Beyond 5G White Paper
Supplementary Volume on“E2E Architecture”

Version 1.0
March 7, 2024
Beyond 5G Promotion Consortium
White Paper Subcommittee

Contents
Preface .............................................................................................................................. 5
Digital-Twin for and by Beyond 5G ................................................................................. 7
1

Introduction ............................................................................................................... 7

2

Digital-Twin Platform Architecture with Beyond 5G ............................................ 8

3

Functional Design ..................................................................................................... 9
3.1 Probabilistic Representation of Digital-Twin ................................................ 9
3.2 Cross-Domain Orchestration of Digital-Twins ............................................ 10

4 Use Case Examples ................................................................................................. 12
4.1 Digital-Twin for Radio Communication Environment ................................ 12
4.2 Digital-Twin for Human-Robot Cooperation ................................................ 13
4.3 Smart Sustainable Mobility .......................................................................... 14
5 Conclusion................................................................................................................ 16
Acknowledgements ....................................................................................................... 16
Optimum collaboration of network functions and computing resource ........................ 18
1

Introduction ............................................................................................................. 18

2 Network Requirements and Technology Trends in the B5G era ......................... 19
3 Concept of Proposed Architecture .......................................................................... 19
3.1 Background .................................................................................................. 19
3.2 Concept of Proposed Architecture .............................................................. 20
4

Proposed Network Architecture ............................................................................. 21

5 Evaluation ............................................................................................................... 22
5.1 Assumed Use-Case ...................................................................................... 22
5.2 Evaluation Results ...................................................................................... 23
6 Conclusion ................................................................................................................ 24
User-centric Network ..................................................................................................... 26
1

Introduction ............................................................................................................. 26

2 User-centric network .............................................................................................. 27
2.1

Overview ...................................................................................................... 27

1

2.2 User-centric network function deployment and management ................. 28
2.3 User-centric RAN with Cell-Free massive MIMO .................................... 29
2.4

Isolated RAN/CN which exists per user/user-group ................................. 30

3 Research activities to realize a User-centric network .......................................... 31
4 Conclusion................................................................................................................ 32
ACKNOWLEDGMENTS .............................................................................................. 33
Intent-based operational plan generation for business utilization of autonomous
networks ................................................................................................................. 34
1

Introduction .......................................................................................................... 34

2

Current intent-based autonomous network architectures and challenges ..... 34

3

Proposed Architecture ......................................................................................... 35

4Conclusion ............................................................................................................ 37

Acknowledgements ....................................................................................................... 38
Task-Oriented 6G Native-AI Network Architecture ..................................................... 39
1

Introduction .......................................................................................................... 39

2

Network Paradigm Change ................................................................................. 40
2.1 Change 1: From Session to Task ................................................................... 40
2.2 Change 2: From single-dimension to multi-dimension heterogeneous
resources .................................................................................................... 41
2.3 Change 3: From Session-control to Task- control ........................................ 41

3

Architecture and Key Technologies .................................................................... 41
3.1 Logical Architecture of TONA ....................................................................... 41
3.2 Deployment Architectures ............................................................................. 42

4

Advantage Analysis ............................................................................................. 43

5Conclusion ............................................................................................................ 44

Envisioning Architectural Transformation towards 6G ................................................ 45
1

Introduction .......................................................................................................... 45

2

Architecture design principles ............................................................................ 46

3

System architecture migration towards 6G ....................................................... 47
3.1

Lessons learnt from 5G migration and deployments ............................. 47

2

3.2

6G system architecture and migration enabler ...................................... 48

3.3

Interworking with legacy systems ........................................................... 49

4

RAN - CN separation and interface.................................................................... 49

5

Logical RAN architecture .................................................................................... 51

6

Conclusion ............................................................................................................ 52

Abbreviation List ........................................................................................................... 533

【Revision History】
Ver. Date
Contents
1.0 2024.3.7
Initial version

Note

4

Preface
In the future society in the 2030s, it is expected that various services that take advantageof the characteristics of Beyond 5G will be realized. In Beyond 5G, coverage expansion
and speedup are advanced by various technologies. In addition, such communication
functions will be highly virtualized and can be controlled by software. The networkarchitecture of Beyond 5G needs to provide the optimum infrastructure functions thatsatisfy the performance required by applications and the quality of experience of users
without users being aware of the infrastructure and technologies of Beyond 5G, thecongestion of communication bands, and security.
In Japan, Beyond 5G is for the advancement of Society 5.0[1], and network architectureis also required to efficiently provide the functions of Beyond 5G infrastructure in orderto advance Society 5.0. In addition, Society 5.0 is expected to develop the economy bysolving various social issues through CPSs (Cyber-Physical Systems), which are highly
integrated virtual and real spaces. CPSs build real spaces on virtual spaces using AI(Artificial Intelligence) and sensors, which are ubiquitous in society, reproduce social
activities on virtual spaces, and bring social value by acting on real spaces. In Beyond
5G, from the viewpoint of realizing CPSs, both virtual spaces and real spaces, which
realize various Society 5.0 services, can be connected, and controlled. In this way, Beyond
5G can integrate real spaces and virtual spaces and handle ubiquitous AI in order torealize Society 5.0, including optimizing its own network functions. In addition to
network functions, it is expected to be an architecture that can optimally provide
computing resources that make AI available.
This Supplementary Part describes the following E2E architecture-related technologies
in order to realize the vision for 2030 and provide Beyond 5G infrastructure functions in
response to the diverse needs of users.
Digital-Twin for and by Beyond 5G
Digital-Twin can be both digital representation of real-world and digital representationof network objects. Federating and jointly optimizing both real-world and network
objects is essential to realize new services in the era of Beyond 5G.
Optimum collaboration of network functions and computing resource
Introducing a technique that enables the optimal use of computing resources by using
computing resources located in different network domains (access network, MEC, core
network, cloud) as one virtualized computing resource.
User-Centric Network

5

Introducing an overview of "user-centric networks", an innovative approach designed toprovide stable, high-speed, and continuous communication services to each user atanytime and anywhere.
Intent-based operational plan generation for business utilization of autonomous
networks
Proposing a method to generate a network operation plan from the user's intention,
which allows the user to activate the operation plan after verifying its validity.
Task-Oriented 6G Native-AI Network Architecture
Introducing a task-oriented native AI network architecture to natively support network
AI, which integrates AI into the network.
Envisioning Architectural Transformation towards 6G
Introducing system architecture migration options to 6G, interworking aspects withlegacy generations, and the recommended design of the overall system architecture,including RAN-CN functional split and logical RAN architecture.
REFERENCE[1] Society 5.0 https://www8.cao.go.jp/cstp/english/society5_0/index.html

This White Paper was prepared with the generous support of many people who
participated in the White Paper Subcommittee. The cooperation of telecommunicationsindustry players and academia experts, as well as representatives of various industries
other than the communications industry has also been substantial. Thanks to everyone’s
participation and support, this White Paper was able to cover a lot of useful information
for future business creation discussions between the industry, academia, andgovernment, and for investigating solutions to social issues, not only in thetelecommunications industry, but also across all industries. We hope that this White
Paper will help Japan create a better future for society and promote significant global
activities.
Kentaro Ishizu, NICT
Mitsuhiro Azuma, NICT

6

Digital-Twin for and by Beyond 5G
Hideyuki Shimonishi, Osaka University
Kentaro Ishizu, Koji Zettsu, NICT
Toshirou Nakahira, NTT
Dai Kanetomo, NEC

Abstract—In this article, we discuss that a Digital-Twin can be both digital
representation of real-world, i.e. Digital-Twin by Beyond 5G, which could be enabled byadvanced Beyond 5G capabilities, and digital representation of network objects, i.e.
Digital-Twin for Beyond 5G, which helps to enable advanced Beyond 5G capabilities.
Federating and jointly optimizing various Digital-Twin instances of both real-world andnetwork objects is essential to realize new services in the era of Beyond 5G, and thus
propose a Digital-Twin architecture which manages various Digital-Twin instances in acommon way so that any Digital-Twin applications can easily utilize them. We thenintroduce probabilistic Digital-Twin, which can improve both efficiency and safety ofmany Digital-Twin use cases by considering uncertainties inherent in the real world,and cross-domain orchestration of Digital-Twins, which will be a key to realize thedigital-first services. Finally, we introduce some of examples of the Digital-Twinsdiscussed in this article, including radio communication environment, human-robot
cooperation, and smart sustainable mobility.
1

Introduction
Digital-Twin is a digital reproduction of objects in physical space (cars, jet engines,

people, buildings, cityscapes, etc.), or potentially in virtual space or so called Metaverse.
It is expected to be an important technology for various ICT systems in factories, aviation,connected cars, smart cities, smart buildings, etc., in realizing advanced Cyber Physical
Systems. The concept of Digital-Twin has been introduced in various literature since the2000s, and widely accepted in recent years when several literatures, such as 0, has been
known. Also, several articles, such as [2], provides an extensive survey on Digital-Twins,
including enabling technologies and technical issues.
As an extension of classical notion of Digital-Twin, which is a one-to-one
correspondence between objects in physical space and objects reproduced in virtual space,
Digital Twin Network [3] has been proposed to represent of networks of multiple objects
so that various objects in real and virtual space share information and cooperate toperform specific tasks in connected cars, smart cities, etc. In addition, Cognitive Digital
Twin [4] focuses on knowledge representation, called ontology, to handle various typesof objects in the real world. This is expected to enable more sophisticated applications by
sharing digital twins between different systems.

7

Digital-twin, sometimes called Network Digital-Twin, is also used to plan, design,
manage, simulate, operate, and control networks, as discussed by ITU-T [5], IOWN
Global Forum Digital [6], and TMforum [7]. In this case, the Digital-Twin is a digital
reproduction of any network devices, edge/cloud computing resources, terminal devices
or robots which has network interfaces, radio communication environments measured or
estimated by various sensing devices, or even logical networks and services.
2

Digital-Twin Platform Architecture with Beyond 5G

The

key

conceptoftheproposed

Digital-Twin

platformarchitectureis:1) Digital-Twin instances of both real-world and network objects can be handled freely
without any distinctions, and2) various Digital-Twin instances can be easily federated and jointly optimized,
so that any Digital-Twin applications can easily utilize them to realize new services inthe era of Beyond 5G.
Figure 1 shows the proposed framework. As above discussed, target physical object
includes any real-world and network objects. Other logical objects such as logicalnetworks could also be included. Any measurement data are collected from them toreconstruct them in the Digital-Twin space, using any sensing/control devices likecameras, radio monitors, ISAC (Integrated Sensing and Communications), and legacy
network managers such as syslog or EMS. Raw data analysis, such as object detection
from video image, could be done here to extract meaningful information from the raw
data. Then, common Digital-Twin functions would be provided by the platform so that
Digital-Twin applications can utilize the Digital-Twin instances through common andopen APIs. The functions include 1) device connectivity function to connect any kinds ofsensing/control devices through common interfaces like WoT or MQTT, 2) device
abstraction function to easily utilize various versions of devices in a common way, 3) data
management functions to manage the data of any Digital-Twin instances, 4) various
analysis functions, such as probabilistic inference discussed below, commonly useful formany Digital-Twin applications, and 5) Data isolation and access control function so thatdifferent applications can share the data and federate each other. Those functions may
use common open source platform like Eclipse Ditto [8].
Figure 2 shows the implementation of Digital-Twin in distributed infrastructure. As its
nature, physical devices, sensing/control functions, as well as raw data analysis are
implemented on local devices or edge computing devices. Digital-Twin functions areapplications are implemented on distributed computing and storage infrastructure andmutually interconnected via high-speed and low-latency communication infrastructure.
We also note that all those functions shown in Figure 1 and Figure 2 are enabled on an
advanced Beyond 5G communication, computation, and storage infrastructure.

