ETSI TR 104 084 V1.1.1 (2026-08)

• Abstract:

  DTR/EE-EEPS58

• Status: Not Published
• Technical Committee: EE EEPS - EE Eco Environmental Product Standards
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 01-Sep-2026
• Completion Date: 19-Aug-2026

TECHNICAL REPORT
Environmental Engineering (EE);
Study of multi-dimensional network energy efficiency metrics

2 ETSI TR 104 084 V1.1.1 (2026-08)

Reference
DTR/EE-EEPS58
Keywords
energy efficiency, metrics, network
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ETSI
3 ETSI TR 104 084 V1.1.1 (2026-08)
Contents
Intellectual Property Rights . 5
Foreword . 5
Modal verbs terminology . 5
Executive summary . 5
Introduction . 6
1 Scope . 7
2 References . 7
2.1 Normative references . 7
2.2 Informative references . 7
3 Definition of terms, symbols and abbreviations . 8
3.1 Terms . 8
3.2 Symbols . 8
3.3 Abbreviations . 8
4 Multi-dimensional network energy efficiency . 9
5 Existing network energy efficiency metrics . 10
6 Performance dimensions . 11
6.1 Introduction . 11
6.2 Service volume . 11
6.2.1 Enhanced Mobile Broadband (eMBB) . 11
6.2.2 Massive Machine Type Communications (mMTC) . 11
6.2.3 Ultra-Reliable Low-Latency Communications (uRLLC) . 11
6.3 Service quality . 12
6.3.1 Enhanced Mobile Broadband (eMBB) . 12
6.3.2 Massive Machine Type Communications (mMTC) . 12
6.3.3 Ultra-Reliable Low-Latency Communications (uRLLC) . 12
6.4 Service availability . 12
6.4.1 Enhanced Mobile Broadband (eMBB) . 12
6.4.2 Massive Machine Type Communications (mMTC) . 12
6.4.3 Ultra-Reliable Low-Latency Communications (uRLLC) . 12
6.5 Summary . 12
7 Multi-dimensional network energy efficiency metric proposals . 13
7.1 General formulation . 13
7.2 Metric based on adjustment factor(s) . 13
7.2.1 General . 13
7.2.2 Limitation of adjustment factors . 15
7.3 Multiplicative metric . 16
7.4 Separate metrics per performance dimension . 16
8 Analysis of metric proposals . 17
8.1 Use cases . 17
8.1.1 Introduction. 17
8.1.2 Mobile Network EE based network optimization . 18
8.1.3 Mobile Network EE based service delivery optimization . 18
8.1.4 Mobile Network EE assessment between different types of mobile network architecture . 18
8.1.5 EE monitoring and reporting . 18
8.2 Evaluation of new and/or revised EE metrics. 18
8.3 Qualitative analysis of multi-dimensional metric proposals . 24
9 Conclusions and recommendations . 25
Annex A: Required measurements for adjustment factors . 27
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4 ETSI TR 104 084 V1.1.1 (2026-08)
A.1 Introduction . 27
A.2 Throughput measurement . 27
A.3 Coverage quality measurement . 28
Annex B: Comparison of existing and proposed quality adjustment factors . 29
B.1 Introduction . 29
B.2 Adjustment factors in the present document . 29
B.3 Existing factors in ETSI ES 203 228 . 30
B.3.1 Introduction . 30
B.3.2 CS traffic . 30
B.3.3 Coverage area . 30
B.4 Observations and discussion . 31
Annex C: Example application of metric based on adjustment factor. 33
C.1 Introduction . 33
C.2 Methodology . 33
C.3 Reference implementation . 33
C.4 Optimization performance . 34
History . 37

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5 ETSI TR 104 084 V1.1.1 (2026-08)
Intellectual Property Rights
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Foreword
This Technical Report (TR) has been produced by ETSI Technical Committee Environmental Engineering (EE).
The present document was developed jointly by ETSI TC EE and ITU-T Study Group 5 and published respectively by
ITU and ETSI as Supplement ITU-T L.Suppl. 62 [i.6] and ETSI Technical Report ETSI TR 104 084 (the present
document), which are technically equivalent.
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.
