Project1 Number: EUJ-01-2016-723171 Project Acronym: 5G … · 2019-09-26 · dissemination activities. In the third year of the project, one deliverable has been submitted. D5.3

Project1 Number: EUJ-01-2016-723171

Project Acronym: 5G-MiEdge

Project title: Millimeter-wave Edge Cloud as an Enabler for 5G Ecosystem

Periodic Technical Report

Part B

Period covered by the report: from 01/07/2018 to 30/06/2019

Periodic report: 3rd

1 The term ‘project’ used in this template equates to an ‘action’ in certain other Horizon 2020 documentation

Explanation of the work carried out by the beneficiaries and Overview of

the progress

This section contains the overall objectives of the project, explained in context with the

relevant WPs and deliverables released in this project period.

1.1 Objectives

The key measurable objectives of the 5G-MiEdge project are:

1.1.1 Objective 1: Research, develop, and prove the 5G based MiEdge concept whose

viability will be evaluated by detailed theoretical and numerical analysis and

prototyped for proof-of-concept.

Objective 1 is the foundation of the whole 5G-MiEdge project. This objective has been worked

on by all the 5G-MiEdge work packages (WP), in particular by:

WP1, to continuously ensure the effective collaboration between the Japanese and

European teams, to define scenarios and use cases that are relevant for the project, and

to finally describe the system architecture that has provided the framework of all the

activities of the technical WPs in the project.

WP2-4, to develop and implement the new technology enablers proposed by the project,

which are needed to fulfil the requirements of the proposed 5G-Miedge architecture.

WP5, to test and showcase the main project results via simulations, prototypes and

proof-of-concepts.

WP6, to disseminate the obtained project results to the broadest possible audience.

In the third year of the project, one deliverable has been submitted:

D1.4 “Final report on joint EU/JP vision, business models and eco-system impact”.

Its content is detailed in Section 2.1.

1.1.2 Objective 2: Develop transmission schemes and protocols of mmWave

access/backhauling aimed to assist the mobile edge cloud with caching/prefetching

so as to realize ultra-high speed and low latency service delivery, resilient to

network bottlenecks, such as e.g. backhaul congestion, users’ density, mission-

critical service deployment, assuming three target scenarios: stadium, office, and

train/station.

The second objective aims to design new technologies to implement the 5G-MiEdge concepts

and meet up the newly defined requirements. WP2 mainly took care of those activities, and in

the third year of the project, two deliverables have been submitted:

D2.2 “Design of mmWave ultra broadband access for 5G”.

D2.4 “Method of site specific deployment of mmWave edge cloud”.

Section 2.2 contains all the details on WP2.

1.1.3 Objective 3: Develop novel ultra-lean and inter-operable control signalling over

3GPP LTE to provide liquid ubiquitous coverage in 5G networks based on

acquisition of context information and forecasting of traffic requirements, in order

to enable a proactive orchestration of communication/computation resources of

the mmWave edge cloud.

The implementation of the newly proposed concepts requires substantial changes to the network

architecture as defined by current standards, and takes into account all the benefits and

drawbacks of using mmWave technology. To fulfill this objective, WP3 has developed and

designed a liquid edge cloud and has proposed a user/application centric orchestration.

In the third year of the project, two deliverables have been submitted:

D3.2 “Integration of mmWave edge cloud into 5G”.

D3.3 “Context information management to create traffic map for mmWave edge cloud”.

All the details of WP3 are presented in Section 2.3.

1.1.4 Objective 4: Develop user/application centric orchestration algorithms and

protocols to adapt radio and computation resources of mmWave edge cloud in 5G

networks by utilizing traffic forecast provided by liquid RAN C-plane to enable

self-organized and proactive reservation of the resources and satisfy low-latency

service requirements.

This objective has been mainly worked on in Task 3.3 of WP3. One deliverable has been

submitted in the third year of the project:

D3.4 “User/application centric orchestration of mmWave edge cloud”.

Quite some effort has been spent on research and development activities, as explained in

Section 2.3.3.

1.1.5 Objective 5: Develop a joint 5G test-bed integrating mmWave edge cloud, liquid

RAN C-plane, and user/application centric orchestration to foster an effective

impact of 5G-MiEdge in both Europe and Japan, particularly in preparation of

2020 Tokyo Olympic Games. The 5G-MiEdge test-bed will liaise actively with the

other EU/JP consortium focusing on the network side as well as to leverage

synergies between alternative 5G concepts.

Objective 5 has been mainly addressed by developing a proof of concept demonstration. Taking

the designs and specifications from Objective 1, hardware from Objective 2, the algorithms

from Objectives 3 and 4, a functional test-bed has been created to further study and finally

assess the project results.

WP4 mainly took care of this Objective. Two deliverables have been submitted in the third year

of the project:

D4.2 “5G-MiEdge Testbed integrating mmWave access, liquid RAN C-plane, and

user/application centric orchestration”.

D4.3 “5G-MiEdge field trials integrated in 5G-Berlin Testbed toward Tokyo Olympic

2020”.

Section 2.4 elaborates more on this Objective.

1.1.6 Objective 6: Contribute to the definition of 5G mobile communications standards

in 3GPP and IEEE, as well as in open fora such as NGMN, Small Cell Forum, and

the International Telecommunication Union (ITU) Industry Specification Group

MEC, in terms of mmWave access, liquid RAN C-plane, and protocols for

user/application centric orchestration by coordination across European and

Japanese partners.

WP5 has taken care of this Objective, working on standardization, spectrum regulation and

dissemination activities. In the third year of the project, one deliverable has been submitted.

D5.3 Final report on dissemination, standards, regulation and exploitation plan

The details of this Objective are presented in section 2.5.

1.2 Milestones

Table 1-1 shows the list of milestones defined in the Description of Work (DoW). The list is

divided into the three years of the project (project periods) and each milestone is marked with

its achieved status and achieved date.

At the end of the project, all milestones were successfully completed.

Table 1-1 List of milestones for the project

First project period: All milestones achieved

Milestone

number

Milestone

name

Related

WPs

Due

date

Achieved Achieved

date

Means of

verification

MS1 Project kick-off WP6 M1 YES M1 Internal Report

MS2 Use cases and

scenarios

defined

WP1 M9 YES M9 D1.1 is available

Second project period: All milestones achieved

Milestone

number

Milestone

name

Related

WPs

Due

date

Achieved Achieved

date

Means of

verification

MS3 System

architecture

and

requirements

WP1 M20 YES M21 D1.3 is available

MS4 Design of

mmWave

antennas

WP2 M24 YES M24 Prototypes

completed and

running

MS5 System level

simulator

WP4 M24 YES M24 Software

released and

validated

Third project period: All milestones achieved

Milestone

number

Milestone

name

Related

WPs

Due

date

Achieved Achieved

date

Means of

verification

MS6 Design of

mmWave edge

cloud

WP2 M30 YES M31 Report

MS7 Integration into

prototypes

WP4 M30 YES M31 Joint/common

prototypes

completed and

running

MS8 Design of

liquid RAN C-

plane

WP3 M32 YES M32 D3.2, D3.3 and

D3.4 are

available

MS9 Algorithm for

user/

application

centric

orchestration

WP3 M32 YES M32 D3.2, D3.3 and

D3.4 are

available

MS10 Field trials in

the city of

Berlin

WP4 M36 YES M37 5G

demonstrators

completed and

running

MS11 Final reports to

EC and

Japanese

Government

All M36 YES M38 Report

Explanation of the work carried out per WP

This section presents the six WPs that compose the 5G-MiEdge project. Each WP is split into

tasks. For each task, we provide details about the performed activities and deliverables, a

selection of highlights and the relationship to other tasks.

