Research and Innovation Action

5G-Enhance
5G Enhanced Mobile Broadband Access Networks in
Crowded Environments

D2.2 – Unified Technical Requirements and Specification
Contractual Delivery Date:

30-06-2019

Actual Delivery Date:

28-06-2019

Work Package

WP2 - Unified use cases, trial scenario, and specifications
between EU and Japan (RTD)

Responsible Beneficiary:
Contributing Beneficiaries:

TUAT
Accelleran, UOULU, VTT, FHG, NICT, TUAT, UEC, CATV

Dissemination Level:

PublicVersion:

V12

PROPRIETARY RIGHTS STATEMENT
This document contains information, which is proprietary to the 5G-Enhance Consortium.

This project has received funding from the European Union’s Horizon 2020 research andinnovation programme under grant agreement No 815056.

This page is left blank intentionally

PROPRIETARY RIGHTS STATEMENT
This document contains information, which is proprietary to the 5G-Enhance Consortium.

Document ID: WP2 / D2.2

Document Information
Document ID:
Version Date:
Total Number of Pages:
Abstract:

Keywords:

D2.2
28-06-2019
45
This document investigates the 5G-Enhance trials technicalrequirements and specifications. Also, it outlines the trials
architecture, utilized technologies, configurations, setup andevaluation procedure.
5G-Enhance, use cases, large scale trials, radio accesstechnologies (RAT), eMBB, micro-operator, multi-connectivity,
vRAN

Authors
Full Name

Beneficiary /Organizatione-mailRole

Ahmed Al-Tahmeesschi
Kenta Umebayashi
Hiroki Iwata
Satya Joshi
Arto Matilainen
Ville Niemelä

TUAT

ahmedaa@go.tuat.ac.jp
ume_k@cc.tuat.ac.jp
iwa-hiro@go.tuat.ac.jp
satya.joshi@oulu.fi
arto.matilainen@oulu.fi
ville.niemela@oulu.fi

EditorContributor
Contributor
Contributor

Mikko Uitto

VTTmikko.uitto@vtt.fi

Contributor

Trevor Moore
Stephen Parker

Accellerantrevor.moore@accelleran.com
stephen.parker@accelleran.com

Contributor

Stanislav Filin
Hikaru Kawasaki
Kazuo Ibuka
Kentaro Ishizu

NICT

sfilin@nict.go.jp

Contributor

Takeo Fujii

UEC

fujii@awcc.uec.ac.jp

Contributor

Marius-Iulian Corici

FHG

marius-iulian.corici@fokus.fraunhofer.deContributor

UOULU

Document ID: WP2 / D2.2

Reviewers
Full Name

Beneficiary /Organisatione-mailDate

Haesik Kim
Koichi Adachi
Arto Matilainen

VTT
UEC
UOULU

haesik.kim@vtt.fi
adachi@awcc.uec.ac.jp
arto.matilainen@oulu.fi

14-06-2019
15-06-2019
18-06-2019

Version history
Version
V01
V02
V03
V04
V05
V06
V11
V12

Date
07-05-2019
24-05-2019
09-06-2019
10-06-2019
11-06-2019
12-06-2019
25-06-2019
28-06-2019

Comments
The first contents
VTT contribution
Trial 1 description is updated
Trial 2 description is updated
Further updates based on teleconference meeting
Editorial work, references
Reviewers' comments are addressed, version ready for PMT approval
Final version

Document ID: WP2 / D2.2

Executive Summary
The objective of D2.2 entitled “Unified technical requirements and specifications”, is to specify
in detail the 5G-Enhance system requirements, specifications, architectures and usedtechnologies that will be later implemented in the trials. D2.2 aims to answer an importantquestion of how big the trials need to be scaled to from a system designer point of view.
Additionally, a survey of 5G networks and categorization use cases and how the trials arealigned with them. Also, a review of system architecture, used technologies, incorporated
hardware devices, expected results, target KPI and a time plan for the trials is provided.
The first trial is a live 3D AR streaming for real-time surgery. This demo requires a high data
rate with a high user density, stringent delay constraint and slow/limited mobility.
The second trial is a live ad-hoc outdoor sport event. This demo requires high data rates witha massive number of users and medium/high mobility
Several technologies have been developed for 5G-Enhance which will have a significant
impact on improving legacy solutions in dense eMBB scenarios. 5G-Enhance is built over two
key components, namely flexible and scalable network design and implementation andenhanced spectrum resource management. The technologies involved include spectrum
management, micro-operator, network management and multi-connectivity.

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List of tables
Table 1 . 5G PPP KPIs and 5G-Enhance contributions ........................................................18
Table 2 . Use case mapping. ................................................................................................19
Table 3 . Incorporated technologies for 5G-Enhance. ...........................................................19

List of figures
Figure 1. ITU IMT2020 use cases and initial 5G deployment position [Mediatek_2018]........16
Figure 2. 5G use case categories definition for broadband access [NGMN_2015]. ..............17
Figure 3. 5G-Enhance trials main characteristics..................................................................18
Figure 4 .5GTN network architecture. ...................................................................................20
Figure 5. Trial 1 network. ......................................................................................................21
Figure 6. Multi-connectivity architecture................................................................................22
Figure 7. Illustration of a virtualized RAN. .............................................................................24
Figure 8. Illustration of multi-connectivity by using Accelleran vRAN cluster controller platform.
.............................................................................................................................................24
Figure 9. Trial 2 system architecture.....................................................................................26
Figure 10. Trial 2 contributing technologies. .........................................................................28
Figure 11. Spectrum measurement system. .........................................................................30
Figure 12. Signal processing for spectrum measurement. ....................................................30
Figure 13. System structure of spectrum resource management based on spectrum database.
.............................................................................................................................................31
Figure 14 (a) Trial 1 hardware architecture in Operating Theatre..........................................32
Figure 14 (b) Trial 1 hardware architecture at Student Theatre. ............................................33
Figure 15. System configuration for spectrum sharing. .........................................................35
Figure 16. Example of reconfiguration for adaptive spectrum sharing. ..................................36
Figure 17. System configuration for spectrum sharing. .........................................................38

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Table of Contents

List of tables ............................................................................................................. 6
List of figures ............................................................................................................ 6
1.

Introduction ..................................................................................................... 11
1.1
1.2

2.

Purpose of the document ...................................................................................11
Structure of the document .................................................................................11

Trials Outline.................................................................................................... 12
2.1
Trials Outline .......................................................................................................122.1.1 Trial 1 Outline ....................................................................................................122.1.2 Trial 2 Outline ....................................................................................................12

3.

5G-Enahace Technical Requirement ............................................................. 13
3.1
Technical requirements for trial 1 ......................................................................14
3.2
Technical requirements for trial 2 ......................................................................15
3.2.1 Trial 2 Network Sharing Requirements ..............................................................15
3.2.2 Trial 2 Spectrum Sharing Requirements ............................................................16
3.3
eMBB in 5G ..........................................................................................................16
3.4
Mapping among technologies, trials and use cases ........................................17

4.

5G-Enhance Trials systems architecture ...................................................... 19
4.1
5GTN network architecture ................................................................................20
4.2
Trial 1 Main Technologies ..................................................................................23
4.3
Open issues for Trial 1 .......................................................................................25
4.4
Trial 2 ...................................................................................................................25
4.4.1 System architecture ..........................................................................................25
4.4.2 Main technologies .............................................................................................27

5.

Trials hardware setup and trial procedure .................................................... 31
5.1
Trial 1 setup and evaluation scenarios..............................................................32
5.1.1 System configuration .........................................................................................32
5.1.2 Evaluation procedure .......................................................................................33
5.1.3 Expected results................................................................................................34
5.2
Trial 2 evaluation scenarios ...............................................................................35
5.2.1 Spectrum sharing ..............................................................................................35
5.2.2 Multi-link transmission of high-data-rate uplink video stream .............................38

6.

Time plan and partners roles.......................................................................... 40
6.1
Time plan and availability ...................................................................................40
6.1.1 Trial 1 ................................................................................................................40
6.1.2 Trial 2 ................................................................................................................40
6.2
Measurements to maximize the project impact ................................................41
6.3
Partners roles ......................................................................................................41
6.3.1 Trial 1 partners and roles ..................................................................................41
6.3.2 Trial 2 partners and roles ..................................................................................42

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7.

Summary and Conclusions ............................................................................ 44

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

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List of Acronyms and Abbreviations
Term

Description

3GPP

3rd generation partnership project

5G

5th generation wireless systems

5G PPP

the 5G infrastructure public private partnership

5GTN

5G test networkAP

access point

APIapplication programming interface

AR/VRaugmented reality / virtual reality

ATSSS

access traffic steering switching and splittingBSbase station

CQI

channel quality indicator

CWC

center for wireless communicationDL

downlink

DTX

discontinuous transmission

eMBB

enhanced mobile broadband

eMBMS

evolved multimedia broadcast multicast service

EPC

evolved packet coreHDhigh definition

IoT

Internet of things

ITU-R

international telecommunication union radio communication sector

KPI

key performance indicator

LoRA

long range [access network]LTElong term evolutionMC

multi-connectivity

MEC

multi-access edge computing

MME

mobility management entity

mMTC

massive machine type communication

MPTCP

multipath transmission control protocolNR

new radio

NFV

network functions virtualization

PLMN

public land mobile networkPU

positive unlabeled learning

RAT

radio access technology

RRU

remote radio unit

QoS/QoE

quality of service / quality of experience

QUIC

quick UDP internet connections

RNC

radio network controller

RSRP

reference signal received power

RSRQ

reference signal received quality

RAN

radio access network

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RSSI

received signal strength indicator

SDR

software defined radioSIMsubcarrier index modulated [signal]

