Beyond 5G White Paper
Supplementary Volume“Sustainability and Energy Efficiency”

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

Preface .............................................................................................................................. 4
1.

Introduction ............................................................................................................. 4

2.

Sustainability ........................................................................................................... 7

3.

2.1

Sustainability in ICT industries ................................................................................ 7

2.2

Sustainable 6G and 6G for Sustainability ............................................................. 11

2.3

Key Value Indicator ............................................................................................... 12

2.4

Sustainable AI ....................................................................................................... 15

Energy Efficiency ................................................................................................... 16
3.1

Energy Efficiency goals for 6G .............................................................................. 16

3.2

Technical Enablers for Energy Efficiency ............................................................. 19
3.2.1 Overall system perspective ........................................................................ 19
3.2.2 Core Network Enablers .............................................................................. 21
3.2.3 Radio Access Network Enablers ................................................................ 22
3.2.3.1

Optimal deployment planning technology for vRAN resources,
exemplified by energy saving ................................................................ 22

3.2.3.1.1 Advancement of RAN operation management ................................. 22
3.2.3.1.2 Architecture of vRAN operation management .................................. 23
3.2.3.1.3 Process flow ........................................................................................... 23
3.2.3.1.3.1 Configuration management information collection............ 24
3.2.3.1.3.2 Formulation of deployment plan ............................................. 24
3.2.3.1.3.3 Optimal deployment calculation .............................................. 24
3.2.3.1.3.4 Deployment execution................................................................ 25
3.2.3.1.4 Energy saving effect by optimal resource deployment .................... 26
3.2.3.1.5 Conclusion .............................................................................................. 28
3.2.3.2

Other RAN architecture and deployment innovations ........................... 29

3.2.4 Air-interface Enablers ................................................................................. 30
3.2.5 Coordination with Non-Terrestrial Network ................................................ 34
3.2.5.1

Conventional O-RAN architecture and Requirement ............................ 35

3.2.5.2

Solution - Power Management for Base Stations Utilizing Pedestrian
Flow Analytics and Non-Terrestrial Networks ....................................... 3513.2.5.3
4.

Conclusion ............................................................................................. 37

Global Standards for Mobile Communication Networks....................................... 38
4.1

Overall Trend of Energy Efficiency Standardization ............................................. 38
4.1.1 3GPP .......................................................................................................... 38
4.1.2 O-RAN Alliance .......................................................................................... 39
4.1.3 ITU and ETSI ............................................................................................ 40
4.1.4 IETF ........................................................................................................... 40

4.2

Case Study on Energy Saving through utilization of O-RAN Alliance RIC
Research and Development .................................................................................. 41

5.

Acknowledgements ................................................................................................ 45

6.

References .............................................................................................................. 45

7.

Contributing Authors ............................................................................................. 49

Abbreviation List ........................................................................................................... 50

2

【Revision History】
Ver.
Date
1.0 2024.3.7
Initial version

Contents3

NotePreface
Sustainability has been the foremost hurdle for humanity to overcome emerging
change of climate and environment in the planet. As has been done by the othercountries in a global partnership, Japan has been striving to make the planet andsociety sustainable. In the ICT industry, Energy efficiency as part of sustainability is ofoverarching importance to contribute to the goal of sustainable developments. Apartfrom that, more expanded role and contribution would be expected by leveraging
mobile communication systems to evolve human life and society far more sustainablein Beyond 5G/6G era. The Beyond 5G white paper (version 2.0) published by Beyond
5G Promotion Consortium shed light on several technology and research directions
towards energy efficiency as sustainability. The focus of this paper is to follow-up thetechnology direction and expose the cutting-edge technical enablers being extensively
researched in academia and industry.
One deliverable is to crystallize the nomenclature of sustainability applied to the ICT
industry, and potential actions taken by the ICT industry, that is, to minimize ICT
footprint as well as maximize ICT handprint. A new concept to evaluate mobile
telecommunication system capabilities from value-oriented viewpoints is explored, i.e.,
Key Value (KV) and its indicator (KVI). Use of AI is navigated with respect tosustainability, i.e., how AI should be accountable for fairness, transparency, security
and privacy.
Another deliverable is a wide range of technical enablers for energy efficiency,
encompassing the overall system in a holistic manner (CN, RAN, Air-IF andcoordination with NTN). A quantified energy saving gain is shown for some advancedenergy efficiency features. For instance, the optimal resource deployment technique for
vRAN resources can reduce 37% of the power consumption in RAN. Another enabler toleverage pedestrian flow analysis and NTN can achieve up to 40% of the power saving.
Finally, standardization activities on sustainability and energy efficiency are outlined
across key standardization organizations for the mobile industry. An analysis andperformance evaluation are provided to envisage the energy saving gain by leveraging
the energy efficiency features supported for RAN Intelligent Controller standardized by
O-RAN Alliance.

1.

Introduction

Sustainable Development has been an imperative goal and action taken by all
countries in a global partnership, such as tackling emerging climate change and4striving to preserve planet nature [1]. In fact, 2023 was the warmest year on recordand close to 1.5 °C above pre-industrial level [2]. Similar climate change has been
observed in Japan that the average annual temperature deviation in 2023 was +1.29 °C over the average temperature for 30 years from 1991 to 2020, as in Figure 2-1.
Further temperature rise would be foreseen if the current situation would be remained
as it is.

Black: Average annual temperature deviation
Blue: Moving average over 5 years
Red: Long term trend
Figure 1-1: Averaged annual temperature deviation in Japan [3]To wrestle with the climate change, Japanese government has pledged to become acarbon neutral society, i.e., to reach net zero emissions by 2050, in accordance with
Paris agreement [4]. The goal towards net zero emission is also an imminent challenge
for the Information and Communication Technology (ICT). From 2000s to today, theICT footprint has almost doubled over the world. Likewise in Japan, a significant
increase in power consumption is predicted by ICT related equipment (infrastructureand end user devices) as communication traffic grows (see Figure 2-2 [5]) unlesstechnical innovations can overcome the expected higher power consumption.

5

Figure 2-2: Trends of communication traffic and energy consumption in the ICT sector(Source: MIC, the Department of Information and Communications Technology of theInformation and Communications Council, materials of the 27th technology strategy
committee [5])To explore the technical innovations toward energy efficiency and sustainable
development, several technology and research directions are provided in the Beyond 5G
White Paper [6]. As a follow-up study, the focus of this white paper is to identify andexpose key technology enablers for sustainability and energy efficiency towards 6G.
Section 2 attempts to crystallize the sustainability for the ICT industry and actions
taken for the sustainable mobile networks. Section 3 disseminates the cutting-edge
technical enablers for energy efficiency being extensively researched in academia andindustry. Finally, Section 4 looks over the global standardization for mobile
communication networks.

6

2.

Sustainability

The term sustainability per se is a broad concept and often encompasses
environmental, economic and societal practices for operating a business, all of which
are grappled by UN SDGs [1]. This section attempts to crystallize the nomenclature ofsustainability applied to the ICT industry and 6G. Given the gaining momentum that
AI will be an integrated part of all future systems, key design principles are presented
as to how fairness, transparency, security, privacy and energy aware deployments of AI
systems should be ensured.
2.1

Sustainability in ICT industries

Sustainability focuses on how the present needs can be fulfilled without compromising
the ability of future generations to meet their own needs. Sustainability is a criticalpart of business models and strategies with meaningful risks and opportunities, going
far beyond simply doing good and compliance. The ICT and mobile industries play avital role in satisfying environmental sustainability. Environmental sustainability
focuses on protecting the three key diversities of our planet as illustrated in Figure 2.11: air, biological living forms and geodiversity of many materials on the earth. The airis the mixture of gases that surrounds earth, which contains important substances,such as oxygen, nitrogen, and carbon dioxide. The air enables a stable environment tolive and operate businesses, which is essential for life to grow and survive. The
biodiversity denotes the variety of life forms and eco-systems on earth which areproviding many essentials elements and services like medicines, clothes, building
materials, fuels, carbon capture (natural defense against climate change), etc. The
geodiversity covers non-biological aspects of nature, such as metals, minerals, andwater. These three diversities are interconnected with each other, and strongly affectedby major drivers coming from human activities; the growing rate of Green House Gas(GHG) emission leading to climate change, the use and change of land use likedeforestation, the resource use with both their extraction and their usage, and thegenerated pollution and waste from human activities. To downsize these higher risks
and impacts, circularity and energy use are the two most important fields where the
ICT and mobile industries can contribute and facilitate higher efficiencies and waste
reductions.

7

Figure 2.1-1: Environmental sustainability and key fields of action
The strategies taken for circularity and energy are as follows [7].