8

Figure 1 Digital-Twin Platform Architecture

Figure 2 Digital-Twin Implementation in Distributed Infrastructure
3

Functional Design

3.1 Probabilistic Representation of Digital-Twin
Safety and trustability are critical for many Digital-Twin applications in many realworld use cases and thus we propose Probabilistic Digital-Twin in which risk
management can be better handled through probabilistic representation of the realworld, and probabilistic prediction and probabilistic control based on the probabilistic
representation. Use cases of the probabilistic Digital-Twin includes following examples.
•

Human Robot Collaboration, autonomous robot, automatic driving (Figure 3 left):
Risk sensitive path/speed/behavior control with probabilistic information for
Improved/optimized/controlled safety.

•

Remote-controlled robot (Figure 3 center): Move slowly in unstable radio
condition, avoid unstable signal area for Robust operation, avoid risks, etc.

9•Network design and control (Figure 3 right): BS (Base Station) location design
and beam forming control based on probabilistic radio environment map for costeffective, robust, application aware network design and control.

Figure 3 Digital-Twin Implementation in Distributed Infrastructure
Figure 4 shows an example of a data structure for probabilistic Digital-Twin. In
between Digital-Twin applications and devices, a space-time structure is composed ofmany objects in the 4D space. These objects may be a physical object in the space (orange
dot) or a status of a lattice point in the 4D space (green dot). Properties of these objects,
such as occupancy status of the lattice point, location of the objects, identity/class of theobjects are expressed as a probability distribution, rather than a specific value.

Figure 4 Data Structure for Probabilistic Digital-Twin
3.2 Cross-Domain Orchestration of Digital-Twins
Today, many smart cities are introducing digital twins, using IoT sensors to collectand monitor urban data. Conventionally, they have put much effort to digitize physicalspace on cyber space in order to analyze and simulate the real world through data forsituation monitoring and decision making. From now, we will focus on feedback to thereal space to implement the results of analysis and simulation, which is called “digitalfirst” paradigm[9]. The digital twin collaboration will be the key to realize the digitalfirst services.
The beyond 5G/6G functional architecture[13] is an open platform to receive a diverse
set of functions. One of the key components is the orchestrator, which is responsible for10finding the right combination of system components and linking them together to meetthe requirements from a CPS service. The orchestrator facilitates the coordination ofdigital twins across industries to realize a myriad of new value-added services.
To facilitate information sharing and interaction between digital twins among
different domains, orchestrators are required to have the functions shown in Figure 5.
The federation function configures and manages federations of digital twins that updatea shared virtual model while maintaining privacy data generated by physical objects
within individual digital twins. The translation function facilitates the formal andsemantic transformation of communications between digital twins in different domains.
The brokering function identifies and authenticates digital twins, relays datatransmission and reception, performs data filtering, real-time delivery, and guaranteesdelivery. The synchronization function synchronizes many-to-many interactions
between physical space entities and cyberspace models between digital twins. The
registry function registers and discovers digital twin components based on their feature
information

Figure 5 Functional Architecture of the Digital Twin Orchestrator

11

4

Use Case Examples

4.1 Digital-Twin for Radio Communication Environment
This use case is to manage the radio communication network via a Digital-Twin.
Stability of radio communication is so crucial for mission critical communications suchas remote robot operations or connected car operations. However, radio communicationis heavily affected by radio environment especially when high frequency radio like mmWave is used for mobile communications. So, it is very important to understand the radio
environment in detail as a Digital-Twin to manage radio communications.
As discussed in Section 3.1, it is quite difficult to monitor, estimate, and predict theradio environment, so we have we proposed a method for constructing the Digital-Twinusing probability distributions [15]. We proposed using a graphical model known as
Markov Random Field (MRF) to depict the spatial structure of the RSRP (Reference
Signal Received Power) map. We then evaluated the proposed method in our own 28 GHz
Local 5G environment and extensively studied the characteristics of indoor millimeterradio communication. We confirmed that each estimated point can be well estimated byprobability distribution using the proposed method, as shown in Figure 6. We also
considered a radio network design in which estimated RSRP value becomes larger thanrequired value plus certain margin at each location. We proposed to set the margin basedon the inferred probability distribution, rather than to set a uniform margin. As shownin Figure 7, when the target coverage rate, i.e. the ratio of points that the observed RSRP
value is within the margin, is set to 90%, the proposed method can achieve this by using
a 1.1σ interval as the margin at each point, whereas the conventional method requires auniform 1.9σ interval average as the margin.

Figure 6 Estimated Radio Environment Map and its Probability Distribution

12

Figure 7 Ratio of points that the observed value is within the margin
4.2 Digital-Twin for Human-Robot Cooperation
Robots are widely utilized in industrial sites due to the decrease in the number ofworkers. However, there are many sites where it is difficult to replace all operations withrobots due to cost and environmental adaptability, and robots and workers need tocoexist. A logistics facility is a such site.
Logistics facilities have become larger and larger in recent years, and robots (AGVsand AMRs) are increasingly used to transfer goods inside them. On the other hand, there
are many tasks that are difficult for robots to handle goods directly, for example picking
and repacking goods, so manual labor is also indispensable. Therefore, workers andtransfer robots coexist. Although there are some sites that separate the space for both
workers and robots, it is desirable for both to be able to coexist safely in the same space
to increase the efficiency of space utilization in the facility. In such cases, the trade-off
between safety and efficiency becomes an issue. A typical transfer robot restrains its
speed so that it can stop when an obstacle including workers approaches, and once it
recognizes the obstacle, it stops. While this ensures safety, it inevitably reduces transfer
efficiency. This trade-off can be resolved by utilizing probabilistic digital twin to predict
the future location of the worker and control robots to consider the risk of collision andspeed reduction. Each of the location prediction and control techniques is introduced indetail below.
However, sensors inevitably have blind spots, and there is a delay between detectionof location of an obstacle and robot control, so obstacle location information at the time
when the robot is operating is needed. To solve this problem, the presence or absence ofobstacles at each time and point in the robot operation area is expressed as a probability,and based on the observed information, the condition of the blind spots and the future
condition at each point are estimated as probabilities (See Figure 8). When estimating
the probability, it is important to understand the relationship of obstacles in space andtime. In other words, for moving obstacles, if the obstacle is within a certain distance in13the direction of movement from the point where it was observed at the previous time,
the probability of its presence is high, but if it is further away than a certain distance,
the probability of its presence is low. We represent such a spatial-temporal relationshipbetween the presence and absence of obstacles as a conditional random field, CRF, andby mapping the observed values, we construct a model that predicts the future situationof obstacles from the current obstacle situation based on the observed values[10].

a: Probability of obstacle existence at each point(yellow is high, red is middle, blue is low)b: Prediction of worker’s future
location
Figure 8 Prediction of future location of obstacle

After the location of future obstacles is estimated, it is necessary to control the routeand speed of the transfer robot to travel safely and efficiently considering real-world
uncertainties. Risk-sensitive stochastic control[11][12] solves these problems. In risksensitive stochastic control, the robot's motion equation is defined as a stochastic
differential equation (see Figure 9-a) because it represents the uncertainties that affectthe robot's motion, such as hardware degradation and ground conditions, as a model. An
evaluation function is used to choose optimal control inputs, and we design it to evaluate
both safety and efficiency, as well as to be sensitive to risk (see Figure 9-b). Although thevalue of the evaluation function will be a probability distribution because stochastic
differential equation is used as equation of motion, it is possible to select the control that
reduces both the value that the smaller is better and the variance as the optimal one. To
determine the actual control inputs, various control inputs are prepared in advance, andthe path and speed determined by solving stochastic differential equations are evaluatedwith the risk-sensitive evaluation function to select the optimal control (See Figure 9-c).

a: Stochastic differential equationb: Risk-sensitive evaluation function

c: Safe and efficient path
Figure 9 Risk-sensitive stochastic control

4.3 Smart Sustainable Mobility
Today, the environment and mobility are major issues for many smart cities. Here weassume the following digital twin; the smart environment digital twin monitors air

14pollution by collecting air quality data from observation stations, while restricting
emissions at major sources when air pollution is expected to worsen; the smart driving
digital twin monitors the driving environment of individual cars using in-vehicle sensors,
while guiding driving maneuvers and travel routes according to changes of theenvironment. The eco-driving assistance, an application of digital twin orchestration
owned by a city officer, aims to improve the city’s environmental quality byrecommending environment-friendly driving maneuvers to drivers and autonomous carsin areas with poor environmental quality. Based on the emission restriction plan
simulated by the smart environment digital twin, the smart driving digital twin
instructs the navigation system to perform driving maneuvers to control emissions.
Furthermore, it enhances the air pollution prediction of the smart environment digitaltwin using environmental sensor data captured by the cars, which enables more effective
eco-driving assistance.
Figure 10 show interactions between these digital twins through the orchestrator
functions. The federation function shares the air pollution prediction model of the smart
environment digital twin with the smart driving digital twin for federated learning using
private data collected by individual car. The brokering function allows application toreceive the emission restriction plan generated by the smart environmental digital twin,
determines the restriction order for cars driving in the restricted area, and can send theorder to the smart driving digital twins of the target cars. The translation function
converts the environmental sensor data collected by the smart driving digital twin ofindividual cars to the format of observation data in the smart environment digital twin
to import the “mobile” observation data for denser prediction of air pollution.
Implementation of the orchestrator framework is promoted for individual digital twin
platforms as a common interface of inter-platform digital twin orchestration. The first
implementation of the orchestrator framework and the use case is being conducted on
NICT xData Platform[14] and Testbed. The framework implementation for IWON is also
being discussed in IOWN Global Forum based on mapping the orchestrator functions to
the IOWN Digital Twin Framework. In addition, integrated architecture of theorchestrator between physical space and cyber space is included in our future work.

15

Figure 10: Smart sustainable mobility use case
5

Conclusion
In this article, we argued that a Digital-Twin can be digital representation of both real-world network objects. Based on this, we proposed a Digital-Twin architecture which
manages various Digital-Twin instances in a common way so that any Digital-Twin
applications can easily utilize them. We then introduced probabilistic Digital-Twin andcross-domain orchestration of Digital-Twins, as well as the use cases including radio
communication environment, human-robot cooperation, and smart sustainable mobility.
Acknowledgements
This work was partly supported by MIC under a grant entitled R&D of ICT Priority
Technology (JPMI00316). Also, this research results were partly obtained from thecommissioned research (No.00701) by National Institute of Information andCommunications Technology (NICT), Japan.

