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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2017.Doi Number
A secure and resilient 6G architecture vision of
the German flagship project 6G-ANNA
Marco Hoffmann1, Gerald Kunzmann1, Torsten Dudda2, Ralf Irmer3, Admela Jukan4, Fellow,
IEEE, Gordana Macher5, Abdullah Ahmad6, Florian R. Beenen7, Arne Bröring19, Felix
Fellhauer7, Gerhard Fettweis8, Fellow, IEEE, Frank H. P. Fitzek8, Senior Member, IEEE,
Norman Franchi10, Member, IEEE, Florian Gast8, Graduate Student Member, IEEE, Bernd
Haberland9, Sandra Hoppe1, Sadaf Joodaki11, Nandish P. Kuruvatti12, Member, IEEE, Chu Li13,
Student Member, IEEE, Miguel Lopez2, Fidan Mehmeti14, Thomas Meyerhoff16, Lorenzo
Miretti15, Giang T. Nguyen8, Member, IEEE, Mohammad Parvini8, Rastin Pries1, Rafael F.
Schaefer8, Senior Member, IEEE, Peter Schneider1, Dominic Schupke16, Stephanie
Strassner17, Henning Stubbe18, Andra M. Voicu2
1Nokia Solution and Networks, 80798 Munich, Germany
2Ericsson, 52134 Herzogenrath, Germany
3Vodafone GmbH, 01067 Dresden, Germany
4Technische Universität Braunschweig, 38106 Braunschweig, Germany
5Smart Mobile Labs, 81379 Munich, Germany
6PHYSEC GmbH, 44803 Bochum, Germany
7Robert Bosch GmbH, Cross-Domain Computing Solutions, 70839 Stuttgart, Germany
8Technische Universität Dresden, 01069 Dresden, Germany
9Nokia Solution and Networks, 70469 Stuttgart, Germany
10Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
11RWTH Aachen University, Aachen, Germany
12Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, 67663 Kaiserslautern, Germany
13Ruhr University Bochum, 44801 Bochum, Germany
14Technische Universität München, 80333 München, Germany
15Fraunhofer Heinrich Hertz Institute, 10587 Berlin, Germany
16Airbus, Central Research & Technology, 81663 Munich, Germany
17Airbus Secure Land Communications GmbH (ASLC), 89077 Ulm, Germany
18Technische Universität München, 85748 Garching bei München, Germany
19Siemens AG, 81739 München, Germany
Corresponding author: Marco Hoffmann (e-mail: marco.hoffmann@nokia.com).
This work was supported by the German Federal Ministry of Education and Research (BMBF) project 6G-ANNA.
ABSTRACT The 6th generation of wireless mobile networks is emerging as a paradigm shifting successor
to unifying the experience across the physical, digital, and human worlds, pushing boundaries on performance
in capacity, throughput, latency, scalability, flexibility, and reliability, while prominently addressing new
major factors, including sustainability, security and privacy, as well as digital inclusion. Many research
institutions and initiatives worldwide have started investigations to make 6G a reality by approximately 2030.
In Germany, federal funding from the German Ministry of Education and Research (BMBF) supports a large-
scale 6G initiative, with its lighthouse project, called 6G-ANNA. The core aim of this project is to develop
the key aspects of a holistic, sustainable, secure, and resilient 6G system design that will simplify and improve
the interaction between humans, digital assets, and the physical environment. This paper shares the vision of
the project’s main technical working areas and advances, spanning topics from radio access, integration of
multiple networks, as well as automation and simplification in networking to new applications and testbed
scenarios, including real-time digital twins and extended reality. The industrial impact and relevance of
standardization makes 6G-ANNA uniquely positioned to lead and realize the vision of next-generation
wireless mobile network technologies, systems, and applications.
INDEX TERMS 6G, communications systems, wireless communications, network architecture, resiliency,
security
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
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I. INTRODUCTION
As the 5G-Advanced [1] specification is coming of age for
broadband cellular networks, 6G is emerging as a paradigm
shifting successor to unifying the experience across physical,
digital, and human worlds [2], beyond 5G richest capabilities
of, “connectivity and enabling a wider set of advanced use
cases for verticals” [3]. 6G leads to providing more
intelligent services, better control, and new experiences to
augmented humans. It is enabled by the following key
enhancements including extended reality (XR), accurate
positioning, resilient timing, and network operation
efficiency (including energy efficiency and simplification)
with rollouts expected in many markets. In addition to the
enhancements offered by 5G, 6G design is also motivated by
the key principles of traditional performance indicators
(KPIs), that is, capacity, throughput, latency, scalability,
flexibility, and reliability, as well as three key value indicators
(KVIs): sustainability, security & privacy, and digital
inclusion.
These KVIs not only drive the vision towards 6G
architecture, but also present central challenges that 6G is
envisioned to solve:
Sustainability is a major design criterion for 6G and needs
to be considered from two perspectives: making networks
more energy efficient and carbon neutral (“footprint”) and
enabling other businesses to provide solutions addressing the
sustainability goals set by the United Nations [4] (we refer to
it as the network’s “handprint”). To this end, every aspect of
the network’s operation needs to be designed to minimize
energy consumption, resulting in more sustainable water use
(e.g., for cooling systems in data centers) and reduced carbon
emissions. Additional improvements in device power savings,
efficient radio transmission, and simplification of the network
architecture are expected to further reduce the energy footprint
despite the ever-growing traffic demand.
Second, improving security and privacy to better
safeguard communication systems and personal data
continues to be a major goal. Various networking scenarios,
including private campus networks and a novel network of
networks (see Section III.B), present new challenges and
opportunities to implement both security and privacy by
design.
Finally, bridging the digital divide (aka digital inclusion)
is a key goal of 6G. Enhanced connectivity on a global scale
will allow access to healthcare, education, and greater
economic opportunities for all humans. To this end, 6G must
provide connectivity everywhere with good network quality
and affordable costs.
The remainder of this paper is organized as follows. This
section provides an overview of the current global 6G
ecosystem by listing key 6G initiatives globally and
positions 6G-ANNA within this ecosystem. This is followed
1
6G-ANNA: https://6g-anna.de/
2
6G Platform Germany: https://www.6g-platform.com/
3
Horizon 2020: http://ec.europa.eu/programmes/horizon2020/en
by an outlook to 6G standardization efforts. Section II
presents the use cases and requirements that 6G-ANNA
derives for 6G. Section III details the envisioned research
directions and the proposed architecture. Finally, Section IV
describes the planned evaluations and proof-of-concepts.
A. CURRENT GLOBAL 6G ECOSYSTEM
Many research institutions and initiatives worldwide have
started investigations to make 6G a reality by approximately
2030. Significant efforts towards this end are underway in
the US, Europe, and China. In particular, the German
Ministry of Education and Research (BMBF) funded a large-
scale initiative, including the 6G lighthouse project, called
6G-ANNA
1
. The project is a consortium of 30 partners from
industry, small and medium enterprises (SME), research
institutions, and universities based in Germany. The project
focuses on developing a blueprint for a functional 6G end-
to-end system architecture designed for energy efficiency
(“footprint”), security, and resiliency, which supports a
variety of use cases, as detailed in Section II. The 6G-ANNA
technical working areas span topics such as radio access,
integration and interaction of multiple networks, automation
and simplification in networking, including digital twins and
extended reality. Several planned testbeds and proof of
concepts (PoCs) will demonstrate the key findings of the
project.
The 6G Platform Germany
2
aims to make scientific
contributions to the content design of 6G and ensure the
scientific organizational support of the processes necessary for
the successful implementation of the German 6G program. It
is home to four 6G research hubs in Germany, namely 6G
RIC, 6GEM, 6Glife, and Open6GHub, which combine the
know-how from universities and research institutions.
Additionally, the 6G Platform is the interface between these
hubs, 18 6G industry projects, including 6G-ANNA, 7
resilience projects, and the optical networking project
AI-NET.
Horizon 2020
3
and the European Smart Networks and
Services Joint Undertaking (SNS JU)
4
set goals to ensure
industrial leadership for Europe in 5G and 6G, pooling
resources from the EU and industry towards 6G research and
innovation. Hexa-X
5
and its successor Hexa-X-II
6
are two
notable EU flagship 6G research projects with strong
participation from cellular service providers (CSPs), major
industries, SME, and academic partners. The foundation for an
end-to-end 6G system architecture was laid by Hexa-X, and
Hexa-X-II continued to define a blueprint and system
validation of the sustainable, inclusive, and trustworthy 6G
platform. The projects also aim to ensure technological
readiness in critical areas and EU strategic autonomy.
4
SNS JU: https://smart-networks.europa.eu/
5
Hexa-X: https://hexa-x.eu/
6
Hexa-X-II: https://hexa-x-ii.eu/
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
VOLUME XX, 2023 9
In the US, the Next G Alliance
7
is an initiative to advance
North American wireless technology leadership over the next
decade with a strong emphasis on technology
commercialization, including the 6G roadmap. Its working
groups cover all aspects of spectrum, technology, applications,
societal and economic needs, and green networking. Several
whitepapers have already been released by related working
groups
8
.
Moreover, the Resilient and Intelligent Next-Generation
Systems (RINGS)
9
program is a noteworthy US NSF-led
initiative to accelerate collaborative academic research in
areas with a potentially significant impact on next-generation
networking and computing systems. It focuses on significantly
improving the resilience of such networked systems.
China has also initiated several 6G activities, including the
IMT-2030 (6G) Promotion Group
10
which has already
published several deliverables on 6G vision and many
technical 6G research areas, and the 6G Alliance of Network
AI (6GANA)
11
focusing on the exploration and promotion of
6G architectural innovation for network artificial intelligence
(AI). The Beyond 5G Promotion Consortium (B5GPC,
Japan)
12
, 5G Forum (South Korea)
13
, and TSDSI (India) [5]
have begun investigations and research on 6G scenarios,
requirements, 6G architecture design, and technological
innovations.
Multiple aspects are common to most 6G initiatives today,
including, but not limited to, spectrum aspects, 6G architecture
and relevant technologies, and use cases and requirements.