A. Minimizing ICT footprint
The global ICT industry is mainly powered by electricity, accounting for up to 5% of
the world's electricity consumption [8]. GHG emissions from the global ICT
industry are up to 2% of the world total emission. The detailed components ofelectricity consumptions and GHG emissions in the ICT sector is shown in Table
2.1-1 [8].
Table 2.1-1: Global estimates of ICT sector emissions, 2020 to 2022 [8]8Furthermore, the ICT industry has been generating an increasing amount ofelectronic waste due to accelerated digitalization. On the network side, the share of
GHG emission, combining both fixed and wireless networks, is up to a quarter of
the total ICT GHG emission impact. The GHG emission from NW is mainly comingfrom its operational phase, and less from the manufacturing of the NW elements.
The GHG emission from NW is then highly dependent on its operation, i.e., NW
electricity consumption. The NW energy efficiency as well as the carbon emission
intensity of the electricity powering the NW, affects the GHG emission level. Thus,
the industry should strive for minimizing any negative impact to GHG emissions
and achieving significant reduction of its direct and indirect GHG emissions.
Potential approaches are exemplified as follows.
i.

Building sustainable operations and supply chain.

ii.

Continually improving product energy efficiency.

iii.

Driving circularity to reduce waste.

iv.

De-risking the potential misuse of technology (e.g., responsible use of AI[9]).

B. Maximizing ICT handprint
In addition to minimizing the ICT footprint, any positive impacts to theenvironment and ecosystem have to be maximized, i.e., ICT handprint9

maximization. Potential approaches to maximize ICT handprint are exemplified asfollows.
i.

Enabling 24/7 low carbon and renewable energy systems.

ii.

Decarbonizing other industries and society.

iii.

Enabling the transition of the energy sector.

iv.

Providing the critical networks for life with low energy consumption andhigh energy efficiency.

v.

Connecting the unconnected through ubiquitous connectivity and education
as well as building digital skills.

Once the target fields and strategies are determined, the next question of interest is
how to measure the actions taken for ICT footprint and handprint. ESG is the broad
rating of commitment to sustainability and other values [10]. ESG is the abbreviation
for the set of Environmental, Social and (corporate) Governance. “Environmental”
represents any actions to protect and regenerate the natural world, e.g., climatechange, reduction of GHG emissions, air and water pollution, water scarcity anddeforestation. “Social” is aimed at ensuring human centric objectives, including humanrights and equality of opportunity for everyone, regardless of age, gender, race
ethnicity and region. The social practices also cover personal data protection andprivacy, product safety, mental health and community impact. Corporate governancedenotes the governance and ethical practices to ensure sustainability in the businessand corporate world, e.g., responsible sourcing, reporting, transparency, compensation
and accountability to ethics, anti-corruption, health, safety and cyber security. Key
components for which ESG is targeted are illustrated in Figure 2.1-2.

10

Figure 2.1-2: Overview of ESG
2.2

Sustainable 6G and 6G for Sustainability

Whilst the commercial 5G services have successfully evolved, academia and industry
are moving ahead towards 6G research and standardization. ITU-R WP5D agreed on
IMT-2030 framework covering usage scenarios and capabilities in June 2023 [11].
Sustainability was identified as one of the motivations and societal considerations forthe development of IMT-2030. Sustainability, or more specifically environmental
sustainability, was defined as one of the new capabilities to support the expanded
usage scenarios of IMT-2030. Thus, 6G per se needs to be capable of achieving
sustainability in ICT industries as described in subsection 3.1, e.g., minimizing the ICT
footprint by reducing GHG emissions and energy consumption from the ICT
infrastructure, which is termed as “Sustainable 6G” [12]. The Sustainable 6G would
contribute to the first order of effects for the life cycle impact of ICT goods, networks
and services as defined by ITU-T [13]. In Recommendation ITU-T L.1410, the firstorder effect is defined as follows.
-

The impacts created by the physical existence of ICTs and the processes
involved, e.g., energy consumption and GHG emissions, e-waste, use ofhazardous substances and use of scarce, non-renewable resources.

Besides that, 6G technologies and deployments can contribute to maximize thehandprint, as described in subsection 3.1, e.g., decarbonizing other industries and11society, enabling the energy sector transition, providing global connectivity, etc., whichis termed as “6G for Sustainability” [12]. The 6G for Sustainability would contribute to
the second order effects and, to some extent, other effects defined in ITU-T L.1410, as
listed below.
-

Second order effects: the impacts and opportunities created by the use andapplication of ICTs. This includes environmental load reduction effects which
can be either actual or potential, such as travel substitution, transportation
optimization, working environment changes, use of environmental control
systems, use of e-business, e-government, etc.

-

Other effects: the impacts and opportunities created by the aggregated effects
on societal structural changes by using ICTs. Particularly for some ICT services
such as teleworking or videoconferencing, the time gained by an end user using
an ICT service which then may cause additional impact e.g., a leisurely driveand economic activities, which are difficult to track. Such additional impacts
are often defined as "rebound effects".

Both sustainable 6G and 6G for sustainability are the twin pillars of any technology
evolution and revolution towards 6G.
2.3

Key Value Indicator

For the existing capabilities supported from the legacy IMT technologies (up to 5G),
performance-oriented values were the key metrics to evaluate technology potentials,
such as peak data rate, spectrum efficiency, latency, and so on. Whilst theperformance-oriented value is valid for 6G to judge the enhanced capabilities as in
Figure 2.3-1, it would not be able to properly evaluate the new IMT-2030 capabilities,
especially for sustainability. Value-oriented indicators are deemed as necessary toevaluate if the candidate features for 6G can offer sustainable 6G and/or 6G for
sustainability described in subsection 2.2.

12

Figure 2.3-1: Capabilities of IMT-2030
6G pre-research initiatives in several regions have been studying how to define valueoriented indicators, as Key Values (KV) and its Indicators (KVI) and how the concept of
KV and KVI can be integrated with the existing performance-oriented KPIs [14 - 16],
albeit this is still in an early application stage. Figure 2.3-2 illustrates an example of
the role of KV to assess sustainability, trustworthiness and inclusiveness defined by
UN SDGs and how it is integrated into the evaluation methodology for 6G use cases
and technologies [14].

13Figure 2.3-2: Example of clustering KPIs and KVs [14]Table 2.3-1 shows a set of KV examples for some use case areas [17]. The introductionof KVIs in conjunction with the traditional KPIs enables to evaluate 6G technologies
and use cases if they can address human and societal challenges from the needed
viewpoints (sustainability, trustworthiness and inclusiveness). With regards to KVI
standardization, 3GPP has started to study how to introduce KV and KVIs with respect
to the upcoming 6G standards [18].
Table 2.3-1: KV examples

Sustainablefood
production

Living and working
everywhere

Personal healthmonitoring &actuation
everywhere

Smart citywith urban
mobility

Emergency
response &warning systems

Use casearea

KV examples

KVI examples

Societalsustainability

Reduced emergency response times; Increased operational efficiency ofinterventions in remote areas

Environmental
sustainability

Increased area of protected and surveyed natural habitats and climate
preserves

Trust

Reported confidence in advanced digital devices, systems, and services incritical missions

Environmental
sustainability

Environmental footprint of urban transport of persons and goods

Personal healthand protection
from harm

Injuries in urban traffic

Privacy andconfidentiality

Reported user control of medical data for storage/transmission/processing

Societalsustainability

Average cost saving in health care system per patient

Trust

Reported trust level for autonomous e-health components; Accuracy rate in ehealth AI-related events’ identification and/or decision making

Societalsustainability

Travelling / commuting time reduction; Access to job market; Life
opportunities in rural areas

Economical
sustainability
and innovation

Cost-efficiency of living and working in rural areas; Number of activities thatcan be performed anywhere

DigitalinclusionAccess to internet in communities and areas

Environmental
sustainability

Environmental footprint of agriculture activities; Energy use in agriculturalactivities

Societalsustainability

Increase in agriculture productivity; Reliability of food production

14

2.4

Sustainable AI

AI is considered as a key technology enabler for designing multiple aspects of the
future 6G networks like coverage adaptation, resource allocation, security, multidomain orchestration. Aspects of AI design like fairness, transparency, security andprivacy are paramount for implementing fair, inclusive and secure services. There
should be mechanisms to identify and remove undesirable algorithmic bias that creeps
into AI systems due creation of generalized models based on finite training data.
Implementations of AI systems should be accountable for their outcomes, therefore
explainability and traceability are key aspects in implementing critical communication
systems. An unwanted side effect is that the same AI mechanisms that enable manysophisticated 6G features also opens another surface for security attacks and therefore
mechanisms need to be studied to consider and overcome this challenge [19]. Since
collection of data from users and other sources is key for the implementation of AI
models, the design and architecture for collection and transport of data and models
need to consider ensuring that privacy of user data is not compromised.
In addition, due to its compute-intensive nature, the energy footprint of AI
implementations is becoming a significant component of the energy budget of theoverall system. With “digital compute”, the energy needs keep scaling up with the everincreasing sophistication of tasks; and techniques like Analog Compute [20] need to bestudied for developing hardware platforms for AI.