SNR

signal-to-noise ratio

TDD

time division duplexinguO

micro operatorUEuser equipment

UL

uplink

URLLC

ultra-reliable low-latency communication

VRvirtual reality

vRAN

virtual RAN

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1.Introduction
The ITU-R defined three main usage cases for 5G, one of them is eMBB. The definition of
eMBB covers two scenarios: wide-area coverage and hotspot one. Each of them has a different
requirement. The hotspot case includes high user densities in confined areas and requiresvery high traffic capacity and user data rates, while the requirement for mobility is low. For thewide-area coverage case, seamless coverage, high data rate and medium to high mobility aredesired [ITU-R_2015].
The 5G-Enhance project main targets are two large scale demonstrations in EU and Japan.
The first demonstration is a multiple angle camera real-time HD surgery streaming toparticipants in either a class or remote places. The second demonstration is HD live multiple
angle cameras streaming of race course which requires wide-area coverage case. The two
demonstrations cover both eMBB use cases and aim to improve on current solutions andmethods by providing flexible and scalable network design and enhanced spectrum resource
management.
1.1 Purpose of the document
This document summarizes the outcome of activities performed in Task 2.2: Unified technicalrequirements and specifications and aims to specify in detail the 5G-Enhance system
requirements, specifications, architectures and used technologies that will be later
implemented in project trials. The topics addressed in this document include:· One of the key aspects for the project success is the specification of how large the twotrials are required to be scaled to, taking into consideration system level developers
view.
· Describe the techniques and technologies which will be incorporated in both trials andhow they improve on the current 4G/5G techniques.
· A detailed description of the trials’ architecture, hardware setup, configurations andrequired equipment.
· Setting a time plan for the rest of the project and every partner involvement in fulfillingthe deliverables.
· The identification of both the expected results and the target KPIs that will be validated
later during the functional tests and trials.
· This document is the second deliverable of the 5G-Enhance project for WP2; hence it
provides a brief overview of the project, including trials description, architecture,
configuration and setup. The considered technologies in the 5G-Enhance project which
will improve on existing technologies and go beyond them. Time plan and target KPI of
the trials.
· Open issues described in this documentation will be discussed in D2.3 Use case pilot
planning report.
1.2 Structure of the document
The structure of this document· Section 2: provides a brief description for both trials, live surgery streaming and sport
ad-hoc.
· Section 3: describes the technical requirements for the trials. The use case mapping
between 5G use case families and project specific use cases, the target KPI.
· Section 4: first concerns with trials architecture, second defines key technologies
utilized in the two trials.
· Section 5: defines the trials configurations, hardware setup and procedure. Also
provides with essential information on how large scale the trials should be implemented
and expected results.
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Section 6: outlines the project time plan, methods to maximize the project impact andoutlines every partner role and involvement.
Section 7: summarizes and concludes the main findings of the performed work in Task
2.2

2.Trials Outline
2.1 Trials Outline
The core of 5G-Enhance project is the performance evaluation of large-scale trials focusing on
eMBB and spectrum/network sharing in both EU and Japan.
In this section, a brief description to the two trials will be provided, emphasizing on thedemonstration of the advantages of the developed technologies in 5G-Enhance to eMBB
scenario for both indoor environment (Trial 1) and outdoor environment (Trial 2).
2.1.1 Trial 1 Outline
Trial 1 is focused on demonstrating advantages of the technologies developed within 5GEnhance project for eMBB scenario in an indoor environment with high density users and highdata rate requirement.
This demonstration covers a scenario in which a live video from hospital operating room isstreamed to the remotely located medical students or consultants, using the 5G network.
Specifically, a hospital environment in Oulu, Finland, is selected for HD multi-angle live video
streaming of an operation using the local 5G wireless network, where students/participants
can watch the stream. The live streaming demonstration includes multiple remote controlled
2D and 3D cameras with the required real time video stream rendering. It also includes various
sensor elements to monitor the physiology of the patient being operated. The demonstration
incorporates AR/VR enabled glasses/goggles or helmets with role identifications. The
integration of AR/VR and (IoT sensors would have an essential impact on enriching thelearning process for students by providing an opportunity to learn from real case scenarios.
Students can be in either a group (class room/theatre room) or individually (remote places).
Moreover, a real-time 3D surgery stream can enable remote consultancy, by which theadvice/feedback of remotely located expertise could be collected as the operation isprogressing.
The goal of this trial is to demonstrate how technologies developed in WP3 of the 5G-Enhance
project contribute to improve user experience in eMBB scenario in an indoor environment witha high density of users. WP3 focuses on the following technologies:· WP3/T3.1: 5G-Enhance ARAT: Advance RAT architecture definition.
· WP3/T3.2: 5G- Enhance ARAT: Interference mitigation techniques for local 5G networks
with dynamic TDD.
· WP3/T3.3: 5G-Enhance ARAT: Multi-connectivity technique.

2.1.2 Trial 2 Outline
Trial 2 is focused on demonstrating advantages of the technologies developed within 5GEnhance project for eMBB scenario in outdoor environment with high density of users requiringhigh data rates within rural space (i.e. the density of users is not continuous enough to be able
to economically sustain a comprehensive wide area operator network).
Ehime bicycle race is selected as an event where expected scenario Trial conditions will be
met including a high concentration of users for a limited duration of time (i.e. the trial time). In
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addition to regular eMBB scenario applications, some of these users will generate high data
rate video content from multiple cameras mounted on race participant bicycles. The generated
video content will be transferred to transmit video streams from different places along the route,etc. Technologies developed within 5G-Enhance project will help to facilitate delivery of suchhigh-data-rate user content using 5G eMBB features.
Specifics of this scenario are that uplink data rate to support high quality video stream is veryhigh. It is very difficult or not efficient to provide support for such type of users via public 5G
network, especially if there are many other users in the same cell cell (e.g. the spectators of
the race).
To implement such scenario in a more efficient way, the following approach is proposed,
considered more suitable for such deployments.
Several points within the bicycle race route are selected, where the most spectators are
expected to be concentrated. In these areas public 5G capacity may not be enough to support
both spectators and high-quality video cameras. In these points, micro operator cells are
deployed to provide additional capacity to be used for uplink video streams.
Also, to ensure smooth video quality, seamless multi-link switching, and aggregation is
implemented in the uplink based on MP-TCP including both public operators and micro
operators through this being able to use efficiently the available capacity across multiple
accesses.
Additionally, a mobile micro operator is foreseen to move along the bicycle race route following
a group of participants as current TV transmission vans are doing. Such mobile micro operator
follows one or several HD video cameras through this providing direct connectivity without
requiring handover. The mobile micro-operator will gather the content from the video cameras
and will forward it efficiently to the infrastructure.
We also consider the case where other micro operators are operating within the bicycle race
area, so there is a need to share same spectrum with those micro operators for an efficient
spectrum usage while being able to satisfy the communication requirements of all theapplications.
The goal of the trial is to demonstrate how technologies developed in WP4 of the 5G-Enhance
project contribute to improve user experience in eMBB scenario in outdoor environment withhigh density of users.
Two stages of Trial 2 are planned:· Pre-trial in Matsuyama (Nov 2019).
· Trial 2 in Shimanami (Oct 2020).
The following WPs will contribute to Trial 2:· WP4/T4.1: 5G-Enhance ASNRM: Network sharing architecture· WP4/T4.2: 5G-Enhance ASNRM: Spectrum measurement and learning· WP4/T4.3: 5G-Enhance ASNRM: Spectrum resource management based on database· WP4/T4.4: 5G-Enhance ASNRM: Link aggregation

3. 5G-Enahace Technical Requirement
5G will enable a fully mobile and connected society. 5G-Enhance aims to take this goal to thenext step by having an improved seamless connection between mobile devices and services
provided for them. In other words, 5G-Enhance addresses the human-centric use cases in theeMBB scenario by having two trials. For Trial 1 (live surgery), users are expected to be able toparticipate in an interactive class for medical students where video and sensors data will be
streamed to them.

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For Trial 2 (sport ad-hoc), it is expected to have HD streaming throughout the race course.
Cameras will be installed on bicycles for HD stream throughout the race course. Hence, theessential goal for 5G-Enahce project is to improve the user experience when accessing
multimedia content, services and data and focuses mainly on services with high bandwidth
requirements.
To achieve these performance requirements, several technologies have been proposed to
improve 5G-Enhance system performance. The detail will be discussed in detail in Section (4)to improve 5G-Enhance system performance. These technologies include multi-connectivity,and virtual-RAN for Trial 1 and spectrum sharing, micro-operator, link-aggregation, for Trial 2.
In order to achieve the required system performance, several issues are needed to be taken
off. The technical requirements will be split into requirements for Trial 1 and Trial 2 which isfurther split into network and spectrum sharing requirements.
3.1 Technical requirements for trial 1
In order to achieve the expected performance in Trial 1, several technical requirements are
essential to be realized in the system, including:••

The 5G-Enhance shall support micro-operator networks. These micro-operator
networks shall provide coverage in predefined areas (class rooms) with dense users.
It is inevitable that streaming services will have an asymmetric uplink/downlink traffic
rate. Hence, 5G-Enhance shall have the capability to support these users’ requirements
for a high dense area scenario. Also, 5G-Enhance shall have the capability of
supporting dynamic uplink/downlink traffic that may evolve over a period of time.

•

The 5G-Enhance system shall support the utilization of a suitable API for HD cameras
and 360 cameras streaming integrated into broadcast/multicast system.

•

MC technique with a suitable API in order to allow high transmission throughput and/or
increased link reliability.

•

The 5G-Enhance users shall support the connection to multiple base stations
simultaneously.

•

The 5G-Enhance system shall support the use of broadcast/multicast in order toincrease both number of users and the utilized bandwidth efficiency in comparison withunicast technique.
The 5G-Enhance system shall support smart network management in dense
environment through the utilization of vRAN clusters and suitable API.