The focus of this report are the activities concluded in the third year of the project.

2.1 Work package 1: Scenario/use cases, business model, and 5G architecture

and ecosystem

Contributors: Intel, FHG, CEA, TI, URom, TTech, KLAB, PANA

The scope and structure of WP1 has not changed during the third year of the project.

This WP has run throughout the lifetime of 5G-MiEdge and has been in charge of some key

aspects of the project:

- Foster and ensure that an effective collaboration between the Japanese and the European

teams takes place, creating a common vision that can maximize the synergies, reduce

the risks and finally avoid all possible deviations from the set common targets.

- Analyze the impact of the project on the existing business models in the wireless

communication markets.

This WP is composed of two Tasks, Task 1.1 that has lasted until the project end, and Task 1.2

that ended in project year 2 (month 20), and for that reason this report does not elaborate on it.

All set objectives of WP1 have been achieved at the end of the project, and no major deviation

in work focus or content is to be reported.

2.1.1 Task 1.1: Joint EU/JP vision for global exploitation of 5G technologies, business

models and impact on the eco-system

Contributors: Intel, FHG, CEA, TI, URom, TTech, KLAB, PANA.

Task period: M01 – M36.

Task status: completed.

Also in the third year of the project all project partners have been very actively contributing to

this important task, the main scope of which is to keep the alignment, leverage on the

complementing strengths and create synergies among the two different teams and ecosystems

of Japan and Europe. This task has been interacting continuously with the other technical WPs

(WP2, WP3, and WP4), so to maximize the return-on investment of the consortium partners

and to leverage in the best way on the project results.

Finally, in synergy with WP5, this task not only has identified international events, venues,

public demonstrations and fora relevant for the focused areas of the project, but also has taken

care of the synergy with other research projects in the wireless ecosystem.

Output

During year 3, a final project vision for the work done throughout the lifetime of the project

and for the forthcoming quarters after its end has been elaborated.

The achieved ecosystem impact, and the co-work and the interactions with other relevant

collaborative research projects has been described, especially focusing on the synergies with

the sibling projects MiEdge+ and 5G!Pagoda.

Business aspects relevant for 5G-MiEdge have been worked on especially during year 3. Those

studies provided an update stakeholder analysis and a survey of the 5G deployment as of June

2019. In addition, an update SWOT analysis, activity that started in D1.3, was performed on

the most promising project use cases, i.e., Omotenashi services, Automated driving and 2020

Tokyo Olympic Games.

Concerning the techno-economic evaluation, other relevant research projects have been

surveyed, and a business model analysis with CAPEX / OPEX discussion was completed. That

analysis was done using the Business Model Canvas (BMC) methodology.

Details on all the above-mentioned points can be found in the project Deliverable D1.4 [D1.4].

A detailed list of international impacting events organized and attended by the project in

synergy with other research projects can be found in the project Deliverable 5.3 [D5.3].

Deliverable

Task 1.1 has finalized and submitted one deliverable at the end of year 3, D1.4 [D1.4].

Del.no. Deliverable name Task no. Due

1.2 Mid-term report on joint EU/JP vision,

business models and eco-system impact

T1.1 M12

1.4 Final report on joint EU/JP vision, business

models and eco-system impact

T1.1 M36

Highlights

Here below it is briefly reported on the most important aspects of the BMC analysis with

CAPEX and OPEX discussion, applied to three flagship use cases identified by the 5G-MiEdge

project. For a full analysis, one can refer to D1.4 [D1.4].

Omotenashi services

The result of the BMC analysis has come out of a brainstorming session among all the project

partners held during a consortium meeting general assembly and is shown in the following

figure.

Figure 2-1 - Business Model Canvas for Omotenashi services.

To better visualize in the business model of the Omotenashi services use case the relationships

among key players, the following figure shows the service and cash flow between them.

Figure 2-2 - Key players of the Omotenashi services use case.

Application/Service

Providers

MNO

Location Owner

Content

Owner

MiEdge RAN

(mmWave RAN +MEC)

Local OperatorHardware

Vendors

Shops

/Retails

$

Hardware

$

Space

$

$Service$

Case2

Space

Case1

Contents

$

End Users

Contents

$

$

Contextinfo

Contextinfo

APIs,Context info

Mobile NWaccess

$

Target Ads

Finally, a CAPEX/OPEX analysis has been performed, together with a cash flow analysis, and

their results are summarized in the following tables.

Table 2-1 - CAPEX/OPEX analysis – Omotenashi services use case

Number of Sites

Comments

10 50

CAPEX (Euro)

300k 1,500k - €30k / site (WiGig signage, MEC server, storage, etc.)

- Fiber network installation cost excluded

OPEX (Euro)

3k / month

5k / month

- Service provisioning, maintenance, etc.

- Application and service providers’ cost excluded

Table 2-2 - Cash flow analysis - Omotenashi services use case

Number of Sites

Assumption

10 50

Amount of content delivery 500/day 2500/day 50 contents are delivered per site in one day

Content sales in total (Euro)

60k/month 300k/month €4 per content

Revenue

(Euro)

Content owners

30k/month 150k/month Revenue share = 50%

Apps/Service providers

15k/month 75k/month Revenue share = 25%

Local operator 15k/month 75k/month Revenue share = 25%

The results are provided for two sub-cases, i.e. for the case of a deployment done considering

10 sites, and for the case of deploying 50 sites.

2020 Tokyo Olympics

The BMC exercise for this use cases is reported here below.

Figure 2-3 - Business Model Canvas for 2020 Tokyo Olympics.

The related CAPEX/OPEX have been summarized in the following table.

Table 2-3 - CAPEX/OPEX breakdown for the 2020 Tokyo Olympics use case

Facility CAPEX

(€k) OPEX

(€k/year)

AP

Stadium gate 600 30

Stands and arena 750 37.5

4k video aggregator 15 0.8

4k video transmitter 50 2.5

Optical fiber

Optical fiber cable 750 0

10G media converter (O/E) 375 18.8

Network equipment

10G-router 5 0.3

10G-L2SW (48 ports) 32 1.6

Edge server (no details) 100 5

Grand total 2,677 96.5

Automated driving

The BMC for this use case is analyzed from the point of view of the local operator, whom we

call the RSU operator, who operates the RSU infrastructure. The resulting BMC is shown in

the following figure.

Figure 2-4 - Business Model Canvas for the Automated driving use case.

The main identified key players are sketched in the following figure.

Application/Service

Providers

RSU Operator

MiEdge RAN

(mmWave RAN +MEC)

Hardware

Vendors

$

RSU, etc.

$

APIs,Context info

OEMs

(Car manufacturers)$

Car, OBU $

Service

$, Space

Data

Government,

Business Partners

$ Data

MNO

Contextinfo

Contextinfo

End Users

$Mobile NWaccess

Figure 2-5 - Key players for the Automated driving use case.

Finally the resulting CAPEX/OPEX and cash flow analysis are reported in the following two

tables .

Table 2-4 - CAPEX/OPEX analysis

2k Intersections Assumption

CAPEX (Euro)

60 mil

- €30k per intersection (RSUs, MEC server, storage, etc.)

