), pp.5611-5619,
2019.
H. Niu, K. Yonekawa, M. Kurokawa, C. Ono, D. Amagata, T. Maekawa, T. Hara, Exploiting CrossDomain Sequence Data for Cross-Domain Recommendation, 12th Forum on Data Engineering
and Information Management (DEIM 2020), 2020.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in
vector space, arXiv preprint arXiv:1301.3781.
“Accepted as an acceleration phase project by JST CREST, "Artificial Intelligence" area - Safe
and easy-to-use "Persona Marketing AI" that connects different industrial fields”, KDDI Research,
Inc., https://www.kddi-research.jp/newsrelease/2021/032202.html, Mar. 2021.
T. Baltrušaitis, C. Ahuja, and L. Morency, “Multimodal machine learning: A survey and taxonomy,”
IEEE Trans. Pattern Anal. Mach. Intell. 41, 2, 423-443, 2019.
B. P. Yuhas, M. H. Goldstein, and T. J. Sejnowski, “Integration of Acoustic and Visual Speech
Signals Using Neural Networks,” IEEE Communications Magazine, 1989.
M. Soleymani, M. Pantic, and T. Pun, “Multimodal emotion recognition in response to videos,”
TAC, 2012.
G. Habault, Y. Nishimura, K. Yoshihara, and C. Ono, “Detecting errors in short-term electricity
demand forecast using people dynamics,” IoTDA, 2019.
G. Habault, S. Wada, R. Kimura, and C. Ono, “Elucidating the Extent by Which Population
Staying Patterns Help Improve Electricity Load Demand Predictions,” IoTDA, 2020.
Xin, Doris, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song and Aditya G. Parameswaran.
“Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities.” ArXiv
abs/1804.05892, 2018.

135[9-30][9-31][9-32][9-33][9-34][9-35][9-36][9-37][9-38][9-39][9-40][9-41][9-42][9-43][9-44][9-45][9-46][9-47][9-48][9-49][9-50][9-51][9-52][9-53][9-54][9-55][9-56]