••The 5G-Enhance system shall support advanced rf resource allocation in dense
environment through the utilization of vRAN clusters and suitable API.

•The 5G-Enhance system shall support high spectral efficiency by dynamicallymanaging inter-cell interferences by using vRAN clusters and suitable API.

•The 5G-Enhance system shall support MEC to provide sufficient amount ofcomputational power for increased number of sites and users.

•

The 5G-Enhance network components will be integrated with 5GTN platform which isoperated by CWC, UOULU and VTT.

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3.2 Technical requirements for trial 2
Several technical requirements for Trial 2, namely network- and spectrum sharing
requirements, which are explained here into two subsections.
3.2.1Trial 2 Network Sharing Requirements
The following technical requirements are essential to enable networks sharing.
•

The 5G-Enhance shall support micro-operator networks that are linked to the internet
through either microwave or optical fiber link.

•

The 5G-Enhance micro-operator networks shall provide coverage within a pre-defined
specific geographical area (race course).

•

To enable users to connect to the micro operator network, the 5G system shall support
unique network identifiers for micro-operator networks. As well as mechanisms for a
UE to identify a micro-operator network.

•

5G-Enhance system shall support a mechanism to enable UE to select which micro
operator network to access. As well as a mechanism to detect the availability of a micro
operator before attempting to access a cell of this network.

•

5G- Enhance system shall utilize a suitable API to provide the connectivity status andgeographic position of all UEs and their radio access points to an authorized user.

•

The 5G-Enhance system shall support micro-operator networks as tenant-centric
network slices with traffic, QoS, and service separation between tenants, e.g.,communication services of one tenant do not interfere with communication services
from another tenant.

•

The 5G-Enhance system shall support micro-operators network deployed in either
licensed or unlicensed bands.

•

Subject to an agreement between the operators/service providers, operator policies
and the regional or national regulatory requirements, the 5G-Enhance system shallsupport mobility/hand-over between a micro operator and a public PLMN.

•

The 5G-Enhance network shall be able to utilize a suitable API to provide theinformation regarding the geographic location of coverage area (e.g. radio cell sector
coverage) to an authorized 3rd party.

•

The 5G-Enhance system shall be able to utilize a suitable API to provide the information
about the allocated and free network service resources in the network to an authorized
user.

•

The 5G-Enhance system shall be able to utilize a suitable API for monitoring theresource utilization in a micro-operator network (radio access point and the transport
network (front, backhaul) by an authorized 3rd party.

•

The 5G-Enhance system shall be able to utilize a suitable API to provide the information
regarding spectrum usage and spectrum usage prediction from the spectrum manager.

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The 5G-Enhance system shall be able to utilize a suitable API for an authorized 3rdparty application to negotiate communication service requirements.

3.2.2Trial 2 Spectrum Sharing Requirements
The following technical requirements are essential to enable spectrum sharing.
•

To realize spectrum sharing, the 5G-Enhance system shall support access to bothlicensed to unlicensed spectrum bands. The supported frequency bands for spectrum
sharing among the micro-operator networks will be both sub 6GHz band and mm-wave
spectrum band.

•

The 5G-Enhance system shall have the capability of supporting dynamic spectrum
allocation between sharing micro operators based on user demands, spectrum
prediction and spectrum map over a period of time.

•

To answer for the problem of spectrum scarcity, 5G-Enhance systems shall allow reuseof the sharing spectrum in dense areas, to improve spectrum utilization and hence
having an improved services.

3.3 eMBB in 5G
For the convenience of a document reader this section copied from D3.1 gives a quick
introduction of 5G use case and some key metrics characterizing user expectations.
5G will enable a fully mobile and connected society. To do so, 5G will cover a broad range ofuse cases, which lay within the spectrum of mMTC, eMBB and URLLC [NGMN_2015].
According to [Mediatek_2018], the initial phase of 5G deployments lays at the eMBB-URLLC
side of the triangle closer to eMBB, see Figure 1. eMBB addresses the human-centric use
cases [Lechuga_2018]. It aims to improve consumer experience when accessing multimedia
content, services and data and focuses mainly on services with high bandwidth
requirements.

Figure 1. ITU IMT2020 use cases and initial 5G deployment position [Mediatek_2018].

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Within the families of broadband access, the use cases have been classified in different
categories by NGMN as early as 2015, as shown in Figure 2 [NGMN_2015].

Figure 2. 5G use case categories definition for broadband access [NGMN_2015].
3.4 Mapping among technologies, trials and use cases
For the convenience of a document reader this section copied from D2.1. This section presents
how technology development and trials in 5G-Enhance map into use cases. There are
numerous studies regarding potential use cases as they reviewed in the previous section. Twouse case families (Broadband access in dense area and broadband access everywhere) are
the most relevant ones from potential use cases described by the NGMN white paper[NGMN_2015].
The first use case family (Broadband access in dense areas) is in-line with the indoor trial(Remote surgery) of 5G-Enhance and captures the growing demand of high data rate services
in urban and crowded area. Many people in small and dense areas create heavy traffic. In
hospital environment, a reliable and consistent service in small and dense areas is required.
The first focus is broadband access in the critical venues use case. This use cases means thatmany users are temporarily located in crowded venues. Cell capacity, throughput, linkreliability, spectral efficiency, energy efficiency, etc. in uplink and downlink are target KPIs.
Among them, throughput and capacity are significantly important. Many research challenges
are associated with this use case family. In 5G-Enhance projects, flexible TDD, multiconnectivity and spectrum sharing technologies are developed and the system performance
will be improved.
The second use case family (Broadband access everywhere) is in-line with the outdoor trial(Ad-hoc outdoor sport event) of 5G-Enhance. 5G-Enhance outdoor trial will be supported by abroadband access service during the cycling event in Ehime, Japan 5G-EnhanceThis use caseincludes many research challenges: Coverage problem, undesired data stream interruptions
and others. Thus, it is important to achieve consistency and provide all users with enoughperformance. The target KPIs are same as the first use case family. However, this use casedoes not represent any extremes but a minimum guaranteed service. In 5G-Enhance projects,
spectrum measurement and learning, spectrum resource database and management andnetwork sharing and management are developed and the target KPIs will be achieved. Figure
3 shows the main characteristics for the trials. In [EU-Commission_2013], the high level KPIs
of the 5G PPP are described. 5G-Enhance will contribute to those high level KPIs. Table 1
highlights the 5G PPP KPIs.

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Figure 3. 5G-Enhance trials main characteristics.
Table 2 shows how the 5G-Enhance research works and how the main used technologiesalign in the trials, 5G-Enhance use cases, system improvement in the form of KPI and use
case families. shows how every technology will assist in improving 5G-Enahnce.
While the used technologies in 5G-Enahce project will be explained in detail in Section 4.2 for
Trial 1 and Section 4.4.2 for Trial 2. Table 3 provides a high-level summary for the proposed
technologies which will be incorporated in Trials 1 and 2 and the metrics they aim to enhance.
In Table 3, (✔) means improve and (✘) means not related. For instance, multi-connectivity
could be utilized to improve either transmission rate or reliability depending on what information
is being sent over each link (same information or different). vRAN clusters provide a better
network and rf resources management by monitoring not only a single eNB but several eNBs
and hence providing a significant improvement optimal resource allocation. Spectrum
measurement, prediction and mapping improves the spectral efficiency of the rf spectrum. Thisis done by offloading the extra spectrum from micro operator to another. MPTCP allocates thetransmission into multiple parallel streams hence increasing transmission throughput.
Table 1 . 5G PPP KPIs and 5G-Enhance contributions
KPIs

Relevance

5G-Enhance contributions

Providing 1000 times Highhigher wireless area
capacity and more varied
servicecapabilities
compared to 2010.

5G-Enhance contributes to higher wirelesscapacity as follows: (1) Developing link andnetwork level techniques to improve the wirelesscapacity (2) Managing the radio and network
resource more efficiently.

Facilitating very dense Medium
deployments of wirelesscommunication links toconnect over 7 trillion
wireless devices serving
over 7 billion people.

5G-Enhance contributes to high connection
density in different approaches as follows: (1)Managing spectrum more efficiently and using
operated bandwidth. (2) Reducing the required
bandwidth for a given user data rate by flexible
TDD. (3) Operating networks by efficient radio andnetwork resource use.

Creatingasecure, Medium
reliable and dependable
Internet with a “zeroperceived” downtime forservices provision.

5G-Enhance achieves secureconnection by multi-connectivity.

