Internet Research Task Force C. Zhou
Internet-Draft H. Yang
Intended status: Informational X. Duan
Expires: January 8, 2022 China Mobile
D. Lopez
A. Pastor
Telefonica I+D
Q. Wu
Huawei
M. Boucadair
C. Jacquenet
Orange
July 7, 2021
Digital Twin Network: Concepts and Reference Architecture
draft-zhou-nmrg-digitaltwin-network-concepts-04
Abstract
Digital Twin technology has been seen as a rapid adoption technology
in Industry 4.0. The application of Digital Twin technology in the
networking field is meant to realize efficient and intelligent
management and accelerate network innovation. This document presents
an overview of the concepts of Digital Twin Network (DTN), provides
the definition and reference architecture, application scenarios, and
then describes the benefits and key challenges of such technology.
Status of This Memo
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This Internet-Draft will expire on January 8, 2022.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Requirements Language . . . . . . . . . . . . . . . . . . . . 3
3. Definitions and Acronyms . . . . . . . . . . . . . . . . . . 4
4. Definition of Digital Twin Network . . . . . . . . . . . . . 4
5. Benefits of Digital Twin Network . . . . . . . . . . . . . . 6
5.1. Lower the Cost of Network Optimization . . . . . . . . . 7
5.2. Optimized Decision Making . . . . . . . . . . . . . . . . 7
5.3. Safer Assessment of Innovative Network Capabilities . . . 7
5.4. Privacy and Regulatory Compliance . . . . . . . . . . . . 8
5.5. Customize Network Operation Training . . . . . . . . . . 8
6. Reference Architecture of Digital Twin Network . . . . . . . 8
7. Challenges to build Digital Twin Network . . . . . . . . . . 11
8. Interaction with IBN . . . . . . . . . . . . . . . . . . . . 12
9. Application Scenarios . . . . . . . . . . . . . . . . . . . . 12
9.1. Human Training . . . . . . . . . . . . . . . . . . . . . 12
9.2. ML Training . . . . . . . . . . . . . . . . . . . . . . . 13
9.3. DevOps-oriented certification . . . . . . . . . . . . . . 13
9.4. Network fuzzing . . . . . . . . . . . . . . . . . . . . . 13
10. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
11. Security Considerations . . . . . . . . . . . . . . . . . . . 14
12. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 14
13. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14
14. Open issues . . . . . . . . . . . . . . . . . . . . . . . . . 15
15. References . . . . . . . . . . . . . . . . . . . . . . . . . 15
15.1. Normative References . . . . . . . . . . . . . . . . . . 15
15.2. Informative References . . . . . . . . . . . . . . . . . 15
Appendix A. Change Logs . . . . . . . . . . . . . . . . . . . . 15
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16
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1. Introduction
With the advent of technologies such as 5G, Industrial Internet of
Things, Edge Computing, and Artificial Intelligence, the ICT
(Information and Communications Technology) and other vertical
industries such as smart cities or smart manufacturers are
transformed dramatically through replacing what is used to be manual
processes with digital processes.
With the fast growing of the network scale and the increased demand
placed on the network, accommodating and adapting dynamically to
customer needs becomes a big challenge to network operators. Indeed,
network operation and maintenance are becoming more complex due to
higher complexity of the managed networks. As such, providing
innovations on network will be more and more difficult due to the
high risk of interfering with existing services and higher trial cost
if no reliable emulation platforms are available.
Digital Twin is the real-time representation of physical entities in
the digital world. It has the characteristics of virtual-reality
interrelation and real-time interaction, iterative operation and
process optimization, as well as full life-cycle, and full business
data-driven. So far, it has been successfully applied in the fields
of intelligent manufacturing, smart city, or complex system operation
and maintenance [Tao2019] to help with not only object design and
testing, but also operation and maintenance.