Executive summary
Existing network energy efficiency metrics are typically only based on the amount of service provided, e.g. transported
data volume, coverage area, or number of connections. Other dimensions of useful work, e.g. the quality of the service
provided, is seldom reflected, and it is also not always clear if/how these metrics can be combined to give a more
complete picture of the energy efficiency of a network. The scope of the present document is to study existing network
energy efficiency metrics and outline proposals on how network energy efficiency metrics capturing multiple
performance dimensions (service volume, service quality, service availability, etc.) can be formulated. These can be
combinations of existing network energy efficiency metrics, but also new proposals capturing performance dimensions
not considered today. The focus has been on network energy efficiency metrics to be used for monitoring and/or
optimization of live networks, even though metrics for reporting and related implications have been also covered.
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6 ETSI TR 104 084 V1.1.1 (2026-08)
The analysis concludes that it is desirable that in addition to the existing mostly service-volume based EE metrics, also
service quality is added into the EE assessment. In particular, service quality based on (average) user throughput may be
valuable to consider in addition to data volume when assessing mobile network data energy efficiency, something that
has also been concluded by 3GPP. It is also desirable to extend the current mobile network coverage energy efficiency
analysis to consider differences in coverage quality in addition to coverage availability. Whether also service
availability should be considered is less obvious, as the analysis conducted shows limited value to add such metrics into
the assessment.
Three multi-dimensional metric proposals, i.e. structures for considering multiple performance dimensions in the EE
assessment, have been defined and assessed in the present document:
• Metric based on adjustment factor(s): A base metric, typically the existing service volume-based EE metrics
already defined, is extended (i.e. multiplied) by a dimension-less adjustment factor. The idea is that the
adjustment factor captures another performance dimension and scales the base metric accordingly.
• Multiplicative metric: In this case, two performance indicators are multiplied and considered in relation to
the network energy consumption.
• Separate metrics per performance dimension: Multiple single-dimensional energy efficiency metrics
represent the different performance dimensions in the network energy efficiency evaluation.
Of these, the second proposal - multiplicative metric - has been assessed as least attractive. It can be difficult to
understand, as it mixes units of different performance metrics and relates them to the energy consumption at the same
time. It may then be difficult to understand how the metric behaves, and what it really tells. The other two have their
pros and cons depending on the use case. Using separate metrics per performance dimension is beneficial for a more
nuanced assessment, as it reveals all details of what is driving the results. However, it may make an optimization
algorithm more complex with more variables to consider. A metric based on adjustment factors may hide nuances, but
with just one or two performance indicators reflected this risk is reduced. Furthermore, if it is constructed in a sound
way it should be easy to understand, e.g. with service volume scaled by a factor based on service quality. It may in
particular be useful for network optimization.
For reporting, it is recommended to keep the existing single dimensional metrics, e.g. mobile network data energy
efficiency, as they have been used for long time, are well-known, and also requested in some reporting frameworks.
Furthermore, the analysis also confirms that contextual factors such as geographical conditions, population density,
topology, climate zones, and also regulatory obligations on coverage and service quality, may have a significant impact
on the energy efficiency assessment and its' results. Therefore, this again emphasizes that the network level energy
efficiency analysis and metrics should not be used for benchmarking of different networks (unless the contextual factors
are the same or compensated for), rather the use case is for assessing improvements over time in a specific network and
optimization of the network.
Introduction
A mobile network needs to fulfil several service requirements, such as being able to deliver a certain service volume
with a given service quality and service availability. A number of different energy efficiency metrics have been defined
throughout the years, e.g. for mobile networks in ETSI ES 203 228 [i.1]. These metrics are typically used to assess the
energy efficiency of the network over time, but can also be used in network optimization algorithms for improving the
network energy efficiency in an automated manner. The metrics should not be used for benchmarking of different
networks, as contextual factors of different networks such as regulatory obligations, emergency service requirements,
geographical and population characteristics, etc. significantly influence the metrics.
These existing network energy efficiency metrics are typically only based on the amount of service provided,
e.g. transported data volume, coverage area, or number of connections. However, the quality of the service provided is
seldom reflected, and it is also not always clear if/how these metrics can be combined to give a more complete picture
of the energy efficiency of a network.
The present document studies existing network energy efficiency metrics and outlines proposals and analysis on how
mobile network energy efficiency metrics capturing multiple performance dimensions (service volume, service quality,
service availability, etc.) can be formulated.

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7 ETSI TR 104 084 V1.1.1 (2026-08)
1 Scope
The present document studies existing network energy efficiency metrics and outlines proposals how network energy
efficiency metrics capturing multiple performance dimensions (service volume, service quality, service availability,
etc.) could be formulated. These can be combinations of existing energy efficiency metrics, but also new proposals
capturing performance dimensions not considered today. The focus is on network energy efficiency metrics to be used
for monitoring and/or optimization of live networks.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long-term validity.