16

REFERENCE[1] Grieves, Michael and Vickers, John. “Digital Twin: Mitigating Unpredictable,
Undesirable Emergent Behavior in Complex Systems”. 10.1007/978-3-319-38756-7_4.,
2017.
[2] A. Fuller, et. al., "Digital Twin: Enabling Technologies, Challenges and Open Research,"
in IEEE Access, vol. 8, pp. 108952-108971, 2020.
[3] Y. Wu, K. Zhang and Y. Zhang, "Digital Twin Networks: A Survey," in IEEE Internet ofThings Journal, vol. 8, no. 18, pp. 13789-13804, 2021.
[4] Xiaochen Zheng, et. al., “The emergence of cognitive digital twin: vision, challenges andopportunities”, International Journal of Production Research, 60:24, 2022.
[5] “Digital twin network – Requirements and architecture”, Recommendation ITU-T
Y.3090, 2022.
[6] “Innovative Optical and Wireless Network Global Forum Vision 2030 and Technical
Directions”,

IOWN

GlobalForum,

availableathttps://iowngf.org/wp-

content/uploads/2023/03/IOWN_GF_WP_Vision_2030_2.0-2.pdf (Accessed: Jan. 2024).
[7] TMforum. available at https://www.tmforum.org/ (Accessed: Jan. 2024).
[8] Eclipse

Ditto:

DigitalTwinframeworkofEclipseIoT,

availableathttps://eclipse.dev/ditto/index.html (Accessed: Jan. 2024).
[9] Toru Yamada "The Digital Twin and its Evolution from the International
Standardization of Smart Cities.", A1-07, Interop Tokyo 2023 (2023）[10] Y. Ohsita, S. Yasuda, T. Kumagai, H. Yoshida, D. Kanetomo, and M, Murata, “Spatiotemporal model that aggregates information from sensors to estimate and predict statesof obstacles for control of moving robots,” IEICE Proceedings Series, 2022
[11] S. Yasuda, T. Kumagai and H. Yoshida, ”Cooperative Transportation Robot System
Using Risk-Sensitive Stochastic Control,” 2021 IEEE/RSJ International Conference onIntelligent Robots and Systems (IROS), Prague, Czech Republic, 2021, pp.5981-5988.
[12] S. Yasuda, T. Kumagai and H. Yoshida, ”Safe and Efficient Dynamic Window Approachfor Differential Mobile Robots With Stochastic Dynamics Using Deterministic Sampling,”
in IEEE Robotics and Automation Letters, vol. 8, no. 5, pp. 2614-2621, May 2023.
[13] Beyond 5G/6G white paper version 3.0, ISBN 978-4-904020-32-6, 2023, available athttps://beyond5g.nict.go.jp/download/index.html (Accessed: Jan, 2024)[14] NICT xData Platform, https://www.xdata.nict.jp/en/ (Accessed: Jan, 2024).
[15] Daiki Kodama, Kenji Ohira, Hideyuki Shimonishi, Toshiro Nakahira, Daisuke
Murayama, and Tomoki Ogawa, “Enhancing Indoor Millimeter Radio Communication:
A Probabilistic Approach to RSS Map Estimation”, in Proc. of IEEE Consumer
Communications & Networking Conference (CCNC), 2024.

17

Optimum collaboration of network functions and computing resource
Minoru Matsumoto, NTT network service systems laboratories.
Hiroki Baba, NTT network service systems laboratories.
Shiku Hirai, NTT network service systems laboratories.

Abstract— In the Beyond 5G era, network operators will have higher flexible
deployment methods of network functions via the virtualized RAN/core network, and the
optimum usage method for distributed computing resource collaborated with network
functions

will

be

important.

Thereason

is

that

high

real-time/broadband

communications (e.g., VR/AR) and AI inference will become popular applications, andrequire higher performance of arithmetic processing power using several types ofcomputing resource (e.g., Memory, CPU, FPGA, and GPU).
To achieve these targets, the network architecture has been studying provided thevirtual computing resource. The technology of network transport is employed APN (AllPhotonic Network), the computing resource deployed in different network domain(access network, MEC, core network, cloud) can be used as virtualized one computing
resource via APN, so the network operator can use optimum computing resource on
demand from requirements of communications and applications.
This paper reports evaluation its feasibilities of the proposed architecture comparedto the existing CPU-based computing one.
1

Introduction
In the B5G era, “integration and cooperation” will progress in the four areas of“network and computing,” “cyber space and physical space,” “analog and digital,” and“mobile communications and fixed communications” as both communication services
themselves. This progress in multifaceted integration and cooperation will generate aneven greater need for end-to-end and seamless linking of information processing andinformation distribution for terminals, the network, and applications, which had been
performed separately in communication services using the 5G network. This is expected
to be led to new communication services in the form of Cyber Physical Systems (CPS),
various environments that use AI integrated communications (cyber space and real
space), and communications that overcome terminal/location limitations and functional
limitations [1],[2].
This paper reports new concept of network architecture on optimum collaboration of
network functions and computing resource in the B5G era.

18

2

Network Requirements and Technology Trends in the B5G era
A variety of use-case scenarios are proposed for features of B5G network such as

extremely high data rates, ultra-low latency/jitter, and expanded coverage in several
SDOs (Standards Developing Organizations). These use-case scenarios include ultra
broadband communication, ubiquitous sensing, mission critical communication,
universal coverage, ultra massive connection, and intelligent connection from viewpointsof network requirement [3]. To achieve these use-case scenarios, several SDOs have beenstudied technology trends in the B5G era shown in Table 1 [4],[5],[6].
Table. 1 B5G Technologies Trends from SDOs

3

Concept of Proposed Architecture

3.1 Background
Operators have already provided audio-voice, video content, and internet data as maincommunication services before the 5G era. Information-processing applications were
deployed on servers on the cloud at a data center, and on terminals. Informationprocessing functions were centralized-located on the cloud and terminals, so that
operators could provide in a form that minimized the amount of information exchanged
between terminals and server over the network.
The data compression/encoding can make reduce the amount of information exchanged
and the synchronization processing that performed between server and terminals
interacted with each other over the network. However, computing resources andfunctions/processing-performance are generally varied for each terminal, so it needed todevelop server applications on the cloud in accordance with network and terminal's
functions/processing performance. Therefore, applications have any rest of problems that
could be used limited by the difference of terminals' specification, and limited processing
performance.

19

The network virtualization has been progressing since the 4G era, and the 5G
architecture have been adopted cloud-native technologies such as Cloud-native Network
Functions (CNFs) are being implemented to the 5th Generation Core (5GC) network. For
low-latency applications, operators need to deploy server applications performing
information processing at edge points serving as entrances to the network without going
through the Internet using Multi-access Edge Computing (MEC) technologies [7].
Virtualized technologies have been used as a common foundation for MEC, and thevirtualized Radio Access Network (vRAN) will also enable virtualized ones to be appliedeven to equipment installed closer to terminals. Therefore, the technology ofcommonality is expected to make MEC applications to be deployed not only on the edgeof the network but also to a wide area including RAN where it can be shared in resourcesof the computing platform.
3.2 Concept of Proposed Architecture
The concept of proposed architecture can achieve a computing service by mixing andclosely linking network functions, and application functions on the computing platformthat has a ubiquitous presence on such a network. This computing service can provide
to combine in a composite manner dispersed computing resources such as several kindsof computing functions (terminal, network, and cloud) and integrates the informationprocessing and communication functions required for services. In a network at the 5G
era, a delay is generated from communications between terminals and server
applications on the cloud, and associated information-conversion processing. However, if
terminals and server applications for services on the cloud are to be consolidated in an
exclusive

computingplatform,

thesecommunications/processing

canbecomeunnecessary make real-time interaction.
The proposed network architecture provides distributed computing functions, so thatvarious connections methods can be supported such as ones using optical wavelengths,
and the conventional Virtual Private Network (VPN) connection on an All-Photonics
Network (APN). In specially, an APN is expected to make it possible to integratecomputing functions to perform communications and run information-processing
applications with higher level of performance and lower power consumption, that isdifficult to achieve by existing protocols (e.g., TCP/IP). Moreover, the proposed networkarchitecture can use suitable volume of hardware accelerators according to requirementsof the information-processing applications and network functions to perform higher data
rate and lower latency processing that combines these applications and network
functions in a composite manner. Therefore, the proposed network architecture can beprovided higher performance for all network regions. The comparison of networkarchitectures in different generations is shown in Fig. 1 [8].

20Fig. 1 Comparison of network architectures in different generations
4

Proposed Network Architecture
The proposed network architecture can make use of both terminal and cloudcomputing resources in addition to those of the network, and configure cloud/terminal
application functions and network ones in an integrated manner. The computing service
functions of this network architecture are to provide computing resources to terminals,
and are being studied for the B5G era. The communication services have been provided
by the network up to the 5G era, but these are expected to achieve on a distributed
computing platform in the B5G era (shown in Fig. 2).
The proposed network architecture can also provide Control-plane function to control
the communication session collaborated with computing functions as a computing
service (shown in Fig. 3). And, it can also provide Data-plane function to add
conventional User-plane function that transfers audio-voice, video, and data as acomputing service. In the B5G era, it can also be expected to provide a service-meshfunction to enable Data-plane to process multiple microservices network-wide in a chainlike manner within the distributed computing method by using a variety of microservices.

Fig. 2 Distributed computing platform in the B5G era

21

Fig. 3 C-plane and U/D-plane in the B5G era
5

Evaluation

5.1 Assumed Use-Case
This paper assumes one use-case which relays ultra-high-resolution video in real timeor deliver 3D video in the metaverse (shown in Fig. 4). These use-cases are a delivery
service for ultra-high-resolution video or 3D content with performing advanced
information processing such as video synthesis or 3D rendering based on the user’sviewing conditions (e.g., terminal capabilities, resolution, and viewing angle). In thissituation, the terminal needs to support advanced video processing functions and/or
hardware-level performance to play back 3D content or render high-resolution video
according to viewing conditions.
On the other hands, it would be desirable to provide abovementioned services to allkinds of terminals including smartphones that are limited in functionality andperformance beyond any locations and functional limitations from customer's viewpoints.
To achieve customer's demands, this paper considers a method to carry out video
processing, 3D-content rendering, and customized functions to be executed on theterminal on a system other than the terminal such as network or cloud, and instantly
display processing results on the terminal. This method makes release the limitations infunctions or performance such as a small screen to handle a new service independent ofterminal functions and performance as in the case of a terminal incapable of 3D displays.
If a terminal has relatively high performance capable to display 3D video and service
requirements are defined to deliver 3D video with low-latency in real time at maximum
resolution, the transmission of materials from the cloud can be synchronized between
memory devices. Moreover, it can be also performed both advanced storage access, andsynthesized on a GPU for 3D video. This architecture can provide the video delivery
using an uncompressed, real-time transmission protocol exploiting the features using a
protocol-independent abstract API layer without considering the upper-level application.