Settles, Burr. "Active learning literature survey." University of Wisconsin, Madison 52.55-66, 2010.
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, Chrisina Jayne. “Imitation Learning: A
Survey of Learning Methods.”, ACM Computing Surveys Article No.21.
https://doi.org/10.1145/3054912 , Apr. 2017.
Adadi, A., & Berrada, M. “Peeking inside the black-box: A survey on Explainable Artificial
Intelligence (XAI)”, IEEE Access, 6, 52138-52160.
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8466590, 2018.
R. M. French, Catastrophic forgetting in connectionist networks, Trends in cognitive sciences,3(4), pp.128-135, 1999.
D. Lopez-Paz, and M. A. Ranzato, “Gradient episodic memory for continual learning”, Proc. 31st
International Conf. Neural Information Processing Systems, pp. 6470-6479, 2017.
J. Kirkpatrick, et al., Overcoming catastrophic forgetting in neural networks. Proceedings of thenational academy of sciences, 114(13), pp.3521-3526, 2017.
J. Serra, D. Suris, M. Miron, and A. Karatzoglou, Overcoming catastrophic forgetting with hardattention to the task, In International Conference on Machine Learning, pp. 4548-4557, 2018.
Z. Chen and B. Liu, “Lifelong Machine Learning (second edition), Synthesis Lectures on Artificial
Intelligence and Machine Learning,” Morgan & Claypool Publishers, 2018.
Goldberg LR, “An Alternative Description of Personality”: The Big-Five Factor Structure. Journal of
personality and social psychology 59(6):1216, 1990.
Schwartz SH, “A proposal for measuring value orientations across nations”, Questionnaire
Package of the European Social Survey 259(290):261, 2003.
A. Kobayashi, Y. Ishikawa, S. Kuroyanagi and A. Minamikawa, "A Study on Psychological Trait
Prediction from Web Access Patterns and Its Utility for Ad Targeting", IEICE Trans. Fundamentals,
104(2), 27-39, 2021.
Y. Ishikawa, A. Kobayashi, A. Minamikawa, and C. Ono, “Predicting a Driver’s Personality from
Daily Driving Behavior,” Proc International Driving Symposium on Human Factors in Driver
Assessment, Training and Vehicle Design (pp. 203-209), June 2019.
A. Kobayashi, Y. Ishikawa and A. Minamikawa “A Study on Effect of Big Five Personality Traits on
Ad Targeting and Creative Design,” In International Conference on Persuasive Technology (pp.
257-269). Springer, Cham, Apr. 2019.
Y. Ishikawa, A. Kobayashi and A. Minamikawa, “Predicting Advertising Appeal from Receiver's
Psychological Traits and Ad Design Features”, In Adjunct Publication of the 27th Conference on
User Modeling, Adaptation and Personalization (pp. 45-49), Jun. 2019.
Y. Ishikawa, A. Kobayashi, and D. Kamisaka, “Modelling and predicting an individual’s perceptionof advertising appeal”, User Model User-Adapted Interaction, https://doi.org/10.1007/s11257-02009287-z, 2021.
Kobayashi, A., Ishikawa, Y., & Sebastian Legaspi, R. (2021, June). Psychographic Matching
between a Call Center Agent and a Customer. In Proceedings of the 29th ACM Conference on
User Modeling, Adaptation and Personalization (pp. 229-234).
Rabinowitz, N., Perbet, F., Song, F., Zhang, C., Eslami, S. A., & Botvinick, M. (2018, July).
Machine theory of mind. In International conference on machine learning (pp. 4218-4227). PMLR.
Cuzzolin, F., Morelli, A., Cirstea, B., & Sahakian, B. J. (2020). Knowing me, knowing you: theoryof mind in AI. Psychological medicine, 50(7), 1057-1061.
Scassellati, B. (2002). Theory of mind for a humanoid robot. Autonomous Robots, 12(1), 13-24.
Legaspi, R., Xu, W., Konishi, T., & Wada, S. (2021, April). Positing a Sense of Agency-Aware
Persuasive AI: Its Theoretical and Computational Frameworks. In International Conference on
Persuasive Technology (pp. 3-18). Springer, Cham.
David, N. (2012). New frontiers in the neuroscience of the sense of agency. Frontiers in humanneuroscience, 6, 161.
Y. Kuriki, W Xu, M. Taya, N. Kobayashi and A. Minamikawa, “An explorative examination ofInformation Content to prevent Smartphone Zombie,” 2021 IEICE general conference, 2021 (inJapanese).
W. Xu, Y. Kuriki, T. Sato, M. Taya and C. Ono, "Does Traffic Information Provided by Smartphones
Increase Detour Behavior?" In 15th International Conference on Persuasive Technology(PERSUASIVE 2020), pp.45-57, Springer, Cham, 2020.
M. Nagata, T. Konishi, M. Honjo, A. Yoneyama, M. Kurokawa and K. Mishima, “Promotion ofwalking activities in the workplace utilizing walking events,” Trans. Digital Practice, IPSJ, Vol.10,
No.4, pp.803-815, 2019 (in Japanese).
“AI R&D and Utilization Principles for KDDI Group,https://www.kddi.com/english/corporate/kddi/public/ai_principles/
Amina Adadi and Mohammed Berrada. “Peeking inside the black-box: A survey on Explainable
Artificial Intelligence (XAI)”. DOI:10.1109/ACCESS.2018.2870052.
P. W. Koh and P. Liang, “Understanding black-box predictions via influence functions,” In136

[9-57]

International Conference on Machine Learning, pp.1885-1894, 2017.
“3rd Knowledge Graph Reasoning Challenge 2020 Entries”, https://challenge.knowledgegraph.jp/results/results2020.html