15

3.

Energy Efficiency

Energy efficiency, i.e., to save energy consumed in mobile communication systems is ofparamount importance for sustainable 6G explained in subsection 3.2. This section
provides an analysis of the current energy consumption in the mobile network and apotential energy efficiency target for 6G. In addition, technical enablers for energy
efficiency are disseminated from a wide range of technology potentials being
extensively researched in academia and industry.
3.1

Energy Efficiency goals for 6G

First of all, in order to set the design goal of energy efficiency towards 6G, it is sensible
to learn how the energy is being consumed in the current mobile networks. Figure 3.1-1
shows the energy consumption for a typical CSP, based on a survey conducted by
NGMN [21] in 2021 over 30 networks with a mixture of multiple generations andmultiple radio access technologies across different domains of operations. It becomes
clear that most of the energy consumption for a CSP is in network operations, amongst
which the RAN infrastructure consumes the largest amount of energy. Besides that,the radio part, which corresponds to the RU, consumes 29% of energy which is thesame amount of energy consumed by BTS air conditioning. With the on-going rollout of
the larger massive MIMO (mMIMO) RUs and the corresponding shift of processing
functionality into the RU, the share of RU energy consumption will continue to grow in
coming years.

Figure 3.1-1: Distribution of energy consumption for CSP operators (based on datafrom [21])16Figure 3.1-2 further breaks down the proportion of the power consumed by different
components at 100% load. The majority of the power consumption is stemmed from thePA, Digital DFE and RFIC, which includes the converters, the interpolation, digitalpre-distortion and crest-factor reduction, and the digital processing integrated circuitthat performs L1-low signal processing functions including Fast FFT processing,
beamforming, channel estimation and the first stage of MIMO detection.

Figure 3.1-2: mMIMO RU power consumption split at 100% load
Although the RU power consumption distribution in Figure 3.1-2 is based on a full load
situation, the RU, in practice, rarely operates at its peak load. There is a survey on theaggregated network traffic profile of Europe BS sites that 80% of BS sites carry only
20% traffic, and the rest of 20% BS sites are busy in carrying 80% of traffic as shown in
Figure 3.1-3 [22]. 20% of BS sites only take 1% of traffic, which can be regarded nearlyin idle mode. Furthermore, the traffic load varies substantially over the daily 24-hour
period even at the same BS site. This traffic trend and characteristics can give a hintthat a potential target to save energy consumption would be the case where and when
middle or low traffic is observed.

17

Figure 3.1-3: Aggregated network traffic profile of Europe BS sites [18]Figure 3.1-4 illustrates a target power consumption for a 6G extreme mMIMO RU(denoted as xMIMO in the figure) by the relative power consumption of a single carrier
for a 5G mMIMO RU as a function of the cell capacity [23]. The peak capacity of 6G
extreme mMIMO with 400 MHz bandwidth and 128 TRX will be 6.7 times higher peakcapacity of 5G mMIMO with 100 MHz bandwidth and 64 TRX. On the other hand, if
the 6G xMIMO RU were built upon the existing technologies, the 18nergyy
consumption at the peak capacity would grow 3 times larger than that of the existing
5G mMIMO RU. If the advanced energy efficiency techniques are leveraged, the power
consumption of 6G xMIMO RU can be mitigated to the level of 2 times increase fromthe existing 5G mMIMO RU. Moreover, when the offered load for the 6G cell is lessthan the peak capacity of a 5G cell, 6G system should consume substantially less power
than the 5G cell as illustrated in Figure 3.1-4. In particular, the power consumption
should gracefully approach the cell switch-off power with decreasing load. The goal
should be to approach an exponential drop in energy consumption as the load decreases
from the high loads and near linear scaling of energy consumption at low loads.

18

Figure 4-4: 6G power consumption goal versus 5G [23]On the other hand, the energy efficiency goals may require some trade-offs inperformance. It is up to use cases and type of services being provided which
performance degradations are acceptable and to what extent. For instance, 3GPP has
made a formal statement that the energy efficiency aspects of a solution in defined in
3GPP is not to be interpreted to take priority or to be alternative to security, privacy,
complexity, etc., and to meeting the requirements and performance targets of thespecific feature(s) the solution addresses [24]. Therefore, the performance goal andenergy efficiency goal need to be well balanced, when applying energy efficiency
solutions for a given use case.
3.2

Technical Enablers for Energy Efficiency

3.2.1

Overall system perspective

Although the energy consumption in RAN is dominant in mobile networks as
exemplified in section 4, the energy consumption from the other potion such as Core
Network (CN) and data centers cannot be ignored, due to the predicted increase driven
by emerging requirements of cloud native network functions and applications. Edge
clouds and edge traffic processing would also be another potential impact to thepredicted energy increase. Hence, a holistic strategy and approach are imperative toensure the end-to-end system wide energy efficiency of 6G system can be achieved.
Figure 3.2-1 shows the central pillar of Green 6G enablers, i.e., energy efficiency
enablers.

19

Figure 3.2-1: Overall system level enablers for Green 6G

A. 3GPP enablers
3GPP enablers driven by IMT-2030 requirements, such as EE requirements and
KVIs, leverage the energy saving techniques for UE and NW supported in 5G and
5G-Adavnced, which will be a baseline for overall 6G system design toward energy
efficiency. In addition, Green 6G design (radio, architecture and CN) can offer thefollowing standardized functionalities.
-

Multi-layer optimized tradeoff of spectrum efficiency versus energy
efficiency.

-

Leaner carrier, wave forms and modulations for low energy use.

-

QoS adaptation based on load and network energy saving state.

-

NF energy exposure to enable dynamic optimizations via AI/ML.

B. Hardware advancements
Hardware advancements are a viable enabler, especially for the air interface
domain, such as fast (deep) sleep modes, advanced power amplifiers, hybrid
beamforming architectures for low digital complexity and enhanced digital frontend
processing. The advanced hardware design is expected to be driven by vendor
innovations in a competitive manner. Potential innovation directions are as follows.
-

Energy saving and scheduler innovations.

-

Advanced signal processing.

-

Smart work-load balancing.

-

Green energy site solutions (virtual power plant, liquid cooling, etc.)-

Enhanced MIMO radios designed with state of art technology components
and architectures enabling increased energy saving gains.

C. AI/ML

20

AI/ML is expected to be a native capability for 6G system. The integrated AI/ML to6G system can be leveraged to select and optimize energy saving algorithms basedon predictions of traffic, radio conditions and mobility.

D. Digital twin
Digital Twin enables multi-purpose virtual network emulations, e.g., for testingenergy saving algorithms and optimizations reducing lab and field-testing efforts.

E. Optimized RAN deployments
RAN and spectrum deployments can be optimized by taking into account energy
use. For instance, coverage, cell/area capacity, interference and mobility are finetuned not only for performance, but also taking balance with energy efficiency.

F. Coordination with NTN
Due to wider coverage offered by satellite access (aka NTN), NTN coverage can beleveraged to save the energy consumption in RAN, e.g., when traffic load is
expected to be low and BSs are turned off. How to save BS power consumption andleverage NTN is elaborated in section 3.2.5.
3.2.2Core Network Enablers

The current 5G Core Network, aka 5GC, standardized by 3GPP up to Rel-18
specification has already supported features contributing to energy efficiency, e.g., NF
scaling in operations. 3GPP has been continuously addressing energy efficiency
solutions and currently studying the following areas from system and CN perspectives
towards Rel-19 specification [25].
-Framework for NW energy consumption exposure.

-

Subscription and policy control to enable NW energy savings as service criteria.

-

5G system enhancements toward energy saving, e.g., energy usage adjustment
for NF from CN aspect, energy saving related decision making, NF selection
leveraging NF energy states, analytics, etc.

On the NW energy consumption exposure, one potential approach is to define an open
API on compute utilization rates and energy consumption metrics associated withdifferent NFs, in order to enable monitoring and traceability of energy expenditure.
Correlation of energy consumption with traffic load and required compute capacity canprovide means for better optimizing allocation of resources. It is also worthwhile
extending the open API to interact with energy information topics related to building,surrounding infrastructure, street gadgets, etc.

21

The on-going energy efficiency standardization for 5G-Advanced is a steppingstone
toward 6G, and further evolutionary or revolutionary innovations would be envisaged.
Some technology directions are as follows.
-

End-to-End coordination across, UE, RAN, CN and edge to avoid domainspecific energy saving measures that indirectly cause increased overall energy
consumption.