22

Therefore, it can provide an environment that facilitates to develop high-definition 3D
video applications.

Fig. 4 Assumed Use-Case (low-latency, real-time delivery of 3D video to multi-types ofterminals)
5.2 Evaluation Results
This paper simulates abovementioned assumed use-case via the proposed networkarchitecture is to connect and chain multiple workloads on multiple accelerators (Smart
NIC, GPU, FPGA and etc.) over network or computer bus connection. And this paper
evaluates validities of proposed “virtualised accelerator pool” between different network
domain via accelerating End to End data transfer by offloading applications and network
workload, and chaining those workloads with hardware connection as shown in Fig. 5.
Evaluation results are three points (throughput, latency, and PDV: Packet Delay
Variation) compared to the existing CPU-based model as shown is Fig. 6. Each result is
confirmed extremely improved compared to CPU-based model.

23

Fig. 5 Proposed “virtualised accelerator pool” between different network domain

Fig. 6 Evaluation results compared to CPU-based model
6 Conclusion
This paper proposes the network architecture in the B5G era which achieves acomputing service by mixing and closely linking network functions, and application
functions on the computing platform that has a ubiquitous presence on such a network,and evaluates its feasibilities compared to the existing CPU-based computing one.
Evaluation results are confirmed to be extremely improved on these performances as
throughput, latency, and PDV on proposed network architecture.

24

REFERENCE[1] Next G Alliance, “Next G Alliance Report: Digital World Experiences,” December
2022. https://www.nextgalliance.org/white_papers/digital-world-experiences[2] Hexa-X, “Deliverable D1.1 6G Vision, use cases and key societal values,” February
2021. https://hexa-x.eu/wp-content/uploads/2021/02/Hexa-X_D1.1.pdf[3] Beyond 5G Promotion Consortium, “Beyond 5G White Paper—Message to the2030s—Version 2.0,” March 2023. https://b5g.jp/doc/whitepaper_en_2-0.pdf[4] Next G Alliance, “Next G Alliance Report: 6G Technologies,” June 2022.
https://www.nextgalliance.org/white_papers/6g-technologies[5] Hexa-X, “Deliverable D5.2 Analysis of 6G architectural enablers’ applicability andinitial technological solutions,” October 2022.
https://hexa-x.eu/wp-content/uploads/2022/10/Hexa-X_D5.2_v1.0.pdf[6] IOWN Global Forum, “Technical Outlook for Mobile Networks Using IOWNTechnology,” Jan. 2022. https://iowngf.org/wp-content/uploads/formidable/21/IOWN-GFRD-Technical-Outlook-for-Mobile-Networks-1.0-1.pdf[7] https://www.verizon.com/business/solutions/5g/edge-computing/public-mec/[8] https://www.rd.ntt/e/ns/inclusivecore/whitepaper_ver2.html

25

User-centric Network
Akio IKAMI, KDDI Research, Inc.
Masayuki KURATA, KDDI Research, Inc.
Masaki SUZUKI, KDDI Research, Inc.
Hiroyuki SHINBO, KDDI Research, Inc.
Atsushi TAGAMI, KDDI Research, Inc.
Yoji KISHI, KDDI Research, Inc.

Abstract—This paper provides an overview of a "User-centric network", an innovative
approach designed to provide each user with a stable, fast, and continuous
communication service anytime and anywhere. This network architecture adopts CellFree massive MIMO technology to realize personalized access from the radio accessnetwork (RAN) side. The principles underlying the concept of "User-centric" are further
extended to the core network (CN) side, and the functionality of the RAN and CN are
converged to improve the communication quality for each user. In addition, this section
addresses several technological challenges towards 6G.
1

Introduction
In the era of 6th Generation (6G) mobile networks, there is an imperative need toaddress increasingly diversified requirements. In addition, the necessity forsustainability is becoming more prominent. To meet these requirements, customizing
mobile networks according to each user's intent is essential. In this paper, we propose a“User-centric network” that optimizes mobile networks for each user and converges theradio access network (RAN) and core network (CN). This user-centric and convergenceapproach aims to provide a more tailored and sustainable solution for the diverse needsof the 6G era, paving the way for a more efficient and user-focused mobile network
infrastructure.
The target use cases have wide global recognition and are introduced in Hexa-X [1]and Next G Alliance [2], which derive the requirements envisioned in User-centric
networks. These include robot/human cooperation, unmanned aerial vehicles (UAVs),
artificial intelligence (AI) utilization, and eXtended reality(XR). The following are theextracted challenges.
(I)

Continuous
Ensuring safety is one of the most essential requirements. For example, itneeds mission critical applications in various locations over 6G such as
constant monitoring and control of robots. This implies a strong need forconsistent, high radio quality anywhere and anytime. In addition, sincecommunication requirements vary for each communication, they need to26archive based on user intent. However, 5th Generation (5G) systems have celledge issues due to increasing path loss and inter-cell interference, leading to
the degradation of radio quality at cell fringes.
(II)

Fast
As the baseline concept in the present 5G core networks, user-plane (U-plane)
network function (NF), e.g., U-plane function (UPF) instances are potentiallydeployed at the edge sites to reduce the transmission delay between terminals
and applications (APPs), which provides a better quality of experience (QoE)
and quality of service (QoS). Terminals include a smart phone, a robot, a UAV
and many kinds of IoT devices, but for simplicity, we refer to all of these as
user equipment (UE) in this paper. In this case, the other U-plane and control
plane (C-plane) NF instances are deployed at the central site. However,signaling for procedures in the C-plane should also be completed quickly forfurther improvement in QoE and QoS.

(III)

StableWith the increase in the number of UEs simultaneously connected to mobile
networks and the frequent utilization of AI in mobile networks, i.e., analytics
generation by O-RAN RAN intelligent controller (RIC) and network data
analytics function (NWDAF), the number and amount of signaling for C-plane
procedures is predicted to increase explosively. However, the 5G system is notdesigned to perform load-balancing for each NF, and thus cannot process Cplane signaling efficiently. For example, to realize the interaction between
RAN and CN in the 5G system, C-plane signaling goes through the central unit
(CU) and access and mobility management function (AMF), which easily
increases the load on the CU and AMF. In a worst-case scenario, theinappropriate handling of C-plane signaling can cause congestion in the Cplane, also known as a signaling storm [3]. Therefore, the mobile network needs
to accommodate heterogeneous connections and an enormous amount ofsignaling to support stable communications.

2User-centric network

2.1 Overview
We aim to realize the above continuous, fast, and stable communication to provide theuser with a highly satisfying service anytime and anywhere to meet the requirements of
the use cases and satisfy user intent. As shown in Fig. 1, a User-centric network is
designed to be deployed across three sites, i.e., antenna, edge, and central sites. In theantenna site, multiple access points (APs) are installed for Cell-Free massive MIMO [4].
In the edge site and central site, each site incorporates a cloud infrastructure in central

27

and edge sites as computing resources, and network function instances are deployed
across those clouds. AP and NF instances are physically isolated for each user or groupof users to prevent failures in a network from spreading to other networks and toenhance reliability and resilience. Each network facilitates the dynamic placement of
APPs and NFs between central and edge sites to achieve fast communications in boththe U-plane and the C-plane. NFs handle not only CN functions but also RAN functions
such as a virtual central processing unit (vCPU) as radio processing of Cell-Free massive
MIMO. The rest of this section introduces the essential concepts for realization of a Usercentric network.

Fig. 1 Overview of User-centric network
2.2 User-centric network function deployment and management
Cloudification of RAN and core NFs will become a native feature in the 6G era, and all
NFs are deployed in computing resources such as O-cloud of O-RAN. However, thisdeployment of NFs on physically distributed servers raises several challenges, e.g.,mechanisms to resolve changes in demand so that the resources of the virtualinfrastructure can be utilized effectively, and minimization of signaling between
distributed clouds. To address these challenges, we have considered a user-centric
network function deployment approach as shown in Fig. 1. In this approach, NFs are
dynamically deployed based on the user’s demand and resource status. Specifically, NFs

28

are placed according to a physical hierarchical structure. This approach is expected toprovide several benefits: reducing of signaling between sites by aggregating NFs, saving
power consumption by launching only the minimum necessary NFs for each service, andplacing them close to users, i.e., in the edge cloud, to reduce latency. In the 5G system,
while the applications and U-plane NFs, e.g., UPF, are supported to be located at edge
sites [5], C-plane NFs are still deployed at the central site. When the C-plane procedures,e.g., service requests and handover, are performed, the latency requirements can beviolated due to transmission delay from/to the central site. Therefore, C-plane NFs
should also be located at the edge site. Additionally, the upstream signaling can be
reduced by locating the C-plane NFs at the edge site. For example, Hexa-X [1] introduces
the use case where UPF instances are increased/decreased adaptively and predictably to
avoid edge site congestion caused by excessive access to applications.
In addition to these strategies, network isolation per user or group of users helps toprevent the spread of failure. If the mobile core network is physically isolated, a failurethat occurs in one NF instance in one network does not affect other NF instances onother networks. Network isolation also enables prompt recovery from the failure. In alegacy system, the cause analysis and restoration work are likely to be time-consuming.
Our approach assumes that the isolated networks can be easily refreshed and
immediately restarted, even without any root-cause analysis. The root-cause analysis
can be performed independently of the recovery process.
2.3 User-centric RAN with Cell-Free massive MIMOIn the 6G era, there is a growing need for stable quality assurance in areas such as
mission critical applications. Providing the necessary radio quality anytime, anywhereis also becoming increasingly important. However, in 5G, radio quality is highly
dependent on the user's location due to cell-edge issues. This results in low radio quality
at cell-edge areas, decreased signal power due to increased path loss, and increased
interference from neighboring base stations. This cell-edge issue makes it difficult tomaintain good radio quality due to movement, which is a bottleneck for QoS and QoE
per user. To assure radio quality anytime, anywhere, User-centric RAN with Cell-Free
massive MIMO technology is being studied in [6] and [7]. This technology aims to provide
appropriate radio quality for the whole area according to user mobility and services, suchas by spotlighting each user. This user-centric approach can balance radio quality
assurance and power consumption in RAN, achieving sustainability. Cell-Free massive
MIMO technology is gaining attention, as it has been demonstrated to enable userspecific wireless area construction through AP clustering and interference removal
technology through vCPU cooperation [6]. To manage these new air interface
technologies, distributed RICs and other management techniques are necessary.

29

2.4 Isolated RAN/CN which exists per user/user-group
Based on the motivation of creating a User-centric network, CN should also be enhanced
toward for each user or for each user-group architecture. To realize the architecture, four
essential ideas are introduced in this section: (a) Service-based architecture (SBA)
extension to RAN, (b) Lightweight NF, (c) NF componentization, and (d) stateless NF.
An overview of the essential concepts is shown in Fig. 2.