A digital twin network platform can be built by applying Digital Twin
technology to networks and creating a virtual image of physical
network facilities (emulation). Through the real-time data
interaction between the physical network and its twin network, the
digital twin network platform might help the network designers to
achieve more simplification, automatic, resilient, and full life-
cycle operation and maintenance. Having an emulation platform that
allows to reliably represent the state of a network is more reliable
than a simulation platform. The emulated platform can thus be used
to assess specific behaviors before actual implementation in the
physical network, tweak the network for better optimized behavior,
run 'what-if' scenarios that can't be tested and evaluated easily in
the physical network. Service impact analysis tasks will also be
facilitated.
2. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
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14 [RFC2119][RFC8174] when, and only when, they appear in all
capitals, as shown here.
3. Definitions and Acronyms
PLM: Product Lifecycle Management
IBN: Intent-Based Networking
AI: Artificial Intelligence
ML: Machine Learning
OAM: Operations, Administration, and Maintenance
CI/CD: Continuous Integration / Continuous Delivery
4. Definition of Digital Twin Network
The concept of a virtual equivalent to a physical product or the
digital twin was first introduced in the Product Lifecycle Management
(PLM) course in 2003 by Scholar Michael Grieves [Grieves2014]. It
has been widely acknowledged in both industry and academic
publications. However, there is no standard definition of "digital
twin network" within the networking industry or SDOs. This document
defines digital twin network as a virtual representation of the
physical network. Such virtual representation of the network is
meant to be used to analyze, diagnose, emulate, and then control the
physical network based on data, model and interface. To that aim, a
real-time and interactive mapping is required between the physical
network and its virtual twin network.
As shown in Figure 1, the digital twin network involve four key
technology elements: data, mapping, models, and interfaces
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+-------------+ +--------------+
| | | |
| Mapping | | Interface |
| | | |
+-------------+-----------------+--------------+
| |
| Analyze, Diagnose |
| |
| +----------------------+ |
| | NETWORK DIGITAL TWIN | |
| +----------------------+ |
+------------+ +------------+
| | Emulate, Control | |
| Models | | Data |
| |------------------------| |
+------------+ +------------+
Figure 1: Key Elements of Digital Twin Network
Data: A digital twin network should maintain historical data and/or
real time data (configuration data, operational state data,
topology data, trace data, metric data, process data, etc.) about
its real-world twin (i.e., physical network) that are required by
the models to represent and understand the states and behaviors of
the real-world twin. The data is characterized as the single
source of the "truth" and populated in the data repository, which
provides timely and accurate data service support for building
various models..
Models: Techniques that involve collecting data from one or more
sources in the real-world twin and developing a comprehensive
representation of the data (e.g., system, entity, process) using
specific models. It is used as emulation and diagnosis basis to
provides dynamics and elements on how live physical network
operates and develop reasoning data utilized for decision-making.
Various models such as service models, data models, dataset
models, or knowledge graph can be used to represent the physical
network assets and then instantiated to serve various network
applications.
Interfaces: Standardized interfaces can ensure the compatibility of
digital twin network. There are two major types of interface: (1)
the interface between the digital twin network platform and the
physical network infrastructure and (2) the interface between
digital twin network platform and applications. The former
provides real time data collection and control on the physical
network; the latter helps deliver application requirements to
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digital twin network platform and exposure the various abilities
to applications.
Mapping: Is used to identify the digital twin and the underlying
entities and establish a real-time interactive mapping between the
physical network and the twin network or between two twin
networks. The mapping can be:
* One to one (pairing, vertical): Synchronize between a physical
network and its virtual twin network with continuous flow.
* One to many (coupling, horizontal): Synchronize among virtual
twin networks with occasional data exchange.
Such mapping provides a good visibility of actual status which
makes it more convenient to analyze and understand what is going
on in the physical network. It also allows using the digital twin
to optimize the performance and maintenance of the physical
network.
The digital twin network constructed based on the four core
technology elements can analyze, diagnose, emulate, and control the
physical network in the whole life cycle with the help of
optimization algorithms, management methods, and expert knowledge.