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Table 2 . Use case mapping.
5G-Enhanceresearches

5G-Enhancetrials

5G-Enhance
use cases

Target KPIs

Use
familiescase

Dynamic TDD

Indoor trial

Remotesurgery,
Broadband inclass room

Throughput,
Spectralefficiency

Broadband
access in dense
areas

Multiconnectivity

Indoor trial

Remotesurgery,
Broadband inclass room

Resilience and Broadband
continuity,
access in dense
Throughput
areas

Spectrummeasurement
and learning

Outdoor trial

Ad-hocbroadband
service for sport
event

SpectralBroadband
efficiency, Radio accessresource
everywhere
efficiency

Spectrum
Outdoor trial
resource
databaseandmanagement

Ad-hocbroadband
service for sport
event

SpectralBroadband
efficiency, Radio accessresource
everywhere
efficiency

Network sharing Outdoor trial
andmanagement

Ad-hocCost, Flexibility, Broadband
broadband
Reconfigurability accessservice for sport
everywhere
event

Table 3 . Incorporated technologies for 5G-Enhance.
5G-Enhance
technology

Throughput

Latency

Flexibility,
Reconfigurability

Area
spectralefficiency

Reliability

vRAN clusters

✔

✔

✔

✔

✔

Multi-connectivity

✔

✔

✘

✘

✔

Multicast

✘

✘

✔

✔

✘

MPTCP

✔

✔

✘

✘

✔

Spectrum Usage learning
and Prediction

✔

✘

✔

✔

✘

Radio Environment Map

✔

✘

✔

✔

✘

4. 5G-Enhance Trials systems architectureThis section describes the system architecture for the trials. The description will include themain network/system elements and functionality for both trials. Moreover, 5G-Enhance
network requires the development of several key techniques to enable the required flexibility
and scalability. The new technologies and concepts which will be incorporated in thedemonstrations are briefly described with the advantages they provide for 5G-Enhance Trials.
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4.1 5GTN network architecture
Trial 1 utilizes 5G test network (5GTN), which is the world’s widest 5G test network with open
access for testing. The network is operated and maintained together by CWC, UOULU and
VTT. 5GTN is available primary in the campus area of UOULU, but it could be extended into
other sites. Some special requirements like national radio frequency specifications are
identified, but more detailed analyse and plan should be done during the project.
5GTN is complete 5G test system from infrastructure to applications and services. It allows
unique testing possibilities from prototype devices to complete solutions in a controlledenvironment. The 5GTN network consists of several sites and is well-connected with national
and worldwide test environments. The 5G Test Network features support the use of 5G
devices, several frequency bands, orchestration functionalities, and system testing tools. In
the trial, we have newest test devices with in-depth analytics for a maximal trial experienceand outcomes. Figure 4 below shows the high-level network architecture of 5GTN.
The network includes the following main components:· Communications infrastructure from LTE evolution to 5G radio access. WiFi and IoT
networks, e.g., LoRa integrated· MEC to bring services and applications close to users access· eMBMS for delivering the live video content efficiently to large group of users
simultaneously in LTE/5G· Core network in cloud environment. Virtualized EPC of 5GTN
· Cloud systems for applications. Backend servers - serve as data storages, dataprocessing and video converters· Secure connection to other test sites in Finland and worldwide

Figure 4 .5GTN network architecture.
Trial 1 network configuration
Trial 1 considers the 5G network application under very high data rate, indoor (limited mobility),
and many users. The trial 1 focuses on the network sharing/management and the application

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of link aggregation and multi-connectivity solutions in the transport and network layers toincrease the data rate.
In Trial 1, we use 5GTN as the main network infrastructure to provide the end-to-end
connection between the hospital operating room and class room. The network configuration of
Trial 1 is shown in Figure 5.

Figure 5. Trial 1 network.

Below we briefly describe this end-to-end network configuration of Trial 1.
· Multiple sensors, smart glasses and video cameras are placed in the operation room
to capture various data relate to the ongoing surgery (e.g., 360-degree video, various
sensor readings on the physiology of the patient being operated, etc.). These sensors
and cameras are connected to the Server-PC via router.
· The router will have a connection to the 5GTN network, which is connected to the end
users (medical students, or consultant). The end users in Trial 1 can be in the same
hospital building or located remotely.
· In Trial 1, the target is to use a locally deployed 5G network (micro-operator) to stream
360-degree video augmented with various sensor reading form the operating room tothe students in the class room. For this, we utilize 5GTN of UOULU-VTT, and virtual
RAN platform developed by Accelleran.

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Multicast/broadcast feature utilizing eMBMS is located in 5GTN at VTT premises andis connected to real cloud management EPC core. Currently eMBMS uses 2.6 GHz
FDD eNBs and MME inside VTT core. Alternatively, eMBMS can also use UOULU core
which allows possibilities for several co-demonstrations. The input data for the eMBMS
will be video stream from the hospital area outputted simultaneously to multiple
students in the class room via eMBMS.
5G-Enhance RAN will provide a platform for multi-connectivity developed in line with
3GPP R16 studies for ATSSS. This is illustrated in Figure 6 below.

Figure 6. Multi-connectivity architecture.
The multi-technology vRAN platform supports radio access based on LTE Advanced,
5GNR and IEEE technologies. Radio resource management in the RAN is managed
dynamically by the RNC which is multi-connectivity and multi-technology aware. This
framework allows the real-world implementation and trialling of algorithms, for example,
cell association, mobility management and link reliability management specified by the
WP3 work on multi-connectivity.
The user terminal is capable of supporting multiple simultaneous radio connections(either as part of advanced technology in the R16 device or through additional software
supporting multiple RF modem terminations).
Traffic steering is managed by the user plane multipath protocol layer, which is present
both in the end user device and in the core network gateway. For the purposes of the
5G Enhance project two implementations of the multipath layer will be investigated –one based on Multipath TCP, the other based on Multipath QUIC.
A key function of the multipath layer is to decide how to distribute the arriving traffic foreach use over the multiple connections available. 5G-Enhance will investigate thebenefits of assistance data from the RAN controller to help to optimise the multipath
distribution decision problem.

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4.2 Trial 1 Main Technologies
The Trial 1 focuses on the flexible and scalable network design, and the application of linkaggregation and multi-connectivity solutions in the transport and network layers to increase
the data rate. The following technologies will be in focus and adopted/developed within the
5G-Enhance project to be incorporated into Trial 1 demonstration:
1. 5GTN:
5GTN is a 5G technology and service development platform including a continuously
evolving RAN and a cloud-based EPC. 5GTN is operated together by CWC, UOULU
and VTT. The RAN part contains both 4G-based commercial technologies for use
cases requiring multiple users, as well as beyond-4G/pre-5G technologies for more
limited scale use cases. The EPC part is fully virtualized, supporting distribution of
network functionalities both in control plane and user plane. The architecture has also
an integrated testing and network management frameworks implemented, enabling
new functionalities in these domains to be built on top of the existing building blocks or
as parallel implementations complementing the existing functionality.
2. vRAN clusters:
Network sharing is related to the evolution of the traditional RAN (4G LTE integrated
eNB) towards a disaggregated and virtualised 5G edge and access network.
Virtualisation of the RAN brings with its opportunities for architectural innovation. In
particular, the introduction of cell cluster resource management capabilities into the
network. This approach allows for a smarter, more optimal, approach to radio resource
management in dense environments than the classical isolated, black box eNB.
Admission control, mobility decisions, interference management can all be managed
much more effectively by a network function with a more global view of the network
than just one cell or cell site. Measurement data from each cell and connected UE isshared to a distributed dataset, which enables rapid integration of alternative or
additional features requiring deep real-time knowledge of network conditions.
This open and disaggregated approach to RAN architecture allows for the integrated
management of multiple technologies in a common framework. The high-levelarchitecture for a virtualized RAN cluster is shown in Figure 7.
3. Multi-connectivity techniques:
MC is defined as the capability of a UE to connect to multiple APs simultaneously. It
provides several benefits for 5G-Enhance networks including:· higher data-rates, as MC is a form of data aggregation, hence additional
resources can be used for data transmission.
· higher reliability through data duplication and/or inter-frequency transmissions· seamless connectivity by reducing the handoff latency and the overall delay.
· reduced jitter with data duplication by minimizing the variance in latency[Aijaz_2018].
· higher energy efficiency by using micro DTX [Ternon_2014].
The multi-connectivity techniques will be adopted in the Trial using Accelleran vRAN
cluster controller platform; for example, as illustrated in Figure 8.

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Figure 7. Illustration of a virtualized RAN.

Figure 8. Illustration of multi-connectivity by using Accelleran vRAN cluster controller
platform.
4. Multicast feature:
In the Trial multiple stream captured by 2D and 3D cameras are optionally streamed tothe end user devices using multicast feature, which can be accomplished either byusing standard multicasting in IP networks or eMBMS within mobile networks (LTE).
Currently, there is also active standardization ongoing towards enabling emBMS in 5G.
eMBMS is the best alternative for accomplishing this since it is operational in 5GTN.
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The technologies implementing multicast/broadcast streaming in mobile networks can
optimize bandwidth usage and improve the spectral efficiency as well as throughput
and are therefore extremely important in eMBB scenarios that focus on delivering thecontent on high throughput. For such scenarios as in our Trial 1, multicasting the same
stream to multiple users in class room is extremely beneficial for decreasing the
network load and waste of resources.
The main components for eMBMS usage include eMBMS server, eMBMS supported
EPC and eNBs as well as end devices with LTE broadcast middleware and player
application. Currently we are using Samsung and Bittium end devices.
4.3 Open issues for Trial 1
There are few open issues for trial 1 network architecture, although the network is alreadyoperational. The main concerns are number of end users, it is uncertain how many students
or specialist use the service in practise, some concerns for availability of user end devices, 5G
devices e.g. smart phones and modems, and capability to install trial 1 system in otherresearch partner sites.
Multicast option in trial 1 can be affected by lacking the support of eMBMS supporting 360/VR
video end device applications that can both receive the multicast stream via 5G mobile network
as well as playback the stream via 360 application. Also, global integration when performing
trials outside of Finland can be difficult.
4.4 Trial 2
4.4.1 System architecture
As was mentioned in the outline (Section 2), Trial 2 is focused on eMBB scenario in outdoor
environment with high density of users mixed with several users requiring high data rate in the
uplink.
Such scenario will be available during Ehime bicycle race, where there will be a large numberof participants and spectators to create dense user environment. In addition, one of the project
partners (Ehime CATV) will be doing commercial TV translation of the bicycle race using
multiple high-definition cameras distributed within the race route and mounted on theparticipant bicycles.
During the event, within the race route there will be areas with high concentration of spectators(e.g., start, finish, mountain part with nice view) and areas with almost no spectators (e.g.,straight roads or bridges).
While in the areas with low concentration of users it could be possible to provide required
quality of service for uplink video stream using only public networks, in the areas with highconcentration of users capacity may be not enough. Deploying micro operator in the areas
with high density of users for the purpose of supporting uplink video stream would significantly
improve video quality, number of simultaneously supported uplink video streams, and also
offload public 5G networks for the benefit of spectators.
Also, it is planned that at least one micro operator would be deployed inside a car and will
follow one or several users equipped with high-data-rate cameras. Such mobile micro operator
will exclusively serve these users. It will use micro operators and public operators for backhaul.
In addition to deploying micro operators, another measure is typically used to improve uplink
video stream quality – multiple links using several radio interfaces. There is a special
equipment between camera and transmitters that splits an uplink stream from camera into
several streams and distribute them to several radio interfaces. On the server side these