-

Energy optimized CN protocols and procedures, e.g., simplified procedures,
analytics-based CN power saving mode, etc.

-

Energy saving via network sharing and roaming.

-

Energy efficiency via integration of NW and compute.

3.2.3

Radio Access Network Enablers

3.2.3.1 Optimal deployment planning technology for vRAN resources, exemplified
by energy saving
It is anticipated that the digitalization of society and the introduction of 5G and
Beyond 5G will pose challenges to the provision of new services in network systems
that rely on traditional dedicated devices. The first challenge is the expected increasein communication traffic and the associated increase in power consumption of network
devices, due to the proliferation of IoT devices, connected cars, and 5G smartphones, aswell as the increase in high-quality image and sound data. The second challenge is thatthe configuration of the network is becoming complex, making it increasingly difficult
to operate manually or to develop/purchase systems by a single vendor.
To address these challenges in the telecommunications industry, the O-RAN Alliance ispromoting the opening up of communication networks and their intelligentization
using AI/ML. With the spread of O-RAN, operators can achieve cost reduction andoperational efficiency, while vendors can achieve product development and ecosystemconstruction. On the other hand, a major trend in recent communication systems andnetworks is the transition from dedicated hardware devices specialized forcommunication to a virtualization platform based on general-purpose IA servers, andthe use of cloud services that enable flexible service provision and scalability isadvancing. Therefore, even in RAN operations, it has become possible to shift fromtraditional fixed operations to dynamic operations.
3.2.3.1.1 Advancement of RAN operation management
To solve these challenges, we are working with the "network service infrastructuretechnology to realize optimal deployment of vRAN resources" that complies with ORAN specifications for ultra-high-speed, large-capacity wireless access networks. This22

"network service infrastructure technology to realize optimal deployment of vRAN
resources" enables safe and automatic redeployment of vRAN resources according totheir purpose (for example, energy saving, local traffic increase, equipment failure,etc.). In this section, we will describe the case for energy saving.
3.2.3.1.2 Architecture of vRAN operation management
In the "network service infrastructure technology to realize optimal deployment ofvRAN resources", we added a rApp that realizes the "resource planning function" and"resource controller function" on the Non-RT RIC framework inside the SMO in the ORAN architecture. Figure 3.2.3.1.2-1 shows a schematic diagram of this technology.

Figure 3.2.3.1.2-1: Schematic diagram of this technology
In this way, we have built a cycle of data collection, analysis, and setting changes
based on the analysis results. The functions of the added rApp are as follows.
⚫ Resource planning function
We will implement energy saving designs (accommodation design and antennasettings) for wireless access networks (antennas, virtual servers, etc.) according totraffic demand.
⚫ Resource controller function
It collects the resource information on O-RAN necessary for power optimization andsafely and reliably reflects the design results in the network.
3.2.3.1.3 Process flow
The process flow of this rApp is as follows.
1.

Configuration management information collection

2.

Formulation of deployment plan

3.

Optimal deployment calculation

resource planning function

23

4.

Deployment execution

3.2.3.1.3.1

resource controller function

Configuration management information collection

In order to use the optimal deployment configuration derived from the optimization
calculation and the obtained calculation results for the deployment process that reflects
them in the network topology, we collect the O-RAN resource information (physicalresources/virtual resources) of the wireless access network and the vRAN topology
information (configuration information of RU/DU/CU). Here, since the rApp collects
information through the interface standardized by O-RAN, it enables the collection ofconfiguration information that is compatible with multiple vendors.
3.2.3.1.3.2

Formulation of deployment plan

We utilized AI to collect and analyze radio usage information gathered from RUs andDUs, and to determine the trigger for demand forecasting of wireless resources anddeployment execution. Specifically, in formulating the RU stop radio transmissionplan, we adopted the CK-MARL, a performance-guaranteed multi-agent reinforcement
learning technology as an inference model, in order to prevent the degradation of NW
communication quality due to the execution of RU stop radio transmission control andto reduce the wide-area large-scale calculation time without falling into a local
optimum. This CK-MARL has the features of performance guarantee of the specified
KPI and coordinated distributed control by multiple AIs [26]. This allows us toformulate deployment plans from the RU stop-wave plan that ensures NW
communication quality and meets the overall optimization in real-time processing.
3.2.3.1.3.3

Optimal deployment calculation

We collect information on the RU, physical layer, and virtual layer of the deployment
area from the configuration management function, gather link (connection) information
between each entity and attribute information of each entity, and calculate the optimal
deployment configuration with power saving as the evaluation index. Figure
3.2.3.1.3.3-1 shows an overview of the calculation of the optimal deployment
configuration.

24

Figure 3.2.3.1.3.3-1: Deployment configuration calculation overview diagram
The deployment configuration calculation is calculated in the following two steps.
① Derive the optimal combination for multiple cells (RU) that the DU
accommodates.
② From the calculation results of ①, derive the optimal combination for the CU
that connects to the DU where the cell is accommodated.
In deriving the optimal combination, we apply quantum computer technology that canquickly solve combination optimization problems that are difficult to solve with current
general-purpose computers.
3.2.3.1.3.4

Deployment execution

Next, based on the results of the optimal deployment calculation, we reconstruct theservers where the RU/DU/CU, which are components of the wireless access network,
are implemented, and the switches on the route. In reconstructing this new network
topology, it is necessary to consider multi-vendor considerations and control
considering the impact on service to users. In addition, it is necessary to shorten theoperation time as the control target is expected to become enormous. To achieve these,
the resource controller function has the following functions. The resource controller
function consists of various control functions that control each element(server/DMS/vRAN/switch/RU) and a deployment execution function that governs
them.
① Automatic creation of various config information and control commands
necessary for reconstruction

Corresponding to networks that vary by cloudMulti-vendor compatibilityAutomatic creation of config files and deployment scripts② Determining the control order of the reconstruction target grouping.
Grouping is the extraction of groups that can be executed in parallel from theconfiguration information list to shorten the reconstruction time. The control
for the components of the wireless access network is as shown in Table
3.2.3.1.3.4-1. In order to minimize the impact on service to users, it is necessary
to consider the order of control.
Table 3.2.3.1.3.4-1: List of controls implemented by this rApp
Control target
Control content

25Server
DMS
vRAN
RU
SwitchPower ON/Power OFF
Creation/Deletion/Resource Reservation/Resource
Release/Bulk Resource Release
Creation/Modification/Deletion
Change (stop wave/start wave)
ChangeWith these functions, we are realizing the optimal resource deployment for energy
saving of large-scale and multi-vendor wireless access networks, while balancing theminimization of service impact on users and the shortening of reconstruction time.
3.2.3.1.4 Energy saving effect by optimal resource deployment
In order to demonstrate the effectiveness of the "network service infrastructuretechnology to realize optimal deployment of vRAN resources", we conducted a computer
simulation using a real environment as the evaluation target area. This time, we usedthe city of Milan, Italy, where available radio wave usage statistics data [27] ispublished, as the evaluation target area. Figure 6 shows a map of Milan, and the redframe (one side 1 [km]) in the figure was used as the evaluation target area.
Furthermore, the red frame was divided into 16 equal small areas. Similarly, using
OpenCellID [28], which is publicly available data on RU placement, we determined theplacement of RUs in Milan. In the figure below, macro cells at the cell tower locations
are placed in the red circles, and small cells in each RU are placed in the blue circles.
UEs were evenly placed in each small area, with 16 UEs each. Figure 3.2.3.1.4-1 shows
the RU and UE placement diagram.

Figure 3.2.3.1.4-1: RU/UE layout diagram
The traffic volume of each UE was determined based on The Telecommunication
datasets. Figure 4 shows the traffic volume for a weekday. In order to confirm the effectof the proposed technology, it is necessary to set the daytime (peak time) and nighttime

26

(off-peak time) time zones. In this evaluation, the following two times were set as
daytime (peak time) and nighttime (off-peak) respectively.
Daytime: 1:30 pm when the traffic volume is maximum

Nighttime: 4:30 am when the traffic volume is minimum

Based on the traffic volume of The Telecommunication datasets at the above timezones, the traffic volume per UE was set as shown in Figure 3.2.3.1.4-2.

Figure 3.2.3.1.4-2: Traffic volume
The wireless conditions were set as shown in Table 3.2.3.1.4-1.

RU for macrocell.
RU for small cell

Table 3.2.3.1.4-1: Wireless condition table
Bandwidth[MHz]Center frequency
[GHz]
20
0.7
100
4.5

Transmit power
[dBm]
48
25

Figure 3.2.3.1.4-3 shows the operating/stopping status of the RU during the day andnight. The solid line connecting the RU and UE indicates the connection relationship.