7
Next G Alliance: https://www.nextgalliance.org
8
https://www.nextgalliance.org/6g-library/
9
NSF RINGS: https://www.nsf.gov/pubs/2022/nsf22590/nsf22590.htm
10
IMT-2030(6G) Promotion Group: https://www.imt2030.org.cn/
11
6G Alliance of Network AI (6GANA): https://www.6g-ana.com/
B. 6G STANDARDIZATION EFFORTS
The earliest commercial and standard-compliant 6G
deployments are expected to become available by late 2029
(Figure 1). The consortium 3GPP (3rd Generation Partnership
Project)
14
is expected to be the key player in developing the
main protocols for the 6G mobile telecommunications
standard, while itself utilizing protocols developed by other
organizations (e.g., IETF) and being extended by initiatives
such as the O-RAN Alliance
15
.
Standards organizations typically work following an 18 to
24 months release cycle, starting with an investigation of
requirements derived from use cases and verticals markets
(e.g., SA1 - Service and system Aspects working group 1 in
3GPP). Next, the services and architecture needed to support
the agreed requirements are studied and specified at a
functional level (e.g., 3GPP SA2), which is followed by
detailed technical specifications (“Stage 3”). Examples of the
latter are the 3GPP CT (Core network and Terminals) working
groups.
To achieve the 2029/2030 deployment target, the first
release of 6G standards needs to be made available by mid-
2028 as Stage 3 specifications. To this end, 3GPP will start
working on 6G requirements by the end of 2023. Technical
studies are expected to take place in 2025 and 2026, and
normative specification work is expected to start in the second
half of 2026. It should be noted that also other (pre-)standards
organizations are preparing for 6G, including the O-RAN
Alliance currently investigating several research questions as
part of a newly established next Generation Research Group
12
Beyond 5G Promotion Consortium (B5GPC): https://b5g.jp/en/
13
5G Forum: http://www.5gforum.org/
14
3GPP: https://www.3gpp.org/
15
O-RAN Alliance: https://www.o-ran.org/
FIGURE 1. 6G timeline highlighting main projects, phases and milestones along the road towards first commercial 6G deployments.
Regulatory
Innovation
Standardisation
6G specs
6G pre-standards 6G reqs. 6G study
consensus building
2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
SA1 req. R20
EU SNS JU Ph1 –HEXA-X II
Industry Alignment Requirements, evaluation cr. Proposals IMT-2030 Spec
US e.g. Next GA
WRC-23 Agenda Item
on New Bands
WRC-27
New Bands
First 3GPP
6G spec
Commercial 6G
deployments
SA2 arch study
RAN req. + techn. studies
3GPP 6G WS
RAN specs
SA+CT specs
Rel-21 package;
Key 6G arch decisions
IMT-2030
Workshop
GER BMBF –6G-ANNA
O-RAN nGRG
6G specs v2
GER BMBF 6G industry projects
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
VOLUME XX, 2023 9
(nGRG). The results and findings from the different research
organizations and 6G initiatives summarized above will play
a critical role in the current ongoing 6G pre-standards
consensus building. Simultaneously, regulatory aspects and
industry alignment remain critically important, such as
spectrum allocation for 6G. The latter will be discussed at
upcoming world radio conferences (WRC), and ITU-R will
move its 2030 vision into a set of IMT-2030 standards by
around 2030.
II. 6G USE CASES AND REQUIREMENTS
Most previously described initiatives have already identified
and published a set of 6G use cases [6][7][8]. The Hexa-X
project [2] is worth noting in this context categorizing its use
cases into six use case families: (i) enabling sustainability,
(ii) hyperconnected resilient network infrastructures,
(iii) trusted embedded networks, (iv) robots to cobots
(collaborating robots), (v) telepresence, and (vi) massive
twinning. In contrast, Next G Alliance divides its use cases
into four groups: (i) network-enabled robotic and autonomous
systems, (ii) multi-sensory extended reality, (iii) distributed
sensing and communications, and (iv) personalized user
experience [6]. The NGMN Alliance also applies four
categories: (i) enhanced human communication, (ii) enhanced
machine communication, (iii) enabling services, and
(iv) network evolution [8]. Thereby, the last category, with
energy efficiency, coverage expansion, and trusted native AI,
does not actually describe use cases but rather focuses on goals
for network evolution towards 6G.
Several of the abovementioned initiatives have also
identified generic requirements for the described families of
use cases. Consequently, many of these requirements have a
quite large range of values. The 6G-ANNA project started to
detail more concrete requirements for selected relevant use
cases, as in most application areas, not all extreme
requirements must be met at the same time. In addition, the
6G-ANNA project puts a specific emphasize on energy
efficiency metrics and extends the requirements regarding
interworking between networks and network generations. For
example, in the 6G-ANNA, UEs can take up different roles,
such as the host of a sub-network, member of split inference,
or federated model training, with corresponding capabilities
exposure, and necessary state synchronization. In addition,
there is currently a lack of agreed proof-points for the KVIs,
that is, how to demonstrate benefits and translate “key values”
into measurable metrics.
6G-ANNA identified several areas that it uniquely
addresses and extends in comparison to other activities. For
instance, while many initiatives consider use cases that cover
the mobility of people and cars, the aspects of trains and
airplanes are still widely missing, whereas 6G-ANNA
addresses these challenging environments. Furthermore,
although the importance of security is highlighted in most 6G
initiatives, specific use cases for security applications are few
and far between. 6G-ANNA includes a use case entitled
“Critical 6G services for remote operators” that demands
connectivity with advanced security protection to guard
against sophisticated malicious attacks. This is only one
example of a security and privacy use case that the 6G-ANNA
addresses.
6G-ANNA, with a primary focus on factory and industrial
environments, as well as mobility aspects for vehicles
(automotive, aerospace, etc.), studies use cases such as:
--“Public safety networks” enables highly robust
emergency systems with specific communication
requirements. In particular, the use case requires very high
network availability and reliability, with a potentially high
number of users (see Section III.A).
--“Dynamic switching of control access to sub-networks
and hosts” includes a scenario in which hosts are connected to
the network of networks (see Section III.B) dynamically
changing their role from client to (sub-)network, and vice
versa.
--“Intelligent network operations and multi-X
orchestration in 6G networks” has a high level of robustness,
reliability, and sustainability requirements, with several
identified technology gaps (see Section III.C).
--“Real-time digital twinning of factory environments”,
where a better control is needed of the factory environments
including improvements in positioning, mapping, and
semantic segmentation. From a technological point of view,
real-time sensor retrieval and (dynamic split) processing and
efficient real-time data reduction are some of the gaps to be
addressed (see Section III.D).
--“Massive multisensory merged reality” offers a portal to
a metaverse experience powered by XR and requires a broad
range of KPIs as well as the privacy KVI (see Section III.A).
--“Critical 6G services for remote operators”, e.g., for
vehicles demanding for connectivity with advanced security
protection to guard against sophisticated malicious attacks
(see Section III.F).
III. RESEARCH DIRECTIONS (in 6G-ANNA)
A. 6G ACCESS
1) SPECTRUM
The spectrum where the 6G cellular networks will be
allowed to transmit imposes fundamental restrictions on: (i)
how the 6G radio access network (RAN) can operate (e.g.,
regulatory restrictions in terms of radiated power and
interference management mechanisms); (ii) what types of
services can be supported (e.g., high-throughput applications
can be supported with large bandwidths, which in turn are
only available in specific, high spectrum bands); and (iii)
what network performance can be achieved (e.g., carrier
frequency and available bandwidth that determine the
coverage, throughput, number of supported mobile devices,
etc.). Therefore, it is crucial to consider the spectrum
possibilities for the operation of 6G before proceeding with
a detailed design of its RAN.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
VOLUME XX, 2023 9
We briefly review the spectrum in which 6G networks
are expected to operate in the future. Figure 2 shows an
overview of the spectrum ranges in which some bands can
already be used by cellular technologies prior to 6G, as well
as ranges where potential new spectrum bands could be
opened for 6G. Overall, these spectrum ranges cover diverse
regulatory access rights, such as licensed exclusive,
unlicensed, and licensed nonexclusive. Regarding the
spectrum that can already be used for mobile cellular
services, we consider Germany as the region of focus for the
6G-ANNA project. This spectrum comprises broadly 700
MHz–3.7 GHz with exclusive (country-wide) licenses, and
3.7–3.8 GHz and mm-wave bands within 24.25–27.5 GHz
with local licenses [9][10].
Regarding the new spectrum (i.e., the dark blue ranges in
Figure 2), it is not yet clear at a global level whether this
spectrum will actually be available for mobile
communications. Overall, sub-THz bands are, in any case,
suitable only for very specific use cases (e.g., high-definition
merged reality in indoor environments), owing to the very
high radio propagation attenuation, despite the very large
bandwidth. By contrast, the range of 7–20 GHz is more
promising for extending the bandwidth while ensuring
sufficient coverage. However, incumbent services already
exist within this range [11]. Thus, it is not yet clear whether
and how many of these bands will be opened for 6G.
Additionally, even if some are to be opened, strictly defined
regulatory rules are expected to coexist with incumbent
technologies and protect them from interference. The next
ITU-R World Radio Conference (WRC-23) will discuss the
identification of bands in the 6–10 GHz range and additional
bands for international mobile telecommunications (IMT)
services [11].
Given these considerations, 6G operation in existing
licensed bands for cellular technologies is crucial because
this spectrum is readily available for pre-6G 3GPP
communication technologies, and it is straightforward to
employ coordinated interference management techniques
within the network of a single licensee. Nonetheless, it is
important to ensure the coexistence of 4G/5G networks with
the same licensee. Thus, migration solutions from 4G/5G to
6G should be carefully considered and are part of ongoing
6G-ANNA research. We note that such solutions were
already developed and standardized by 3GPP for 4G and 5G
technologies, that is, dynamic spectrum sharing (DSS) [12].