- Exclude fiber network installation cost

OPEX (Euro)

2 mil/year

- €1k per intersection (Infra provisioning, maintenance, etc.)

- Exclude application/service providers’ cost

Table 2-5 - Cash flow analysis

Estimated Assumption

Number of vehicles 82 mil Registered vehicle in Japan (2018)

OBU equipped vehicles 820k 1% penetration rate

Total revenue per year (Euro)

39 mil €4 per month, i.e. €48 per year

Details on the analysis, the assumed constraints and comments on the results, as well as a full

description of the BMC methodology, can be found in the project Deliverable D1.4.

Relationship to other tasks

This task mainly feeds WP5.

This task is fed from the work of all technical WPs (WP2-WP4).

2.1.2 Task 1.2: Use cases, scenarios and system architecture

Contributors: FHG, CEA, Intel, TI, URom, TTech, KLAB, PANA.

Task period: M01 – M20.

Task status: completed in year 2.

2.2 Work package 2: Millimeter-wave edge cloud for 5G RAN deployment

paradigm

Contributors: PANA, FHG, CEA, TI, URom, TTech

WP2 aims to provide ultra-broadband access with multi-Gbps throughput, while supporting

mmWave backhaul and relay in order to extend coverage area as well as to improve resiliency

against blocking. The key technologies include not only mmWave physical layer design, but

also sophisticated resource allocation (including MIMO and coordinated beamforming),

mmWave high-gain array antenna, mmWave AP deployment design etc.

2.2.3 Task 2.1: mmWave ultra broadband access for highest capacity 5G scenarios

Contributors: PANA, FHG, CEA, TI, URom, TTech

Task period: M04 – M30

Task status: completed

This task focuses on mmWave ultra broadband access technologies. In order to achieve multi-

gigabit throughput while dealing with densely populated environment, various techniques such

as spatial multiplexing, multi-link coordination as well as calibration techniques for super high-

speed mmWave access are investigated. Ultra lean signalling/control plane is also developed

for efficient user management in a dense environment.

Output

Spatial multiplexing (MIMO, massive MIMO) for mmWave (Subtask 2.1.1):

o Proposed a round Robin (RR) based user scheduling algorithm for the mmWave

MU-MIMO systems using hybrid beamforming (HBF), which is called beam RR

algorithm and jointly selects the beam pattern for analog beamforming (ABF) and

the user station (STA) for each selected beam; and evaluated the performance of the

proposed beam RR algorithm in mmWave HBF MU-MIMO systems with a line of

sight (LOS)-component-only channel model over stadium scenario with high

density STAs.

Multi-link coordination of mmWave access to control interference and blocking (Subtask

2.1.2):

o Proposed an algorithm for radio resource allocation in the uplink transmissions,

exploiting multi-link communications to reduce power consumption while

maintaining a minimum data rate, avoiding interference among neighboring cells.

Two different sources of interference are considered: inter-cell interference, caused

by users in other cells, and intra-cell interference, caused by users in the same cell

considering that they can communicate on the same time-frequency blocks. Our

proposed algorithm selects the precoding matrices of mobile users via a Successive

Convex Approximation method to deal with the non-convexity caused by the

interference.

Channel bonding and higher order modulation for super high speed mmWave access

including phase noise compensation and pre-distortion (Subtask 2.1.3):

o Evaluated two types of channel estimation schemes with multiple channel

estimation preamble for the mmWave MU-MIMO OFDM systems, and proposed a

phase-noise-robust (PN-robust) channel estimation method.

Ultra lean signalling/control plane for mmWave (Subtask 2.1.4):

o Completed system performance evaluation of the proposed scheme under stringent

condition where up to six users are connected to the signage at 30 cm spacing.

Successfully verified that each user can achieve more than 1 Gbps throughput in

high-density user environment. The results have been reported in D2.2 [D2.2].

The work performed in this task has led to the several publications, some of which are listed

below.

o Y. Chang, and K. Fukawa, “Phase Noise Compensation for mm-Wave MU-MIMO

OFDM Systems: Phase-Noise-Robust Channel Estimation and PER Characteristics

Evaluation,” SmartCom 2018, Thailand, October 2018, Best Paper Award

o T. Urushihara, N. Shirakata, K. Takinami, “60 GHz Wireless System Employing

Multi-Link Connection Targeting for V2X Applications,” SmartCom 2018,

Thailand, October 2018.

Deliverable

Task 2.1 contributed to deliverable D2.2.


2.1 Requirement and scenario definition for

mmWave access, antenna and area planning

for mmWave edge cloud

T2.1,

T2.2,

T2.3

M12

2.2 Design of mmWave ultra broadband access for

5G

T2.1 M30

Highlights

Through three years of the project, Task 2.1 investigated several key technologies, including

spatial multiplexing, multi-link coordination, channel bonding/higher order modulation and

ultra-lean signalling/control plane, which have been successfully verified by either simulation

or measurements. Table 2-6 summarizes relationships between key technologies and

requirements of five use cases specified in 5G-MiEdge. In principle, each technology can be

applied to all the use cases, but highlighted cells indicate most stringent requirements, which

can be achieved by introducing the key technology listed in the right column.

Table 2-6 - Relationship between requirements and key technologies developed in WP2

Use Cases

Key technologies Omotenashi

services

Moving

hotspot

2020 Tokyo

Olympic

Dynamic

crowd

Automated

driving

1

System

rate > 6.6 Gbps

> 80 Gbps

(backhaul)

500 Gbps

(viewing area) 7.5 Gbps -

Spatial

multiplexing

(Subtask 2.2.1) Area/

distance 1000 m2 > 10 m

40,000 m2

(viewing area) 40 m×40 m > 150 m

2 Blocking Severe Severe

in vehicle

Severe in

viewing area Severe

Severe

in V2I Multi-link

coordination

(Subtask 2.2.2) 3 Mobility 4 km/h

120 km/h

(backhaul on

train)

- 4 km/h 70 km/h

4 Peak user

rate > 2 Gbps

> 2.15/0.54

Gbps

(DL/UL)

> 4.2 Gbps

(Gates) 50 Mbps 1 Gbps

Channel bonding

and higher order

modulation

(Subtask 2.2.3)

5 User

density

0.4 users/m2

(Train

station)

2 users/m2

(Bus)

1.5 users/m2

(Viewing area)

0.18

users/m2

0.2

vehicles/m/

lane

Ultra lean

signalling/control

plane(Subtask

2.2.4)

The details of the outcomes have been reported in Deliverable D2.2 [D2.2], whose highlights

are summarized in the following.

Spatial multiplexing (MIMO, massive MIMO) for mmWave

The mmWave multi-user MIMO systems has been investigated, targeting for the stadium

scenario with high-density user stations. The performance was evaluated using hybrid

beamforming with a line of sight component-only channel model, employing user-scheduling

algorithm. The proposed beam round Robin algorithm that considers the joint beam and user

selection can reduce the inter user interference between the selected stations. The evaluation

results show that the average system capacity is larger than 17 bps/Hz, when the maximum

number of selected STAs is 4, and both the analogue beamforming and digital beamforming

(zero forcing) are adapted.

Multi-link coordination of mmWave access to control interference and blocking

This strategy aims to develop an efficient method for handling users’ mutual interferences in

case of spatial division multiple access in the uplink direction, when users are served in the

same frequency band at the same time. The convenience of exploiting multi-link

communications by coordinating multiple access points is shown by numerical results. The

presented algorithms aim at finding the users’ precoding matrices in order to minimize the

transmit power under QoS constraints, taking into account interferences. The algorithm shows

good performance in terms of power consumption and convergence time of a Successive

Convex Approximation method used to handle the non-convexity of the problem.