One of the objectives of such control is to master the digital twin
network environment and its elements to derive the required system
behavior, e.g., provide:
o repeatability: that is the capacity to replicate network
conditions on-demand.
o reproducibility: i.e., the ability to replay successions of
events, possibly under controlled variations.
5. Benefits of Digital Twin Network
Digital twin network can help enable closed-loop network management
across the entire lifecycle, from deployment and emulation, to
visualized assessment, physical deployment, and continuous
verification. In doing so, network operators (and end-users to some
extent) can get a global, systemic, and consistent view of the
network. Also, network operators can safely exercise the enforcement
of network planning policies, deployment procedures, etc., without
jeopardizing the daily operation of the physical network.
The benefits of digital twin network can be classified into: low cost
of network optimization, optimized and safer decision-making, safer
testing of innovative network capabilities (including "what if"
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scenarios), privacy and regulatory compliance, and customize network
operation training. The following subsections further elaborate on
such benefits.
5.1. Lower the Cost of Network Optimization
Large scale networks are complex to operate. Since there is no
effective platform for simulation, network optimization designs have
to be tested on the physical network at the cost of jeopardizing its
daily operation and possibly degrading the quality of the services
supported by the network. Such assessment greatly increases network
operator's Operational Expenditure (OPEX) budgets too.
With a digital twin network platform, network operators can safely
emulate candidate optimization solutions before deploying them in the
physical network. In addition, the operator's OPEX on the real
physical network deployment will be greatly decreased accordingly at
the cost of the complexity of the assessment and the resources
involved.
5.2. Optimized Decision Making
Traditional network operation and management mainly focus on
deploying and managing running services, but hardly support
predictive maintenance techniques.
Digital twin network can combine data acquisition, big data
processing, and AI modeling to assess the status of the network, but
also to predict future trends, and better organize predictive
maintenance. The ability to reproduce network behaviors under
various conditions facilitates the corresponding assessment of the
various evolution options as often as required.
5.3. Safer Assessment of Innovative Network Capabilities
Testing a new feature in an operational network is not only complex,
it is also extremely risky.
As mentioned above, digital twin network can greatly help assessing
innovative network capabilities without jeopardizing the daily
operation of the physical network. In addition, it also helps
researchers to explore network innovation (e.g., new network
protocols, network AI/ML applications) efficiently, and network
operators to deploy new technologies quickly with lower risks. Take
AI/ ML application as example, it is a conflict between the
continuous high reliability requirement (i.e., 99.999%) of network
and the slow learning speed or phase-in learning steps of AI/ML
algorithms. With digital twin network platform, AI/ML can complete
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the learning and training with the sufficient data before deploying
the model in the real network. This will greatly encourage more
network AI innovations in future networks.
5.4. Privacy and Regulatory Compliance
The requirements on data confidentiality and privacy on network
providers increase the complexity of network management, as decisions
made by computation logics such as an SDN controller may rely upon
the payloads content. As a result, the improvement of data-driven
management requires complementary techniques that can provide a
strict control based upon security mechanisms to guarantee data
privacy protection and regulatory compliance. Some examples of these
techniques include payload inspection, including decryption with user
explicit consents, or data anonymization mechanisms.
Given digital twin network operation assumes the mapping between real
traffic or services and the traffic used by the digital twin network
for assessment purposes in particular, the need for privacy is of the
utmost importance. The lack of personal data permits to lower the
privacy requirements and simplifies the use of privacy-preserving
techniques.
5.5. Customize Network Operation Training
Network architectures can be complex, and their operation requires
expert personnel. Digital twin network offers an opportunity to
train staff for customized networks and specific user needs. Two
salient examples are the application of new network architectures and
protocols or the use of cyber-ranges to train security experts in the
threat detection and mitigation.
6. Reference Architecture of Digital Twin Network
Based on the definition of the key digital twin network technology
elements introduced in Section 4, a digital twin network architecture
is depicted in Figure 2. The digital twin network architecture is
broken down into three layers: Application Layer, Network Digital
Twin Layer and Physical Network Layer.