Consequently, future spectrum-sharing techniques between
5G and 6G can be based on lessons learned from DSS, which
will also be explored in 6G-ANNA.
2) ADAPTIVE PHYSICAL LAYER
For 6G radio access, further evolution of the physical layer
(PHY) is expected, fulfilling the broad requirement range of
the previously mentioned 6G use cases, while operating in
different frequency ranges and deployment scenarios. The
6G-ANNA project investigates the key technology enablers
for making the PHY more adaptive. Furthermore, 6G-ANNA
analyzes the benefits of the integration of AI in PHY and
develops concepts for massive and distributed multiple-input
multiple-output (MIMO) antenna systems.
Using a single PHY design to implement different 6G use
cases is not optimal in terms of the operational cost and
energy consumption. This is because of the various
requirements and deployment scenarios that affect the
channel characteristics, coverage, and traffic over time and
area. For instance, conventional cellular networks exhibit
non-uniform coverage and user distribution as well as
varying data usage patterns throughout the day. This can be
translated into different spectral efficiency (SE)
requirements in time and space, based on actual data rate
demands, available radio resources, channel status, and
interference conditions.
Consequently, it is inefficient to concentrate the physical
layer design on achieving a high SE, which is rarely used.
Although adaptive modulation and coding schemes (MCS)
and orthogonal frequency division multiplex (OFDM)
numerologies, such as employed 5G PHY, provide a solution
to configure the transmission parameters according to SE
requirements, this approach does not consider the potential
of utilizing other energy-efficient modulation schemes with
optimized analogue hardware. Moreover, in linear
modulations, analogue-digital converter (ADC) power
consumption is expected to present an energy-consumption
bottleneck for bandwidths beyond 300 MHz [13].
Therefore, the PHY adaptability needs to be extended to
multiple modulation schemes with corresponding hardware
(HW) options, and switching between them needs to be
enabled based on the SE requirements of energy
consumption. Such an approach has been denoted as the
“Gearbox PHY” [14], where multiple options (gears) are
optimized in terms of hardware and modulation for different
SE ranges. For example, the lowest gear could employ pulse
modulation, which is suitable for a low SE. The second gear
operates with 1-bit ADCs and zero-crossing modulation
(ZXM). The third gear can be designed with constant
envelope modulation and a low-resolution ADC. The highest
SE requirements can be supported by gears that employ
linear modulation and MIMO schemes, etc. Changing the
type of modulation requires the full switching of the
transceiver chain. Additionally, within one gear, flexible
adaptation of the design with traditional modulation and
coding schemes can provide a fine-tuned SE.
FIGURE 2. Spectrum range for future 6G radio access, illustrating
spectrum already available for 5G (cyan) and possible new spectrum
for 6G (dark blue).
Frequency [GHz]
1310 30 100 300
current 5G spectrum ranges possible new 6G spectrum ranges
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
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VOLUME XX, 2023 9
Accordingly, there are three emerging research areas:
1) baseband transceiver design considering adaptive
modulation for individual gears; 2) underlying radio
architecture considering frequency band, bandwidth, and
hardware constraints; and 3) analysis of the conditions for
switching the gears.
One of the further key aspects of 6G wireless systems is
expected to be their "by-design" ability to learn and adapt to
dynamic environments where artificial intelligence (AI)
methods play the central role [15]. Computational learning
agents have the potential to enable 6G wireless systems to
cope with real-time changes in their environment, such as
channel variation, signal strength, or interference, resulting
in improved network performance.
AI concepts in the 6G PHY are expected to employ
learning-based methods in the physical layer to replace or
complement traditional transceiver functions, such as
channel estimation (CE), synchronization, signal, and
modulation detection. Alternatively, it can perform these
tasks jointly in the system; for example, in joint estimation
and detection [16].
A major obstacle to 6G-AI integration is the lack of
transparency and dependability of AI algorithms. This
opaque behavior and lack of model explainability hinder the
widespread adoption of AI-empowered services in real
systems. The 6G-ANNA project addresses the need to
develop AI schemes that deliver the same level of
dependability and clarity as classical model-based
optimization algorithms [17].
3) DISTRIBUTED/MASSIVE MIMO
Massive MIMO has been one of the main technological
innovations for 5G RAN because of its potential to achieve
dramatic spectral, energy, and hardware efficiency gains at a
relatively low cost and complexity [18][19]. However,
considerable research effort is still needed to bring massive
MIMO technology to maturity and to develop commercially
attractive solutions that can deliver the full promised gains in
practical scenarios [20]. Of particular interest is its extension
to distributed MIMO deployments and its implementation
via virtualization and cloudification concepts that enable
flexible “cell-free” operations offering more uniform
coverage and quality of service [21][22][23]. In this context,
a key research challenge is the design of PHY algorithms and
deployment architectures with scalable fronthaul overhead
and computational complexity. The development of
satisfactory solutions is currently prevented by the limited
theoretical understanding of nonideal distributed MIMO
systems. For instance, the long-lasting open problem of
optimally distributed precoding/combining under limited
channel state information sharing [23][24] was solved only
recently in [25] using the theory of decentralized decision
making. We believe that future research on distributed
MIMO systems should focus on fundamental questions, such
as “which task should be performed where, and on the basis
of which information?”. Promising approaches covered in
6G-ANNA may include analytics tools, as in [25][26], or
decentralized learning methods, as in [27].
The trend in 5G and 5G Advanced massive MIMO is to
increase the number of base station antennas while limiting
the antenna area, as well as to enable site reuse and
deployment of compact antenna systems. However,
increasing the physical size of the base station antenna arrays
is also beneficial, as it increases the spatial resolvability (i.e.,
the possibility of targeting specific UEs) of the array and
changes the nature of the propagation channel by increasing
the probability of line-of-sight between users and antennas,
both of which can be used to increase the spectrum and
energy efficiency. While spatial multiplexing and distributed
computation techniques from massive MIMO and
distributed MIMO can be applied to very large aperture
massive antenna arrays, further reductions in complexity and
spectrum efficiency gains can be obtained by new, low-
complexity techniques tailored to very large arrays to help
bring to practice the potential gains from such arrays.
4) RAN PROTOCOLS & RADIO RESOURCE
MANAGEMENT (RRM)
Further research topics within the scope of 6G-ANNA are
the 6G RAN protocols and architecture for user and control
planes, including mobility, as well as radio resource
management (RRM). The overall objective is to simplify the
protocol stack, resulting in lower operating costs while
maintaining flexibility for optimally handling the diverse and
demanding QoS requirements for 6G use cases. In particular,
Link Layer (OSI layer 2) aspects in the area of uplink
scheduling, latency reduction, retransmission schemes, re-
ordering of packets, air-interface security, and QoS are
expected to help achieve significant improvements
compared with 5G.
6G networks introduce a new architectural challenge and
requirement, that is, the integration of sub-networks, such as
with flexible duplex schemes and the related architecture and
system modifications in RAN protocols. Owing to this so-
called densification, managing the radio resources in sub-
networks becomes more critical. In addition to the spectrum
issues that must be considered, mobility within the network
adds another level of complexity to RRM. Higher mobility
leads to higher Doppler spreads, causing spectral
broadening, and lower coherence times, which consequently
complicate the actual channel estimation. Because of
mobility, the interference pattern changes over time, and
considering some of the 6G services with stricter latency
requirements, it is anticipated that the typical reactive
approaches applied in 5G for interference and RRM might
need to be enhanced by proactive components [28][29]. To
cater to the diverse requirements of these time-critical
services, the interference management must be proactive. To
this end, 6G networks should be able to predict the demand
and availability of radio resources that are determining the
current spectrum usage in terms of time, frequency, and
location.
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
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VOLUME XX, 2023 9
One way to achieve this is through the integration of
native and distributed AI/ML approaches as an add-on to
existing signal-processing algorithms. This includes aspects
of interference estimations/management, parameter
optimization, and access schemes for energy efficiency,
security, resilience, and mobility. Together with the 6G
Network of Networks design principles that enable further
decomposition of the network into individual sub-networks,
the resilience of distributed AI/ML approaches in terms of
reliability and RRM needs to be further analyzed to
investigate the impact of decomposition on their overall
performance [30].
5) CLOUD-BASED RAN ARCHITECTURE
Research on the 6G RAN architecture design has three
aspects: 1) the functional modules and their connecting
interfaces as defined by the standard, 2) the resulting potential
implementation modularization (vendor-specific choice, e.g.,
based on hardware platforms and technologies), and 3) the
resulting potential deployment options (typically according to
operator-specific needs). The functional architecture should
thereby allow for implementation and deployment choices to
make best use of available RAN resources, available
hardware, and technologies, as well as to best serve in the
required deployment scenarios. One key technology candidate
to be considered for flexible implementations is the cloud-
based architecture, that is, one based on a virtualized network
function (VNF), which is seen as an integral part in the design
of the 6G RAN.
In the envisioned 6G market, a mixture of communication
service providers (CSPs), webscalers, and enterprises in
shared networks is expected, with the need for security,
isolation of services and service automation, and the
requirement of the use of generic processing platforms in
addition to vendor-specific solutions, by SW-based solutions
and HW accelerator pools. All of these are enabled by the
cloud-based RAN approach, and furthermore, would enable
RAN as-a-service solutions for pay as you grow opportunity.
6G-ANNA proposes to analyze the functional de-
composition and placement options in cloud-based
implementations and the impact on performance metrics such
as energy consumption, throughput, latency, reliability, and
security.
Furthermore, in 6G networks, a higher variance in the
QoS requirements is expected. Therefore, improvements to
the concept of network slicing will be investigated in this
project, for example, slices or service types as a combination
of network slices with VNF dimensioning and dynamic
scaling with AI-based workload models and traffic
prediction. This more flexible placement of VNFs per slice
or service type may enable higher pooling gains and energy
efficiency while meeting the service requirements. For
example, it would enable guarantees for certain services that
require high robustness, such as those needed for public
safety networks.
FIGURE 31. 6G-ANNA cloud-RAN components.