27

Figure 3.2.3.1.4-3: RU stop status during the day (left) and night (right)
It shows the effect of reducing power consumption by stopping the RU. The number of
each device that can be accommodated per 1CU was defined as RU:DU:CU=32:4:1. Inother words, when the RU is stopped, the DU and CU are also stopped according to theabove number ratio.
Next, the power consumption of each device was defined as shown in Table 3.2.3.1.4-2.
The power consumption of the device set to the stop state was set to 0 [W], which isequivalent to the power off, assuming the remote stop/recovery function of the device.
Table 3.2.3.1.4-2: Equipment power consumption table
RU(macro)
RU(small)
DU
Power consumption
150
200
500
during operation [W]Power consumption in000
stop state[W]

CU
400
0

In this evaluation, the number of RUs operating in the evaluation target area was 4
during the day and 1 at night. From the equipment configuration ratio, one CU canaccommodate 32 RUs, so it can accommodate eight times the evaluation target area.
Assuming that there were eight areas similar to the evaluation target area and the
RUs were stopped in the same way, the total number of operating units and the total
power consumption would be as shown in Table 3.2.3.1.4-3.
Table 3.2.3.1.4-3: Table of operating units and power consumption
RU(macro)
RU(small)
DU
CU
Total power
consumption[W]
Daytime
8
24
4
1
8400
Nighttime
8
0
1
1
2100
Therefore, compared to the conventional case where the RU operates both during the
day and at night, the power consumption when the device is stopped at night using the
proposed method is about 63 [%], and a reduction of about 37 [%] is possible. From this,it was found that this rApp can be expected to have an energy saving effect.
3.2.3.1.5 Conclusion
Sub-clause 3.2.3.1 presents an optimal resource deployment technique for virtualized
RAN resources. As a result of the simulation based on this technology, the energy
saving effect of about 37% was confirmed. This technology is considered to be effective

28

for optimal deployment of resources according to the purpose in addition to energy
saving. This research result was obtained through research commissioned by National
Institute of Information and Communications Technology (NICT) (JPJ012368C04801).
3.2.3.2 Other RAN architecture and deployment innovations
RAN architecture design would have direct or indirect impact to save energy
consumption. 6G RAN architecture should also be designed from the viewpoint ofenergy efficiency. Some potential design principles are provided below [29].

I.

Centralization
In case of disaggregated RAN architecture, the location of network functions
would be relevant to energy consumption. Whilst the RU needs to be installedat the cell site, other network functions in RAN can be placed further back intoa more central site such as the edge cloud or a RAN hub site. One drawback ofcentralization from the energy consumption point of view is the energy required
to transport the information to the central site for processing. Nonetheless,central processing can lower energy consumption with statistical pooling gains
and lower climate control energy consumption using centralized baseband
liquid cooling solutions. Energy consumption for processing and cooling ofcompute is expected to outweigh energy consumption for optical NW transport.

II.

RAN acceleration for cloud implementation
Apart from the RAN centralization, implementation of RAN functions into thecloud infrastructure is an emerging trend in the recent deployment, which ismost likely a viable deployment for 6G RAN. The cloud native deployment can
reduce network operational expenses, and also enables rapid introduction of
new services, for example, introducing a new network slice for a particularbusiness or traffic type. On the other hand, implementation of signal processing
intensive physical layer functions on a CPU or GPU is not energy efficient. The
acceleration with ASICs will be tailored for RAN specific processing to mitigate
power consumption, when 6G comes into the market. It is also imperative tosupport a solution to completely power down the acceleration cards when not inuse. Under light traffic conditions, not all deployed acceleration cards will be
required to stay active as multiple RUs will be pooled and terminated at thesame DU.

III.

Densification
Coverage densification by small cells (e.g., pico or femto) or multi-TRP basestations can also contribute to energy efficient network. Smaller cells transmit
substantially less power than traditional macro base stations. Since the macro
supports a wide area, a large number of small cells each consuming a smaller
amount of power is required to provide the same coverage. Each small cell29

needs to be designed only for a small capacity as the service area is muchsmaller, thus smaller arrays with fewer layers are sufficient. Similar to RF
power, the digital power could also be small. At low loads, the small cells can beswitched off dynamically according to traffic patterns within the cell, since only
a subset of the small cells will have to serve users, albeit this is only possible
where there is umbrella macro coverage.
3.2.4

Air-interface Enablers

As analyzed in section 3.1, the energy consumption in RAN is the largest portion in
today’s network operation. Amongst RAN, the power consumption in an RU is an
emerging challenge due to mMIMO deployment that many parts of the physical layer(PHY) signal processing have been shifted into the RU to keep the required fronthaulbandwidth small. Even in the existing 4G/5G radio, there exist some operational
solutions by NW implementation. The followings are some examples [23].

A. Micro-discontinuous transmission (μDTX)
The idea of μDTX is that sparse data traffic is grouped into high power bursts
with low backoff, and thereby creating idle transmission periods at which theanalog components are pushed into micro-sleep mode. μDTX is hence aimed at
improving PA energy consumption in low load regime. Although μDTX cancontribute to improve the PA portion of energy consumption in RU (17% as in
Figure 4-1), the whole power consumption in RU cannot be close to zero energy
operation even in low load. This is because the digital processing power
consumption does not decrease with the load since DFE and BB processing
platform cannot be pushed into sleep mode.

B. Energy efficient scheduling and link adaptation
In legacy mobile networks, scheduling and link adaptation (LA) strategies aim
at maximizing the spectral efficiency by using MIMO and OFDM with highconstellation order and channel coding rate. Whilst it is favorable at peak load,
it does not provide the best tradeoff between spectrum efficiency and energy
efficiency at medium to high load even in combination with μDTX. An energy
efficient scheduling/LA approach, which selects the best combination of energy
efficiency functionalities offered by the PHY, can provide significant EE gains
particularly in the medium to high load regime. For instance, reducing the
average Tx power is preferable over μDTX because the spectrum efficiency is alogarithmic function of the Tx power and linear with respect to thetransmission time [26]. Instead of transmitting short bursts each with highspectral efficiency, stretching data transmissions over multiple slots allows a30

linear reduction in spectrum efficiency (i.e., reduced constellation size and coderate) but an exponential reduction in the required total radiated power.

C. Antenna muting
Antenna muting is a technique to limit the number of active Tx chains,depending on the required total radiated power and array gain. The remaining
set of active Pas still operates in their optimized low backoff regime. In
addition, part of the analog and digital front end can be sent into sleep mode oreven deactivated. A key challenge with antenna muting is the loss in antenna
gain which affects the coverage. Another challenge is the restriction on the
supported number of MIMO layers which affects cell capacity and spectrum
efficiency.
In terms of the standard solutions defined in 3GPP, NW energy saving for 5G, i.e., 5G
NR, has been studied for their specification in Release 18. The following new features
have introduced as the outcome of Release 18 study and specification work [48].
a. SSB-less SCell in inter-band CA for FR1 and co-located cells
This feature enables to omit the regular SSB transmission over SCells if colocated with PCell, even in case of inter-band CA.
b. PDSCH power domain adaptation and antenna adaptation by means of CSI
enhancements
This feature enables the gNB to adjust the Tx power of PDSCH, according to
the CSI feedback from UE. Likewise, antenna elements are enabled or disabled,
according to the CSI feedback from UE indicating the performance of different
candidate antenna patterns.
c. Cell DTX/DRX
The functionality of discontinuous transmission and discontinuous reception at
gNB is introduced to reduce gNB Tx/Rx activity time.
d. Inter-node signaling for requesting SSB-beam reactivation
Per-beam level activation and deactivation is managed via the peer-to-peer
signalling between gNBs via Xn interface.
e. Restricting paging in a limited area of the cell
With the beam level information where UE is camping on, paging transmission
toward UE can be restricted per beam level granularity.
f.

Preventing legacy UEs camping on cells in DTX/DRX operations
The functionality avoids legacy UEs camping on cells in DTX/DRX operations.

31To approach a further drop in energy consumption in mid-to-low load regimes, when a
new radio interface is designed for 6G, several innovative directions can be considered
[29].
I.