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streams are merged back into one stream. Trial 2 also uses this multi-link technology basedon MP-TCP.
HD cameras mounted on race participant bicycles are moving along the race route. While
moving, they experience different areas:•
Areas with only public operator service•
Areas with both public operator service and micro operator service.
There is a need to adaptively switch multiple links from one camera among these public 5G
operators and micro operators. To assist in this adaptive multi-link management, a special
system is deployed. It contains radio environment map that helps to select location and timeto switch links between different base stations (public and non-public) and to adaptively
distribute video traffic between active links. This system helps to reduce overhead associatedwith switching radio interfaces between different cells, frequencies, and networks.
Micro operators use specific frequency band. Public operators or other radio systems do not
operate in this band. However, within this band, different micro operators could share the same
frequency.
To emulate such situation during Trial 2 several co-located micro operators are deployed,
where one micro operator serves high-definition video camera users, while other operator
serves different type service, such as shopping mall (uO Y in Figure 9). These co-located micro
operators share the same frequency band. For the spectrum usage prediction, spectrum
measurement and spectrum usage modelling are necessary.
Trial 2 system architecture is shown in Figure 9. It includes the following key elements
highlighted in the text above:•
Users with high definition cameras supporting several radio interfaceso Including terminal side multi-link support software•
Public 5G networks•
Micro operators•
Radio environment map databaseo Connected to multi-link manager•
Spectrum usage prediction databaseo Connected to spectrum sharing manager.

Figure 9. Trial 2 system architecture.
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4.4.2Main technologies
Different technologies developed within WP 4 contribute to Trial 2. Brief description is asfollows.
T4.1: Network sharing architecture:•
Micro operator – micro operator eNB and CN are developed and will be used in Trial 2.
•
Mobile micro operator – at least one micro operator eNB will be installed on a car and will
follow one or several high-definition cameras during the race together with a selfbackhauled edge node which enables the content pre-processing before using the side of
the road infrastructure.
•
Adaptive reconfigurability for spectrum sharing – micro operator could adaptively change
central frequency and bandwidth.
•
Millimeter band technology – some of micro operators could operate around 30 GHz.
T4.2: Spectrum measurement and learning:•
Spectrum measurement – using portable spectrum analyzers, spatial-temporal spectrum
usage measurement is performed•
Spectrum usage modeling and prediction – based on the results on spectrum usage
measurement, spatial-temporal spectrum usage model is created using learningalgorithms•
Spectrum sharing support – based on the spectrum usage model,
policies/recommendations for spectrum sharing are provided to co-located micro
operators.
T4.3: Spectrum resource management based on database:•Spectrum measurement – using special software on smartphones, signal strength fromdifferent PLMN operators and micro operators is measured and reported to a database•
Radio environment map database – based on the signal strength and location information,
radio environment database is created•Multi-link transmission support – based on the UE location information, radio environment
database could send recommendations when to switch links between different cells.
T4.4: Link aggregation:•
Multi-link connectivity based on MP-TCP – based on MP-TCP multi-link transmission is
implemented for high-data-rate uplink video stream upload•
Multi-link cluster handover – joint handover decisions for a cluster of co-located cells•
Multi-link traffic switching and splitting management – management of high-data-rateuplink traffic switching and splitting among multiple links with PLMN operators and micro
operators.
Figure 10 summarizes how WP 4 R&D contributes to Trial 2. They are described below in moredetails.

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Figure 10. Trial 2 contributing technologies.
4.4.2.1 Micro operator
Micro operator for Trial 2 uses LTE eNodeB and 4G CN as a basis.
Two new features are implemented on top of 4G eNodeB:•
Support of mmWave frequency band•
Support of fast adaptive reconfiguration of central frequency and bandwidth.
One new feature implemented on top of 4G eNB support of the millimeter wave frequency
band. Central frequency is around 28 GHz and bandwidth in around 20 MHz. In addition toseveral such eNBs, there are several devices available that could communicate with such
eNBs. This will allow to consider both regular and microwave frequency bands currently
considered for 5G and Beyond 5G systems in dense environments.
Another feature implemented on top of 4G eNB is support of the fast adaptive reconfigurationof central frequency and bandwidth for spectrum sharing. Central frequency is around 2.6
GHz. Bandwidth is 5MHz or 10MHz.
Fast real-time spectrum sharing would require specific PHY and MAC design of eNB andterminals and thus is not supported in Trail 2. However, in case of long-term predictable
spectrum usage variation (for example, within one day or one week) micro operator could
reconfigure fast enough to do adaptive spectrum sharing. This case is considered in Trial 2.
Four new features are implemented on top of 4G CN:•
Interface with spectrum usage prediction database•
Spectrum sharing manager•
Interface with radio environment map database•
Multi link manager.
Spectrum Usage Prediction Database collects spectrum usage measurements fromspectrum analyzers for several co-located micro operators, creates spectrum usage model foreach of such operator and assist these micro operators in adaptive spectrum sharing.

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Micro operator CN has an interface with the spectrum usage prediction database. This
interface is used to receive spectrum sharing policies/recommendations from the database.
The function inside a micro operator CN that implements such interface is called Spectrum
Sharing Manager. In Figure 9 only one instance of such function is shown, serving three micro
operators. How many such functions are used depends on deployment scenario: one per eachmicro operator or one per several micro operators.
Spectrum Sharing Manager analyzes current configuration (central frequency and bandwidth),
traffic load, number of users etc. received from each of the micro operators, as well as
spectrum sharing policies/recommendations received from the database. Based on thisinformation, Spectrum Sharing Manager could make decisions to reconfigure one or several
co-located micro operators sharing the same frequency band.
Radio Environment Map Database collects signal strength of PLMN operators and micro
operators and creates radio environment map. This map shows coverage from different
operators along the route of the bicycle race. Based on this map and device locations, radio
environment map database provides recommendations to multi-link transmission management
function when to switch links between the operators.
Micro operator CN has an interface with the radio environment map database. This interface
is used to receive recommendations when to switch links between operators from thedatabase. The function inside a micro operator CN that implements such interface is called
Multi Link Manager. In Figure 9 only one instance of such function is shown. How many suchfunctions are used depends on deployment scenario.
Multi Link Manager analyses number of multi-link users, their QoS requirements, locations,multi-link configuration, available operators, their available capacity, recommendations fromradio environment map database. Based on this information, Multi Link Manager makes
decisions to adaptively reconfigure multi-link connections. Multi-link connections are
implemented using MP-TCP.
Mobile micro operator is a micro operator equipment installed on a car that can follow specificparticipants of a race. Mobile micro operator uses LTE eNB and an adapted 5G CN. It uses
3.5 GHz or 3.7 GHz and bandwidth up to 20 MHz. It cannot use directional antenna forbackhauling e because of the movement. Instead, it will use other micro operators and public
operators to connect to the Internet.
4.4.2.2 Spectrum Usage Prediction
Several spectrum measurement systems will be used, where each one consists of several
portable real-time spectrum analyzers and/or SDR sensors with device control PC, systemmanager, local data server and local storage. Figures 11 and 12 show the spectrum
measurement system and signal processing for spectrum measurement in each device control
PC, respectively. The system manager can control the device parameters including
measurement time and period and measurement center frequency and frequency bandwidth.
The proposed system will focus on measuring the RSSI value obtained by FFT-based power
spectrum estimation. The bandwidth is between 20 and 40 MHz depending on themeasurement device. Then, the spectrum usage detection by energy detection and signal area
estimation are performed to acquire the spectrum usage in the measurement area. The
detection mask size setting and noise floor estimation are utilized to improve the accuracy ofthe spectrum usage detection. The data server is responsible for the fusion process of all theobserved data by devices and duty cycle estimation.

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Figure 11. Spectrum measurement system.

Figure 12. Signal processing for spectrum measurement.
Learning algorithms for spectrum usage modelling and prediction are focused on two topics:
the model of spectrum usage in the time domain and prediction of spectrum usage ratio (dutycycle). For the spectrum modelling part, a flexible and scalable spectrum usage model in thetime dimension is proposed based on mixture distribution in which more than one distribution
is employed. The accuracy obtained by mixture distribution can be further improved by setting
the number of distributions and their parameters appropriately. For this, we employ a
nonparametric Bayesian model.
For the prediction part, two approaches are being investigated. The first approach is based on
autoregressive moving based prediction of the duty-cycle. The second approach is based onmachine learning. Multiple deep learning algorithms such as Convolutional Neural Networks
and Long Short-Term Memory are being investigated to accurately estimate the duty-cycle.
Based on the obtained results the total available spectrum can be adaptively allocated between
serving micro operators seamlessly. Hence, maximizes the spectral efficiency and provides animproved QoS for users.

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4.4.2.3 Radio Environment Map
A software is developed on top of android operating system that periodically collects RSRP
measurement and send it to database together with location information. Several mobile nodes
will be incorporated for measurements such as smart phones, vehicles and wireless LAN
terminals. Radio environment map is created in the database. For each of the measured points
this radio environment map includes the following information:•
Location of mobile sensor•
PLMN ID
•
Cell ID•
Frequency Band•
RSRP (signal strength), RSRQ, RSSI, SNR, CQI•
Time of measurement
The gathered information is thoroughly analyzed on the database and used to improve thereliability, spectrum sharing and area design for the proposed system. In this task, thespectrum resource management for 5G systems is discussed for improving the spectrum
efficiency and satisfying the demand of users. Also, it is expected that radio environment map
database will assist MP-TCP algorithm for adaptive multi-link transmission.
The basic structure of spectrum resource management system with spectrum database isshown in Figure 13.