Figure 3 illustrates the 6G cloud-RAN technology
components investigated in this project. Flexible function
placement is therein possible in more centralized/far-edge
cloud implementations or more distributed on-premise cloud
environments, particularly for slice-specific components. In
this way, it allows balancing between scalability gains
achievable with centralized processing and latency gains
achievable with distributed processing close to the radio
interface. Slicing may be applied to functions such as UE-
control, including radio resource control (RRC), and further
functions such as access control/management, as well as to the
user plane protocol stack, for which modularization and
simplification compared with 5G is expected. This
decomposition into slices or service types may include the
functionality of both a 5G-defined centralized unit (CU) and a
distributed unit (DU). Radio resource management (RRM),
including scheduling for the network and sub-networks,
including potential enhancements by AI, may be instantiated
per radio resource group, such as multiple cells or carriers. The
physical layer, where project focus lies on MIMO
enhancements, security, and AI integration, may also be sliced
and different instances may be created as per “gear” as
described above. The Cloud-RAN interfaces with the core
network and multiple radio units.
B. NETWORK OF NETWORKS (NON)
In our context, a network refers to a group of interconnected
hardware devices and software components that can
communicate with each other and share data, resources, and
services. Hardware components refer to conventional
computing and communication devices such as computers,
servers, routers, switches, and base stations. In comparison,
software components can be digital twins of hardware devices
for monitoring, control, and optimization purposes. Various
individual networks include purpose-built technologies that
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
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VOLUME XX, 2023 9
cause deployment, interoperation, and scaling issues.
Therefore, developing protocols and standardization for
tightly integrated networks is crucial, which in 6G-ANNA is
referred to as network of networks (NoN).
1) 6G-ANNA NETWORK OF NETWORKS (NoN)
A NoN is a compound of different networks and services that
enable the efficient usage of network resources. In this section,
the envisioned architecture and environment for sub-networks
-a part of the NoN architecture- are described along with their
desired key characteristics. The architectural classification of
sub-networks, envisioned management functions,
interworking, physical access, and mobility aspects are
explained.
As shown in Figure 4, a network within a NoN can
comprise of one or several networks (here shown in layers),
may peer with networks on the same layer, may lay on top of
“lower layer” networks, and must offer access to higher layer
networks or hosts. A host is the data endpoint or user and
connects to a network. In most cases, the host is separated
from the network, while in some cases, as in the context of
sub-networks, it is a part of the network. By allowing other
hosts to attach, it may also turn into a network and vice versa.
Figure 4 depicts two example networks: network 1, which is
an operator network, and network 2, which is an edge-sub-
network. The two networks interact via well-defined
interfaces.
2) 6G SUB-NETWORKS
As part of the NoN concept, 6G-ANNA defines the concept of
6G sub-networks providing spatially limited means of
communication with extreme demands in terms of bandwidth,
latency, reliability, and availability. As the name sub-network
suggests, it is integrated and managed by a (public or private)
operator network (ON). Access to the ON and the associated
sub-network devices (SN-UEs) is provided in a bidirectional
manner. This means that a sub-network provides both uplinks
and downlinks to an ON and can serve as a relay to another
sub-network, as illustrated in Figure 4. In the context of a
Network of Networks (NoN), 6G sub-networks are a possible
manifestation of a specialized network that integrates into the
NoN to allow for an end-to-end design of a 6G system.
It should be noted that in 6G-ANNA we envision sub-
networking to be different from a traditional cellular approach
using base stations with a limited range (e.g., pico/femto cells).
Although sub-networks also allow uplink communication of
the associated devices to an ON through a gateway instance as
part of a sub-network controller (SNC), the main goal of sub-
networking is to enable 6G-based communication within the
sub-network or devices belonging to different sub-networks in
the local vicinity. Furthermore, specialized services (e.g.,
computing capabilities) are provided within the sub-network.
Because of the spatially limited transmission range and
tailored protocol stacks in the gateway, low-energy devices are
suitable for sub-networking, which is critical for future
networks. For example, wearable electronics for augmented
reality rely on low power consumption and require high data
throughput and low latencies. Other use cases investigated in
6G-ANNA include public safety, industrial automation, and
mobility in vehicular and aeronautical environments. In
Figure 5, two local automotive sub-networks exist that allow
wireless sensor data transmission, which requires extreme data
rates and reliability. In this way, traditional wired connections
may be supplemented or even replaced by 6G local
communication, increasing system reliability through
redundancy, reducing wiring costs, and optimizing the weight
of the communication system (and thus the overall weight of
the vehicle itself).
FIGURE 5. Two in-car sub-networks with wireless sensor
communication. Furthermore, 6G ON uplink and inter-sub-network
connectivity is provided.
As indicated before, in 6G-ANNA, sub-networks are regarded
as edge networks, implying a typically small spatial distance
of the connected UEs. From an architectural point of view,
sub-networks are a hybrid solution that combines features
from private networks and local sidelink-based
communication. Sub-networks are envisioned to include tens
to hundreds of devices with communication being more
locally contained, whereas private 5G or 6G based campus
networks typically encompass thousands of devices and traffic
stretches across many different (control) networks. In
addition, sub-networks are typically under different
ownership. Furthermore, sub-networks can operate
autonomously without the availability of an ON for a limited
FIGURE 4 6G-ANNA network of networks is a compound of different
networks and services enabling an efficient usage of the network
resources.
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
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VOLUME XX, 2023 9
time, allowing the sub-network to continue providing its
services, while some features might be limited by sub-network
capabilities. An overview of the different network types is
presented in Table 1 [31][32].
The management and orchestration of a sub-network may be
handled by a sub-network controller (SNC). As such, the SNC
provides functionality for autonomous operation to maintain
the sub-network in service for at least a limited amount of time
in case of unavailability of the 6G ON. Because sub-networks
are regarded as highly specific networks and tailored to their
respective use cases, the SNC is envisioned to implement
management functions that address the individual
communication requirements of the SN-UEs towards the
network. This may include interworking with time-sensitive
networking (TSN), resource allocation, and positioning. In
addition, the SNC provides gateway functionality for traffic
entering and leaving the sub-network via the link to the ON.
The desired communication demands can be achieved by
providing flexibility at the physical layer. For SN-UEs,
different radio access technologies, such as 3GPP-based
systems (i.e., 4G/5G/6G sidelink) or non-3GPP systems (i.e.,
WiFi, Bluetooth, ultra-wideband (UWB)) may be used
depending on the configuration. For high performance
applications requiring extreme reliability, non-contention-
based access strategies on licensed spectrum with large
bandwidths are required, as provided by 3GPP technologies
in, e.g., the Frequency Range 2 (or FR2), meaning the
millimeter wave (mmWave) frequencies between 24.25 GHz
and 52.6 GHz.
With mobility as one of the key characteristics, sub-
networks will allow for seamless roaming of SN-UEs across
different sub-networks and to the ON, as well as the mobility
of the sub-network itself. Additionally, the nesting of sub-
networks (i.e., a sub-network inside another sub-network) is
also a possible feature that allows a more flexible form of
communication in, for example, an automated factory site.
Related designs for sub-networks have been discussed in
academia [33] and standardization [34]. In 6G-ANNA, we
enrich these approaches with our visions and expertise in the
consortium, collect requirements for our use cases, and
develop solutions that enable sub-networks to fulfil the
demands, ultimately contributing to and shaping the
standardization of 6G.
3) RESILIENCE
The term resilience stems from the Latin verb “resiliere”,
which means “to bounce back”. Most definitions of resilience
concentrate on the ability of a system to “bounce back” after
the changing and challenging conditions while restoring a
level of service within a suitable time upon the degradation
[35][36]. NextG Alliance defines resilience concerning 5G
and 6G networks as the “network’s ability to meet a diverse
set of service objectives and to be able to identify, anticipate,
detect, and respond to the evolution of the state of the
network.” [37]. By adapting and recovering, resiliency shall
provide the means to provide reliable operation and high
network availability. For systems requiring high resilience and
availability, redundancy and the so-called hardening are the
tools used to achieve this requirement for critical
infrastructure. APCO International, an organization of public
safety communications professionals, has provided a Guide
for Public Safety Grade Site Hardening of the network
infrastructure, approved by ANSI, and shows the effort and
costs involved in hardening a network against environmental
risks [38]. For example, immediate and long-term backup
power sources induce costs that increase with the number of
sites requiring hardening. In addition, the critical infrastructure
must adapt on the fly to challenging conditions and be easy to
use. It should be able to add capacity where and when needed,
at short notice, to provide temporary coverage for remote areas
where no grid power supply may exist, and to offer new ways
to connect back to the network to ensure that people remain
connected [38].
The 6G concept as a Network of Networks, including sub-
networks, offers the opportunity to increase the resilience of
networks and provide alternative means for costly physical
hardening concepts. In particular, 6G-ANNA plans to ensure
the resilience of autonomously functional sub-networks,
creating fallback options to avoid segmentation. Fast and
efficient handovers will be orchestrated using artificial
intelligence and automation solutions. Furthermore,
6G-ANNA will go beyond maintaining transport services and
offer resilient storage services by leveraging and incorporating
in-network computing and an information-centric networking
(ICN) approach. This includes the development of a
corresponding architecture that integrates both
communication and computing.
TABLE I
COMPARISON OF DIFFERENT NETWORK CLASSES
Public Network
Campus Network
Sub-Network
Sidelink
Dimension
Country/Region
Building / Company site
<~100 m
<~100 m
Number of Nodes
Millions
Thousands
Hundreds
Two
Spectrum
Licensed
Licensed
Licensed/Unlicensed
Licensed/Unlicensed
Autonomy from ON
-
Depends on deployment
Configurable
Mode 1/3: no
Mode 2/4: yes
Access
3GPP
3GPP
3GPP, non-3GPP
wired/wireless
3GPP
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VOLUME XX, 2023 9
C. AUTOMATION
Automation generally refers to the automated operation and
optimization of flexible, secure, and sustainable networks and
related end devices while considering user requirements and
needs. Since today's networks are complex to design and
manage, partly because of the new technologies in wireless
communications, and partly because of novel computing
paradigms, such as computation based on digital twins (see
Section III.D), automation needs to be accompanied with
"simplification" in network management for any 6G system
that is expected to generate and process immense amounts of
data. New applications and services, such as the exploitation
of robots for various medical interventions [39], using
holograms in communications [40], and massive latency-
critical Industrial Internet of Things (IIoT) [41], etc., in
addition require the management of demanding traffic needs
in terms of different metrics of interest, which span beyond the
bandwidth, such as energy efficiency or privacy. Complying
with these requirements is not possible in current cellular
networks [42], and they present a major challenge for 6G.