Channel bonding and higher order modulation for super high-speed mmWave access

including phase noise compensation and pre-distortion

A phase-noise-robust channel estimation for mmWave MU-MIMO OFDM systems is proposed

and evaluated. The packet error rate (PER) results of a simple frequency-domain phase noise

compensation (PNC) scheme using the proposed channel estimation method are shown.

Comparing with the system with perfect channel state information (CSI), the degradation of

PER performance of the proposed simple PNC scheme with the estimated CSI is around 3 dB

in signal-to-noise power ratio (SNR); however, it still outperforms the conventional common-

phase-error (CPE)-only compensation scheme, when the total phase noise level is −88 dBc/Hz

@ 1MHz offset. In addition, the maximum throughput is around 27 Gbps, when the number of

STAs is 4, the bandwidth is 1.815 GHz, and the secondary modulation schemes are 64QAM.

Ultra lean signalling/control plane for mmWave

The cooperative WiGig/Wi-Fi connection management scheme is proposed to minimize latency

due to WiGig connection while reducing overhead of mmWave control signals. Measurement

shows that the WiGig connection can be established before the download request from stations

with about 90% probability, validating the effectiveness of the proposed scheme. Besides, the

system has been extended to a link aggregated system, targeting for the moving hotspot use

case. By utilizing three WiGig channels (CH1:58.32GHz，CH2:60.48GHz，CH3:62.64GHz)

combined with polarization MIMO (vertical/horizontal), the prototype aggregates up to six

mmWave links, achieving 10 Gbps total throughput which has been successfully verified by

measurements.


The outcomes have been transferred to WP4 and the results were reported in WP4

deliverables (D4.2 and D4.3).

2.2.4 Task 2.2: Design of mmWave antenna for specific scenarios toward Tokyo

Olympic 2020

Contributors: TTech, FHG, TI


Task status: completed in second period

This task has been completed in second period; therefore, there is no update in the third year.

The task focuses on the development of mmWave antennas for various use case and selected

scenarios, together with WP1, toward Tokyo Olympic 2020. The antenna prototypes will

include a planar array antenna for gate system (mmWave shower) where large contents such as

videos, application software etc. are downloaded instantaneously at the entrance gate. High gain

antennas will also be investigated for mmWave backhaul.

2.2.5 Task 2.3: Site specific deployment of mmWave edge cloud with

caching/prefetching and relay

Contributors: FHG, URom, CEA, TTech



This task is tightly connected to WP1 and focuses on deployment of mmWave edge cloud

systems in specific scenarios. It also builds the foundation for hardware development and

outdoor deployment in WP4.

At first the deployment method is defined to match the scenario requirements regarding

throughput and user density and to provide caching, prefetching and relay capabilities. After

the successful deployment, the radio resources are dynamically optimized. Based on the current

and predicted traffic load, the backhaul links and allocated channels are adapted.

With the submission of Deliverable D2.4 in M26, this task was successfully completed.

Output

This task was split into multiple subtasks, which were handled and presented in Deliverable

D2.4.

Overall architecture of mmWave edge cloud in selected scenarios

Explaining the system architecture, requirements and specific deployment constraints of

mmWave edge cloud networks for selected 5G-MiEdge scenarios.

Design and analysis for site specific deployment of mmWave edge cloud

Site-specific deployment of mmWave edge cloud systems needs careful planning and

evaluation of the used concepts and technologies. Detailed analysis for three key design

components:

Modeling and optimization of multi radio access technology (RAT) heterogeneous network

(HetNet)

Interference management in mesh backhaul networks

Dynamic resource allocation, considering the best trade-off between queues and power

consumption

Mesh backhaul prototyping

The base for the final evaluation of all developed ideas in large field trials, this task covered the

details and specific constraints on the developed prototype for selected 5G-MiEdge scenarios.

Deliverable



2.1 Requirement and scenario definition for

mmWave access, antenna and area planning

for mmWave edge cloud

T2.1,

T2.2,

T2.3

M12

2.4 Method of site specific deployment of

mmWave edge cloud

T2.2 M26

Highlights

Two of the highlights of this task are the development of the overall architecture, which leads

to the mesh backhaul prototyping. Details were published in Deliverable D2.4 [D2.4].

Overall architecture of mmWave edge cloud in selected scenarios

While the architecture shows strong similarities for each scenario that can be efficiently

exploited in the hardware prototyping, there are also some distinct differences that need to be

considered carefully. Figure 2-6 presents the developed system architecture for one of the

specified 5G-MiEdge scenarios, the Outdoor Dynamic Crowd.

Figure 2-6 - System architecture of outdoor dynamic crowd

In this scenario, the wireless backhaul meshed networks are compliant to the 3GPP architecture.

The 3GPP network provides both control plane and data plane for both UE and mesh wireless

backhaul. About mesh wireless backhaul, since mesh relay is yet to be discussed in 3GPP for

backhaul link, donor may be an anchor point of the relay network. In this figure, the MSF

collects the users’ context information (location, user profiles, subscriptions etc.), as well as

other content-related information, such as content popularity, and data from the (mobile and

fixed) MEHs to optimize the MEH resource usage. The MSF also has a sub-function dedicated

to controlling the local mesh backhaul network. This sub-function collects context-information

of the mesh routers themselves e.g. location, radio link condition, link availability etc., to

dynamically construct the wireless backhaul, transferring data plane to the supported UEs.

Using these sub-functions and functions of MSF, data can be pre-fetched from the AS to the

MEH located at the small cell gateway.

Design and analysis of site-specific deployment of mmWave edge cloud

A dynamic user assignment and resource allocation algorithm for computation offloading in

the edge cloud is described in [D2.4]. In particular, the strategy aims at finding the best trade-

off between delay and power consumption in the long-term average. The assignment strategy

takes into account radio and computation resources jointly, and uses a suitable penalty function

discouraging small cell switch described in details in [MDB19]. Indeed, each handover comes

with the drawback of having to transfer the application state from the current MEH to the target

MEH. This delay can be detrimental for the application and should be avoided as much as

possible. The dynamic resource allocation is performed through the tools of Stochastic

Lyapunov optimization, which allow solving a long-term problem in a per-slot fashion with

good performance corroborated by numerical results. The numerical results are shown in Figure

2-7, comparing our strategy with a classical SNR based assignment in terms of trade-off

between average queue length (i.e. average delay), and average power consumption. In the

Figure, 𝑁tr denotes the number of time slots needed to transfer the application state. Detailed

description of the algorithm can be found in [D2.4] and [MDB19].

Figure 2-7 - Tradeoff between average queue length and average power consumption

Mesh backhaul prototyping

The requirements on the nodes, which have then been developed in WP4, are as follows:

Data rates of multiple Gbps

Latency below 1 ms

Range of over 200 m

Reduced interference in dense deployments

Multi-Access edge computing

Power saving

Central orchestration

They were meant for deployment in our testbeds in Berlin and Tokyo, sharing similar hardware

to cover all the defined 5G-MiEdge use-cases and scenarios. A possible testbed architecture is

shown in Figure 2-8. It consists of a SDN controller for the necessary central orchestration over

the specified liquid RAN C-plane.