Fig. 2 Overview of each isolated network which exists for each user or user-group(a) SBA extension to RAN is an essential enabler for efficient interaction between the
RAN and CN [8]. In the current architecture, the access is likely to concentrate at AMF,
resulting in a bottleneck. If both the RAN and CN are converted to SBA, instances of
network functions can directly interact with each other without proxy functionality.
Additionally, full-SBA eliminates specific interfaces and protocols between the RAN and
CN, which means that the next generation application protocol (NGAP) over the stream
control transmission protocol (SCTP), i.e., N2 session, is not needed anymore. Reducing
the variation of protocols contributes to easier deployment and operational verificationin cloud-native environments, especially in the case where generic equipment is used.
(b) Lightweight NF is a technology for reducing the image size of an NF instance. Forsmoother lifecycle management, in an isolated network, the functions of NF instances
are carefully selected according to the requirements of users and services. For example,
if a group of UEs do not need mobility management, e.g., sensors, etc., the NF instances
turn off the mobility management function. By dropping subfunctions from the NF, theimage size of an NF instance is reduced for fast instantiation and termination. However,30

the choice of subfunction from NF leads to the multiple generation of NF images. The
mechanism to handle different versions is a matter for future study.
(c) NF componentization reduces HTTP signaling between NF instances. Excessive Cplane signaling can lead to overloading or congestion, called a signaling storm [3], which
degrades user QoE and QoS. "Component" is introduced to drastically mitigate thesignaling load. First, it groups NFs that frequently exchange signaling messages witheach other and (re)locates them on the same node. Then, HTTP messages between NFs
on the same node are replaced with interaction via shared memory. In addition to
reducing the number of messages, in-memory communication eliminates the HTTP
parsing and de-parsing processes, reducing network header overhead and decreasing
kernel processing latency [9].
(d) Stateless NF is the removal of context from an NF instance. In the 5G system, AMF
and session management function (SMF) are stateful and capable of managing thecontexts of UE and session. In the context of virtualization, NFs are supported to bescaled out/in for low power consumption and load balancing. However, it is difficult toscale out/in stateful NFs flexibly and quickly because the contexts need to be properly
handled. Therefore, we remove the context from NF itself by storing context in thecommon storage, assuming componentized NF instances.
3

Research activities to realize a User-centric network
We describe the activities of KDDI Research, Inc. to realize a User-centric network. We

have started evaluations of the RAN for a User-centric network (User-centric RAN) byconducting computer simulations and using a testbed with the element technologies: APclustering technology [10] and CPU cooperation technology [7]. AP clustering is a method
in Cell-Free massive MIMO that selects cooperating APs for each user to communicate,
thereby ensuring wireless quality and reducing the computational load of wireless signal
processing. CPU cooperation is a method that coordinates virtualized CPUs distributed
across different buildings to achieve both interference suppression effects and reduction
in computational load for wireless signal processing.
The simulation results for the element technologies are shown in Figure 3. Compared
to traditional cellular networks, it is evident that a User-centric network can provide
higher quality over a wider area. Furthermore, we discovered that better quality can beprovided by combining AP clustering technology with CPU inter-cooperation. In addition,
we have constructed an initial testbed with commercial smart phones as shown on theleft side of Figure 4. In the testbed, we evaluated IP-level data communication between
terminals and servers with AP clustering technology [11]. As shown on the right side of
Figure 4, the throughput of the User-centric network is stable compared to the cellular
network.

31

Fig. 3 Results of simulation comparison with elemental technologies

Fig. 4 Experimental results in initial User-centric RAN testbed
4

Conclusion

In this paper, we proposed a "User-centric network" that offers stable, fast, andcontinuous communication anytime and anywhere by optimizing mobile networks foreach user and integrating the RAN and CN. Our approach ensures that the mobile
network can accommodate heterogeneous connections and an enormous amount ofsignaling, supporting stable communications. In addition, KDDI Research has
investigated User-centric RAN with Cell-Free massive MIMO in testbeds utilizing
element technologies such as AP clustering technology and CPU cooperation technology.
Moving forward, KDDI Research will continue research and technology development
towards 6G by combining various element technologies (Section 2) of the User-centric
network.

32

ACKNOWLEDGMENTS
The research results of User-centric RAN are obtained from the commissioned research(JPJ012368C00401) by National Institute of Information and Communications
Technology (NICT), Japan.
REFERENCES[1] Hexa-X. Accessed: Aug. 29,2023. [Online]. Available: https://hexa-x.eu/[2] Next

G

Alliance.

Accessed:Aug.

29,2023.

[Online].

Available:

https://www.nextgalliance.org/[3] Ahmad, Ijaz, et al. “Overview of 5G security challenges and solutions.”
IEEE Communications Standards Magazine 2.1 (2018): 36-43.
[4] H. Q. Ngo et al., “Cell-free massive MIMO: uniformly great service for everyone,” in2015 IEEE 16th Int. Workshop on Signal Process. Advances in Wireless Commun.
(SPAWC), Jun. 2015, pp. 201–205.
[5] Filali,Abderrahime,

et

al.

"Multi-accessedge

computing:A

survey."IEEE Access 8 (2020): 197017-197046.
[6] T. Murakami et al., “Analysis of CPU Placement of Cell-Free Massive MIMO for
User-centric RAN," NOMS 2022 IEEE/IFIP Network Operations and Management
Symposium, 2022, pp. 1-7[7] A. Ikami et al., “Interference suppression for distributed CPU deployments in CellFree massive MIMO”, the 2022 IEEE 96th Vehicular Technology Conference(VTC2022-Fall).
[8] Choi, Jinho, et al. “RAN-CN Converged Control-Plane for 6G Cellular Networks.”
GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022.
[9] Jain, Vivek, et al. "L25gc: A low latency 5g core network based on high-performance
nfv platforms." Proceedings of the ACM SIGCOMM 2022 Conference. 2022.
[10] Y. Tsukamoto, et al. “User-centric AP Clustering with Deep Reinforcement Learning
for Cell-Free Massive MIMO,” In Proceedings of the Int'l ACM Symposium on
Mobility Management and Wireless Access (MobiWac 2023).
[11] KDDI research Inc. Accessed: Aug. 29,2023. [Online]. Available:
https://www.kddi-research.jp/english/newsrelease/2023/052302.html

33

Intent-based operational plan generation for business utilization ofautonomous networks
Takayuki Kuroda (NEC)Abstract— Intent-based autonomous network operation technologies are attracting
attention toward advanced automation of network operations. However, there is concernthat the lack of a means for users to check the behavior of automatic operations inadvance in conventional technologies is a barrier to their practical application. Therefore,
we propose a method for generating network operational plans from intent. This allows
users to activate the operational plan after checking its validity. In this article, we
describe the functionality of the proposed technique, followed by a brief overview of themethod.
1

Introduction
Increasingly complex networks are becoming difficult to provide quickly and stably byhand, requiring a high degree of automation of operations [1][2]. The intent-based
approach is promising as a fundamental approach to automate network operations [3].
Intent is information that expresses requirements in an abstract and declarative manner.
According to intent-based automation technology, a machine interprets the intent andperforms the construction and operation of the network. This allows users to easily build
the desired network by simply entering high-level requirements without having to enter
detailed information. However, conventional technologies do not provide a means forusers to confirm the automatically determined behavior of operation and managementin advance, and there is concern that this may be a barrier to the practical applicationof autonomous networks. Therefore, we propose a method to automate operation by
generating network operational plans from intents and applying them after checking
their validity. In this paper, we describe the functionality of the proposed technique,followed by a brief overview of the method.
2

Current intent-based autonomous network architectures and challenges
In current intent-based network architectures, the construction and operation phases

are in one cycle. Figure 1(a) shows the architecture described in [4]. In this approach,the intent is translated into the network configuration; when the automated operation
function receives the intent from the user, it translates the intent and activates it
immediately. When a problem is detected by assurance, a response is consideredaccording to the intent.
There are two problems with this architecture. First, in this architecture, problematic
events are observed in assurance and then addressed in translation. Preferably, at the34

time of initial activation, configuration and policies should be planned in anticipation ofchanges that may occur during operation. Second, users are not provided with a means
to know in advance the range of changes that the target network can handle during
operation and how to deal with them. In actual operation, the decision to start operation
is made only after clarifying the operational plan and confirming its safety and adequacyin advance before deployment. Otherwise, unexpected costs will be incurred andoperations will be black boxed, making it impossible to respond to unexpected problems.

(b) Our architecture(a) Current typical architecture

Operational plan(for review)

IntentTranslation
Assurance

Operational plan(confirmed)

Resolution

IntentTranslation

Operation

Activation

Monitoring

Target Network

Execution

Target Network

Fig. 1 Comparison between existing intent-based autonomous network architecture
and ours.
3

Proposed Architecture
Figure 1(b) shows the architecture of our proposed intent-based autonomous

networking. In our method, intent is translated into operational plan, which includes theconfigurations of the network to be built, the expected changes in the network and theirscope, and the actions to be taken. The phases of using this method are divided into aplanning phase and an operational phase. In the planning phase, the user repeatedly
adjusts the intent and reviews the operational plan to create an appropriate one. Then,in the operation phase, the user instructs to deploy and start operation of the network
based on the confirmed operational plan.

35

Indicator A
legendA: high
Network topology
and configuration
A: middle
Workflow to
update network
A: low
B: low

B: middle

B: high
Indicator B

Fig. 2 Concept of operational plan.
Figure 2 shows the concept of operational plan. The operational plan describes theindicators specified by the user, the appropriate network configuration for each intervalof the value of the indicator (hereafter, this interval is called the state), and the workflow
for changing from each network configuration to the other network configuration. For
example, the ideal configuration when both indicator A and indicator B are "low" isshown in the lower left state. The ideal configuration when the value of indicator A is
"middle" and the value of indicator B is "low" is shown in the middle left state. By
referring to the operational plan, the user can check the operational behavior in detail.

The level whichmonitoring result requires
A: md
B: md

…

Judgement of necessary action according
to the plan and monitoring resultA:
md

Updating workflow
…

lo
lo

md B:

Operation
The level whichcurrent network is for
A: lo
B: lo

Monitoring

Execution

Updating tasks

Add
Add
xx
Addxx
xx
Target Network

Fig. 3 Automated operation flow with using operational plan.
Figure 3 shows the automated operation flow with using operational plan. The
automated operation function consists of three modules: monitoring, operation, andexecution. First, monitoring reveals the new ideal state, while operation refers to the36operational plan and extracts workflows to change the current configuration to the ideal
configuration if the ideal state is different from the current state. In Execution, the target
network is updated to the desired configuration by executing the extracted workflows.

Traffic
1000

～

…
…
…

…

Functional req.
Connect X to Y

…

901
…

Indicators and ranges
Traffic : [10, 1000]
Load : [10, 1000]…

Intent

～

200
…

101
100
～

Non-Functional req.
• throughput• delay
etc..