Adaptive waveforms
OFDM has been a chosen waveform for recent cellular standards, e.g., 4G LTE
and 5G NR, especially for downlink. OFDM along with enhancements tocompute capabilities enables better spectral efficiency over wider bandwidthand facilitate adoption of MIMO. A drawback of OFDM waveform is its highpeak to average power ratio (PAPR), which requires larger backoff in PA
operation to achieve better PA linearity at the cost of lower PA efficiency. For
uplink, in contrast, a single carrier waveform, i.e., DFT spread OFDM has beenused since 4G LTE whose low-PAPR characteristics significantly improved
coverage and reduced the power consumption at UE. For 5G NR, both CPOFDM and DFT spread OFDM are supported for UL, which can be semistatically configured by NW.
For 6G, support of multiple waveforms can be considered for both downlink anduplink, from which an appropriate one can be chosen depending on the trafficload, e.g., to facilitate energy saving. Figure 3.2.4-1 shows the PAPR of different
waveforms without clipping or digital pre-distortion (DPD) operations [29]. The
figure reveals the fact that single-carrier waveforms have substantially lower
PAPR compared to OFDM. A lower PAPR will translate into better PA
efficiency by operating closer to the peak output power of the PA. This can betranslated into lower power consumption by adjusting the drain voltage and/or
bias current of the PA. At lower loads, there will be no need to multiplex
multiple users across the frequency domain. Thus, it will be beneficial to switchfrom OFDM to a single carrier (SC) waveform such as SC-FDE or SC-FDMA tomake energy consumption comparable to that of single carrier systems withnarrow bandwidth. Such an adaptive waveform technique has already been
discussed for 5G-Advanced in 3GPP. Nevertheless, the technique per se is a
viable enabler from the viewpoint of energy efficiency for 6G.

32

Figure 3.2.4-1: Waveform choices for 6G
II.

Adaptive in-band guard sub-carriers
Although the use of multiple waveforms is investigated, it is also worth to
explore a technique to balance energy efficiency and spectral efficiency with
OFDM. One idea is leveraging some of the in-band tones as guard sub-carriers.
In medium or low load regimes, a small subset of sub-carriers is sufficient totransmit data. The rest of unused sub-carriers are the guard bands, as
illustrated in Figure 3.2.4-2. By limiting the use of sub-carriers as in the figure,the required spurious emissions are mitigated, resulting in reduced PAPR.

Figure 3.2.4-2: Concept of adaptive in-band guard sub-carriers
III.

Database-driven spectrum mask adaptation

33

An alternative way to increasing the efficiency of PA is to operate it in the nonlinear regime with limited or no PA backoff. Nevertheless, one of the challenges
to operate PA with the non-linearity is to incur higher out of band emissions. If
the requirements on the out of band emissions can be relaxed wheneverpossible, the PA efficiency is vastly increased. It hinges on whether the adjacentband receivers are in proximity. A spectrum database could be used to learn
spectrum occupancy in the adjacent band in the surrounding area. In manycases, the bands adjacent to the cellular bands involve devices that are
relatively static or known to follow deterministic trajectories, hence it should bepossible to compute their presence in a given area and apply the spectrum mask
accordingly.
IV.

Dynamically scalable arrays
As explained for the antenna muting, one challenging issue when turning off
transceivers is the resulting impact on coverage. Typically, the coverage islimited for uplink due to the limited Tx power from UE. It is hence possible toturn off antenna elements on the downlink but maintain the receiver chain toreceive uplink signals. Beyond that point, muting will require coverageenhancement solutions such as repeated transmissions. Downlink and uplinkcoverage should be analyzed for various antenna configurations to determine
what additional coverage solutions should be introduced for some of theconfigurations as needed. If control channel coverage is the limiting factor, thensolutions, such as relying on control channels on a different carrier with carrier
aggregation, can be considered.

3.2.5

Coordination with Non-Terrestrial Network

Current mobile communication traffic continues to increase at an annual rate of 2030%. Therefore, communication networks in beyond 5G (B5G)/6G era will need toefficiently accommodate the ever-increasing traffic and also promptly provide diverse
communication network services such as ultra-high speed, large capacity, ultra-low
latency, and multiple simultaneous connections. To enhance the flexibility of network
components and fully automate network operations and management, open radio
access network (O-RAN) has recently received much attention [30] as a next-generation
promising technology for B5G/6G RANs. O-RAN Alliance discusses the specifications of
O-RAN, which have two major characteristics: (1) "openness" that enables rapid service
provision and incorporation of equipment from multiple vendors, and (2) "intelligence"that enables automation of network operations.

34

Meanwhile, current mobile networks provide diverse service types handling various
frequency bands and coverage scales such as existing 4G-LTE, 5G, and local 5G
services, and consequently, the type and the number of base stations (BSs) havecontinuously been increasing. When 6G services will be commercialized in the future,
existing service systems will need to remain in place for the time being, while new 6G
BSs will need to be installed. Thus, power consumption of BSs in RANs is a serious
problem [30] - [33] and it will be more remarkable in beyond 5G/6G era.
3.2.5.1 Conventional O-RAN architecture and Requirement
Figure 3.2.5.1-1 is a schematic diagram of the standard O-RAN architecture [30], inwhich the components of base stations are separated and interconnected. The
components include (i) CU (Central Unit) as a data processing unit, (ii) DU(Distributed Unit) as a radio signal processing unit, and RU (Radio Unit) as anantenna unit, and RIC (RAN Intelligent Controller) as a control unit for the entire ORAN which performs resource control, service quality control, traffic control, and so on.
The RIC is further divided into Near-Realtime (Near-RT) RIC, which requires less than
1 s processing time, and Non-Realtime (Non-RT) RIC which requires more than 1 s
processing time. The specified interfaces between each component are (i) A1 between
Non-RT RIC and Near-RT RIC, (ii) E2 between Near-RT RIC and CU/DU, (iii) F1
between CU and DU, (iv) O1 between Non-RT RIC and other three components, and (v)Open Fronthaul between DU and RU. UE (User Element) is a mobile terminal, whichis connected to the mobile core network and the Internet via the O-RAN. We require to
reduce the power consumption of O-RAN BSs while maintaining the continuity of data
communications and quality of services for mobile users.

Figure 3.2.5.1-1: Standard O-RAN architecture
3.2.5.2 Solution - Power Management for Base Stations Utilizing Pedestrian Flow
Analytics and Non-Terrestrial Networks
To address the problem of power consumption of O-RAN, NICT proposes a power
management architecture for BSs to realize the BS power-on/-off control utilizing

35

analytics results of pedestrian flow (i.e., the time-varying number of mobile terminals
in the coverage area of each BS) and non-terrestrial networks (NTN) [34] - [36]. Figure
3.2.5.2-1 illustrates a schematic of the proposed architecture, which extends the ORAN architecture so that the pedestrian flow prediction results and NTN caneffectively be utilized to achieve higher energy efficiency. Inside the Non-RT RIC
installed in the mobile core, an analytics engine is deployed for each BS and predicts
the pedestrian flow in a specific period by learning the past data of pedestrian flow. For
example, Long Short-Term Memory can be applied as a deep learning for theprediction. The Non-RT RIC also equips a power scheduling engine to predetermine
power-on/-off of multiple BSs for each time. The proposed scheduling algorithm
maximizes the number of BSs that can be turned off based on the pedestrian flow
prediction results. The Near-RT RIC is deployed in each BS to execute the power-on/off of the corresponding CU/DU. UEs existing in coverage areas in which the BSs areturned off can access NTN such as LEO (Low Earthe Orbit) and HAPS (High-altitude
Platform Station) to reduce the power consumption of O-RAN while maintaining thecontinuity of data communications, the bitrate, and so on.

Figure 3.2.5.2-1: Proposed power management architecture36

In [34][35], NICT evaluated the power-saving effect obtained by the proposedarchitecture. KDDI Location Data [37] of 42 mesh areas of Koganei City in Tokyo, wereused as real data of pedestrian flow time-varying every 15 minutes. The pedestrianflow for 1 weekday was predicted by training the past 56 weekdays’ data. Besides, thedatabase disclosed in [38] was used as mobile traffic data time-varying every hour. The
power consumption of CU, DU, and RU were set as 337W, 514W, and 84W,
respectively, by using measurement results on a B5G mobile testbed [39], and that of
NTN as 2770W. Figure 3.2.5.2-2 shows the relative power consumption of the proposedarchitecture utilizing NTN, where that of the conventional method turning on all ORAN BSs without using NTN is set as 1. The communication capacity of NTN waschanged in the range from 100 Mbps to 1.3 Gbps. A larger capacity of NTN can
accommodate more UEs’ traffic, and consequently can turn off more BSs. For example,
the proposed architecture can reduce the power consumption by up to 40% when the
NTN can accommodate Ues’ traffic of 400 Mbps.

Figure 3.2.5.2-2: Relative power consumption of our proposed architecture
3.2.5.3 Conclusion
The proposed power management architecture can highly contribute to energy-saving
for O-RANs. In future work, we need to investigate power management for unusual
situations such as bad weather, events, and disasters.

37

4.