Figure 13. System structure of spectrum resource management based on spectrum
database.

5.Trials hardware setup and trial procedure
The trials hardware design and setup have been carefully investigated taking into accountseveral aspects including human personal, available budget and equipment while ensuringthat the scale of trials will provide a valid proof of concept on the possible enhancement for the
future 5G systems which we believe will have a significant impact on currently deployed 5G
networks through the utilization of proposed algorithms and techniques in 5G-Enhance project.
In this section the hardware setup and procedure for both trials will be described.

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5.1 Trial 1 setup and evaluation scenarios
5.1.1 System configuration
Figures 14 (a) and (b) illustrates the general configuration for Trial 1. The extent to which
research results, algorithms or techniques can be implemented into a deployable network
solution (and Trial 1 deployment) will be established during the project.
The main components of Figure 14 (a) and (b) are:
· Server-PC: A server PC, where data (including video, audio, sensor data) generated
in the operating room is stored and processed. Operating room components (e.g.,smart glasses, 360 video camera, sensors) and end user devices of the class room
(e.g., VR glasses, tablets, smart-phones) are connected to the Server-PC via a router(LTE/5G or Wi-Fi).
· 360 video camera (Ricoh Theta V): It is a 360-degree camera and records overall
video of the operating room. Multiple streams capture by the camera (for 360 view) is
stitched and processed to get a single data stream, which is transferred to the ServerPC.
· Sensors: Various sensors elements will be deployed to monitor the physiology of thepatient being operated. In the demo, Shimmer3 GSR is used to monitor heartbeats.
Other sensor elements will be considered during the project.
· Smart glasses (Vuzix M300): It is a headset, that can be used by a doctor or nurse in
the operating room. Vuzix can capture video/audio and transfer to Server-PC. Vuzix
also has a small display, where doctor/nurse can see sensor data c. Thus, Vuzix M300
headset has dual purpose in the demo; either to assist doctor/nurse with sensor data,or to feed the audio/video (doctor/nurse view) of the operating room to the end users.

Figure 14 (a) Trial 1 hardware architecture in Operating Theatre.
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Figure 15 (b) Trial 1 hardware architecture at Student Theatre.
···

End user devices: It can be VR glasses, tables, or smart-phones with or without
eMBMS support. End devices get connected to the Server-PC via 5GTN core androuter or multicast routing. End users will have opportunity to view video captured by
360 video camera and smart glasses (doctor’s view), audio, and sensors data.
Small Cells: The RAN platform will support a range of small cell options including LTE,
multicarrier LTE Advanced and 5GNR as well as the potential to include WiFi based
APs.
Virtual RAN Server Platform: The RAN control functions will be hosted on an off-theshelf small form factor server supporting 10GB ethernet networking interfaces. vRAN
software will be managed via open standard network orchestration (Kubernetes) andprovide tools for remote management and monitoring of the network and user activity.

5.1.2 Evaluation procedure
The preparation of Trial 1 will be carried out step by step as follows:· Step 1: First, UOULU will test the 5GTN to make sure that the core-network is fully
functional and available to all partners to deploy the algorithms/techniques developedin the project.
· Step 2: By using the basic hardware available in UOULU, pre-trial will be organized by
UOULU and report the status of the network to all involved partners. Initial setup will
be based on LTE and WiFi connections. Then UOULU, will work toward obtaining andintegrating the 5G capable devices.

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Step 3: Accelleran vRAN cluster platform will be integrated with 5G Core.
Interoperability with end user devices will be tested and proven. MC testing will be
performed to demonstrate performance improvement vs Single Connectivity.
Step 4: Test procedure carried out in Step 1 to Step 3 uses single cast transmission
techniques to stream live video to the end user devices. In order to optimally utilize
bandwidth and improve spectral efficiency, VTT will deploy multicast transmission
techniques via eMBMS.
Step 5: Finally, the complete trial 1 setup will be tested by integrating 5G capabledevices.

5.1.3 Expected results
In the prototype of demo in 2018, each hardware component (camera and sensor) wereconnected using existing technologies; 4G (LTE), Wi-Fi and Bluetooth radios and PC levelcomputing power for video and data analysis and processing. Also singlecast is used fordelivering the streams to users. The data rate was around 10s Mb/s, which was offed justacceptable quality of service with rather long latencies, uncontrolled connection and service
breaks and low-resolution videos.
During Trial 1 in 2020, it is expected that 5G devices and modems are available. New HD and
360 cameras, new sensors and next generation end user devices will be used. Required
transmission data rate and need for data processing (video, audio and data processing andanalysing) will increase. Several HD cameras will be used, where they are used in the sameroom at the same time. Also, number of sites and viewers in remote education and remoteconsultancy trial cases will be bigger than in the first phase. Requirements for network andcomputing will be more demanding.
Compared to situation 2018, the following improvement are planned for 2020 (trail 1):·

Data and video stream data rate increased >100s Mb/s – local 5G and high capacity
internet connections needed.

·

Support larger number of end users (around 10 users in demo, however the developed
system is scalable) and several multi-remote connections.

·

Quality of service will meet expectations of end users, for example HD video is in use
and major end user device problems i.e. over-heating of smart glasses solved orunderstood.

·

Efficient use of bandwidth through the utilization of broadcast/multicast technologies.
For instance, 10 simultaneous users require 10x10Mbit/s = 100Mbit/s total network
capacity only for video streaming in unicast way. While when using broadcast/multicast
only single 10 Mbit/s is needed for delivering the same content to all users. Hence
increasing number of multicast users compared to singlecast. Also, decreasing thespectrum usage and network bandwidth compared to singlecast.
High achievable data rates through using MC techniques. For instance, a user
connected to single AP can obtain 10Mbit/s. While when utilizing MC the same user
can connect to several APs (N) and the results data rate is Nx10Mbit/s.

··

Multiconnectivity will provide much improved flexibility to tune QOS profiles to deliver
high-throughput on the one hand or high-reliability delivery on the other. Dynamic
intelligent management of the virtualised RAN network resources supports innovative

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schemes for multiconnection management to significantly improve the user
experience at cell edge and improve the reliability of handover during mobility.
·

Trial 1 will enable remote class room and consultancy. For example, surgery room in
Oulu Finland, and student class room or consultancy could be Japan.

5.2 Trial 2 evaluation scenarios
5.2.1Spectrum sharing
It is expected that one scenario of spectrum allocation for private operators would be allocationof some band for shared use by several micro operators. This would be different micro
operators serving different groups of users, for example, inside factories, within shopping mallor entertainment park, within a service area on a highway.
If two or more micro operators are co-located, that is, could cause harmful interference to eachother when operating on the same frequency, there is a need to manage spectrum sharing
among them. This will improve spectrum usage efficiency and QoS support.
Within Trial 2 it is planned to evaluate such spectrum sharing scenario.
5.2.1.1 System configuration
System configuration for spectrum sharing evaluation is shown in Figure 15.

Figure 16. System configuration for spectrum sharing.
Two micro operators are co-located, uO XN and uO Y. Micro operator uO Y serves regularusers, for example, highway service area users. Micro operator uO XN serves bicycle race
participants that have cameras mounted on their bicycles.
Micro operator uO Y that serves regular users is available every day, so it is possible to do
spectrum usage measurements in advance and create spectrum usage model. These
measurements and spectrum usage model are stored in spectrum usage prediction database.

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Both micro operators uO XN and uO Y have modified eNBs that allow them to perform adaptive
spectrum sharing with a reasonable speed. In other words, they can reconfigure their eNB
central frequency and bandwidth without long interruption of service.
There is a spectrum sharing manager as part of a micro operator CN. In Trial 2 for simplicity
there is only one instance of spectrum sharing manager for two micro operators. Based onmessages from spectrum usage prediction database and measurements from microoperators, spectrum sharing manager reconfigures central frequencies and bandwidth of micro
operators uO XN and uO Y. Example of such reconfiguration is shown in Figure 16.

Figure 17. Example of reconfiguration for adaptive spectrum sharing.
5.2.1.2 Evaluation procedure
Evaluation procedure for the spectrum sharing scenario has two stages:· Measurement and modelling· Adaptive spectrum sharing.
Measurement and modelling stage is as follows:
1. Long-term spectrum usage measurements are performed for different locations withincoverage area of micro operator uO Y.
2. Measurements are stored in the spectrum usage prediction database.
3. Spatial-temporal model of spectrum usage of micro operator uO Y is created.
4. The model should show that there are some changes in the spectrum usage of micro
operator uO Y during the day or week of the bicycle race that allow to do beneficial
spectrum sharing with micro operator uO XN.
Adaptive spectrum sharing stage is as follows:
1. Micro operators uO Y and uO XN are co-located and share the same spectrum by using
different frequency bands (otherwise they will cause harmful interference to each other).
2. At some point of time during the period of bicycle race spectrum usage prediction
database predicts that number of users of micro operator uO Y has decreased and there
is a possibility to give more spectrum to micro operator uO XN.
3. Spectrum usage prediction database sends a message to spectrum sharing manager
containing information that micro operator uO Y has decreased its spectrum usage.
4. Spectrum sharing manager sends a message to micro operator uO Y requesting to reportcurrent spectrum usage.
5. If spectrum usage prediction and spectrum usage measurements for micro operator uO Y
are similar and show that there is some available spectrum, spectrum sharing manager

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requests central frequency and bandwidth reconfiguration to micro operators uO Y anduO XN.
5.2.1.3 Expected results··