10-3

10-2

10-1

100

Average power consumption (W)

106

107

108

Avera

ge

sum

qu

eue

le

ng

th (

bits)

SNR-based, Ntr = 50

SNR-based, Ntr = 10

Matching-based, Ntr = 50

Matching-based, Ntr = 10

Figure 2-8 - Testbed architecture [D2.4]

As well as a several 5G-MiEdge nodes with multiple mmWave backhaul links, edge

computation capabilities for computation and caching/prefetching, as well as mmWave access

for the user.

This testbed was then deployed in the labs at Fraunhofer HHI in Berlin and Tokyo Institute of

Technology, first indoor for initial evaluations and then outdoor for larger scale and other

selected scenarios.


There is a close connectivity between Task 2.3: Site-specific deployment of mmWave

edge cloud with caching/prefetching and relay and Task 1.2: Use cases, scenarios and

system architecture.

Task 2.3 is based on the specifications of scenarios and use cases that are developed in

Task 1.2, yet those specifications are shaped and presented to be usable for the

deployment of an mmWave edge cloud system.

Task 2.3 provides the base requirements to the hardware development in Task 4.2 and

4.3

2.2.6 Risk Assessment

As described in Section 2.6.2.4, the risks that were identified and constantly monitored in

WP2 are shown in Table 2-7.

Table 2-7 Technical Risks in WP2

Progress of each task has been continuously monitored and discussed in the monthly meetings

and face-to-face GA meetings. Through collaborative work among project partners, all the

solution design has been finished as scheduled. Outcomes were reported in two deliverables of

D2.2 and D2.4, which are accessible to the public from the 5G-MiEdge webpage.

To be aware of competitive technologies, WP partners have continuous monitored a trend of

standardization, such as 3GPPP and IEEE, as well as major conference/journal proceedings, in

order to identify the novelty of their developed technologies. The outcomes were also presented

at several international conferences.

WP2

WP3

Solution design is not

finished in time or

includes design

mistakes

Low Reviews of technical work is conducted by work package

leaders and steering committee to ensure consistency among

WPs and fulfillment of the project objectives. When necessary,

solution options to this risk are developed by a team of

multiple partners and feedback is received from the design,

implementation, and validation activities in WP1 and WP4.

WP2

WP3

Emergence of

competitive

technologies

Low A continuous technological watch in standards and major

conference/journal proceedings will highlight the

shortcomings, and enable a fast reaction to such threats.

2.3 Work package 3: Design of 5G liquid edge cloud for user/application centric

orchestration

Contributors: KLAB, CEA, URom, TTech, Intel

This WP focuses on designing control signalling for joint radio and computation resource

orchestration algorithm for distributed mmWave edge cloud of 5G wireless heterogeneous

networks. It is composed of mmWave edge cloud integration into 5G mobile network, context

information management for traffic map prediction and user/application centric orchestration

to realize 5G liquid edge cloud.

2.3.1 Task T3.1: Integration of mmWave edge cloud into 5G cellular networks with inter

operable control plane

Contributors: KLAB, Intel



This task focuses on integration of mmWave edge cloud into the 5G mobile network. Control

signalling is analyzed so to better serve contents and services, which are deployed by utilizing

edge cloud in the network in a distributed manner. In the third period, it was studied and

evaluated how to allocate limited resources of edge computing optimally and to control for

migrating services along with user handover between hosts of edge cloud.

Output

In the third year period, the following approaches were studied and evaluated in terms of the

task objectives.

Service continuity via carry-on state service handover

o In order to shorten service disruption time when a user equipment (UE) moves to an

area of another more appropriate edge host, our approach resumes the ongoing

service at the destination edge host by the UE sending the service state parameters

held in the UE itself as progress information of the service. We evaluated efficiency

of the approach. This result led to a conference paper [YS18].

Virtual machine (VM) instantiation exploiting mobility prediction

o Mobility prediction for proactive VM instantiation was evaluated. It is shown that

accurate prediction of a handover would help avoiding or reducing the effects of

three drawbacks for the VM instantiation: too late relocation, too early relocation

and relocation to a wrong edge host.

Appropriate workload placement in edge cloud

o A method of appropriate workload placement considering both computation and

communication delay in distributed computing resources of the edge cloud was

proposed and evaluated. This result led to a conference paper [YS19].

Deliverable



3.1 Architecture of mmWave edge cloud and

requirement for control signalling

T3.1,

T3.2,

T3.3

M18

3.2 Integration of mmWave edge cloud into 5G

cellular networks

T3.1 M32

Highlights

Through three years of the project, Task 3.1 studied integration of mmWave edge cloud into

the 5G mobile networks. We defined its system architecture and control signalling, and

focused on underlying issues of resource allocation from limited resources of edge cloud and

service migration between hosts of edge cloud on user mobility. The details of the outcomes

have been reported in D3.2. Highlights are summarized in the following.

(1) Service continuity via carry-on state service handover

It is necessary to provide a measure for service continuity when a user moves across edge

hosts. In the conventional studies, migration of VM or container between edge hosts were

proposed for it. However, they took long time to migrate a VM or a container between edge

hosts, so the service provisioning would be interrupted during the migration. It would be

critical for latency strict services to have this service downtime. Our approach resumes the

ongoing service at the destination edge host by the UE sending the service state parameters

held in the UE itself as progress information of the service instead of the VM migration. By

performing this way, amount of data to migrate between edge hosts will be decreased. Then,

the service downtime is shortened. Moreover, we proposed a signalling procedure to trigger a

UE to request a resume of the service to a destination edge host. Figure 2-9 (a) and (b) show

service downtime and backhaul resource consumption when the conventional studies and our

approach are compared respectively. These results were presented in a conference paper

[YS18]

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Reduced

(a) Service downtime (b) Backhaul resource consumption

Figure 2-9 - Service downtime and backhaul resource consumption due to service migration between

edge hosts

(2) VM instantiation exploiting mobility prediction

We also elaborated on a different approach than the previous approach in (1), to proactively

instantiate the VM in the next target edge host. In order to save storage resources avoiding to

replicate VMs in too many nodes while guaranteeing service continuity during handovers,

mobility prediction was studied. Our approach was based on Kalman filtering, coupled with the

concept of relocation group. We proved more accurate prediction (lower 𝜎𝑤2 ) of user trajectory

achieves lower number of instantiated and migrated VMs, and higher service continuity

probability. Examples of evaluation results are shown in Figure 2-10.

(a) Instantiated VMs

(b) Migrated VMs

Figure 2-10 - Tradeoff for service continuity probability

(3) Appropriate workload placement in edge cloud

To improve the efficiency of edge cloud resource utilization, it was proposed to organize edge

servers into a hierarchical architecture. Instead of serving UEs from a flat collection of edge

servers, the new concept proposes to configure tiers of edge servers in the hierarchy topology

and opportunistically aggregate and serve the peak loads that exceed the capacities of edge

servers in lower tiers to other edge servers in higher tiers of the hierarchy. We proposed a

method of workload placement considering both computation and communication delay in the

hierarchical architecture. We proved average task completion times could be shortened more

by our approach than the conventional approach, which considered just computation delay for

workload placement. Figure 2-11 (a) and (b) show comparisons of average task completion

times for conventional and our proposed workload placement for backhaul bandwidth of

100Mbps and 10Gbps respectively. These results were presented in a conference paper

[YS19].