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+---------------------------------------------------------+
| +-------+ +-------+ +-------+ |
| | App 1 | | App 2 | ... | App n | Application|
| +-------+ +-------+ +-------+ |
+-------------^-------------------+-----------------------+
|Capability Exposure|intent input
| |
+---------------------------------v-----------------------+
| Network Digital Twin|
| +--------+ +------------------------+ +--------+ |
| | | | Service Mapping Models | | | |
| | | | +------------------+ | | | |
| | Data +---> |Functional Models | +---> Digital| |
| | Repo- | | +-----+-----^------+ | | Twin | |
| | sitory | | | | | | Entity | |
| | | | +-----v-----+------+ | | Mgmt | |
| | <---+ | Basic Models | <---+ | |
| | | | +------------------+ | | | |
| +--------+ +------------------------+ +--------+ |
+--------^------------------------------------------------+
| |
| data collection | control
+-------------------------------------v-------------------+
| Physical Network |
| |
+---------------------------------------------------------+
Figure 2: Reference Architecture of Digital Twin Network
1. The lowest layer is the Physical Network. (All) network elements
in the physical network exchange massive network data and control
with network digital twin entity, through twin southbound
interfaces. As the physical object of the network twin, the
physical network can be a mobile access network, a transport
network, a mobile core, a backbone, etc. The network can also be
a data center network, a campus enterprise network, an industrial
Internet of Things, etc. The network can span across a single
network domain or multiple network domains.
2. The Intermediate layer is the Network Digital Twin. This layer
includes three key subsystems: Data Repository subsystem, Service
Mapping Models subsystem, and Digital Twin Entity Management
subsystem.
* Data Repository subsystem is responsible for collecting and
storing various network data for building various models by
collecting and updating the real-time operational data of
various network elements through the twin southbound
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interface, and providing data services (e.g., fast retrieval,
concurrent conflict, batch service) and unified interfaces to
Service Mapping Models subsystem.
* Service Mapping Models complete data modeling, provides data
model instances for various network applications, and
maximizes the agility and programmability of network services.
The data models include two major types: basic and functional
models.
+ Basic models refer to the network element model and network
topology model of the network digital twin based on the
basic configuration, environment information, operational
state, link topology and other information of the network
element, to complete the real-time accurate
characterization of the physical network.
+ Functional models refer to various data models such as
network analysis, simulation, diagnosis, prediction,
assurance, etc. The functional models can be constructed
and expanded by multiple dimensions: by network type, there
can be models serving for a single or multiple network
domains; by function type, it can be divided into state
monitoring, traffic analysis, security exercise, fault
diagnosis, quality assurance and other models; by network
lifecycle management, it can be divided into planning,
construction, maintenance, optimization and operation. it
can also be divided into general model and special-purpose
model. Specifically, multiple dimensions can be combined
to create a data model for more specific application
scenarios.
* Digital Twin Entity Management completes the management
function of digital twin network, records the life-cycle of
the entity, visualizes and controls various elements of the
network digital twin, including topology management, model
management and security management.
3. Top layer is Application Layer. Various applications (e.g., OAM,
IBN) can effectively run over a digital twin network platform to
implement either conventional or innovative network operations,
with low cost and less service impact on real networks. Network
applications raise requirements that need to be addressed by the
digital twin network. Such requirements are exchanged through a
northbound interface; then the service is emulated by various
twin service instances. Once checked, the changes can be safely
deployed in the physical network.