The use of digital twins, discussed in more detail in the rest
of this section, as a relatively new concept in automation, is
considered beneficial for optimal resource allocation in the
next generation of cellular networks, especially in industrial
applications. In many cases of optimization, resource
allocation policies need to be changed across several
dimensions (physical resource blocks (PRBs), computing
resources, etc.). This involves solving different optimization
problems in a traditional manner and with the help of machine
learning and AI.
AI/ML has been acknowledged as one of the main enablers
to achieve significant improvements in network automation.
This is especially visible in scenarios in which the entire
network is expected to self-adapt and self-react to changes and
disruptions with minimal or no human intervention. 6G-
ANNA aims to identify the most effective and trustable
models that solve specific problems and meet the automation
requirements, along with aspects of network predictability.
The other side of AI/ML is its increased usage in advanced
applications, including predictive maintenance for machines,
digital healthcare, and indoor localization in shopping malls.
In the future, the vast distribution of such AI-driven
applications must be appropriately addressed by the 6G
network. Thus, 6G-ANNA is working on efficient methods for
distributed training (e.g., Federated Learning [49]) and the
execution of neural networks via edge computing resources in
the network, as this is the preferred deployment option for
applications that cannot rely on cloud computing facilities
(e.g., owing to strong privacy or latency requirements).
Therefore, these methods must be designed in such a way that
they work with limited compute and network capacities.
Automating the configuration of the network in conjunction
with such AI/ML applications is being addressed by
6G-ANNA. Automatically configuring the network according
to specified application requirements can be addressed
through intent-based networking, which is currently under
discussion in multiple standardization forums, such as TM
Forum[50], ETSI ZSM [51] and 3GPP SA5 [52]. Although
intents are not a new concept of autonomous networks, the
implementation of such autonomous capabilities is an open
and challenging task.
In summary, the automation of 6G networks is of
paramount importance for achieving desirable performance as
well as large adoption of 6G networks. 6G network
automation needs to consider all parts of the network (i.e.,
radio access, core network, central and edge cloud, etc.) to
realize holistic and end-to-end optimized network operations.
D. DIGITAL TWINS & EXTENDED REALITY
A digital twin (DT) is a digital replica of physical assets,
processes, and systems that are synchronized at specific
frequencies and fidelities. DTs use real-time and historical
data to represent the past and present, and allow the simulation
of predicted futures.
While DTs have been in use for some years, for example, in
the form of asset administration shells (AAS) in industry, for
management and optimization in the network domain, and
building information models (BIM) for the building sector, the
potential and power of DTs increase with the timeliness and
accuracy of the DTs. This requires concurrent updates based
on real-world data, which are the basis of so-called digital
shadows. In today’s systems, there are often limitations due to
limited bandwidth, non-negligible delays, and computational
limitations to efficiently process the huge amounts of data
needed to keep the DT up to date.
6G can support real-time DTs in various ways: joint communi-
cation and sensing can be integrated to update the DTs, in-
network compression to reduce the large amount of required
data, precise positioning and mapping to generate an exact
copy of the real world, and a tight integration of network and
compute resources to allow for an efficient handling of DTs.
Network digital twins (NDT) offer benefits for planning,
deployment, and operations. Interaction of NDT with factory
applications and its digital twin enables us to carry out
predictive analysis in the digital world for both runtime
network optimization as well as for appropriately adapting the
(factory) application behavior. Such a joint interaction of
digital twins also potentially leads to fault identification and
prevention.
While an NDT can be used for real-time optimization of
the network, digital twins of factories can be used for
optimization of production processes and logistics, and
environmental digital twins can be used to create an immersive
experience for extended reality (XR) users.
XR, as the umbrella term for virtual reality, augmented
reality, and mixed reality, will change how we interact
between the real and virtual worlds. Although Wi-Fi and 5G
can already support XR users to a certain degree, a fully
immersive experience requires 6G. Current XR devices are
rather bulky, tethered to a smartphone, or connected to a PC.
The reason for this is the compute intense rendering of virtual
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VOLUME XX, 2023 9
objects. To enable lightweight, energy-efficient devices, the
computation must be offloaded to a nearby edge/proximity
cloud, which must also be equipped with a powerful graphics
processing unit (GPU). The downside of offloading is that it
requires low delays, rather large bandwidth on both downlink
and uplink, and privacy aspects must be considered as
sensitive camera data is streamed. Thus, to enable an
immersive XR experience, a close interaction between the XR
applications, the underlying network, and the compute
resources is required, which 6G-ANNA proposes to
demonstrate.
6G-ANNA considers enabling real-time digital twins and
an immersive XR experience based on a precise positioning
and mapping service. Depending on the use case, the precision
should be up to the centimeter level and 1° accuracy. This
requires a fusion of various radio- and vision-based
positioning mechanisms. To allow real-time updates of the
digital twins, the mapping service should also work in real
time, meaning that changes in the environment are detected
immediately and adjusted in the maps used by the digital twin.
E. SUSTAINABILITY
A sustainable network is one of the key requirements of 6G.
Having a sustainable 6G architecture that can provide
performance guarantees on multiple, usually very stringent,
QoS metrics and for different use cases is of paramount
importance [42]. The trade-off between sustainability on the
one hand and reconfigurability, on the other, is a critical issue
that needs to be addressed. To address this challenge,
adequate models that can capture the largest possible extent
of power consumption as a function of various traffic
parameters are needed. These models, or the analytical
results obtained from them, will be validated in 6G-ANNA
with real measurements from testbeds.
The reduction in energy consumption targeting 6G
networks has already been considered in [43], where the
achievable performance is compared for different power
control methods. Alternative methods [44] emphasize the
need to use AI/ML techniques to minimize energy
consumption and to use intelligent reflecting surfaces.
Intelligent reflecting surfaces were also proposed in [45],
together with exploiting cell-free and airborne access
networks, with the ultimate goal of achieving an energy self-
sustainable 6G.
6G-ANNA plans to also introduce energy savings in
other ways. For example, depending on the traffic load in the
network at certain periods of time and to maintain low energy
usage, an automated decision to shut off certain edge clouds
and reassign the tasks to other edge clouds can be made.
Alternatively, certain User Plane Functions (UPF) can be
shut down and the traffic from the base stations can be
redirected to other UPFs of the core network to save energy.
16
https://kubernetes.io/
17
https://gyroidos.github.io/
In both cases, these decisions can be seen as solutions to
optimization problems, where the constraints are the QoS
requirements of the users, with the objective of minimizing
energy consumption. Solving these optimization problems
and obtaining the corresponding algorithms for sustainable
policies are the focus of 6G-ANNA.
F. SECURITY
Thus far, 4G networks have been remarkably robust to any
type of attack. 5G networks build on this field-proven
technology and add additional security features, for example,
for better protection of permanent subscriber identities.
However, it cannot be denied that for 6G, the attack surface
of mobile networks increases because of more features
provided by the networks, more complex software, use of
AI/ML, more diverse network structures, and more
heterogeneity of platforms and stakeholders. At the same
time, more critical services rely on networks, attracting
more, and more capable attackers.
The trend of virtualizing network functions and running
them on cloud platforms rather than on custom hardware
brings specific security challenges, particularly if a cloud is
not exclusively used by a mobile network but also hosts other
workloads. Methods for reliable isolation of workloads,
guaranteeing availability of an agreed amount of resources,
and ensuring the integrity of the platform and software stacks
at boot and during runtime are needed. 6G-ANNA analyses
the state-of-the-art, identifies potential gaps, and works on
improving and automating the applicable security
mechanisms. It also considers secure workload orchestration
and management, including attestation techniques and fine-
grained continuous monitoring of the overall system and its
components.
A specific challenge arises when workloads need to be
deployed in data centers that are not fully trusted, so data
must be protected against the data center operator during
processing. 6G-ANNA will provide a secure runtime
environment for containerized 6G services using
confidential computing techniques. Thus, the cloud operator
is excluded from the trusted computing base. The runtime
environment provides integrity and confidentiality for data
at rest, in use, and in transit as well as remote attestation for
integrity verification. Another key aspect is the automated
and secure orchestration of containerized workloads in
heterogeneous cloud environments, for example, utilizing
Kubernetes
16
. To achieve a small overall code size and strong
isolation guarantees, the OS-level virtualization platform
GyroidOS
17
[46] and the microkernel seL4
18
[47] will be
used.
The increasing reliance on software components calls for
verified, secure software that also allows fulfillment of
increasing compliance obligations. The software
18
https://sel4.systems/
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development process must include steps and tools to
automatically ensure security and compliance. To cope with
the task to perform compliance tests frequently, that is, not
only per major release, but also each time the system is
updated (e.g., a security patch is added), 6G-ANNA aims to
provide tools for the automation of compliance testing.
AI/ML is expected to be used pervasively in 6G
networks, and threats, such as data or model poisoning or
interference attacks, must be considered. As many
potentially sensitive data may need to be processed to
generate ML models, privacy is a specific concern; therefore,
techniques such as federated learning or privacy-preserving
feature extraction must be considered. 6G-ANNA
investigates how to properly secure the complete AI/ML
pipeline, resulting in AI/ML systems that are not only efficient
and scalable, but also privacy-preserving and secure.