Chapter 10[10-1][10-2][10-3][10-4][10-5][10-6][10-7][10-8][10-9][10-10][10-11][10-12][10-13][10-14][10-15][10-16][10-17][10-18][10-19]T. Kameoka, K. Sase, S. Yoshimoto, T. Hachisu, S. Saga, Y. Kuroda, T. Yanai, S. Ishimaru, M.
Kawaguchi, K. Kiyokawa, Y. Vibol and A. Shirai, “The Beginning of Online and VR Conferences,”
Journal of the Virtual Reality Society of Japan, 2020, Vol. 25, No. 2, p. 35-43, 2020 (in Japanese).
Matt Zalaznick, “How about using a digital avatar on a virtual campus?,” University Business,
https://universitybusiness.com/digital-avatar-virtual-college-campus-virbela-edtech/, Aug. 6, 2020.
“U.S. eXp's Virtual Office Sales Rise Sevenfold due to Corona Virus Pandemic”, NIKKEI,
https://www.nikkei.com/article/DGXMZO60130810Z00C20A6I00000/,
Jun. 9, 2020 (in Japanese).
“Virtual Shibuya, A Distribution Platform Authorized by Shibuya Ward, Opens on May 19”, KDDI,https://news.kddi.com/kddi/corporate/newsrelease/2020/05/15/4437.html,
May 15, 2020 (in Japanese).
““Shibuya Evolves with ‘Entertainment x 5G’; KDDI, Shibuya City Tourism Association, and Shibuya
Future Design Launch ‘Shibuya Entertainment Tech Promotion Project’”, KDDI etc.,
https://news.kddi.com/kddi/corporate/newsrelease/2019/08/28/3979.html,
Aug. 28, 2019 (in Japanese).
"NTT Develops Non-Rigid Object Recognition Technology with a Small Number of Database
Images -Not Only Rigid 3D Objects but Non-Rigid Objects Such as Soft Packaging or Cloth
Products can be Recognized with Low Preparation Cost-," NTT,
https://group.ntt/en/newsrelease/2018/11/26/181126b.html, Nov. 26, 2018.
T. Yoneyama, E. Murakami, Y. Oguro, H. Kubo, K. Yamaguchi, and Y.Sakamoto, “Holographic headmounted display with correct accommodation and vergence stimuli, ”Opt. Eng. 57(6), 061619, 2018.
T. Horiuchi, “Sound Media Technology Reflecting the Viewer’s Intention of Watching and Listening,”
Journal of IEICE, Vo1.104, No.1, pp.22-26, Jan. 2021.
Cisco Visual Networking Index (VNI) Forecast for 2016 – 2021.
Cisco Mobile VNI Forecast for 2017 to 2022.
JVET-S2001, “Versatile Video Coding (Draft 10)”.
“World's First Successful Demonstration of 4K Live Transmission using Real-time Codec
Compatible with the Latest Video Coding Format H.266|VVC”, KDDI Research Inc.,https://www.kddi-research.jp/newsrelease/2020/122301.html, Dec. 23, 2020 (in Japanese).
“World’s first 8K video live transmission experiment using real-time H.266|VVC codec”, KDDI
Research Inc.,https://www.kddi-research.jp/english/newsrelease/2021/041901.html, Apr. 19, 2021.
“Development of AR Streaming Technology for Photo-Realistic 3D Avatars -Realizes Transmissionof Virtual Humans to Smartphones at One-Third the Existing Transmission Rate -”, KDDI ResearchInc., https://www.kddi-research.jp/newsrelease/2021/021801.html, Feb. 18, 2021 (in Japanese).
“Creating New Experiences with 5G and XR, Launching the Creative Team ‘au VISION STUDIO’,”
KDDI, https://news.kddi.com/kddi/corporate/newsrelease/2021/03/10/5006.html,
Mar. 10, 2021 (in Japanese).
“Launching an Initiative to Support DX in the Apparel Industry Utilizing 5G, and Virtual Humans”,
KDDI, https://news.kddi.com/kddi/corporate/topic/2021/09/10/5401.html, Sep. 10, 2021 (inJapanese).
“We Can Share the Feeling of Cheers Even in an Online Environment. Development of ‘Sync
Glass’ a Communication System That Uses Haptic Technology to Connect with People at a
Distance”, KDDI Research Inc.,https://www.kddi-research.jp/newsrelease/2021/031501.html, Mar. 15, 2021 (in Japanese).
MPEG-3DG: "MPEG Point Cloud Compression", http://www.mpeg-pcc.org/“KDDI Research realize the ultra-low latency video transmission of 50ms for a remotely operating
robot – remote operation is possible without a gap between visual cognition and operation”,
KDDI Research Inc., https://www.kddi-research.jp/newsrelease/2020/070901.html,
Jul. 9, 2020 (in Japanese).