Global Standards for Mobile Communication Networks

4.1

Overall Trend of Energy Efficiency Standardization

In the field of mobile networks, reducing energy consumption and associated OPEX
(operating expenses) has been a crucial challenge for communication service providers.
Particularly during large-scale disasters such as earthquakes and tsunamis, ensuringcommunication means, including emergency call, becomes a vital task even in cases ofpower loss at base stations. Mobile operators have been working on various measures
to address this issue. These efforts are expected to play a significant role in ensuring
the sustainability of communication networks and the efficient use of energy as wemove towards Beyond 5G/6G.
Furthermore, in the context of Beyond 5G/6G, as communication networks continue toexpand and become more densely populated, the introduction of further energyefficient technologies and improvements in energy efficiency are essential to minimize
environmental impact. International standardization bodies, such as the International
Telecommunication Union (ITU), the 3rd Generation Partnership Project (3GPP), andthe O-RAN Alliance, focus on energy efficiency and sustainability as crucial elementsin the development of new communication standards, for instance, as defined in theusage scenarios of IMT-2030 defined by ITU-R in Figure 2.3-1. This includes not only
the power consumption of base stations and network equipments but also theintroduction of low-power devices like IoT, virtualized networks, and other measures.
Efforts are also underway to minimize environmental impact through the use ofrenewable energy and the introduction of technologies to reduce unnecessary energy
waste.
4.1.1

3GPP

3GPP is an organization that promotes the standardization of mobile communication
technologies, developing standards for generations such as 3G, 4G, and 5G. Within
3GPP, standardization activities related to Energy Saving and Energy Efficiency havebeen underway since the LTE era (Rel-8), as part of the efforts within the Self
Organized Network (SON) framework.
Functions like Minimization of Drive Test (MDT), which collects information oncommunication quality from commercial terminals to reduce drive tests, and featuresthat allow to switch off specific cells under certain conditions, such as using OAM(Operation, Administration, and Maintenance) information to ensure predeterminedcommunication quality and coverage (by other base stations or frequencies), have been
discussed and standardized in forums like 3GPP SA5 and RAN3.

38

Furthermore, in the context of 5G/5G Advanced, with the utilization of IoT, big data,
and the evolution of AI/ML, there is ongoing consideration of more advanced Energy
Saving and Energy Efficiency. In the core network, the introduction of Service-BasedArchitecture has increased the scalability of Network Functions through virtualizationand cloud infrastructure, significantly enhancing the flexibility of network resource
management with NW slice functionality, contributing to power consumption
optimization.
In the RAN, with the introduction of 5G NR (New Radio) and new frequency bands, aswell as communication technologies like beamforming and MIMO, efforts are being
made to standardize technologies to maximize energy efficiency alongside expanding
frequency utilization efficiency. Initiatives for smart energy management andminimizing energy waste through dynamic network adjustments are also being
pursued.
In 3GPP Rel-19, discussions have begun on a Study Item specialized in Energy
Efficiency and Energy Saving, where the scope is to identify potential enhancements on
5GS (5G System) including network energy related information exposure,enhancement for subscription and policy control to enable energy efficiency as service
criteria and also potential enhancements to take into account the availability of
renewable energy may also be considered [41]4.1.2

O-RAN AllianceIn the O-RAN Alliance, consideration of Network Energy Saving and Energy Efficiency
is also progressing. The O-RAN Alliance further subdivides network devices such asbase stations that constitute the Radio Access Network (RAN) defined by 3GPP into
logical functional units. It explores the interface specifications for flexible and effective
network configurations and commercial deployments using virtualization
infrastructure. The alliance is advancing discussions on features that enable moreadvanced network operations using RAN Intelligent Controller (RIC) and O-Cloud,with Energy Saving/Energy Efficiency being a crucial element in this context.
Within the nodes defined by O-RAN (O-RU, O-DU, O-CU) and the logical componentsof the network defined by 3GPP (Cell, Carrier, gNB, etc.), research is conducted on thereal-time monitoring and reporting of energy consumption and energy efficiency. The
following functions are under consideration:-

Switching off/on of carriers and cells-

RF channel reconfiguration-

Selection of advanced sleep modes

39-

Energy-saving mode for O-Cloud resources

While building upon considerations in 3GPP, the O-RAN Alliance plays a crucial role inachieving even more flexible and advanced Energy Efficiency through the utilizationand expansion of RIC and O-Cloud.
4.1.3

ITU and ETSIIn ITU-T, Study Group 5 (SG5) is the lead study group on EMF, environment, climate
action, sustainable digitalization and circular economy, etc. In ITU-T SG5 provides
guidance and specifications for the safe and sustainable use of ICT, including ICT
products, equipment and installation. SG5 also develops international standards
promoting the use of ICT for accelerating a sustainable digital transformation andclimate change adaptation and mitigation actions in line with the UN SDG, the
UNFCCC Paris Agreement and the Glasgow Climate Pact [42].
In ETSI, the energy efficiency matters are handled by the Technical Committee (TC)
Environmental Engineering (EE) which is responsible for defining the equipment
engineering, the bonding and grounding, the power supply interface and environmental
aspects for telecommunication infrastructures and equipment [43].
There is an energy efficiency related standard jointly developed by ITU-T SG5 andETSI TC EE. The scope of the joint effort with ITU-T and ETSI is to provide a better
understanding of the energy efficiency of mobile networks in particular considering the
networks’ evolution in different periods of time [44]. The focus of the standard(Recommendation ITU-T L.1331/ ETSI ES 203 228) is on metrics for energy efficiency
and methods of assessing (and measuring) energy efficiency in operational networks.
For instance, how to measure the energy efficiency ratio is defined from extensive
viewpoints, such as, site efficiency, data volume, area coverage, End-to-End latency,
number of subscribers, NW slices, etc. [44]. The energy efficiency metrics defined in thestandard is applicable to GSM, UMTS, LTE and 5G NR. Furthermore, ETSI has
standardized the measurement method for the evaluation of BTS power consumption &energy consumption with static load [45]. For these energy efficiency related standards,
the target is NW energy efficiency and UE side is out of the scope.
4.1.4

IETF

IETF has founded a new program for discussing environmental impacts andsustainability of internet technology, that is, the E-Impact Program [46]. The scope of
the E-Impact Program is to investigate trends, issues, improvement opportunities,
ideas, best practices, and subsequent direction of work related to Internet technology,

40

architecture, and operations, including visibility and efficiency on energy and otherenvironmentally impacting attributes. One of the active projects is to outline theguideline of sustainability considerations when designing a new protocols andextensions specified in the IETF standards [47].
4.2

Case Study on Energy Saving through utilization of O-RAN Alliance RIC

Research and Development
As a case study for the utilization of Energy Saving in mobile networks, various
research and development efforts are underway. Within the NICT (National Instituteof Information and Communications Technology) research project titled ‘Research andDevelopment on Frequency Efficiency Improvement through Antenna Transmission
Control and Power Control Optimization of Open RAN,’ one of the scopes involves
research and development on Energy Saving utilizing the RIC (RAN Intelligent
Controller) specified by the O-RAN Alliance. In the context of the Open RAN
environment, which becomes even more crucial in Beyond 5G/6G, RIC is positioned asa vital component to enable the autonomous provision of flexible and dynamic services.

Figure 4.2-1: System Configuration of the RIC Control Platform
Table 4.2-1: Components of the RIC Control Platform
Elements
Near-RT RIC
Non-RT RIC

Summary
Near-real-time RIC implementation
compliant with O-RAN.WG3.RICARCH.
Non-real-time RIC implementation
compliant with O-RAN.WG2.Non-RTRIC-ARCH.

41

Related IF
A1/E2/O1
A1/R1

O-Cloud

API Gateway
AI/DATA Platform

inventory
FOCOM(Federated O-Cloud
Orchestration)
NFO (Network
Function
Orchestration)（Network Function
Orchestration)
OAM (Operation andManagement)（Operation andManagement (OAM)

IMS (Infrastructure Management
Services): manages O-Cloud resources
and resource management software.
DMS (Deployment Management
Services): Manages NF Deployment on
O-Cloud, providing functions such as
instantiating, monitoring, and stopping
NF Deployment.
API gateway functionality. Northband
system integration.
Provides AI/ML and data analysis
functions, AI/ML model training, AI
support resource optimization, and AI
support recommendations on LCM andrApp.
Provides Inventory Management (IM)functionality for CU, DU, RU, O-Cloud,rApp, xApp, RAN data, etc.
Work with LCM and Inventory to provide
management capabilities for O-Cloud's
infrastructure resources and software.
Provides NF Deployments management
functionality on the O-Cloud for acollection of Network Functions (NF) in
conjunction with LCM and Inventory.

O2-IMS

Provides Fault Management (FM)functionality.
Provides Performance Management (PM)functionality.
Provides Configuration Management(CM) functionality.
Provide Observability. Example: ORAN/O-Cloud logs, metrics/KPIs, traces,
etc. to be collected via O1/O2.