In contrast, AI/ML provides a significant opportunity to
improve network security by efficient early detection of
anomalies and attacks. 6G-ANNA will provide a system that
enhances classical methods for traffic classification and
anomaly detection using AI/ML methods, such that attacks
can be detected efficiently even in huge data streams,
including ciphered traffic. Detecting suspicious behavior
allows a more detailed analysis, and if needed, mitigation
measures will be proposed, or, in the case of full automation,
immediately triggered.
Considering the timeframe for 6G, the threat posed by
quantum computers breaking all public key cryptography
used today is highly relevant. Luckily, research on quantum-
safe, or “post quantum” algorithms (PQAs) is well
underway. However, the potential impact of adopting PQAs
on mobile network performance is not yet well understood.
6G-ANNA will investigate in depth the performance
properties of different PQAs and their suitability for various
use cases, including those where the capabilities of the end
devices are restricted; however, the security requirements are
still high.
In light of cryptographic methods being endangered by
quantum computers, Physical Layer Security (PLS) methods
may become important for 6G. PLS is an opportunistic
technique for designing security algorithms based on channel
reciprocity, which is inherent to wireless channels between
legitimate communicating nodes. This privilege is not shared
by the eavesdropper, which is located at a distance of at least
half the wavelength of the carrier. This principle can be used
to extract secret keys, perform authentication, etc., which can
complement the security protocols running in the upper
layers. Furthermore, it is possible to design an intelligent and
overhead-aware security framework that uses
computationally intensive upper-layer security protocols
only when PLS indicates a problem. A specific PLS
technique investigated in 6G-ANNA in the context of
wireless ad hoc networks is friendly jamming, where the
wireless channel is continuously jammed in a controlled
manner so that only authorized devices can cancel the
interference and recover the signals carrying the
communication. The use of intelligent reflective surfaces
(IRS) is another technique to be explored in 6G-ANNA to
improve PLS performance. By intelligently configuring the
IRS, transmission to a legitimate user can be enhanced while
suppressing transmission to the eavesdropper, resulting in a
significant improvement in security.
6G-ANNA envisions that future 6G networks will support
the integration of sub-networks that may use diverse wireless
technologies and may be operated by various stakeholders.
There are many use cases for this, including public safety.
Security is an obvious requirement in this context, and
6GANNA will investigate how networks and stakeholders can
interact to provide sound security in a highly dynamic network
of networks. A special aspect is the integration of PLS in the
network of networks case, which may meet stringent security
requirements in industrial contexts.
The need for strong privacy calls for specific so-called
Privacy Enhancing Technologies (PET), which are important
in the context of AI/ML, as mentioned above. In particular,
homomorphic encryption allows outsourcing data processing
to a third party without revealing the data or processing
results in the clear to the third party. 6G-ANNA will
implement an algorithm using deep neural networks for
either data cryptography and compression and investigate the
trade-off between resource costs and performance when
using homomorphic encryption. Another approach in the
field of PET is the concept of an “anonymous network” that
aims to minimize the amount of private user information
revealed to network nodes. 6G-ANNA investigates how to
improve such methods and how to best apply them in 6G
networks, particularly in the context of network of networks.
One aspect of holistic security for the 6G infrastructure is
physical security, that is, controlling physical access to
hardware components. With the rise of edge computing, a
greater number of devices are expected to be deployed in
public or shared environments, at the cost of a greater risk of
physical tampering. 6G-ANNA will investigate methods for
securing critical hardware at a system level. Examples of such
hardware are fixed access points or radio station components.
Anti-Tamper Radio (ATR) [48] is a promising technology for
system level hardware tamper detection. ATR works on the
radar principle, using microwaves to sense the physical
environment. The ATR sends a special radio signal that
spreads everywhere in the system and is reflected by the walls
and hardware components. All of these reflections cause a
signal to reach the receiver, which is as characteristic of the
system as a fingerprint. Tiny changes to the system are
sufficient to have a noticeable effect on the fingerprint. Here,
the radio fingerprint can be analyzed using ML/AI to extract
information about the physical integrity of the hardware and
take necessary countermeasures, if required. The magnitude of
change required to trigger the anomaly detection is calibrated
at initialization and automatically adjusts to environmental
changes.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
VOLUME XX, 2023 9
IV. EVALUATION
The 6G-ANNA project will not only focus on concept work.
The most relevant concepts developed in 6G‑ANNA will be
implemented, demonstrated, and optimized (where
applicable) in the following Proof of Concepts (PoCs). These
are (as structured per topic):
6G-Access
• Slice-oriented radio access network with
synchronized transport system
• ML-based PHY layer transceiver functionalities on
the open platform of the 6GEM research hub
19
• 6G in production environments using a distributed
load generation system designed in 6GEM
• 6G wireless fingerprinting for anomaly detection
in production facilities and proximity detection for
occupational safety
Mobility
• Intra- and inter-communication of vehicles
• Secure communication between drones
• Subnets for drones and vehicle
Safety & Resilience
• Advanced end-to-end security based on 6G-
specific functionalities for critical services,
including air-cab connectivity for ground control
• Dynamic placement of functions (including
replicas) to optimize energy efficiency
Digital Twinning
• Real-time digital twin for automation in
production environment
• Application of digital twins for vehicles
(automotive) or in-vehicle sub-networks
• Digital twins for network optimization and
resource management
Extended Reality (XR)
• XR applications via 6G in robotics for remote
teaching, user training, and/or plant prototyping
• 6G XR platform for processing, rendering, and
positioning XR services
Production
• Over-the-air updates in manufacturing and
automotive environments
• Management and optimization of heterogeneous
networks and industrial edge computing resources
for running distributed applications and AI
For this purpose, various testbeds are provided by the project
partners, ranging from production halls to mock-ups (e.g., an
air cab) and mobile radio testbeds to a drone test site.
Furthermore, collaboration with the testbeds of the 6G
research hubs is planned.
19
6GEM: https://www.6gem.de/en/
V. CONCLUSION
The German lighthouse project 6G-ANNA contributes,
based on its vision and research activities, to the definition
of an 6G E2E architecture and system design with a focus on
German and European use cases and scenarios.
In this article, the main research directions of 6G-ANNA are
described, including 6G RAN, network of networks,
automation & simplification, digital twinning & extended
reality, security, privacy and sustainability.
Based on the project use cases, the planned areas of
evaluation, 6G-Access, mobility, safety & resilience, digital
twinning, extended reality, and production were defined.
By mid 2024, the 6G-ANNA project is entrusted with
providing a detailed description and analysis of the technical
concepts proposed in this paper. By the end of the project
(June 2025), the project partners plan to extend and refine the
technical work and testbeds/PoCs, and to provide final
evaluations and large-scale demonstrations of the proposed
solutions. 6G-ANNA activities will be aligned with the other
6G research and industry projects in Germany and Europe via
the German 6G platform, as well as more broadly aligned with
global efforts in next-generation mobile networking.
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VOLUME XX, 2023 9
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This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
VOLUME XX, 2023 9
MARCO HOFFMANN studied computer science
and received the Dr. rer. nat. degree from
Technische Universität München (TUM), Munich,
Germany in 2005.
In 2004 he joined the Research and
Development Department of Siemens. Currently
he is Technology Expert for softwarization,
cloudification and 6G architecture and. Marco has
more than 15 years of experience in the
telecommunication industry. He was consortium
leader and board member of several national and international research
projects and member of company internal and nation-wide Future Internet
strategy teams. Currently, he leads the German BMBF lighthouse project
6G-ANNA. He is author of numerous conference papers, journal articles,
patents and patent applications.
GERALD KUNZMANN received the PhD
degree in electrical engineering and information
technology from Technische Universität München
(TUM), Germany in 2010.
He had been working in NTT DOCOMO
Eurolabs in Munich, where he was responsible for
various European ICT research projects,
university collaborations, and open-source
projects on topics such as information-centric
networking, Quality-of-Experience (QoE),
network management, and Network Functions
Virtualization (NFV). Between 2015 and 2020, he was acting as a delegate
in ETSI ISG NFV. He had also been acting as a delegate to 3GPP WGs SA1
and SA2 with focus on machine-type communication, network automation,
artificial intelligence, and machine learning. Since 2020, he is a Senior
Network Architect at NOKIA Strategy & Technology, Munich, Germany
and has a leadership position in Nokia's internal 6G research &
standardization program.
Dr. Kunzmann is a major contributor to bi-lateral 6G collaborations and
funded 6G research projects and leads the Architecture & System Design
work package in the German 6G lighthouse project 6G-ANNA.
TORSTEN DUDDA works as a Master
Researcher at Ericsson in Aachen, Germany. His
current research focusses on evolving radio
network architectures and protocols towards 6G.
For this purpose he also participates with a
coordinating role in the 6G-ANNA project.
Torsten joined Ericsson in 2012. He graduated as
a diploma engineer in electrical engineering and
information technology from RWTH-Aachen
University, Germany.
DR. RALF IRMER (SM’09) is Chief Innovation
Architect at Vodafone Germany and he heads the
new global Vodafone Tech Innovation Center
Dresden (DE). Ralf holds a PhD from Dresden
University of Technology and he has studied in
Dresden and Edinburgh. He joined Vodafone
Group R&D at the headquarters in the UK in 2005.
He was responsible for wireless access innovation
and initiated the first 5G programme of Vodafone
Group and co-authored the 5G white paper of NGMN.
His passion was and is to drive 5G/6G and technology innovation with
impact to business verticals – such as public safety, healthcare, railways,
automotive, industry production, and sports. In 2015, Ralf joined Vodafone
Germany for the spectrum auction. Ralf Irmer is now focusing more on 6G.
He leads one Germanys large 6G projects (“6G Health”) with 19 partners
and has a leading part in 6G projects on security, resilience and system
architecture - such as 6G-ANNA.
Ralf Irmer is in the advisory board of 5G Lab Germany, advisor to 6G
programme of Fraunhofer, the “6G research and Innovation Cluster (6G-
RIC)”, the national 6G platform and of the Industrial Radio Lab Germany.
Ralf has filed more than 10 patents and has published more than 40 research
papers. He is a senior IEEE member since 2009.