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7. Challenges to build Digital Twin Network
As mentioned in the above section, digital twin networks can bring
many benefits to network management as well as facilitate the
introduction of innovative network capabilities. However, building
an effective and efficient digital twin network system remains a
challenge. The following is a list of the major challenges:
o Large scale challenge: The digital twin of large-scale networks
will significantly increase the complexity of data acquisition and
storage, the design and implementation of models. And the
requirements of software and hardware of the system will be even
more constraining.
o Interoperability: It is difficult to establish a unified digital
twin platform with a unified data model in the whole network
domain due to the inconsistency of technical implementations and
the heterogeneity of vendor technologies.
o Data modeling difficulties: Based on large-scale network data,
data modeling should not only focus on ensuring the accuracy of
model functions, but also need to consider the flexibility and
scalability of the model. Balancing these requirements further
increase the complexity of building efficient and hierarchical
functional data models.
o Real-time requirement: For services with real-time requirements,
the processing of model simulation and verification through a
digital twin network will increase the service delay, so the
function and process of the data model need to be based on
automated processing mechanism under various network application
scenarios; at the same time, the real-time requirements will
further increase performance requirements on the system software
and hardware.
o Security risks: the digital twin network synchronizes all the data
of physical networks in real time, which inevitably augments the
attack surface, with a higher risk of information leakage, in
particular.
To address these challenges, the digital twin network needs
continuous optimization and breakthrough on key enabling technologies
including data acquisition, data storage, data modeling, network
visualization, interface standardization, and security assurance, so
as to meet the requirements of compatibility, reliability, real-time
and security.
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8. Interaction with IBN
Implementing Intent-Based Networking (IBN) is an innovative
technology for life-cycle network management. Future network will be
possibly Intent-based, which means that users can input their
abstract 'intent' to the network, instead of detailed policies or
configurations on the network devices.
[I-D.irtf-nmrg-ibn-concepts-definitions] clarifies the concept of
"Intent" and provides an overview of IBN functionalities. The key
characteristic of an IBN system is that user's intent can be assured
automatically via continuously adjusting the policies and validating
the real-time situation.
IBN can envisaged in a digital twin network context to show how
digital twin network improves the efficiency of deploying network
innovation. To lower the impact on real networks, several rounds of
adjustment and validation can be emulated on the digital twin network
platform instead of directly on physical network. Therefore, digital
twin network can be an important enabler platform to implement IBN
system and speed up the deployment of IBN in customer's network.
9. Application Scenarios
Digital twin network can be applied to solve different problems in
network management and operation.
9.1. Human Training
The usual approach to network Operations, Administration, and
Maintenance (OAM) with procedures applied by humans is open to errors
in all these procedures, with impact in network availability and
resilience. Response procedures and actions for most relevant
operational requests and incidents are commonly defined to reduce
errors to a minimum. The progressive automation of these procedures,
such as predictive control or closed loop management, reduce the
faults and response time, but still there is the need of a human-in-
the-loop for multiples actions. These processes are not intuitive
and require training to learn how to respond.
The use of digital twin network for this purpose in different network
management activities will improve the operators performance. One
common example is cybersecurity incident handling, where cyber-range
exercises are executed periodically to train security practitioners.
Digital twin network will offer realistic environments, fitted to the
real production networks.
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9.2. ML Training
Machine Learning requires data and their context to be available in
order to apply it. A common approach in the network management
environment has been to simulate or import data in a specific
environment (the ML developer lab), where they are used to train the
selected model, while later, when the model is deployed in
production, re-train or adjust to the production environment context.
This demands a specific adaption period.
Digital twin network simplifies the complete ML lifecycle development
by providing a realistic environment, including network topologies,
to generate the data required in a well-aligned context. Dataset
generated belongs to the digital twin network and not to the
production network, allowing information access by third parties,
without impacting data privacy.
9.3. DevOps-oriented certification
The potential application of CI/CD models network management
operations increases the risk associated to deployment of non-
validated updates, what conflicts with the goal of the certification
requirements applied by network service providers. A solution for
addressing these certification requirements is to verify the specific
impacts of updates on service assurance and SLAs using a digital twin
network environment replicating the network particularities, as a
previous step to production release.
Digital twin network control functional block supports such dynamic
mechanisms required by DevOps procedures.
9.4. Network fuzzing
Network management dependency on programmability increases systems
complexity. The behavior of new protocol stacks, API parameters, and
interactions among complex software components are examples that
imply higher risk to errors or vulnerabilities in software and
configuration.