…

10
10～100101～200

901～1000

Load

Fig. 4 Generation of operational plan from intent.
In the planning phase, the translation function of the intent generates an operational
plan from the intent. This is shown in Figure 4. In addition to functional and nonfunctional requirements, the intent in this method specifies the indicator to be monitored
and the range of values to be supported. Using the aforementioned intent, the translation
function generates an operational plan as follows. First, by dividing the range of valuesof an indicator into multiple intervals, a state consisting of multiple indicators is
generated. Next, the network configuration is automatically designed for each state. In
the automatic design, the value of the indicator in the state is added to the non-functional
requirements, and the function to monitor the value of the indicator is added to thefunctional requirements. Finally, for each generated configuration pair, a workflow is
generated to transition between configurations. Automatic network configuration andworkflow generation based on intent can be achieved by other results of the author's
research group [5][6].
4Conclusion
This paper introduces intent-based operational plan generation technology forbusiness utilization of autonomous networks. This technology allows users to safely
execute operations by knowing the contents of operations in advance. In the future, wewill continue to refine and put the technology to practical use, as well as develop amethod for explaining the contents of an operational plan more concisely.

37

Acknowledgements
These

research

results

wereobtained

from

the

commissioned

research(No.JPJ012368C04801) by National Institute of Information andCommunications Technology (NICT) , Japan
REFERENCE[1] TM Forum. Autonomous Networks: Empowering Digital Transformation For Smart
Societies and Industries. TMForum White Paper, 2020.
[2] ETSI, “Intent driven management services for mobile networks”, TS 128 312
V17.0.1 (3GPP TS 28.312 version 17.0.1 Release 17), Jul. 2022.
[3] A. Clemm, L. Ciavaglia, L. Granville, and J. Tantsura, Intent-Based Networking
Concepts and Definitions, ITU, Geneva, Switzerland, Feb. 2021.
[4] A. Leivadeas and M. Falkner, "A Survey on Intent-Based Networking," in IEEE
Communications Surveys & Tutorials, vol. 25, no. 1, pp. 625-655, Firstquarter 2023,
doi: 10.1109/COMST.2022.3215919.
[5] T. Kuroda, Y. Yakuwa, T. Maruyama, T. Kuwahara, K. Satoda, “Automation ofIntent-based Service Operation with Models and AI/ML”, The Intelligence Provisioning
for Network and Service Management in Softwarized Networks workshop (IPSN) 2022
[6] T. Kuroda, M. Nakanoya, A. Kitano, A. Gokhale, “The Configuration-Oriented
Planning for Fully Declarative IT System Provisioning Automation”, IEEE/IFIP
Network Operations and Management Symposium (NOMS) 201638

Task-Oriented 6G Native-AI Network Architecture
Peng Chenghui, Huawei Technologies
Wang Jun, Huawei Technologies
Yang Yang, The Hong Kong University of Science and Technology
Koshimizu Takashi, Huawei Technologies Japan

Abstract— The vision for 6G networks is to offer pervasive intelligence and internet ofintelligence, in which the networks natively support artificial intelligence (AI), empowersmart applications and scenarios in various fields, and create a "ubiquitous-intelligence"
world. In this vision, the traditional session-oriented architecture cannot achieve flexible
per-user customization, ultimate performance, security and reliability required by future
AI services. In addition, users' requirements for personalized AI services may become akey feature in the near future. By integrating AI in the network, the network AI has
more advantages than cloud/MEC AI, such as better QoS assurance, lower latency, lesstransmission and computing overhead, and stronger security and privacy. Therefore,
this article proposes the task-oriented native-AI network architecture (TONA), tonatively support the network AI. By introducing task control and quality of AI services(QoAIS) assurance mechanisms at the control layer of 6G [1].
1Introduction

This explains the needs of Native-AI based 6G Wireless Network Architecture and listsof reason that requires to shift to task-oriented system mechanism. The proposed NW
architecture called Task-Oriented native-AI network architecture (TONA), to natively
support the network AI that create a "ubiquitous-intelligence" world. Reflecting theproceeding transformation, this article further proposes TONA to meet personalized AI
service demand and requirements. This article mainly:(1) Introduces three-layer logical architecture of task management and control system,and designs the task lifecycle management procedures, which include thecollaborationofmulti-dimension

heterogeneous

resources(communication,

computing, data, and algorithm) and multi-node at the control layer.
(2) Defines task-specific QoAIS indicators for the mapping from Service Level Agreement(SLA) indicators — e.g., service requirement zone (SRZ) and user satisfaction ratio(USR) — to QoAIS indicators, and discusses task-level QoS assurance to meet
individual requirements of different users.
(3) Compares the network AI and cloud/mobile edge computing (MEC) in terms of QoAIS
indicators. Thanks to providing the AI executing environments closer to UE, TONA39

is anticipated to have some advantages, such as better data privacy protection, lower
latency, and lower energy consumption.
2

Network Paradigm Change

The TONA, as shown in Figure 1, introduces the orchestration and control functions aswell as the resource layer in network AI. The control function uses control layer signaling
to control multi-nodes (UEs, base stations, and CN NEs) and heterogeneous resources inreal-time. We believe that the 6G network architecture requires the following changes
in the design paradigm:(1) Change 1: The object to be managed and controlled in network are changed fromsessions to tasks.
(2) Change 2: The resources of the object are changed from one dimension to multidimensions, from homogeneous to heterogeneous.
(3) Change 3: The object control mechanism are changed from session-control to taskcontrol.
(4) Change 4: The performance indicators of the object are changed from session-QoS totask-QoS.

Fig.1 Network paradigm changes
2.1 Change 1: From Session to Task
AI tasks differ from traditional sessions in terms of technical objectives and methods.
In terms of technical purposes, a traditional communications system provides session
services, typically between terminals or between terminals and application servers, totransmit user data (including voice). Conversely, network AI (i.e., NE intelligence andnetwork intelligence) aims to provide intelligent services for networks and improve40

communication network efficiency. Service intelligence seeks to provide app-specificintelligent services for third parties. Thus, sessions and AI tasks have different purposes.
2.2 Change 2: From single-dimension to multi-dimension heterogeneous resources
The traditional wireless system establishes tunnels and allocates radio resources fordata transmission. Conversely, TONA implements collaboration among heterogeneous
resources of connection, computing, data and model/algorithm to execute AI tasks. Takean AI inference task as an example.
2.3 Change 3: From Session-control to Task- control
Unlike session control, task management and control in network AI includes thefollowing functions: (1) Decomposing and mapping from external services to internaltasks, (2) Decomposing and mapping from service QoS to task QoS, and (3) Providing
heterogeneous and multi-node collaboration mechanisms to orchestrate and control
heterogeneous resources of multiple nodes at the infrastructure layer in real-time (toimplement distributed serial or parallel processing of tasks and real-time QoS
assurance).
3

Architecture and Key Technologies

This section describes the logical architecture and deployment options of TONA, andQoAIS details.
3.1 Logical Architecture of TONAFirst, we introduce fundamental basic concepts in wireless network. A communications
system consists of a management domain and a control domain. The Operations
Administration and Maintenance (OAM) deployed in management domain is used tooperate and manage NEs through non-real-time (usually within minutes) management
plane signaling. The control domain is deployed on core network (CN) NEs, base stations,and terminals, and features with real-time controlling signaling (usually within
milliseconds). For example, an E2E tunnel for a voice call can be established within
dozens of milliseconds by control signaling.
Unlike the centralized, homogeneous, and stable AI environment provided by the cloud,the network AI faces the following technical challenges when embedded in the wirelessnetworks: (1) AI needs to be distributed on numerous CN NEs, base stations, and UEs.
Therefore, it is necessary to consider how to manage the massive number of nodes
efficiently in the architecture design. (2) The computing, memory, data, and algorithm
capabilities of different nodes vary significantly, requiring the architecture design to also
consider how to efficiently manage these heterogeneous nodes efficiently. (3) The
dynamic variation of the channel status and the computing load need to be factored into

41

the architecture design. To address the aforementioned challenges, TONA includes twological functions, as shown in Figure 2: (1) AI orchestration and management, called
Network AI Management & Orchestration (NAMO); and (2) task control. NAMO
decomposes and maps AI services to tasks and orchestrates the AI service flows. It is not
performed in real-time and is generally deployed in the management domain. Task
control introduces the Task Anchor (TA), Task Scheduler (TS), and Task Executor (TE)functions in the control domain in three layers. This layered design strikes a balance
between the task scope and real-time task scheduling, and effectively manages thenumerous, heterogeneous nodes and aware of dynamic change of heterogeneous
resources (e.g. channel status and computing load).

Fig.2 Logical architecture of TONA
3.2 Deployment Architectures
The statuses of TEs (e.g., the CPU load, memory, electricity, and UE channel status)change in real-time. As such, deploying TA and TS close to each other can reduce themanagement delay. According to the design logic of wireless networks, the CN and RAN
need to be decoupled as much as possible. For example, the CN should be independentof RAN Radio Resource Management (RRM) and Radio Transmission Technology (RTT)
algorithms. Therefore, this article recommends that TA/TS be deployed on RAN and CN,
named RAN TA/TS and CN TA/TS, respectively. This way will allow TA to manage TEs
in real-time flexibly. Four deployment scenarios of TONA are shown in Figure 3 to42

describe the necessity and rationality of CN TA and RAN TA. These scenarios are only
examples — there may be other deployment scenarios and architectures.
Scenario 1: gNodeB + UEs. In this scenario, the gNodeB serves as both TA and TS, andthe UEs serve as TEs. Here, a UE is a computing provider and task executor, which
accepts task assignment and scheduling from the gNodeB. The Uu interface and Radio
Resource Control (RRC) layer between the gNodeB and the UE can be enhanced tosupport task controlling and scheduling purposes.
Scenario 2: CU + DUs. In this scenario, the CU serves as both TA and TS, and the DUs
serve as TEs. Here, a DU is the computing provider and task executor. The F1 interface
and F1-AP layer between the CU and the DU can be enhanced to support task controllingand scheduling purposes.