ADMELA JUKAN is Chair Professor of Commu-
nication Networks at the Technische Universität
Braunschweig in Germany. Professor Jukan is a
Fellow of the IEEE. She serves as Senior Editor in
the IEEE Journal of Selected Areas in
Communications. She is a co-Editor in Chief of
the Elsevier Journal on Optical Switching and
Networking (OSN). She was an elected Chair of
the IEEE Optical Network Technical Committee,
ONTC (2014-2015). She has chaired and co-
chaired several international conferences, including IEEE/ACM IWqoS,
IEEE ANTS, IFIP ONDM, IEEE ICC and IEEE GLOBECOM. She is
recipient of an Award of Excellence for the BMBF/CELTIC project "100Gb
Ethernet" and was also awarded the IBM Innovation Award (2009). She was
Dean of Studies for a joint degree program between computer science and
engineering (IST, 2017-2019). Prof. Jukan was an elected Distinguished
Lecturer of the IEEE Communications Society, 2015-2017.
GORDANA MACHER was born in Čakovec,
Croatia in 1973. She received her Dipl.-Ing. (FH)
degree (M.Eng.) in computer engineering from the
University of Applied Sciences in Ulm, Germany
in 2005. From 2012 to 2021 she was Research
Engineer and Project Manager at the Institut für
Rundfunktechnik (IRT, Institute for Broadcasting
Technology) in Munich, Germany. Since 2022 she
is Senior Project Manager at Smart Mobile Labs
(SML) in Munich, Germany. Her main research
focus is on campus networks and cloud computing.
ABDULLAH AHMAD was born in Aligarh, UP,
India in 1994. He received his B.Tech degree in
electronics engineering from Aligarh Muslim
University in 2017 and his M.Sc. in
communication systems and networks from TH
Köln in 2022. From 2021 to 2022 he was involved
in wireless ranging research with the Institut für
Nachrichtentechnik at TH Köln. Since then, he has
joined PHYSEC GmbH as an M.Sc. engineer in
Bochum, NRW, Germany. His research interests
lie in wireless communications, RF sensing and
cyber-physical security. Mr. Ahmad was the recipient of IEEE InCiTe best
paper award in 2017 and DAAD end-of-study scholarship in 2022.
FLORIAN BEENEN received his B.Sc. degree in
2018 and his M.Sc. degree in 2021 in computer
engineering from FH Wedel, University of
Applied Sciences and Heidelberg University,
Germany, respectively. Since 2022 he is active as
a PhD candidate at Robert Bosch GmbH,
Germany and supervised by the Chair of
Communication Networks at the University of
Tübingen, Germany. His research interests include
6G Sub-Networks, vehicular communication
networks and Time-Sensitive Networking.
ARNE BRÖRING is a computer scientist and
received his MSc in 2007 from the University of
Münster (DE), and a Ph.D. in 2012 from the
University of Twente (NL). From 2007-2013 he
worked as a researcher at the University of
Münster (DE) and at the 52°North initiative. In
2008, he worked as a researcher at the University
of Windsor (CA) on sensor network management.
In 2014, he worked for the Environmental Systems
Research Institute (ESRI) in Zurich (CH) on
designs of Smarter Cities infrastructures. Since 2015, he works as researcher
at Siemens (Munich, DE) and currently in the position of a Senior Key
Expert Research Scientist. He is author of over 90 publications. His interests
range from distributed systems, over indoor localization, to the Semantic
Web. He has been technical & scientific coordinator of large research
projects (e.g., BIG IoT and IntellIoT with over 16 Mio € budget combined).
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
VOLUME XX, 2023 9
FELIX FELLHAUER received the Ph.D. degree
in electrical engineering and information
technology from University of Stuttgart in 2020,
after his studies at Aschaffenburg, University of
Applied Sciences (B. Sc. in 2012) and University
of Stuttgart (M. Sc. in 2015).
He was working for the Institute of
Telecommunications at University of Stuttgart,
Germany, as a Research Assistant with main-focus
on 60 GHz Hybrid-MIMO and its applications for
WiFi standards. Currently he is working as a Communication System Expert
at Robert Bosch GmbH, division on Cross-Domain Computing Solutions in
Stuttgart, Germany. His main interests are advanced development and
systems engineering with a focus on mobility applications of wired and
wireless communications.
Dr. Felix Fellhauer is an active member of IEEE 802.3, IEEE 1722
working groups, and the OpenAlliance.
GERHARD P. FETTWEIS (F’09) earned a
Ph.D. under H. Meyr at RWTH Aachen in 1990.
After a postdoc at IBM Research, San Jose, he
joined TCSI, Berkeley, USA. Since 1994 he is
Vodafone Chair Professor at TU Dresden,
Germany. Since 2018 he is also founding
director/CEO of the Barkhausen Institute.
He researches wireless transmission and chip
design, coordinates the 5G++Lab Germany and
the German Cluster-for-Future SEMECO. His
team spun-out 19 tech and 3 non-tech startups, and he initiated 4 platform
companies.
Prof. Fettweis is member of the German Academy of Sciences
(Leopoldina), and the German Academy of Engineering (acatech), and
active in helping organize IEEE conferences.
FRANK H.P. FITZEK (SM) received the Ph.D. (Dr.-Ing.) degree in
electrical engineering from the Technical University Berlin, Germany, in
2002. He is currently a Professor and the Head of the Deutsche Telekom
Chair of Communication Networks, TU Dresden. He is also the Spokesman
of the DFG Cluster of Excellence CeTI and the 6G-life Hub in Germany.
He became an Adjunct Professor at the University of Ferrara, Italy, in 2002.
In 2003, he joined Aalborg University, Denmark, as an Associate Professor
and later became a Professor. His current research interests include 5G/6G
communication networks, in-network computing, network coding,
compressed sensing, post-Shannon theory, quantum, molecular
communication, and human–machine interaction in the virtual worlds.
NORMAN FRANCHI (M’12) is a full professor
(W3) at the Friedrich-Alexander-University
(FAU) of Erlangen-Nuremberg, Germany, where
he heads the Chair of Electrical Smart City
Systems. He holds a Dr.-Ing. (Ph.D.E.E., 2015)
and Dipl.-Ing. (M.S.E.E., 2007) degree in
Electrical Engineering, both from FAU. From
2007 to 2011, he worked in the automotive R&D
sector as a system and application engineer for
advanced networked control system design. From
2012 to 2015, he was a research associate at the Institute for Electronics
Engineering, FAU, focused on software-defined radio-based V2X
communications. From 2015 to 2021, he was with Gerhard Fettweis'
Vodafone Chair at Dresden University of Technology (TU Dresden), where
he was the senior research group leader for resilient mobile communications
systems and 5G industrial campus networks. From 2019 to 2020, he was
managing director of the 5G Lab GmbH, Germany. In 2020, he founded the
company AI Networks (AIN) GmbH, Germany, a technology start-up for
the design, optimization, and operation of IIoT networks. He is a member
of the IEEE ISAC initiative, Open 6G Hub Germany, 6G Platform
Germany, and 5G++ Lab Germany. Furthermore, he is an advisory board
member of the Industrial Radio Lab Germany (IRLG) and the KI Park e.V.,
Germany. His research interests include 6G, joint communications and
sensing, resilient and secure systems, campus networks, IIoT, Open RAN,
V2X, resilient energy (micro) grids, green and sustainable ICT, and IoT
Technologies for Smart Cities and Countries.
FLORIAN GAST (GS’23) received the Dipl.-
Ing. degree (with distinction) in electrical
engineering from Technical University Dresden,
Dresden, Germany, in 2021, where he is currently
pursuing a Ph.D. degree with the Vodafone Chair
Mobile Communication Systems. His current
research interests revolve around sustainable and
energy-efficient communication networks. As
such, he focuses on channel estimation for systems
with 1-bit quantization at the receiver, as well as
investigating new highly flexible physical layer designs.
Mr. Gast is the co-author of a paper that received the best student paper
award at the IEEE Statistical Signal Processing Workshop 2021 and the
recipient of the BASF Schwarzheide award for the best diploma thesis in
2021.
BERND HABERLAND was born in Stuttgart,
Germany 1956. He received the M.S. degrees in
communication technology, digital signal
processing and computer science at the University
of Erlangen/Nuremberg, Germany in 1983.
Since 2016 he is working in Nokia in the Nokia
Bell organization in Germany. His current focus is
on research work on mobile networking and RAN
cloud technologies with virtualization solutions of
Base Station functions for 5G and 6G. In this
context he is working in an architecture team to
define Native Cloud RAN for 6G and he is leading the work on different
PoCs in this area with an international Bell Labs team from Murray Hill,
Antwerp, Stuttgart and Paris. As a further focus he is currently working as
a task leader inside the work package 2 of the 6G lighthouse project in
Germany (ANNA) with the target of an enhanced network slice orientation
inside the RAN. He has published numerous research papers in journals and
scientific conferences and holds many related essential patents for mobile
communication technologies.
Mr. Haberland was awarded as an Alcatel Fellow 2004 and as a Bell Labs
Fellow 2010 in recognition of my extensive contributions and
breakthroughs in various generations of Mobile Radio Networks in
particular in Baseband and Transceiver solutions of Base Stations.
SANDRA HOPPE received her Ph.D. degree
from Technische Universität Dresden in 2021 and
the M.Sc. degree in electrical engineering from
Technische Universität München, Germany in
2016. In the same year she joined Airbus, Munich,
Germany, where she was working on high-
capacity communication for aerial vehicles.
In 2020, she joined Nokia Strategy &
Technology in Munich, focusing on non-terrestrial
networks and core network research and
standardization.
SADAF JOODAKI received her B.S. and M.S.
degree in Electrical Engineering, Telecommuni-
cation Systems from Amirkabir University of
Technology, Iran in 2014 and 2017, respectively.
During her M.S. degree, she worked as a teaching
assistant for the Digital Communication course.