The following referenced documents may be useful in implementing an ETSI deliverable or add to the reader's
understanding, but are not required for conformance to the present document.
[i.1] ETSI ES 203 228 (V1.4.1): "Environmental Engineering (EE); Assessment of mobile network
energy efficiency".
[i.2] ETSI TS 128 554 (V19.5.0): "5G; Management and orchestration; 5G end to end Key Performance
Indicators (KPI) (3GPP TS 28.554 version 19.5.0 Release 19)".
[i.3] ETSI TR 128 913 (V18.0.1), "5G; Study on new aspects of Energy Efficiency (EE) for 5G phase 2
(3GPP TR 28.913 version 18.0.1 Release 18)".
[i.4] ETSI TS 132 450 (V18.0.0): "Universal Mobile Telecommunications System (UMTS); LTE;
Telecommunication management; Key Performance Indicators (KPI) for Evolved Universal
Terrestrial Radio Access Network (E-UTRAN): Definitions (3GPP TS 32.450 version 18.0.0
Release 18)".
[i.5] 3GPP TR 28.880 (V19.0.0 - 2024-12): "Study on energy efficiency and energy saving aspects of
5G networks and services (Release 19)".
[i.6] ITU-T L.Suppl. 62: "Study of Multi-dimensional network energy efficiency metrics".
[i.7] Recommendation ITU-T L.1015 (05/19): "Criteria for evaluation of the environmental impact of
mobile phones".
[i.8] Recommendation ITU-T K.114 (08/22): "Electromagnetic compatibility requirements and
measurement methods for digital cellular mobile communication base station equipment".
[i.9] Recommendation ITU-T E.805 (12/19): "Strategies to establish quality regulatory frameworks".
[i.10] ETSI ES 202 336-12 (V1.3.1): "Environmental Engineering (EE); Monitoring and control
interface for infrastructure equipment (power, cooling and building environment systems used in
telecommunication networks); Part 12: ICT equipment power, energy and environmental
parameters monitoring information model".
[i.11] JRC144975 (2026) Baldini, G. Cerutti I: "EU Code of Conduct for the sustainability of
telecommunications networks".
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8 ETSI TR 104 084 V1.1.1 (2026-08)
3 Definition of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the following terms apply:
end-to-end latency: time taken to transfer a given piece of information from a source to a destination, measured at the
communication interface, from the moment it is transmitted by the source to the moment it is successfully received at
the destination
NOTE: As defined in [i.1].
energy efficiency: relation between the useful output and energy/power consumption
NOTE: As defined in [i.1].
multi-dimensional network energy efficiency: network energy efficiency assessment or metric based on multiple
performance indicators from different performance dimensions
reliability: probability that a product functions as required under given conditions, including maintenance, for a given
duration without failure
NOTE: As defined in [i.7].
service availability: availability of the service provided by the network, for example in time or space
service quality: quality of the telecommunication services provided by an operator
NOTE: As defined in [i.9].
service volume: volume of the service provided by the network, for example data volume, coverage area, or number of
connections
throughput: number of payload bits successfully received per second for a reference measurement channel under a
specified reference condition
NOTE: As defined in [i.8].
3.2 Symbols
Void.