Chapter 11137

[11-1][11-2][11-3][11-4][11-5][11-6][11-7][11-8][11-9][11-10][11-11][11-12][11-13]

Headquarters for Japan’s Economic Revitalization, Japan: “Summary of Japan’s Robot Strategy It’s vision, strategy and action plan-”,https://www.kantei.go.jp/jp/singi/keizaisaisei/pdf/robot_point_150210EN.pdf, Feb. 10, 2015.
“Environment change surrounding robots and future direction – Plan for Promoting Social Change
Taking Advantage of Robots –", Council for Promoting Social Change Taking Advantage of Robots
in Ministry of Economy, Trade and Industry,
https://www.meti.go.jp/shingikai/mono_info_service/robot_shakaihenkaku/pdf/20190724_report_0
1.pdf, Jul. 24, 2019 (in Japanese).
“LG Electronics' Promise of AI for an Even Better Life Delivered at CES 2019 Keynote”, LG
electronics,
https://www.lg.com/ca_en/about-lg/press-and-media/LG-Electronics-Promise-of-AI-for-an-EvenBetter-Life-Delivered-at-CES-2019-Keynote, 2019.
“TIS provides ‘Service robot integration business’ which realizes human task outsourcing andsharing by robots – resolving social issues such as labor shortage and work productivity by
RoboticBase, a platform which comprehensively manages multiple robots”, TIS,
https://www.tis.co.jp/news/2018/tis_news/20181016_1.html,
Oct. 16, 2018 (in Japanese).
J. Xu, et al., "Fast and Accurate Whole-Body Pose Estimation in the Wild and Its Applications," ITE
Transactions on Media Technology and Applications, Volume 9, Issue 1, pp. 63-70, 2021.
“KDDI Research develops Japan’s first USV which can be remotely controlled by a smartphone
connecting to mobile network,”
KDDI Research Inc., https://www.kddi-research.jp/newsrelease/2020/111901.html,
Nov. 19, 2020 (in Japanese).
“World’s first development of ‘aerial-and-underwater-assembled drone’”,KDDI Corporation, KDDI Research, Inc., Prodrone Co., Ltd.,
https://news.kddi.com/kddi/corporate/newsrelease/2021/06/10/5181.html,
June 10, 2021 (in Japanese).
“World’s first successful communication experiments with an undersea smartphone by the blue LED
optical wireless communication technology,”
KDDI Research Inc., https://www.kddi-research.jp/newsrelease/2019/100201.html,
Oct. 2, 2019 (in Japanese).
“KDDI Research realize the ultra-low latency video transmission of 50ms for a remotely operating
robot – remote operation is possible without a gap between visual cognition and operation,”
KDDI Research Inc., https://www.kddi-research.jp/newsrelease/2020/070901.html,
Jul. 9, 2020 (in Japanese).
“Japan’s first delivery of 5G pre-service by high definition video transmission and 4K real-time
broadcast in the drone race,” KDDI and KDDI Research Inc.,https://news.kddi.com/kddi/corporate/newsrelease/2019/10/15/4077.html,
Oct. 15, 2019 (in Japanese) (redisplaying).
“Robotics Hub”, Panasonic, https://tech.panasonic.com/jp/robot/about.html"Future Robotics Organization," Waseda University, https://www.waseda.jp/inst/fro/en/"’Robot Kobo (workshop)’ established for co-creating new services using robots – towards robot
friendly society by harmonizing communication control and robot control –”,KDDI Research, Inc.
https://www.kddi-research.jp/newsrelease/2021/090801.html,Sep. 8, 2021 (in Japanese).

Chapter 12[12-1][12-2]

M. Miyake, "Standardization of security in communication field", 6th Standardization Strategy WG,
Subcommittee on Technology Strategy, Department of Information and Communications
Technology, Information and Communications Council, Ministry of Internal Affairs andCommunications, Japan,
https://www.soumu.go.jp/main_content/000690766.pdf, Mar. 31, 2020 (in Japanese).
"Launch of Joint Research Projects with Outstanding Researchers Overseas -Toward realizing
KDDI Accelerate 5.0, our concept of the future society-,” KDDI Research Inc.,https://www.kddi-research.jp/english/newsrelease/2020/102601.html,
Oct 26, 2020 (redisplaying).

（Note） The above citations are as of the time when this white paper was published.

138

Revision history
Data

Ver.

Update2021/03/24

1.0First version released

2021/06/02

1.0.1

Correction of errors

2021/10/22

2.0

Contents are added or updated,
e.g. Section 5.4.6: "Communications in space" andSection 8.4.5: "Quantum Computing" as well as
several figures in Chapter 2.

2021/12/14

2.0.1

Correction of errors

139© 2021 KDDI CORPORATION and KDDI Research, Inc.