0.99 0.992 0.994 0.996 0.998 1

Service continuity probability

200

250

300

350

400

Ave

rage

num

ber

of in

stan

tiate

d V

Ms

w

2 = 0.1

w

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w

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0.99 0.992 0.994 0.996 0.998 1

Service continuity probability

2

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6

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rag

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of

mig

rate

d V

Ms

w

2 = 0.1

w

2 = 0.05

w

2 = 0.01

(a) 100 Mbps

(b) 10 Gbps

Figure 2-11 - Task completion time for backhaul bandwidth


This task is based on the use cases defined by WP1.

This task provided information to T1.2 for the definition of the whole 5G-MiEdge

architecture.

The results will be input to Task 4.2 for PoC definition.

The results will be input to Task 5.2 for standardization activities.

2.3.2 Task T3.2: Context information management for traffic map prediction

Contributors: CEA, URom, KLAB, Intel



This task focuses on defining new procedures to transport and share measured context (e.g.,

location, traffic, action, etc.) of users with the edge, on understanding the impact and roles of

terminals, (e.g. identify and define measurements on the terminal side to fulfil liquid resource

management, or new procedures and functional splits between terminals and the edge cloud),

and finally on the management of liquid resource, which requires adapted signalling for joint

communication and computing cluster formation. Cluster formation either can require a

centralized intelligence for orchestrating its formation and update or can be accomplished by

distributed intelligence.

Output

On the management and exploitation of context information for content and computation

caching:

o Conception of a strategy for finding the optimal trade-off between the transport and

caching energy costs associated with the distribution of contents in information

networks. This study led to two publications [BSC18] and [SCM18].

0

128

256

384

512

640

30

50

70

90

110

130

0 30 60 90 120

# o

f UE

Ave

. co

mp

leti

on

tim

e [m

sec]

Simulation time lapse [seconds]

ConventionalProposed# of UE

Shorter time

0

100

200

300

400

500

600

700

30

50

70

90

110

130

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# o

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Ave

. co

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tim

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sec]

Simulation time lapse [seconds]

Conventional

Proposed

# of UE

Shorter time

o Finalization of the analysis of computation caching policies for the exploitation of

context information and the performance improvement of computation offloading

and edge computing. These results are presented in [dC18] and [dC19].

o Submission of a patent proposal on methods for the exploitation of computation

caching: E. Calvanese Strinati and N. di Pietro, “Connected cache empowered edge

cloud computing offloading”.

On learning algorithms for physical and application layer parameters and context

information:

o Advancements in three different applications, based on graph topology inference

from data: i) construction of the radio environment map (REM); ii) recovery of the

spatial pattern of wireless data traffic from sparse measurements; iii) prediction of

file popularity across space and time. These results were obtained thanks to the

method proposed in [SBD19].

Deliverable





T3.1,

T3.2,

T3.3

M18

3.3 Context information management to create

traffic map for mmWave edge cloud

T3.2 M32

Highlights

D3.3 provides the detailed final description of the results and activities carried out in the

framework of Task 3.2 of 5G-MiEdge. These studies focus on management, exploitation, and

learning of context information, with direct applications to two caching problems and to the

construction of radio environment and traffic maps.

In particular, our results on content and computation caching contribute to the reduction of

network costs and to a considerable optimization of the exploitation of resources. The word

“cost” includes several meanings: energetic costs, time delays, volume of data flowing through

the network, amount of control signalling, etc. Cutting these costs directly implies a higher

scalability of the system and an easier implementation of the proposed techniques. Moreover,

optimized caching and transport strategies globally reduce the flow of data through the network

(or concentrate it in moments of the day when it is more sustainable), thus streamlining and

speeding up communications.

In addition, 5G-MiEdge developed methods for learning context information for a smart and

efficient usage of network resources. These methods, based on inference of graph topology

from available data (e.g. current traffic), allow predicting and estimating different parameters

across space and time, thus obtaining run-time awareness of the operating environment

conditions. As a result, proactive resource allocations strategies and prefetching algorithms can

be optimized, improving the management of data and communication traffic and the overall

network QoS.

In the following, we briefly summarize the achieved results and the activities described in D3.3.

(1) Proactive caching and transport optimization

We propose to incorporate the information-centric networks (ICN) strategy in the edge cloud

to have a robust mechanism to handle mobility in the mmWave scenario. ICN is a networking

infrastructure tailored for content delivery, based on a name-data-routing. In ICN, content

objects move across the network according to users’ requests and are retrieved by their name,

and each network entity is equipped with limited storage capabilities. This helps in reaching

contents without having explicit knowledge of their storage location, reducing access delay and

network bandwidth utilization. The question becomes then how to distribute contents through

the network. We address this question by finding the optimal trade-off between content

replication and delivery time.

Our proposed strategy is proactive with respect to the users’ requests, as contents are pre-

fetched depending on the distribution of their (estimated) popularity. In particular, we

developed a dynamic energy-efficient strategy that jointly optimizes caching and delivery costs

within each cluster of nodes, i.e. moderate-size network in which each node (entities like mobile

edge hosts (MEHs)) has storage capabilities. The content distribution results as a trade-off

between replication and delivery time in an energy-efficient dynamic way to find network

configuration evolution in terms of placement and routing of content objects over time.

As an example of numerical results, Figure 2-12 shows the energy cost comparison between

our proposed strategy and a non-proactive state-of-the-art solution [LTV15], with respect to the

transport cost parameter 𝜆. Note that proactivity yields considerable energy savings for low λ

values, taking benefit from the optimal transport strategy.

Figure 2-12 - Total average energy cost vs. transport cost parameter

(2) Computation caching

In the context of computation offloading, we proposed and analyzed several computation

caching policies, based on three quantities that characterize an offloadable task: its popularity,

the size of its input, and the size of its result. The numerical results presented in [dC18] and

[dC19] highlight numerous benefits of computation caching techniques. First, computation

caching helps in reducing by considerable percentages the uplink traffic from UEs to the edge

cloud, in the framework of computation offloading, as shown in Figure 2-13. This contributes

to mitigate the “tsunami” of uplink traffic that is foreseen for future 5G communications.

Moreover, more offloading requests can be treated per time unit by the same small cell, thus

increasing the quality of the provided services and allowing the same MEH to serve more UEs.

This is mainly due to the fact that the average computation delay required by offloadable tasks

is shortened, because already-cached task results do not need to be computed again. This also

entails a reduction of the computational capacity needed at the MEHs’ level to guarantee a

certain performance. Finally, the numerical results obtained in the scenario with small cell

federation not only confirm and strengthen the previous points, but also show that well-designed

computation caching policies can be sufficient to guarantee high performance, making

federation not essential to achieve high gains. This is particularly beneficial in scenarios where

small cell clustering induces high fixed costs (due to backhaul implementation, overhead

communications, etc.).

Figure 2-13 - Reduction of uplink traffic and corresponding computational work load as a function

of the small cell cache size and the applied computation caching policy, in a scenario with 50000

offloadable tasks

(3) Learning algorithms for physical and application layer parameters and context

information, based on graph topology inference from data

Associating a graph-based representation with a dataset plays a crucial role in determining and

extracting relevant information from the data. By modelling the observations as random

variables or processes, the graph topology typically reflects correlations among signals defined

over its vertices. In [SBD19], we proposed a method to associate a graph topology with the

observed signal in order to make the signal band-limited over the inferred graph. Enforcing this

band-limited property enables then the use of sampling theory to recover the overall signal from

a subset of values. This property is appealing in all applications where it is convenient to reduce

the number of observations and has a key importance for the applications studied in the

framework of 5G-MiEdge. Namely, we applied our innovative algorithms [SBD19] for

efficiently building radio environment maps (REMs) starting from a limited amount of

observations, based on the representation of the field to be reconstructed over a graph. The

starting point of the proposed approach is that the relationships between the field values in

different points in space can be properly represented through a graph whose topology captures

the correlation among different points. An example of our REM construction is given in Figure

2-14 -.