3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
2G Second Generation
3G Third Generation
rd
3GPP 3 Generation Partnership Project
4G Fourth Generation
5G Fifth Generation
B2B Business-to-Business
B2C Business-to-Consumer
BS Base Station
CO2e Carbon Dioxide equivalent
CoA Coverage Area
CS Circuit Switched
CSP Communication Service Provider
DL DownLink
DV Data Volume
EC Energy Consumption
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9 ETSI TR 104 084 V1.1.1 (2026-08)
EE Energy Efficiency
eMBB enhanced Mobile Broadband
eNB evolved Node B
FDD Frequency Division Duplex
FWA Fixed Wireless Access
gNB Next Generation Node B
HW Hardware
ICT Information and Communication Technologies
IoT Internet of Things
IP Internet Protocol
KPI Key Performance Indicator
LTE Long Term Evolution
MDEE Multi-Dimensional Energy Efficiency
mMTC massive Machine Type Communications
MN Mobile Network
MNO Mobile Network Operator
MTC Machine Type Communication
NG-RAN New Generation Radio Access Network
NR New Radio
NSA Non-Stand Alone
NTN Non-Terrestrial Networks
O&M Operation and Maintenance
OPEX OPerational EXpenditures
O-RAN Open RAN
OTT Over The Top
PDCP Packet Data Convergence Protocol
QCI QoS Class Identifier
QoS Quality of Service
RAB Radio Access Bearer
RAN Radio Access Network
RAT Radio Access Technology
RF Radio Frequency
RLC Radio Link Control
RRC Radio Resource Control
RSRP Reference Signal Received Power
SA Stand-Alone
SDU Service Data Unit
TTI Transmission Time Interval
UE User Equipment
UL Uplink
uRLLC ultra-Reliable Low Latency Communication
VoLTE Voice over LTE
VoNR Voice over NR
4 Multi-dimensional network energy efficiency
A Mobile Network (MN) needs to fulfill several service requirements. It need to provide reliable coverage where the
users want to access service, it need to provide enough capacity so that the traffic demand can be served, and it need to
provide a service quality such that the users are satisfied with their experience of the service. Mobile Network Operators
(MNOs) are continuously investing in their networks to make sure these service requirements, which evolve over time,
are fulfilled. These investments are typically materialized in equipment consuming energy, and energy efficiency is an
important objective. Energy efficiency is often defined as the useful output of a product or service divided by the energy
consumption of the product or the service:
������ ����
������ ���������� =
������ �����������
A number of different energy efficiency metrics have been defined throughout the years, e.g. for mobile networks in
ETSI ES 203 228 [i.1]. These network energy efficiency metrics are typically based on the amount of service provided,
e.g. transported data volume, coverage area, or number of connections.
ETSI
10 ETSI TR 104 084 V1.1.1 (2026-08)
However, a drawback with these metrics is that they only consider the amount of service, but not the quality of the
service provided which also is an important useful output of the network. In fact, mobile networks today may have
similar data volume energy efficiency, but the user quality may vary greatly.
With the continuous development of networks, new services and new scenarios emerge. The focus of services varies in
different phases and scenarios of network development, both in terms of increasing traffic demand to service, and the
requirements for service quality. To allow for a more comprehensive energy efficiency evaluation as well as
optimization in different network development phases and scenarios, it is desirable that also other performance
dimensions than the service volume is reflected, e.g. the service quality.
5 Existing network energy efficiency metrics
ETSI ES 203 228 [i.1] defines mobile network energy consumption metrics and mobile network energy efficiency
metrics based on a number of performance metrics coupled to use case families.
For the enhanced Mobile Broadband (eMBB) use case family, the energy efficiency metrics Mobile Network data
Energy Efficiency (EE ) and Mobile Network coverage Energy Efficiency (EE ) measured in bit/J and m /J,
MN,DV MN,CoA
respectively, are defined. They are derived from performance metrics of the Mobile Network (MN) under investigation,
in these cases the total data volume (DV ) delivered by all its equipment and its global coverage area (CoA ),
MN MN
respectively, and divided by the mobile network energy consumption (EC ):
MN
��
��
�� =
��,��
��
��
���
��
�� =
��,���
��
��
Mobile Network coverage Energy Efficiency (EE ) is mainly used to complement Mobile Network data Energy
MN,CoA
Efficiency (EE ) for mobile networks handling low data volumes, in particular in rural or deep rural areas. It is
MN,DV
worth noting, that EE is the only one of the defined mobile network energy efficiency metrics that involves a
MN,CoA
quality factor. In ETSI ES 203 228 [i.1], the coverage quality factor (CoA_Q) measures the performances of the
network within the actually covered fraction of the planned total coverage area. UE reports such as failed call attempts
are used to determine how well the users within the coverage area are covered, and the coverage quality factor is on a
high level defined as:
Q = 1 - "percentage of users/sessions with coverage failure"
For the ultra-Reliable Low Latency Communications (uRLLC) use case family, ETSI ES 203 228 [i.1] defines a mobile
network energy efficiency metric based on the end-to-end latency of the mobile network (T ). The mobile network
e2e,MN
latency energy efficiency (EE ) is defined as the inverse ratio of the end-to-end user plane latency and the energy
MN,L
consumed by the MN:
�
�� =
��,�
� ∗��
���,�� ��
-1
where �� is expressed in ms /J.
��,�
For the massive Machine Type Communications (mMTC) use cases, ETSI ES 203 228 [i.1] defines a mobile network
energy efficiency metric based on the number of subscribers (N registered to the network. In this case, the EE
MMTC)
metric is defined as follows:
�
����
�� =
��,����
��
��
For more detailed information on how these mobile network energy efficiency metrics are calculated, and how the
mobile network energy consumption (EC ) as well as the respective performance metrics are measured, see ETSI
MN
ES 203 228 [i.1].