Based on the spectrum usage database and appropriate spectrum prediction
algorithms, the required spectrum at uO Y can be forecasted. Hence, the extra
frequency resources can be offloaded to uO Xn and thus providing the necessary
resources for high data rate users especially when advanced techniques such as
carrier/link aggregation is being used.
The main targeted metric here iswhich is defined as the ratio
between the total allocated spectrum between sharing micro operators to the actuallyused spectrum measured at spectrum sharing micro operators.
=··

The reason for using
and not spectral efficiency metric is
because of different modulation types and path losses affect the obtained throughput
and we want a more generic metric to identify spectrum sharing. Hence, thewill be utilized.
Another target sub-metric is the prediction error of spectrum usage ratio (i.e., duty
cycle), which is defined as RMSE (Root Mean Square Error) between the duty cycle
measurement and the predicted duty cycle. Specifically, it is expressed as follows.
( ) =

·

·

·

1

( ( ) − ( ))where N indicates the number of duty cycle measurements, Ψ( ) is the duty cycle( )measurement at time instant t and Ψ( ) is the predicted one. Minimizing
leads to accurate estimation for the statistical information (accurate database). Hence
having an improved spectrum sharing support.
Current networks do not support spectrum sharing and the spectrum allocation is fixed.
This can create a scenario where one area has more spectrum that it actually needs
while a neighbouring cell is overcrowded with users. Based on our proposed algorithms(accurate spectrum detection, spectrum modelling and prediction), we aim to adaptively
allocate the available bandwidth based on demand requirements between frequency
sharing uOs. Hence increasing themetric.
At this point in the project, defining accurate values for the improvement inandis a difficult task. As spectrum usage andmeasurements differ from one area to another. In other words, it is site specific.
Nevertheless, the target is to provide enough spectrum resources for streaming
cameras when they enter uO Xn coverage area through efficient spectrum allocation.
Improvement numbers can be obtained after measuring spectrum usage on site(coverage area of uO Y) and comparing the results with the total allocated frequency
to estimate spectrum usage ratio.

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5.2.2Multi-link transmission of high-data-rate uplink video stream
5.2.2.1 System configuration
System configuration for multi-link transmission of high-data-rate uplink video stream is shownin Figure 17.

Figure 18. System configuration for spectrum sharing.
HD cameras generating high-data-rate uplink traffic are mounted on bicycles of several race
participants. They move along the race route.
HD cameras are connected to a device implementing MP-TCP client software that can split
high-data-rate stream from the camera into several links. Correspondingly, this device also
has several UEs, capable of connecting to different public operators and micro operators(having radio interface capability and subscription).
Bicycle race route is covered by several public operators. Also, some areas of particularinterest for the race viewers are additionally covered by micro operators.
When a user that has high-data-rate uplink stream, MP-TCP client software, and multiple UEs
moves along the route of the race, it has to switch links between eNBs of different operatorsand distribute traffic between the links depending on each link capacity.
To assist seamless switching between eNBs of different operators, radio environment map
database is created. Based on predicted location information of users with cameras, radio
environment map could provide information on available eNBs to connect to.
To use such information, multi-link manager function is used in a CN. In Trial 2 for simplicity
only one multi-link manager is deployed. Based on messages from radio environment map
database and measurements from operators and terminals, multi-link manager performs
reconfiguration of each uplink multi-link transmission user.
One micro operator is mounted on a car. This car follows one or several users with cameras.
It uses micro operators and public operators as a backhauling connection and acts as a mobile
terminal for the public operators and the other micro-operators deployed at the side of the road.

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5.2.2.2 Evaluation procedure
Evaluation procedure for the multi-link transmission of high-data-rate uplink video stream has
two stages:· Measurement and creating radio environment map· Adaptive multi-link transmission.
Measurement and creating radio environment map stage is as follows:
1. Measurements are performed along the route of the bicycle race
2. Each measurement includes several parameters such asa. Parameters related to frequency band, PLMN ID and cell ID
b. Parameters related to signal strengthc. Parameters related to time and location of the measurement.
3. These measurements are collected in the radio environment map database.
4. Radio environment map is created based on processing the measurements.
Adaptive multi-link transmission stage is as follows:
1. A user with high-definition camera, MP-TCP support and multiple UE support moves along
the route of the bicycle race.
2. This user periodically reports its location to multi-link manager, which processes it andsends predicted future user location information to the radio environment map database.
3. Also, multi-link manager sends current multi-link configuration of this user to the radio
environment map database.
4. In response, radio environment map database sends to the multi-link manager
recommended link configurations for some of the future locations together with a reason tochange connection (for example, end of coverage area for one of the micro operator eNBs
with which there is an active connection).
5. After that and periodically, terminals and public and private operators provide additional
measurements to the multi-link manager. Such measurements may include:a. Measurements related to signal strength for active and potential multi-linkconnections
b. Measurements related to available capacity of the cells of public operators andmicro operators
c. Measurements related to actual throughput and other QoS parameters on each of
the active links
6. Taking into account all received information, multi-link manager makes decision on multilink reconfiguration for each of such users. In particular, multi-link manager performs
reconfiguration of the following parameters:a. Number of links usedb. To which eNB each used link is connectedc. Total supported traffic rate
d. Which part of traffic distributed to each link
7. Micro operator mounted on a car that follows one or several high-data-rate users is also
viewed as one of the multi-link users (for the purpose of backhauling connection). The
mobile micro-operator will be evaluated taking into consideration the backhauling qualityof the video as well as the direct video connectivity quality, practically assessing the selfbackhauling capabilities of the proposed infrastructure.

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5.2.2.3 Expected results
During the previous bicycle race in Ehime in 2018, each camera was connected to the multilink
device that was connected to four LTE USB cards. Each card was able to provide around 1
Mb/s data rate, which was combined to have total of 4 Mb/s data rate.
Also there were areas where data rate on all four LTE USB cards decreased considerably forsome period of time, probably due to coverage in the area with a lot of mountains and tunnels.
During Trial 2 in 2020, it is expected that HD cameras will be used. Expected data rate percamera is 10-15 Mb/s. Also, it is expected that several cameras will be used, where they could
be nearby at the same time. Also, there would be stationary cameras, for example, on thebridges or other places with interesting and scenic view.
Compared to 2018 race, the following improvement are planned for 2020 (trail 2):· Video stream data rate increased from 4 Mb/s to 10-15 Mb/s per camera.
· We try to avoid areas of interest that will not support required data rate (coverage
extension using micro operators and mobile micro operator).
· Increase number of cameras that could be used in parallel and in nearby location in orderto support multi-angle interactive view of the race.
An additional benefit of using micro operators would be offloading most part of high-data-rateuplink traffic from public operators.

6.Time plan and partners roles
6.1 Time plan and availability
6.1.1 Trial 1· 28th of March, 2019: project plan ready and use case presentation and query for medical
students (digitalization in medical studies workshop at Oulu university hospital/TestLabwith OuluHealth)· 29th of March: 5GEnhance partners on-site visit Oulu .
· End of April (Week 17): Workshop in Japan .
· 12th of June, 2019: 360 video/remote education technical prototype at Pre-HIMMS.
· 11th -13th of June, 2019: HIMSS Europe 2019 and Health 2.0 in Helsinki (healthcare
information and management systems society event) .
· September 2019, Initial implementation of 4G MC prototype will be demonstrated(simple algorithms).
· December 2019, 4G MC prototype available for testing with v2 algorithms.
· By the end of 2019, 360 remote education/consultation technical demo/prototype
available for 5G Enhance, first tests with multicasting.
· April 2020, 4G/5G MC HW available for trial.
· By the end of 2020, the eMBB enabled 360 remote education/consultation prototype isready to be used in Finland and Japan.
6.1.2 Trial 2· April 2019, project partners visited Trial 2 site at Ehime prefecture. Potential points for
micro operator installation have been selected. Several installation points were checked
for possibility to connect to Internet via mmWave point-to-point connection link.
· May – June 2019, development of evaluation scenarios and metrics for Trial 1.
· July 2019 – March 2020, each partner develops their system (micro operator, spectrum
usage prediction database, radio environment map database, mobile micro operator).
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NICT will develop protocols for interfaces between micro operator CN functions and two
databases.
November 2019, pre-trial will take place in Ehime, Japan. During the pre-trial, eachpartner can field test their developed system and algorithms.
April – August 2020, integration of all partners equipment followed by laboratory tests
for the integrated equipment.
April – September 2020, measurements collection for spectrum usage prediction andradio environment map.
October 2020, final integration and tests for equipment and technologies. Trial 2 during
bicycle race and HD video streaming from multiple cameras mounted on race
participants bicycles to stream different places along the route in Ehime, Japan.

·
·
·
·

6.2 Measurements to maximize the project impact
To accelerate the adoption and impact of the technologies developed in 5G-Enhance, we haveplanned to implement suitable steps in terms of dissemination to industry, in learned
publications and conferences and moreover in terms of standardization contributions.
···

5G-Enhance will organize a workshop within IEEE VTC Fall 2019, between 22–25
September 2019, in Honolulu, Hawaii, USA. The workshop title is “5G and Beyond*Technologies for Ultra-Dense Environments “We are aiming to attend events such as IEEE Globecom, EuCNC2020, or PIMRC werea demonstration and tutorials of latest 5G-Enhance findings/results will be provided topeer researchers.
Publication of scientific results and of practical experiences to the largest scientificand industrial communities, high profile conferences and journals. Target events/conferences include: IEEE ICC, IEEE PIMRC, IEEE GLOBECOM, IEEE INFOCOM
and IEEE WCNC. We will also target top peer-reviewed journals, including, amongst
others, IEEE Transactions on Wireless Communications, IEEE JSAC, and IEEE
Communication Magazine. The project targets to have at least 2 publications
submitted per year to top journals and 5 per year to conferences.