Fig.3 Four deployment scenarios of TONA
4

Advantage Analysis

Compared with cloud/MEC AI, the TONA and QoAIS have the following advantages(summarized in Table 2) in meeting users' customized AI service requirements:(1) QoAIS assurance
Dynamic wireless environments require joint optimization of the heterogeneous
resources (connection and three AI resources) to achieve precise QoAIS assurance.
(2) Latency
TONA computing is distributed on NEs closer to UEs or even directly on UEs to processdata locally. This not only successfully achieves real-time and low-latency AI services,
but also significantly reduces data transmission. In the cloud/MEC AI mode, a large

43amount of data needs to be transmitted to the cloud/MEC for training, meaning that E2E
data transmission takes longer to complete.
(3) Overhead
TONA can optimally allocate resources through the real-time collaboration mechanismof the heterogeneous resources, maximizing the overall resource utilization and reducing
the transmission and computing overheads. Conversely, because the cloud/MEC AI
cannot adapt to dynamic environments, it allocates resources based on only the
maximum resource consumption to ensure QoAIS. As a result, the overall resource
utilization is low, and the resource overhead is high.
(4) Security
TONA has native data security and privacy protection capabilities because it processes
data inside the network. Unlike TONA, the cloud/MEC AI protects data privacy only at
the application layer.
5

Conclusion

To meet the 6G vision of pervasive intelligence and internet of intelligence, TONA is
proposed to support efficient collaboration of heterogeneous resources and multi-node in
wireless networks, and to provide new services in the form of tasks at the network layer.
By bringing new dimensions of resources to 6G networks (i.e., computing, data, andmodel/algorithm), this architecture enables the SLA assurance of computing related
services such as AI services, further explores the application scenarios of 6G networks,
and enriches the value of wireless networks. Furthermore, the task concept and TONA
proposed in this article support not only AI tasks, but also sensing-, computing- and dataprocessing-specific tasks.
REFERENCE[1] IEEE Network, “Task-Oriented 6G Native-AI Network Architecture”, October 2023,
https://ieeexplore.ieee.org/document/10273257

44

Envisioning Architectural Transformation towards 6G
Hideaki Takahashi, Nokia
Gerald Kunzmann, Nokia
Hannu Flinck, Nokia
Heiko Straulino, Nokia

Abstract - This article covers system architecture migration options towards 6G,
interworking aspects with the legacy generations, as well as a suggested design for theoverall system architecture including RAN-CN functional split and logical RAN
architecture. On the architecture migration, 6G system architecture is studied byleveraging the 5G system architecture principles, such as reusability, modularity,
support of cloud native service-based architecture and resiliency, while adding principles
to support 6G key values, sustainability, digital inclusion, and trustworthiness. Theinterworking design shall support seamless service continuity between legacy systems
and 6G. as part of the overall system architecture, the necessity of RAN-CN separationand the functional split between those two domains is being analyzed. Finally, the logical
6G RAN architecture is analyzed covering both the emerging trend of cloud native
deployments as well as the classical RAN deployments.
1

Introduction

Approximately every ten years, the next generation of mobile networks is rolled out toenrich human life and society with evolutional innovations from the previous generation.
Whilst the commercial 5G services have been successfully evolved, 6G research is in fullswing together with strong momentum not only by individual organizations, but also
from pre-competitive joint research initiatives in several geographical regions.
Meanwhile, 6G standardization is gearing up towards the market roll-out around 2030.
ITU-R WP5D has agreed on IMT-2030 framework covering usage scenarios andcapabilities [1]. Following the ITU-R decision on IMT-2030, 3GPP has agreed on 6G
standardization timeline on high level [2]. Technology innovations are supposed to bestudied soon in 3GPP, encompassing overall system architecture. This article navigatesarchitectural transformation and associated decompositions which are the tenets ofarchitectural innovations towards 6G. Section 2 crystallizes design principles driving
technology innovations across all the architectural transformations. Section 3 andonwards describes some of the key architectural innovations: migration, RAN-CNfunctional split and logical RAN architecture.

45

2Architecture design principles

6G system architecture needs to enable a set of new use cases and unleash additionalmonetization capabilities, whilst still supporting existing use cases in an optimal
manner [3]. To achieve this, the following principles are considered central pillars of theoverall 6G architectural design.
A. Simplification
Architectural solutions should be lean in targeting performance requirements, whilst
limiting complexity and avoiding over-optimization. Features and associated parameters
should be designed for practical deployments and performances achieved by commercial
devices. It is quintessential to make 6G consumable to its customers and limit costs for
all phases from standardization, development, deployment to operation.
B. Energy Efficiency
6G per se should be sustainable to limit the carbon footprint of 6G systems as the numberof UEs and traffic continues to grow exponentially. Besides that, the 6G system should
maximize its handprint by enabling other industries to meet their sustainability goals[4, 5].
C. Native AI/ML in radio, system and management
Native AI/ML integration into the overall system is aimed at evolving the overall levelof network intelligence and its perception of the environments. The AI-nativeness should
cover management, control and user plane operation within the entire 6G system in aholistic way.
D. Multi-cloud nativeness
Embracing

multi-cloudnativetechnologies

into

6G

systemcanempower

telecommunication providers to drive innovations, agility, and enhanced customer
experiences. Multi-cloud native 6G system enables to integrate advanced technologies
smoothly, e.g., AI/ML as well as enhancing resiliency and fault tolerance of the entire
telecommunication system.
E. Programmability
6G should be able to offer a service innovation platform for MNOs and enterprises whichembarks on multi-party value ecosystems. Exposing and leveraging the right open APIs
at the proper abstraction levels is essential to leverage the capabilities of open hardware
and software platforms.
F. Autonomy
Whilst the NW automation has been one of the principles since the legacy generations,e.g., SON, 6G should take it to the next level towards fully autonomous networks
throughout their whole lifecycle from design, deployment, operation to decommissioning.
It is even further indispensable in 6G era to manage complex NW and service offering inan economic way.

46

G. Security and privacy
6G as a system should be trustworthy and resilient. It needs also be protected against
new attack surfaces emerging from distributed clouds, pervasive AI, usage/exposure ofsensitive data and potential quantum computer attacks on cryptographic algorithms.
H. Universal Access Convergence
Seamless service experience should be supported across the growing number of radio
technologies and access networks in terrestrial as well as non-terrestrial (satellites,
HAPS, etc.).
3

System architecture migration towards 6G

3.1

Lessons learnt from 5G migration and deployments

When designing 6G system architecture and migration from 5G, it is worth to have inmind what was observed and learned from the commercial 5G migration anddeployments. Some key learnings are outlined below.
Learning #1: A key learning from 4G to 5G migration was that many architecture options
were standardized, including bearer type options in MR-DC, whereas only one NonStandalone (NSA) option (i.e., EN-DC) and NR Standalone option have been
implemented and deployed as of now. Furthermore, allowing a phased 5G deployment
with NSA and SA has resulted in less migration toward NR SA, preventing to fully
leverage 5G capabilities, such as URLLC, slicing, etc.
Learning #2: There is a solution to share a spectrum for 4G and 5G, a.k.a. Dynamic
Spectrum Sharing (DSS) which was expected to contribute to migrate 4G to 5G. However,
DSS has a considerable drawback to deteriorate spectrum efficiency. DSS enhancements
to compensate the drawback was introduced in later 5G specifications, which are all
optional for UE to support.
Learning #3: For 4G to 5G migration, NSA was chosen for the case where the existing
spectra are used for 4G in conjunction with a new 5G spectrum, in spite of the drawbackof compromising UL coverage due to semi-static UL Tx power split and the complexityof tight coordination between two gNBs. It was thought as challenging to re-farm all theexisting 4G spectra to 5G since Day-1 5G deployment. The performance drawback of DSS
was also the hurdle to migrate the existing spectra to 5G.
Learning #4: In addition to the RAN architecture with a single logical entity for basestation (i.e., gNB), 5G RAN supported the disaggregated architecture for which RAN
functionalities are split into 2 entities, Central Unit (CU) and Distributed Unite (DU).
Whilst the CU-DU split architecture was thought as beneficial, enabling flexible RAN
functional allocation and suitable to cloud based infrastructure, CU-DU split resulted inhigher latency than E-UTRAN (LTE). Furthermore, not all of the standardized

47

disaggregation options are implemented and deployed, which infers that multiple
standardized split options are counterproductive.
3.2

6G system architecture and migration enabler

Based on the experience learned from 5G as described above, the number of architecture
options standardized for 6G should be limited to the one which is essential to migrate
towards 6G smoothly for leveraging the full technology potential of 6G from its Day-1
deployment. The best approach is to standardize, develop and deploy only a single SAarchitecture for 6G as illustrated in Figure 3.2-1. 6G SA architecture should be designed
to fulfill the design principles, e.g., energy efficient, implementation friendly, cloudnative and trustworthy, as described in clause 2.

Figure 3.2-1: 6G standalone architecture
If 6G architecture supports only the standalone mode of operation, the similar concern
on spectrum re-farming from 4G to 5G (Learning #2 in subsection 3.1) would beenvisaged for 5G to 6G migration. Thus, necessity of the NSA mode of operation might
be claimed in spite of the drawback observed in Learning #3 in subsection 3.1.
Nonetheless, a revolutionary spectrum sharing innovation, Multi-Radio Spectrum
Sharing (MRSS) can iron out the issue observed in 4G to 5G migration by achieving
significantly higher efficiency than 4G-5G DSS. MRSS enables self-adjusting refarming
and is desirable to be an integral part of the 6G radio design from the initial standards.
MRSS also enables to aggregate a new 6G frequency band(s) and the existing frequency
band, whereas the legacy 5G UE can be accommodated over the MRSS cell, as illustrated
in Figure 3.2-2. Carrier Aggregation can be used to aggregate the existing frequency
band(s) and the new frequency band(s) within 6G radio. UL CA as well as DL CA should
be supported from Day-1 deployment to achieve high 6G capacity and performance overthe new 6G spectrum as well as the existing spectrum. Thus, MRSS is a key enabler tosmoothly migrate from 5G to 6G with a single SA architecture and diminish the necessityof NSA.

48

Figure 3.2-2: Migration from 5G to 6G SA with MRSS
3.3

Interworking with legacy systems

Apart from the architecture migration towards 6G, interworking between 6G and 5G isanother quintessential enabler to support smooth migration. It is realistic to assume thescenario where 6G radio coverage is sparse compared to the existing 5G and 4G radio
coverage. UEs need to move from 6G to 5G, and vice versa, for coverage reasons. Thus,
the interworking between 6G and 5G needs to support UE mobility for all states to besupported (e.g., idle, inactive and connected as in 5G). For UE in the connected state,
support of seamless mobility is deemed as necessary for essential services supported
from legacy generations, e.g., IMS voice/video services. An open question is the necessityof interworking with 4G (i.e., E-UTRA connected to EPC). Albeit a desirable approachfor functional simplification is to avoid the interworking with 4G, we have to consider
the 4G to 5G migration situation predicted for the time of initial 6G deployments.
4

RAN - CN separation and interface

The mobile telecommunication systems standardized in 3GPP have been comprised of
two network domains, i.e., Core Network (CN) and Radio Access Network (RAN) since
2G (GSM). Clear domain separation between RAN and CN is one of the basic tenets forthe overall system architecture to facilitate independent technology evolution as well as
different scalability and sharing needs of the two NW domains. Therefore, it is sensible
to stick to the RAN-CN separation for 6G system, whereas a common solution across
RAN and CN might be beneficial and should be considered for some areas, e.g., data49

management and network analytics. With RAN-CN separation, an important aspect tobe studied in a holistic way is how to design the interface between RAN and CN enabling
independent evolution, different scalability and sharing needs.
Figure 4-1 shows the envisioned 6G system architecture built upon 5G system. 5G
system leverages architecture principles to offer innovative services efficiently, e.g.,reusability and modularity, supports cloud native service-based architecture andresiliency. Whilst 6G offers an opportunity for innovation, it is imperative to build 6G
system upon 5G system evolution to sustain a balance between innovation and protectionof existing investments. As illustrated in Figure 4-1, some Network Functions (NFs) inthe proposed 6G system architecture are shared between 6G and 5G, whereas there
would be some new NFs dedicated for 6G. The figure shows our current assumptions,and study is on-going which NFs to be shared with 5G and which new NFs are needed
to support 6G RAN and new 6G services.