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11 ETSI TR 104 084 V1.1.1 (2026-08)
6 Performance dimensions
6.1 Introduction
A mobile network needs to provide several services, and fulfil many requirements. For long time mobile networks have
been used for voice communication, then for consuming multimedia services, and with the introduction of 5G more and
more of machine type communications. All of these communication forms are characterized by different Key
Performance Indicators (KPIs), telling how well the network is delivering its services.
The performance indicators can typically be divided into different classes, or dimensions. The three common
performance dimensions are the following:
• Service volume, typically measuring the volume or amount of service provided. Examples can be the data
volume delivered, the area covered, the number of supported connections, etc.
• Service quality, measuring the quality of the service provided. Examples are user throughput, coverage quality
or connection quality.
• Service availability, typically measuring the availability of the service. An obvious example in a wireless
network is the area coverage of the service, but one can also envision other KPIs belonging to this
performance dimension such as service availability in time.
One can probably formulate more performance dimensions, but these are a good starting point in order to keep the
complexity down. Furthermore, as can be seen of the examples, some KPIs may fall into more than one of the
performance dimensions depending on the type of service and how important the different KPIs are. Therefore, how to
classify different KPIs typically depends on type of service or use case. This is in line with how energy efficiency KPIs
have been defined in ETSI ES 203 228 [i.1], as they are typically based on the most important performance KPI for the
use case.
To exemplify this, the three 5G use case families eMBB, mMTC and uRLLC will be examined, and KPIs belonging to
the three performance dimensions will be identified for each of the use case families.
6.2 Service volume
6.2.1 Enhanced Mobile Broadband (eMBB)
Mobile broadband is still the absolutely dominating use case for mobile networks, and is expected to continue be so
even though other use cases are emerging today. As can be seen from the existing eMBB energy efficiency metrics, two
important performance metrics for eMBB are the total data volume (DV ) delivered and the coverage area (CoA ),
MN MN
respectively. These can be seen as belonging to the performance dimension service volume, but representing two
different sub use cases; capacity sites and coverage sites.
6.2.2 Massive Machine Type Communications (mMTC)
For the mMTC use case family, an important performance indicator is the number of connected users, or as already
used in ETSI ES 203 228 [i.1] the number of subscribers (N registered to the network. This is clearly a service
MMTC)
volume performance indicator.
6.2.3 Ultra-Reliable Low-Latency Communications (uRLLC)
The uRLLC use case family covers many different use cases. In some use cases it is the reliability that is the most
important performance matric, while in others it is low latency that is the most important metric, and in some both are
equally important. Furthermore, there may also be use cases where data volume is an important performance metric in
addition to the reliability and low latency metrics.
So far, ETSI ES 203 228 [i.1] has defined the uRLLC energy efficiency metric based on the latency metric (or rather the
inverse of it), (1/T ). It can therefore be seen as a service volume performance indicator (the lower latency, the
e2e,MN
better).
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12 ETSI TR 104 084 V1.1.1 (2026-08)
However, 3GPP has in [i.2] defined an uRLLC energy efficiency metric based on both latency and data volume, (DV
MN
/T ). This KPI is applicable for the cases where, for example, the uRLLC is deployed and operators want to evaluate
e2e,MN
the Energy Efficiency at different periods of time, such as the busy hours in the morning and the idle hours in the mid
night, in which both latency performance and the data volume performance can vary.
6.3 Service quality
6.3.1 Enhanced Mobile Broadband (eMBB)
Already present in ETSI ES 203 228 [i.1], is the coverage quality factor (CoA_Q), which adds a service quality
performance dimension to the eMBB sub use case coverage sites. However, as currently defined the coverage quality
factor is more a service availability indicator, and an alternative definition may be needed. For capacity sites, where the
service volume is measured in data volume, a commonly used service quality indicator is the user throughput, which
can be measured both in DL and UL and considered separately or jointly.
6.3.2 Massive Machine Type Communications (mMTC)
For mMTC, connection quality is a natural service quality indicator.
6.3.3 Ultra-Reliable Low-Latency Communications (uRLLC)
Performance of uRLLC is in addition to low latency also reflected by its reliability. The lower the latency or higher the
reliability of uRLLC, the higher its performance is. 3GPP discuss this in [i.3], that an operator may want to assess
energy efficiency with respect to reliability of uRLLC, e.g. for communication services requiring very high reliability,
such as Cloud/Edge/Split Rendering, Gaming or Interactive Data Exchanging, wireless road-side infrastructure
backhaul, etc. Reliability is a very important performance metric for such use cases. Reliability can be considered to be
a service quality indicator.