Figure 2-14 - REM: true field (background), reconstructed field (circles)

Moreover, we applied our methods for filtering and recovery of signals defined over a graph to

a data set, which gathers information on the outgoing calls activity generated by the Telecom

Italia cellular network over the city of Milan. In particular, we inferred the graph topology

reflecting the relations among data traffic values in different points in space and time and then

we used the graphical representation to derive optimal sampling algorithms and signal recovery.

We were able to numerically show the goodness of our algorithms through a comparison

between the known real data and the inferred data. Then, we extended our graph-based

algorithms to build a traffic map predictor based on a finite number of training data. A

numerical example is reported in Figure 2-13, showing the mean prediction error as a function

of the number of samples taken from previous days. The figure shows the prediction of the

wireless traffic at a given time over a set of access points, based on the monitoring of the traffic

over a subset of access points in the previous days. The figure compares the performance

obtained using an arbitrary selection of the subset of cells used to monitor the traffic and an

optimal method proposed in the paper [SBD19]. From Figure 2-15 - Normalized mean square

prediction error vs. number of spatial samples, we can observe that, for the same number of

monitored cells, the proposed method provides a significant performance improvement.

Figure 2-15 - Normalized mean square prediction error vs. number of spatial samples

Finally, we applied our learning algorithms to predict the file popularity within the coverage of

a base station (BS) for caching applications. Clearly, an efficient proactive content delivery can

be performed only if we know the probability that a file is going to be requested. In general, the

popularity of a file varies across time and space. We proposed a mechanism to recover the

estimation over all BSs, by exploiting the spatial correlation among popularities over nearby

nodes, where the neighborhood relations are established by the graph connecting all BSs. Given

the knowledge of the popularity over a certain subset of nodes, we managed to recover the

popularity across all other nodes.


T3.2 takes input from T1.2 about the use cases of interest.

The learning mechanisms implemented in T3.2 are a key enabler for the proactive

resource allocation strategies developed in T3.3.

T3.2 contributed to dissemination (T5.1) with journal and conference papers and a

book chapter.

T3.2 contributed to exploitation (T5.1) via the submission of a patent proposal.

2.3.3 Task T3.3: User/application centric orchestration to realize 5G liquid edge cloud

Contributors: URom, CEA, TTech, Intel



The user/application centric paradigm requires a joint optimization of communication /

computation / storage resource allocation, taking explicitly into account application layer

parameters. This task aims at providing an energy efficient design satisfying application-

specific latency constraints. This requires in some cases a proactive allocation, based on the

learning techniques developed in Task 3.2.

Output

On the joint management of radio, computation and storage resources:

o Definition of static and dynamic strategies for joint allocation of communication

and computation resources for computation offloading in the edge cloud to

reduce the devices’ energy consumption. This work led to two publications: a

conference paper [Mer19], and the submission of a journal paper [Mer19-2].

o Exploitation of data prefetching in the edge cloud in scenarios with limited

backhaul resource to improve the system data rate and reduce the access latency.

On the load distribution and clustering within the edge cloud:

o Load distribution among MEHs and clustering to reduce the network energy

consumption while providing low latency services to the end users. This work

led to the publication [OUEIS19].

o Dynamic ON/OFF strategies to find a suitable clustering of users to be served

by the macro cell’s MEH or the small cell’s MEH and switch off base station to

reduce the system energy consumption.

On the resilient design and the detection of network criticalities:

o On the analysis of block erasure channel coding techniques to counteract

blocking events typical of mmWave links. This work led to the submission of a

journal paper [dMC18]

o On a method to control mmWave meshed backhaul for efficient operation of

mmWave small cell overlay HetNet.

Deliverable





T3.1,

T3.2,

T3.3

M18

3.4 User/application centric orchestration of

mmWave edge cloud

T3.3 M32

Highlights

D3.4 provides a detailed description of the activities related to task 3.3 of the project, focused

on resource allocation strategies, load distribution and clustering, resilient design and detection

of network criticalities for an effective management of the edge cloud. All the results are briefly

summarized here below and described in details in D3.4.

(1) Optimal assignment and resource allocation for computation offloading

We consider the mmWave edge cloud scenario serving multiple users. The objective is to assign

UEs to APs and MEHs and to jointly allocate radio and computation resources. The detailed

description of the algorithm can be found in D3.4 and [Sar18]. In Figure 2-16 we show the gain

of our strategy with respect to a classical SNR based association in terms of power consumption

for different latency constraints

Figure 2-16 - Overall UEs transmit power vs. Latency

(2) Jointly optimal resource allocation in dynamic scenarios with power consumption and

average delay trade-off

In dynamic scenarios, in which the applications running on mobile devices continuously

generate data to be processed at the MEH, we can model the system with communication queues

at the UE and computation queues at the MEH. As described in details in D3.4 and the related

publications [Mer19], [Mer19-2], the aim is to find a good trade-off between total sum queue

length and energy consumption exploiting the tools of stochastic Lyapunov optimization. In

Figure 2-17, the reliability is shown, i.e. the probability that the total queue length exceeds the

value on the abscissa. As we can see, all the UEs meet their constraints on the out-of-service

probability.

Figure 2-17 - Probability that the user sum queue length exceeds the value on the abscissa

(3) Data prefetching

As described in D3.4, we assume a scenario composed of heterogeneous networks with limited

backhaul resource. In such environment, the method to allocate the limited resources greatly

affects the performance of the network. The prefetching of data to MEH in advance reduces

access delay significantly, as will be seen from the numerical results, which is important for

latency-sensitive applications. Figure 2-18 shows the average access delay of the different

algorithms, as a function of the backhaul capacity.

Figure 2-18 - Avg. access delay of the different algorithms

(4) Load distribution

This subsection introduces novel algorithms for the computational load distribution among

MEHs. Our goal is to federate APs and MEHs in order to minimize the energy consumption

under delay constraints. The detailed description of the algorithm for load distribution can be

found in D3.3 and the related publication [OUEIS19]. In Figure 2-19, we show the user

satisfaction ratio in terms of latency constrained computation offloading vs. the maximum

number of users per AP.

Figure 2-19 - User satisfaction ratio vs. maximum number of users per SAP-MEH

(5) Dynamic ON/OFF strategies

In this section, we consider the network architecture as shown in where mmWave edge cloud

can be switched on and off in adaptation to the forecasted data traffic demand. The detailed

description can be found in D3.4. Here we show the result in Figure 2-20 in terms of ON/OFF

status of small cells.

Figure 2-20 - ON/OFF status

(6) Robust design based on multi-link communications and block erasure coding against

blocking

One of the major drawbacks of mmWave communications is that they are prone to blocking

events due to human body and obstacles. In this section, we propose away to compensate

blocking effects, based on multi-link communications between a UE and the edge cloud and on

error-correcting codes for block-erasure channels. The idea is to exploit diversity and channel

coding to guarantee information retrieval in case of blocking events over some mmWave links.