Figure 4-1:

Envisioned 6G System ArchitectureWith respect to the RAN-CN interface, several options can be considered as listed below:
Option A. Point-to-point based on evolved NGAPThe N2 reference point between RAN-CN interface is kept for 6G system. The
application protocol over N2, i.e., NGAP is evolved for new 6G services andfeatures. This option sticks to the strict domain separation between RAN and CN
as all communications between the two domains need to go through 6G N2
reference point between 6G MM and 6G RAN in Figure 4-1. ASN.1 encoding iscontinued to use NGAP information elements, which can be optimized for low
latency.
Option B. Hybrid approach with SBI and evolved NGAP
The basic procedures like Mobility Management and Session Management
continue using the N2 based reference point (i.e., NGAP over SCTP). In contrast,
auxiliary services (like analytics, positioning, etc.) and/or new 6G services (e.g.,sensing) are realized as SBI.
Option C. Full SBI

50

The 6G N2 is fully transitioned into a Service-Based Interface (SBI), i.e., using
service operations (requests and responses) for the respective service APIs overHTTP2/3 as in the CN. 6G RAN and CN NFs expose services towards each otherand communicate directly over SBI without relaying any NFs (like AMF in 5GCore).
All options are valid and expected to be studied further from the viewpoints of technicalgain, complexity of implementation, etc.
5Logical RAN architecture

In E-UTRAN (a.k.a., 4G LTE), a single logical entity is defined, i.e., eNB. It is up to NW
implementation whether and how to disaggregate RAN functionalities. In 5G, standards
support both a disaggregated and a non-disaggregated architecture. It is expected thatboth architectures will continue to be supported for 6G RAN, as illustrated in Figure 5-1.
In this article, the classic RAN and the disaggregated RAN are defined as follows:-

Classic RAN: consisting of monolithic Node Bs (i.e., Base Station) without any
disaggregation (like eNB/gNB in 4G/5G).

-

Disaggregated RAN: built upon cloud-native environments where RAN protocol
stack implementation is neatly divided into functional components and thesefunctional components are packed into containers.

Figure 5-1:

Logical 6G RAN architecture

Whilst classical RAN deployments are likely to be present even in 6G era, enhanced
support of disaggregated RAN is worthwhile exploring to offer a wide range of 6G use
cases and deployments. The disaggregated RAN architecture allows flexibility of RAN
function placements aiming at a future-proof deployment. Nonetheless, the lesson learnt
from 5G standardization and deployment, i.e., Learning #4 in subsection 3.1, should bereflected into 6G RAN disaggregation work and ironed out for 6G RAN by focusing on
only essential open interfaces and optimizing to achieve lower latency comparable toclassical RAN deployments.

51

6Conclusion

This article presented design principles of architectural transformation towards 6G. The
candidate system architecture, migration enablers, RAN-CN IF and logical RAN
architecture were explored from the lessons learnt from 5G migration and deployments.
REFERENCE[1] Recommendation ITU-R M.2160-0 (11/2023), “Framework and overall objectives ofthe future development of IMT for 2030 and beyond,”
https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.2160-0-202311-I!!MSW-E.docx[2] 3GPP SP-231693, “High-level Considerations for 6G Timeline,” TSG Chairs.
https://www.3gpp.org/ftp/tsg_sa/TSG_SA/TSGS_102_Edinburgh_2023-12/Docs/SP231693.zip[3] H. Takahashi, et. al., “Envisioning 6G Outlook and Technical Enablers,” IEICE Trans.
Commun., Vol.e106-B. No.9, September 2023.
https://search.ieice.org/bin/pdf_link.php?category=B&lang=E&year=2023&fname=e106b_9_724&abst=[4] H. Holma and H. Viswanathan, “In the 6G era, we won’t need to sacrifice sustainability
for the sake of performance,” Nokia Blog, November 2022.
https://www.bell-labs.com/institute/blog/in-the-6g-era-we-wont-need-to-sacrificesustainability-for-the-sake-of-performance/[5] S. Mukherjee, “Creating the future: maximizing our handprint and minimizing ourfootprint,” Nokia Blog, June 2023.
https://www.nokia.com/blog/creating-the-future-maximizing-our-handprint-andminimizing-our-footprint/52

Abbreviation List
Abbreviation

Explanation

6DoF

Six Degrees of Freedom

3GPP

Third Generation Partnership Project

ACO-OFDM

Asymmetrically Clipped Optical OFDM

AGV

Automated Guided Vehicle

AI

Artificial Intelligence

AIaaS

Artificial Intelligence as a Service

API

Application Programming Interface

APN

All Photonic Network

APs

Access Points

AR

Augmented Reality

ASIC

Application Specific Integrated Circuit

BaaS

Banking as a Service

BMI

Brain Machine Interface

BtoBtoC

Business to Business to Consumer

BtoC

Business to Customer

BW

Band Width

BWA

Broadband Wireless Access

CASE

Connected, Autonomous/Automated, Shared, Electric

CAPEX

Capital Expenditure

CMOS

Complementary Metal Oxide Semiconductor

CPRI

Common Public Radio Interface

CPS

Cyber Physical System

CPU

Central Processing Unit

CSI

Channel State Information

DaaS

Data as a Service

DCO-OFDM

Direct Current biased Optical OFDM

DDoSDistributed Denial of Service

D-MIMO

Distributed MIMO53

Abbreviation

Explanation

DMM

Distributed Mobility Management

DNA

Deoxyribonucleic Acid

DRAM

Dynamic Random Access Memory

DSRC

Dedicated Short Range Communication

DT

Digital Twins

DTC

Digital Twin Computing

DX

Digital Transformation

ECC

Elliptic Curve Cryptography

eCPRI

enhanced CPRI

EESS

Earth Exploration Satellite Service

eMBB

enhanced Mobile Broadband

ECElectronic Commerce

ECC

Elliptic Curve Cryptography

ECU

Engine Control Unit

EHF

Extremely High Frequency

ETC

Electronic Toll Collection System

EV

Electric Vehicle

FA

Factory Automation

FDD

Frequency Division Duplex

FG-AN

Focus Group on Autonomous Networks

FH

Fronthaul

FPGA

Field Programmable Gate ArrayFRFrequency Range

FS

Fixed Service

FSPL

Free Space Path LossFW

FirewallGDP

Gross Domestic Product

GEO

Geostationary Orbit

GPU

Graphics Processing UnitGPS

Global Positioning System

GSO

Geostationary Earth Orbit

54

Abbreviation

Explanation

HAPS

High Altitude Platform Station

HARQ

Hybrid Automatic Repeat Request

HEMS

Home Energy Management System

HMD

Head Mounted Display

HMI

Human Machine Interface

HUD

Head-up Display

I/F

InterfaceIC

Integrated Circuit

ICDT

Information, Communication, and Data Technology

ICT

Information and Communication Technology

IDS

Intrusion Detection System

IFFT

Inverse Fast Fourier Transform

IFoF

Intermediate Frequency over Fiber

IoTInternet of Things

IP

Internet Protocol

IPS

Induced Pluripotent Stem

IPv6

Internet Protocol version 6

ISAC

Integrated Sensing and Communication

ISAC-OW

Integrated Sensing and Communication with Optical Wireless

ISRU

In-Situ Resource Utilization

ISS

International Space Station

IT

Information Technology

ITSIntelligent Transport Systems

KEM

Key Encapsulation Mechanism

KPI

Key Performance Indicator

LCT

Laser Communication Terminals

LEDLight-Emitting DiodeLEO

Low Earth Orbit Satellite

LiDAR

Light Detection and Ranging

LLS

Lower Layer Split

LOS

Light of Sight

55

Abbreviation

Explanation

LTE

Long Tern Evolution

MaaS

Mobility as a service

MAC

Media Access Control

MCS

Modulation and Coding Scheme

MEC

Multi-access Edge Computing

MIMO

Multiple-Input and Multiple-Output

ML

Machine Learning

ｍMTC

massive Machine Type Communication

MR

Mixed Reality

MS

Mobile Service

MTP

Motion to Photon

MUP

Mobile User Plane

NBI

Northbound Interface

NGSO

Non-Geostationary Orbit

NIST

National Institute of Standards and Technology

NLOS

Non-Line of Sight

NOMA

Non-Orthogonal Multiple Access

NPU

Neural network Processing UnitNR

New Radio

NTN

Non-Terrestrial Network

O&M

Operation and Maintenance

OAM

Orbital Angular Momentum

ODD

Operational Design Domain

OFDM

Orthogonal Frequency Division Multiplexing

ONAP

Open Network Automation Platform

OPEX

Operating Expense

O-RAN

Open Radio Access Network

OSINT

Open-Source INTelligence

OTA

Over The Air

OWC

Optical Wireless Communication

PA

Process Automation56

Abbreviation

Explanation

PA

Power Amplifier

PCB

Printed Circuit Board

PCR

Polymerase Chain Reaction

PD-NOMAPower Domain Non-Orthogonal Multiple Access

PDs

Photodetectors

PF

Platform

PHR

Personal Healthcare Record

PHV

Plug-in Hybrid Vehicle

PHY

Physical Layer

PII

Personally Identifiable Information

PMIPv6

Proxy Mobile IPv6

PPM

Privacy Policy Manager

PPP

Purchasing Power Parity

PUE

Power Usage Efficiency

QKD

Quantum Key Distribution

QoE

Quality of Experience

RaaS

Retail as a Service

RaaS

Robotics as a Service

RACH

Random Access Channel

RAN

Radio Access Network

RAS

Radio Astronomy Service

RAT

Radio Access Technology

RFRadio Frequency

RIS

Reconfigurable Intelligent Surface

RMSE

Root Mean Square Error

RoFRadio over Fiber

RSA

Rivest-Shamir-Adleman

RTT

Round Trip Time

SDGs

Sustainable Development Goals

SDN

Software Defined Networking

SEAL

Service Enabler Architecture Layer for Verticals

57

Abbreviation

Explanation

SISO

Single-Input Single-Output

SNS

Social Networking Services

SPOF

Single Point of Failure

SR

Segment Routing

SRS

Sounding Reference Signal

SRv6

Segment Routing version 6

SRv6 MUP

Segment Routing IPv6 Mobile User Plane

TAT

Turn-around-Time

TCO

Total Cost of Ownership

TDD

Time Division Duplex

TEE

Trusted Execution Environment

ToF

Time-of-Flight

TRP

Transmission Reception Points

UAV

Unmanned Aerial Vehicle

UCN

User Centric Network

UE

User Equipment

UI

User Interface

UMTS

Universal Traffic Management System

URLLC

Ultra-Reliable and Low Latency Communications

UX

User Experience

V2X

Vehicle To Everything

VHTS

Very High Throughput SatelliteVR

Virtual Reality

vRAN

Virtual Radio Access Network

XaaS

X as a Service

X-OFDM

Mixed OFDM

XR

Extended Reality

ZSM

Zero-touch network and Service Management58