Digital twin network allows to apply fuzzing testing techniques on a
twin network environment, with interactions and conditions similar to
the production network, permitting to identify and solve
vulnerabilities, bugs and zero-days attacks before production
delivery.
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10. Summary
Research on digital twin network has just started. This document
presents an overview of the digital twin network concepts. Looking
forward, further elaboration on digital twin network scenarios,
requirements, architecture, and key enabling technologies should be
promoted by the industry, so as to accelerate the implementation and
deployment of digital twin network.
11. Security Considerations
This document describes concepts and definitions of digital twin
network. As such, the below security considerations remain high
level, i.e., in the form of principles, guidelines or requirements.
Security considerations of the digital twin network include:
o Secure the digital twin system itself.
o Data privacy protection.
Securing the digital twin network system aims at making the digital
twin system operationally secure by implementing security mechanisms
and applying security best practices. In the context of digital twin
network, such mechanisms and practices may consist in data
verification and model validation, mapping operations between
physical network and digital counterpart network by authenticated and
authorized users only.
Synchronizing the data between the physical and the digital twin
networks may increase the risk of sensitive data and information
leakage. Strict control and security mechanisms must be provided and
enabled to prevent data leaks.
12. Acknowledgements
Diego Lopez and Antonio Pastor were partly supported by the European
Commission under Horizon 2020 grant agreement no. 833685 (SPIDER),
and grant agreement no. 871808 (INSPIRE-5Gplus).
13. IANA Considerations
This document has no requests to IANA.
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14. Open issues
o Investigate related digital twin network work and identify the
differences and commonality, e.g., How is this concept and
architecture different from digital twin for industry application?
How can existing network management models be re-used?
15. References
15.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
15.2. Informative References
[Grieves2014]
Grieves, M., "Digital twin: Manufacturing excellence
through virtual factory replication", 2003.
[I-D.irtf-nmrg-ibn-concepts-definitions]
Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
Tantsura, "Intent-Based Networking - Concepts and
Definitions", draft-irtf-nmrg-ibn-concepts-definitions-03
(work in progress), February 2021.
[Tao2019] Tao, F., Zhang, H., Liu, A., and A. Nee, "Digital Twin in
Industry: State-of-the-Art. IEEE Transactions on
Industrial Informatics, vol. 15, no. 4.", April 2019.
Appendix A. Change Logs
v03 - v04
o Change the I-D title from "Concepts of Digital Twin Network" to
"Digital Twin Network: Concepts and Reference Architecture".
o Update data definition and models definitions to clarify their
difference.
o Remove the orchestration element and consolidated into control
functionality building block in the digital twin network.
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o Clarify the mapping relation (one to one, and one to many) in the
mapping definition.
o Add explanation text for continuous verification.
v02 - v03
o Split interaction with IBN part as a separate section.
o Fill security section;
o Clarify the motivation in the introduction section;
o Use new boilerplate for requirements language section;
o Key elements definition update.
o Other editorial changes.
o Add open issues section.
o Add section on application scenarios.
Authors' Addresses
Cheng Zhou
China Mobile
Beijing 100053
China
Email: zhouchengyjy@chinamobile.com
Hongwei Yang
China Mobile
Beijing 100053
China
Email: yanghongwei@chinamobile.com
Xiaodong Duan
China Mobile
Beijing 100053
China
Email: duanxiaodong@chinamobile.com
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Diego Lopez
Telefonica I+D
Seville
Spain
Email: diego.r.lopez@telefonica.com
Antonio Pastor
Telefonica I+D
Madrid
Spain
Email: antonio.pastorperales@telefonica.com
Qin Wu
Huawei
101 Software Avenue, Yuhua District
Nanjing, Jiangsu 210012
China
Email: bill.wu@huawei.com
Mohamed Boucadair
Orange
Rennes 35000
France
Email: mohamed.boucadair@orange.com
Christian Jacquenet
Orange
Rennes 35000
France
Email: christian.jacquenet@orange.com
Zhou, et al. Expires January 8, 2022 [Page 17]