After her M.S. degree, she worked as a Research
Assistant at the Digital Communication Research
Laboratory (DCRL) of Amirkabir University of
Technology. Her research was in the field of
communication systems, mainly wireless communication systems such as
BTS and user implementation of IEEE 802.16e2009 and LTE systems. In
June 2021, she joined the DSP chair, RWTH Aachen University in
Germany, as a Research Assistant. During this time, she studied and
simulated 5G NR transmitter and receiver using "C" and "Python"
programming languages.
Her research focuses on AI-based modules in the physical layer and AI-
based algorithms in cell-free massive MIMO systems specifically to
improve energy efficiency.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
VOLUME XX, 2023 9
NANDISH P. KURUVATTI (S’13–M’18)
received the M.Sc. and Ph.D. (Dr.-Ing.) degrees in
electrical and computer engineering from
Technische Universität Kaiserslautern, Germany,
in 2012 and 2020, respectively. He is currently a
Senior Researcher at the Division of Wireless
Communications and Radio Navigation,
Technische Universität Kaiserslautern. During his
research career, he has contributed to several EU
and BMBF funded projects, as well as bilateral
industrial projects. His main research interests include radio resource
management, mobility management, D2D, V2X communications, and
intelligence in networks.
CHU LI (S’10) received the B.Sc. degree in
electrical engineering from Wuhan University of
Science and Technology, Wuhan, China, in 2013,
the M.Sc. degree in electrical engineering from
Technische Universität Dresden, Germany, in
2017. She is currently pursuing the Ph.D. degree
with the Institute of Digital Communication
Systems, Ruhr-Universität Bochum, Germany.
Her research interests include signal processing in
intelligent reflecting surfaces assisted systems,
physical layer security and distributed machine learning in wireless
networks.
MIGUEL LÓPEZ received the PhD in
Mathematics from the University of British
Columbia, Vancouver, Canada, in 1996. He joined
Ericsson in Stockholm, Sweden in 1998. He has
worked with signal processing, algorithm
development, standardization of cellular
technologies and IEEE 802.11 standardization. He
is currently Expert in Physical Layer Design at
Ericsson Research, Ericsson GmbH,
Herzogenrath, Germany. His research interests
include signal processing for wireless
communications, transmitter algorithms, receiver
algorithms and physical layer design.
FIDAN MEHMETI received his graduate degree
in Electrical and Computer Engineering from the
University of Prishtina, Kosovo, in 2009. He
obtained his Ph.D. degree in 2015 at Institute
Eurecom/Telecom ParisTech, France. After that,
he was a Post-doctoral Scholar at the University of
Waterloo, Canada, North Carolina State
University and Penn State University, USA. He is
now working as a Senior Researcher and Lecturer
at the Technical University of Munich, Germany.
His research interests lie within the broad area of wireless networks, with an
emphasis on performance modeling, analysis, and optimization.
THOMAS MEYERHOFF received his diploma
in computer science engineering (equivalent to
M.S.) and a Ph.D degree in wireless
communication engineering from the Hamburg
University of Technology, Germany in 2009 and
2018. From 2009 till 2013, he was a Research
Assistant with the Institute of Communications at
Hamburg University of Technology. In 2013, he
joined Airbus and has been involved in research
and standardization of wireless communication systems for aerospace. His
research interest includes the development of wireless avionics intra
communication systems, the study of aeronautical radio channel conditions
and physical layer design.
LORENZO MIRETTI received the B.Sc. and
M.Sc. degrees in Telecommunication Engineering
from Politecnico di Torino in 2015 and 2018,
respectively, and the Ph.D. degree in wireless
communications from EURECOM and Sorbonne
Université in 2021.
He is currently a post-doctoral researcher with
the Technical University of Berlin and a research
fellow with the Fraunhofer Heinrich Hertz
Institute, Berlin, Germany. He investigates novel
solutions for next generation wireless networks, such as cell-free massive
MIMO and sub-THz mobile access networks.
GIANG T. NGUYEN (M’16) is currently an
Assistant Professor, heading the Haptic
Communication Systems research group at the
Cluster of Excellence for Tactile Internet with
Human-in-the-Loop (CeTI) and Faculty of
Electrical and Computer Engineering, TU
Dresden, Germany. He received a Ph.D. degree in
Computer Science from TU Dresden in 2016. His
research interests include network softwarization,
in-network computing, and distributed systems,
aiming at networked systems’ low latency, flexibility, and resilience to
facilitate haptic communication.
MOHAMMAD PARVINI received the M.Sc.
degree in communication systems from Tarbiat
Modares University, Tehran, Iran, in 2021.
He is currently pursuing his PhD at the
Vodafone Chair for Mobile Communications
Systems at TU Dresden, Germany.
RASTIN PRIES is a research project manager at
Nokia. He currently co-leads the German BMBF
lighthouse project 6G-ANNA. Rastin holds a
master and Ph.D. degree in computer science from
University of Wuerzburg, Germany. He has more
than 10 years of experience in the tele-
communication industry and joined Nokia,
Munich, Germany in 2015. He was the consortium
leader of several national and international
research projects. His research focuses on
everything related to extended reality such as edge
computing, localization and mapping, and digital twinning. He is the author
of numerous scientific conference and journal papers as well as patents.
RAFAEL F. SCHAEFER (S’08–M’12–SM’17)
received the Dipl.-Ing. degree in electrical
engineering and computer science from the
Technische Universitt Berlin, Germany, in 2007,
and the Dr.-Ing. degree in electrical engineering
from the Technische Universitt Mnchen,
Germany, in 2012.
Since 2022 he is a Professor and the Head of the
Chair of Information Theory and Machine
Learning, Technische Universitt Dresden,
Germany. Before joining TU Dresden, he was a
Postdoctoral Research Fellow with Princeton University, an Assistant
Professor with Technische Universitt Berlin, and a Professor with
Universitt Siegen. Among his publications is the recent book Information
Theoretic Security and Privacy of Information Systems (Cambridge
University Press, 2017). He was a recipient of the VDE Johann-Philipp-Reis
Award in 2013. He received the Best Paper Award of the German
Information Technology Society (ITG-Preis) in 2016. He is currently an
Associate Editor of the IEEE TRANSACTIONS ON INFORMATION
FORENSICS AND SECURITY and the IEEE TRANSACTIONS ON
COMMUNICATIONS.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
VOLUME XX, 2023 9
PETER SCHNEIDER received the Diploma
degree in mathematics from the Julius-
Maximilians-Universität Würzburg, Germany. He
started his professional career as a Researcher on
new software architectures at the Siemens
Corporate Technology Division. He moved on to
the Siemens Communication Division, where he
investigated and prototyped new, innovative
communication solutions, and subsequently
became a Systems Engineer at the Siemens Mobile
Core Network. Since about 20 years, he has been
focusing on network security research at Siemens, Nokia Siemens
Networks, and Nokia. In 2015, he joined the Nokia Bell Labs Security
Research Group as a Senior Expert for mobile network security. He is the
author of various mobile network related security concepts, articles,
tutorials, and book chapters, as well as numerous patents and patent
applications.
DOMINIC A. SCHUPKE is a research leader in
reliable communication networks, currently
focusing on Wireless Communications at Airbus,
Munich, Germany. He is also a lecturer in
Network Planning at Technical University of
Munich (TUM). Prior to Airbus, he was with
Nokia, Siemens, and TUM. He studied Electrical
Engineering and Information Technology at
RWTH Aachen, Imperial College London, and
TUM, from where he received a Dr.-Ing. degree
(summa cum laude). Dominic is Senior Member of IEEE and author or co-
author of more than 150 journal and conference papers (Google Scholar h-
index 34). His recent research addresses aerospace networks.
STEPHANIE STRASSNER was born in
Heidenheim (Brenz) in 1973. She received B.S.
and M.S. degrees in geography in 2001 from the
university of Wuerzburg and B.S. degrees in
economics in 1995 from the academy in
Heidenheim (Brenz). Since then she has held
various positions in the telecommunication sector,
starting 2001-2006 at Vodafone in Munich as
capacity planning engineer for 2G and 3G RAN and 2006 – 2016 as trainer
and consultant for 3G and 4G 3GPP specifications, software and drive test
solutions at tfk technologies in Munich. In 2016 she joined Kathrein
Solutions GmbH as trainer and O&M engineer for active indoor antenna
systems. Since 2019 she is working for Airbus Secure Land
Communications in Ulm as Technical Solution Manager for Broadband
Solutions and MCX in the Public Safety Sector.
HENNING STUBBE received a B.Sc. in computer science from the
University of Rostock, Germany, in 2015 and an M.Sc. degree in computer
science from the Technical University of Munich (TUM), Germany, in
2018. He is currently pursuing a Ph.D. degree in computer science at TUM
at the Chair of Network Architectures and Services with chairholder Prof.
Dr.-Ing. Georg Carle. Since 2019, he has been a Research Associate with
TUM at the chair mentioned above. His research interests include software-
defined, programmable networking with a particular focus on high-
performance benchmarking. He is further contributing to the development
of next-generation 6G infrastructures. Moreover, he investigates the
creation of reproducible experiments and the development of the
methodology and tools to make them portable between different testbeds.
ANDRA M. VOICU received the B.Sc. degree in
electronics and telecommunications engineering
from University POLITEHNICA of Bucharest in
2011 and the M.Sc. degree in communications
engineering and Ph.D. in electrical engineering
and information technology from RWTH Aachen
University in 2013 and 2020, respectively.
From 2014 to 2019 she was a Research Assistant
with the Self-Organized Networks group, Institute
for Networked Systems, RWTH Aachen
University and from 2020 to 2021 she was a Postdoctoral Researcher with
the Mobile Communications and Computing group, RWTH Aachen
University. Since 2021 she has been an Experienced Researcher with
Ericsson Research, Ericsson GmbH, Herzogenrath, Germany. Her research
interests include mobile radio networks, spectrum sharing, and wireless
technology standardization.
Dr. Voicu was a recipient of the IEEE International Symposium on
Dynamic Spectrum Access Networks (DySPAN) Best Paper Award in
2017, and the Information and Communication Technology (ICT) Young
Researcher Award, RWTH Aachen University in 2019.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3313505
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