O1/O2

O2-DMS

R1
A1

O2-IMS
O2-DMS

O1/O2
O1-CM
O1-FM/O1-PM/
O2-FM/O2-PM

In the consideration of Energy Saving in mobile communication systems, including
Open RAN, it is crucial to establish platforms and environments that implement
various functions to reduce the power consumption of RAN (Radio Access Network)
devices. This includes real-time monitoring and analysis of the utilization of various
resources such as cloud servers, as well as traffic conditions. Utilizing Artificial
Intelligence (AI) and Machine Learning (ML), a platform for implementing appropriate
control based on real-time monitoring becomes essential.
In this research and development, the focus is on the development of a power control
system based on ML for real-time analysis of service models and base station
configurations. Additionally, a big data platform is being developed to collect power42

consumption data from RAN and core networks, as well as user traffic. The goal is todemonstrate a power reduction of over 10% across the entire network, contributing to
improved frequency efficiency, with the baseline being the scenario where the power
control system is not utilized. The following outlines the power control-related
functions that constitute the scope of this research and development:-“Cell/Carrier Switch On/Off”: Based on predictions or current load conditions,specific cells/carriers are turned off to reduce the power consumption of theRadio Access Network (RAN). For instance, in scenarios where capacity cells(aimed at capacity enhancement) are completely overlaid by coverage cells(aimed at coverage provision), as described in Section 5.1.3.3 of 3GPP TS
28.310, capacity cells can be turned off during low-traffic times to offload allcommunication to coverage cells.

-“RF Channel Reconfiguration”: Based on predictions or current load conditions,control of the number of transmission channels in the Radio Frequency (RF) is
adjusted to reduce the power consumption of the RAN. For example, during
periods of low traffic, the MIMO channel count can be changed from 4x4 to 2x4.

-

“Advanced Sleep Modes”: Based on predictions or current load conditions,power-saving states of the O-RAN Radio Unit (O-RU) are controlled according
to the Advanced Sleep Modes under consideration by the O-RAN Working
Group 1’s Use Case Task Group. This helps in reducing the power consumptionof the RAN.

-

“Cloud Resource Energy Saving”: Based on predictions or current loadconditions, power-saving states of various components in the edge cloud (e.g., Cstates for CPU) are controlled to reduce the power consumption of the edge
cloud.

The Cell OFF function involves the following operating principles:-Specific Cell Deactivation: The function identifies specific cells within the
network and deactivates them.

-Power Consumption Reduction: The primary objective is to reduce the power
consumption of the Radio Access Network (RAN).

-

Example Scenario: As an illustration, during low-traffic periods, the function
may deactivate capacity cells (cells designed for capacity enhancement) if they
are entirely overlaid by coverage cells (cells designed for coverage provision).
This action facilitates the offloading of all communications to the coverage cells,
optimizing power usage in the network.

43

Figure 4.2-2: Overview of the Cell OFF Function
To achieve maximum power savings across the entire network, it is crucial to flexibly
and dynamically select the most optimal sleep modes for each cell, taking into accountvariations in traffic load. Leveraging the Cell OFF function and offloading User
Equipment (UE) being serviced to a coverage cell covering the same area as the current
serving cell is expected to further reduce the power consumption of capacity cells. Thisapproach allows for consideration of traffic load fluctuations in each cell and aims toenhance power efficiency.

Figure 4.2-3: Power reduction example with Cell OFF [W]
Furthermore, power control technologies for the edge cloud are also crucial. Dynamic
control of CPU cores and operating frequencies based on traffic trends, without
impacting the performance of the control plane and user plane, is being pursued.
Additionally, advancements include dynamic orchestration techniques for virtual
network functions based on traffic demand and predictions, along with verification of
power control effects in the edge cloud contributing to improved energy efficiency.

44

The research in this section was supported by Japan government. Part of the research
results were obtained from the commissioned research (No. JPJ012368C06501) by
National Institute of Information and Communications Technology (NICT) , Japan.

5.

Acknowledgements

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

6.

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[34] T. Miyazawa et al., IEICE Technical Report, RCS2023-124, Nagano, Japan, Aug.
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[40] https://www.itu.int/en/ITU-R/study-groups/rsg5/rwp5d/imt2030/Pages/default.aspx[41] 3GPP TR 23.700-66 Study on Energy Efficiency and Energy Saving(Release 19)[42] ITU-T SG5 Webpage, https://www.itu.int/en/ITU-T/studygroups/20222024/05/Pages/default.aspx[43] ETSI TC EE Webpage, https://www.etsi.org/committee/1395-ee[44] Recommendation ITU-T L.1331/ETSI ES 203 228 v1.4.1 (2022-04), “Assessment ofmobile network energy efficiency,”[45] ETSI ES 202 706-1 v1.7.1 (2022-08), “Metrics and measurement method for energy
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48

7.

Contributing Authors

Chapter 3.2.3.1: Optimal deployment planning technology for vRAN resources,
exemplified by energy saving
Fujitsu Limited: Masayuki Satsu and Tatsuaki Oyabu
Chapter 3.2.5: Coordination with Non-Terrestrial Network (Power Management forBase Stations Utilizing Pedestrian Flow Analytics and Non-Terrestrial Networks)
National Institute of Information and Communications Technology: Takaya Miyazawa
Chapter 4: Global Standards for Mobile Communication Networks
Rakuten Mobile: Kenichiro Aoyagi
Others:Nokia: Hideaki Takahashi, Ali Rezaki, Daniela Laselva, Gerald Kunzmann, Jinfeng
Du, Kimmo Kettunen, Laurent-Walter Goix, Navin Hathiramani, Olivier Guyot, Satish
K., Shohreh Ahvar

49

Abbreviation List
Abbreviation

Explanation

3GPP

Third Generation Partnership Project

AI

Artificial Intelligence

API

Application Programming Interface

APs

Access Points

AR

Augmented Reality

ASIC

Application Specific Integrated Circuit

BS

Base Station

BTS

Base Transceiver Station

CA

Carrier Aggregation

CK-MARL
CN

Constrained KPI-managing Multi-Agent Reinforcement
Learning
Core Network

CPU

Central Processing Unit

CSI

Channel State Information

CSP

Communication Service Provider

CU

Central Unit

DFE

Digital Front End

DFT

Discrete Fourier Transform

DRX

Discontinuous Reception

DTX

Discontinuous Transmission

DU

Distributed Unit

EMF

Electromagnetic Field

ESG

Environmental, Social and (corporate) Governance

ETSI

European Telecommunications Standards Institute

FDE

Frequency Domain Equalization

FDMA

Frequency Division Multiple Access

FFT

Fast Fourier Transform

FR

Frequency Range

GEO

Geostationary Orbit

GHG

Green House Gas

GPU

Graphics Processing Unit

50

Abbreviation

Explanation

GSM

Global System for Mobile communication

HAPS

High Altitude Platform Station

IETF

Internet Engineering Task Force

I/F

InterfaceIC

Integrated Circuit

ICT

Information and Communication Technology

IoT

Internet of Things

IMTInternational Mobile Telecommunications

IT

Information Technology

ITU

International Telecommunication Union

ITU-R

ITU Radiocommunication sector

ITU-T

ITU Telecommunication standardization sector

KPI

Key Performance Indicator

KV

Kay Value

KVI

Key Value Indicator

LEO

Low Earth Orbit Satellite

LTE

Long Tern Evolution

MDT

Minimization of Drive Test

MIC

Ministry of Internal affairs and Communications

MIMO

Multiple-Input and Multiple-Output

mMIMO

massive MIMO

ML

Machine Learning

NF

Network FunctionNR

New Radio

NTN

Non-Terrestrial Network

NW

Network

OFDM

Orthogonal Frequency Division Multiplexing

OPEX

Operating Expense

O-RAN

Open Radio Access Network

PA

Power Amplifier

PAPR

Peak to Average Power Ratio

51

Abbreviation

Explanation

PCell

Primary Cell

PDSCH

Physical Downlink Shared Channel

PHY

Physical Layer

QoS

Quality of Service

RAN

Radio Access NetworkrApp

Non-RT RIC Application

RAT

Radio Access Technology

RFRadio Frequency

RFIC

Radio Frequency Integrated Circuit

RIC

RAN Intelligent Controller

RT

Real Time

RU

Radio Unit

SCell

Secondary Cell

SDGs

Sustainable Development Goals

SMO

Service Management and Orchestration

SON

Self Organized Network

SSB

SS/PBCH block

TRP

Transmission Reception Points

UE

User Equipment

UMTS

Universal Mobile Telecommunication System

UN

United Nations

UNFCCC

United Nations Framework Convention on Climate Change

vRAN

Virtual Radio Access Network52