6.4 Service availability
6.4.1 Enhanced Mobile Broadband (eMBB)
The straight-forward indicator of service availability in an eMBB scenario would be the coverage, e.g. the coverage
area where a targeted service quality can be maintained or guaranteed. At the same time, the coverage area is already
used as the service volume indicator for coverage sites, which need to be considered in the continued analysis. One way
forward could be to use a coverage quality indicator (CoA_Q) as service availability metric, as mentioned in clause
6.3.1 above. Further availability indicators could be cell availability or RAN availability, which provides the average
time availability duration per cell or the entire RAN, respectively [i.2].
6.4.2 Massive Machine Type Communications (mMTC)
For mMTC, the same service availability performance indicators as for eMBB can be considered.
6.4.3 Ultra-Reliable Low-Latency Communications (uRLLC)
Also here, the same service availability performance indicators as for eMBB can be considered.
6.5 Summary
Table 1 summarizes the key performance indicators, mapped to performance dimensions, for the different use case
families.
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13 ETSI TR 104 084 V1.1.1 (2026-08)
Table 1: Main performance indicators for use case families
Use case family Main performance indicators, divided on performance dimensions
Service volume Service quality Service availability
Enhanced mobile Data volume (capacity sites) User throughput (capacity Coverage (area or quality)
broadband (eMBB) Coverage area (coverage sites) alternatively
sites) Coverage quality (coverage Cell or RAN
sites) availability
Massive machine type Number of connections or Connection quality See eMBB
communications (mMTC) subscribers
Ultra-reliable low latency
Data volume Reliability See eMBB
communications (uRLLC) 1/Latency

7 Multi-dimensional network energy efficiency metric
proposals
7.1 General formulation
To allow for a more comprehensive network energy efficiency evaluation, and to better reflect the full performance of
the network in the energy efficiency metric, the network energy efficiency metrics should take multiple performance
dimensions into account. A general formulation, based on the high-level definition of energy efficiency in clause 4
above, can be as follows.
� (��� ��� �����,��� ��� !�"����,��� ��� " "��"#�����,… )
������ ���������� =
������ �����������
This formulation indicates that the numerator should be a function of the service volume, service quality, service
availability (and potential other performance dimensions), hence reflecting several performance dimensions. However,
this general formulation should not be interpreted strictly mathematical, but rather reflecting the philosophy to consider
several performance dimensions when assessing energy efficiency.
There are several reasons for why this could be beneficial. One is that 5G networks, not only target at carrying more
data volume and providing a better coverage, but also at providing differentiation via Quality of Service (QoS). The
current volume-based network energy efficiency metrics fail to capture MNOs' efforts to provide such QoS
differentiation. Another is network optimization, where energy saving algorithms in certain cases may be misled by the
volume-based energy efficiency metrics. Having the quality dimension reflected in the network energy efficiency
metrics would solve many of these issues.
The service function in the numerator can be defined in different ways, and the following sections will outline some
alternatives with their pros and cons.
7.2 Metric based on adjustment factor(s)
7.2.1 General
One straight-forward approach to formulate the multi-dimensional metric is to take the existing volume-based metrics
as starting point, and to introduce adjustment factors to reflect the other performance dimensions. The adjustment
factors would be dimensionless, in order not to affect the unit of the metric.
As an example, it is here illustrated how such an adjusted energy efficiency metric could be formulated for the eMBB
use case family, i.e. how to adjust the network data energy efficiency metric to take service quality into account:
�"�" �����
������ ���������� = ������
!�"����
������ �����������
The adjustment factor, in this case Factorquality, should represent the service quality. In this simple example, it is defined
as the average downlink user throughput in relation to the targeted downlink user throughput that the MNO wants to
offer to its customers:
ETSI
14 ETSI TR 104 084 V1.1.1 (2026-08)
� ��"�� $������� ���� �%����%���
������ =
!�"����
� ��"�� �"���� $������� ���� �%����%���
Hence, it describes how well the network is able to deliver the intended service quality, in this case, downlink user
throughput. The average downlink throughput is easy to calculate based on standardized counters available in the
operation and maintenance (O&M) system (see [i.4] and [i.2]), which is an advantage. For details about this, see
Annex A.
As an example, take the networks introduced in Table 2 and assume that the average targeted downlink user throughput
is 10 Mbit/s. Then the adjustm
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