6.3 Partners roles
5G-Enhance network requires the development of several key techniques to enable therequired flexibility and scalability of the network in design/implementation as well as enhancing
spectrum resource
management. A brief description of every partner role is provided in this subsection. Tosummarize every partner role, Figure 10 shows the main partners involved in the 5G-Enhance
project.

6.3.1 Trial 1 partners and roles
The work is divided between partners as follows for Trial 1:• UOULU will participate and lead the architecture design and configuration in the Trial
1. Oulu will handle the planning and execution of Trial 1. UOULU 5G test network has
key components for the network sharing based on SDN (software defined network) /NFV (network function virtualization). One of the key tasks is the design of merging the
key-components/technologies in order to meet the 5G KPIs requirements for 3D remoteclass for a real-time surgery trial. Oulu main contributions to 5G-Enhance work
packages are to WP1, WP2, WP3, WP4, WP5 and support in WP6.
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VTT will focus on developing and integrating 5G multicasting algorithms as a part of a
testbed for improved throughput and energy efficiency. Algorithms for cell selection ina multi-connectivity scenario will be also developed in conjunction with Trial 1, but live
demonstration for multi-connectivity can be difficult. Also, VTT will participate inestablishing the interconnection between FHG 5G Testbed and 5GTN in order toperform a system-wide evaluation of the trial scene if needed. Moreover, the evaluationand test capabilities for network sharing within 5GTN will be done. VTT main
contributions to 5G-Enhance work packages are to WP1, WP2, WP3, WP5 and WP6/•

Accelleran is experienced in small cell and distributed cloud RAN solutions, hence
they will support Trial 1 by providing small cell and vRAN equipment which supports
the testing of multipath connectivity, as well as assisting in the vRAN deployment.
Accelleran will ensure that the platform has the required multi-connectivity capabilities
to allow evaluation of the gains such an approach and protocols can offer in a 5G dense
multi-operator network. Accelleran will help in the definition of trials within realistic
scope of available and project development functionality, products and capabilities aswell as the definition of the multi-connectivity use-cases. Accelleran will ensure thatmaximum benefit to the project can be brought by reusing developments between
partners Accelleran and Fraunhofer FOKUS from other R&I projects. Accelleran main
contributions to 5G-Enhance work packages are to WP3 as well as supporting WP2,
WP4, WP5 and WP6.

6.3.2 Trial 2 partners and roles
The work is divided between partners as follows for Trial 2:• NICT will participate and lead in the eMBB Trial 2 as well as will handle the planningand execution of Trial 2. NICT will develop, 5G test Networks that will be used to deploy,
test, validate and demonstrate the concepts of 5G-Enhance in regard to the trial. NICT
will contribute to the definition of procedures to enable the spectrum, network sharing
and multi-connectivity aspects of the use of unified cases. NICT will developarchitectures and provide high-level 5G procedures to support network sharing in
addition to contributing to 3GPP standardization. Also, NICT will ensure that thedeveloped technologies are supported by the 5G prototype system. Moreover, NICT
will contribute to database architectures to enable efficient spectrum utilization. NICT
will contribute to the link aggregation by the creation of novel MPTCP schedulers.
MPTCP enable connections to distinct systems such as WiFi and LTE concomitantly
and experience higher data rates owing to its capabilities of splitting data throughmultiple TCP subflows. NICT main contributions to 5G-Enhance work packages are to
WP1, WP2, WP4 and WP5 as well as supporting WP6.
•TUAT is to develop the spectrum management/sharing system to be used between
macro base station and uO concept (uO proposed by the University of Oulu). The goal
is to achieve both a reliable spectrum sharing and efficient spectrum allocation between
macro operator and uOs through the utilization of smart spectrum concept. TUAT has
developed a comprehensive spectrum measurement system and learning algorithm toprovide proper information to the database. For Trial 1, the enhanced spectrum
resource management based on the obtained information for spectrum usage in thetime domain. TUAT will provide spectrum measurements (based on spectrum
analyzers and SDRs) in the time domain to extract channel usage and other usefulstatistics. Also, the development of learning algorithms for spectrum usage modelingand prediction to be used in spectrum sharing paradigm. The proposed
ideas/techniques will be investigated through both analytical and comprehensive

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demonstration experiments. TUAT main contributions to 5G-Enhance work packages
are to WP2, WP4 and WP5 as well as supporting WP6.
•

UEC is to develop the spectrum management / sharing system to be used between
macro base station and uO concept (uO proposed by the University of Oulu). The goal
is to achieve both a reliable spectrum utilization between macro operator and uO] andefficient spectrum allocation between multiple uOs through the utilization of smart
spectrum concept. UEC has developed a comprehensive spectrum measurement
system. For Trial 2, the enhanced spectrum resource management based on theobtained information (spectrum usage map in the time-space domain) will be
developed. UEC will provide spectrum measurement based on RSRP (Reference
Signal Received Power) extracted from multiple Android smartphones. The RSRP
information can be used to plot the coverage map for the uO and hence provide
statistical radio environment information which improves the handover and adaptively
control the traffic bandwidth. Moreover, UEC will contribute to the design of spectrum
database and a spectrum management server for 5G-Enhance Trial 2. The collected
information. UEC main contributions to 5G-Enhance work packages are to WP2, WP3
and WP5 as well as supporting WP6.
Fraunhofer Fokus (FHG) will contribute to the architectural design for network sharing
at core network components. Mainly, FHG will operate the eMBB enhanced core
network components and the developed edge node components according to themobile micro operator case definition in Trial 2. Also, FHG will investigate different
techniques to improve user experience and optimize bandwidth such as evolved
Multimedia Broadcast Multicast Service (eMBMS), link aggregation and TCP flow
optimization. Finally, FHG will support other partners by setting up the trail components
in their testbeds. FHG main contributions to 5G-Enhance work packages are to WP1,
WP2, WP4, WP5 and WP6.

•

Ehime CATV is a local broadcast service provider in Japan and hence CATV will
provide support for Trial 2. CATV will contribute to the discussions related to the trial
by providing feedback from the perspective of a local broadcast service provider toensure that the trial outcome satisfies their intended services. Also, CATV will provide
equipment to support the trial such as fixed cameras, mobile cameras to be attachedat the helmet of the racers, the multi-angle broadcast servers and visualization app forthe trial. Also, CATV will provide local network infrastructure to assist with the trial local
broadcasting. Finally, CATV will act as a link with locals for trial organization purposes.
CATV is mainly contributing to 5G-Enhance work packages WP5 and WP6 whileproviding assistance to WP2.

•JCTA Japan Cable and Telecommunications Association accelerates wireless service
business in CATV industries in Japan. Together with Ehime CATV and Regional
Wireless Japan, JCTA joined this project to contribute by providing experimental
opportunities.

•

Regional Wireless Japan (RWJ) is a wireless service platform enabler assigned by
JCTA. Cable operators including Ehime CATV use RWJ’s platform to offer MVNO, WiFi, IoT and Regional BWA services. RWJ will support Ehime CATV in the discussions
related to the trial by providing feedback from the perspective of the candidate local 5G
service enabler to ensure that the trial outcome is in line with cable industry’s wirelessstrategy.

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7.Summary and Conclusions
This is the second deliverable of 5G-Enhance WP2, hence it summarized the outcome ofactivities performed in Task 2.2 entitled “Unified technical requirements and specifications”,
and specified in detail the 5G-enhance system requirements, specifications, architectures andused technologies that will be later implemented in project trials.
A brief outline of the two planned trials 3D remote streaming for a real-time surgery in EU andmulti-angle HD video streaming for an ad-hoc outdoor sport event in Japan is provided. Thenthe main technologies used in Trials 1 and 2 and how they improve on current systems. The
system hardware setup and configuration for the Trial 1 (real-time surgery stream) and Trial 2(race course stream) is discussed. Several technologies have been developed for 5G-Enhance
which will have a signification impact on improving legacy solutions in dense eMBB scenarios.
5G-Enhance is built over two key components, namely flexible and scalable network design
and implementation and enhanced spectrum resource management. The technologies
involved include spectrum management, micro-operator, network management, vRAN
clusters, multicast and multi-connectivity.
A recap for use case scenarios and expected results in terms of KPI is given. The project use
cases are categorized against the eMBB in 5G. The mapping between proposed technologies,
trials and use cases shows a good alignment with eMBB use case in 5G.
Finally, from the work plan, we can conclude that WP2 is still on plan and on schedule.

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References[3GPP-TR38.802]

3GPP, “Technical specification group radio access network; Service
requirements for the 5G systems; Study on New Radio AccessTechnology; Physical Layer Aspects (Release 14); 3GPP TR 38.802
V14.2.0, 2017.

[Aijaz_2018]

Adnan Aijaz, “Packet Duplication in Dual Connectivity Enabled 5G
Wireless
Networks:
Overview
andChallenges,”
http://arxiv.org/abs/1804.01058v1, April 2018.

[EU-Commission_2013]

EU Commission, 5G-PPP contract & technical annex,“ 2013.
[Online].
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[Komulainen_2013]

P. Komulainen, A. Tölli, and M. Juntti, “Effective CSI Signaling and
Decentralized Beam Coordination in TDD Multi-Cell MIMO
Systems,” in IEEE Transactions on Signal Processing, vol. 61, no. 9,
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[Mediatek_2018]

Mediatek, “5G NR A new era for enhanced mobile broadband,”2018. [online] https://cdn-www.mediatek.com/page/MediaTek5G-NR-White-Paper-PDF5GNRWP.pdf[NGMN_2015]

NGMN Alliance, “NGMN 5G White Paper,” 125 p., February 2015.

[Ternon_2014]

E. Ternon, P. Agyapong, L. Hu, and A. Dekorsy, “Database-aided
energy savings in next generation dual connectivity heterogeneous
networks,” in IEEE WCNC’14, pp. 2811-2816, May 2014.

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