In particular, in D3.4 and the related publication [dMC18], we gave a theoretical analysis for

the necessary coding rate that allows reconstructing the overall information from the bits

received over the non-blocked channels. In Figure 2-21 -, we show the necessary coding rate

vs. the number of obstacles to guarantee different outage probabilities.

Figure 2-21 - The maximum allowed coding rate needed to guarantee that the outage probability is

smaller than a given fixed value.

(7) Multi-route multiplexing on mmWave mesh backhauling against overloaded edge cloud

In the environment of dynamic crowd scenario, network densification with high number of

mmWave small cells overlaid on the current LTE cells is effective to accommodate traffic in

peak hours. However, having many small cells leads to the problem of high CAPEX and OPEX.

One solution to relax the problem is to use mmWave meshed network for the backhauling of

small cells since CAPEX can be reduced by avoiding deployment cost of wired backhaul.

Furthermore, OPEX can also be reduced by introducing dynamic ON/OFF and flexible path

creation in the backhaul network in accordance with the time-variant and spatially non-uniform

traffic. The prominent objective of the traffic and energy management algorithm is to reduce

energy consumption of mmWave meshed network by switching off as many mmWave Small

Cell BSs (SC-BSs) as possible in an area while satisfying users’ traffic demands. As it is hard

to optimize ON/OFF status of mmWave SC-BSs and backhaul paths all at once, the algorithm

involves three steps, described in details in D3.4. An example of the formed mmWave meshed

networks are shown in Figure 2-22.

(b) Formed mmWave meshed network at PM 3:00.

Figure 2-22 - Dynamic formation of mmWave meshed network.


T3.3 takes input from T1.1 and T1.2 on uses cases and architecture and contributed to the

overall architecture.

Clustering algorithm based on content popularities uses parameters learned in T3.2

T3.3 is also strictly associated to the work performed in WP2. In particular, the data plane

methods and the caching strategies developed in WP2 are closely followed and

incorporated in the development of T3.3.

T3.3 contributed to dissemination (T5.1) with journal and conference papers and a book

chapter.




Table 2-8 - Technical Risks in WP3

Our research results need to be used in real deployments in the market in the future. Therefore,

it must be avoided that our research deviates from the direction of the 5G discussions in

standardization bodies. In order to determine the direction of our research along with the 5G

discussions, WP3 defined the baseline architecture for the project. At first, statuses in

standardization activities were carefully investigated in terms of discussions on the radio access

network (RAN), the service and system aspects (SA) for 5G in 3GPP and on MEC in ETSI.

WP3 deeply verified related standards defined in [TS23.501], [TS23.502], [TS38.300],

[TS23.402], [TS36.300], [TS36.301], [MEC003], [MEC010-2], [MEC012], [MEC013] and

[MEC018]. Then, the common architecture were established for the project and delivered in the

deliverable [D3.1]. It was shared among the project members as the baseline of studies. WP3

leader led its discussions to agree on this baseline architecture among relevant project members.

Thanks to definition of this baseline, it was avoided that our studies returned to the beginning.

Moreover, WP3 leader emphasized necessity of novel points in researches many times, e.g. at

the GA meetings and monthly teleconferences. WP3 members investigated conference papers

and journals related to their research topics individually to identify novel points on their studies.

Additionally, WP3 had some opportunities to review study status of each WP3 member at the

GA meetings and teleconferences. The members gave comments on studies of other members.

Then, inherent shortcomings were avoided in our studies.

WP2

WP3


finished in time or

includes design

mistakes







WP2

WP3

Emergence of

competitive

technologies




2.4 Work package 4: 5G System Evaluation and Proof of Concept

Contributors: FHG, Intel, TI, URom, TTech, KLAB, PANA

Work package 4 is dedicated to the evaluation of the 5G system performance enhanced by the

MiEdge concepts using system level simulation tools and real world field-tests in the 5G Berlin

Testbed for indoor and outdoor. The work includes numerical system simulations on high

performance computing clusters to capture system relevant KPIs and effects in repeated and

controlled modelling environments representing typical use cases under investigation. After all

necessary steps of development, integration and testing are completed, the key MiEdge network

components we will conduct field trials for specific use cases and scenarios, which were

evaluated by system level simulations before. With D4.1 the simulations and system level

performance evaluation was completed and the implementation and design for the testbed

deployments is currently in progress for the upcoming deliverables D4.2 and D4.3.

The Over-the-Air tests are being conducted in the 5G Berlin and Tokyo Tech testbeds in real

world environment for feasibility proof of concept and evaluation of the key achievements of

the 5G-MiEdge project.

Joint evaluations and demonstrations between EU and Japan are the central scope of WP4.

2.4.1 Task T4.1: System level performance evaluation

Contributors: TTech, FHG, Intel, URom, PANA


Task status: completed in second period

Task 4.1 focuses on evaluation of the 5G-MiEdge technology components and their

orchestrated overall network performance using system level simulation methodology enabling

flexible mapping of scenarios and use cases to system level simulation tasks and extraction of

5G system performance relevant KPIs.

2.4.2 Task T4.2: Development of common/joint 5G-MiEdge Testbed

Contributors: FHG, TI, TTech, KLAB, PANA



Task 4.2 focuses on the setup of a joint testbed. This includes development and testing of 5G-

MiEdge technology components and orchestration in a real world deployment. We finished the

initial setup of a testbed in both Fraunhofer Heinrich-Hertz Institute in Berlin and Tokyo

Institute of Technology. Having almost identical setups on both sites enables us to transfer

progress and the latest achievements. With to the tight collaboration between the two partners,

they even visited the other sites to help on integrating our latest technology. Thanks to the

partnership, we can now work on realization of the planned features with maximum efficiency.

This task completed in M30 with the submission of Deliverable D4.2 [D4.2].

Output

This task focused on several subtasks that were handled and presented in Deliverable D4.2

[D4.2], with some additions in the final WP4 Deliverable D4.3 [D4.3].

Development of MiEdge AP

The developed MiEdge AP consists of two fundamental technologies:

mmWave access and edge cloud capabilities

mmWave backhaul

Those technologies were combined into the MiEdge AP. Throughout the second and third year

of the project there were numerous setups to evaluate the performance of the latest features.

Development of mmWave MiEdge Shower with integrated mmWave massive antennas

For each developed use-case and scenario, there are different KPIs to be satisfied and with the

development of the mmWave MiEdge Shower, we aim to improve the coverage in confined

areas like stadium gates with a very consistent radiation pattern.

Development of liquid RAN C-plane over 5G cellular networks and 5G terminals

To orchestrate the developed features like the power state of a Small Cell Base Station (SC-

BS) and reconfiguration of the mmWave mesh backhaul network, the C-plane is the key

component.

Evaluation of Rainfall Effects

As mmWave is one of the fundamental technologies in this project, there were careful

evaluations of the propagation characteristics in different levels of rainfall. From a light

summer rain to monsoon-like storms, the system needs to handle the load, even in extreme

situations.

Testbed evaluation of 5G-MiEdge network components and algorithms

The final part of Task 4.2 was to evaluate the combination of all developed components and

algorithms in one device. There were many long-term experiments and deployments to make

sure the different components work well together.

Deliverables



4.2 5G-MiEdge Testbed integrating mmWave

access, liquid RAN C-plane, and

user/application centric orchestration

T4.2 M30

Highlights

The highlights in this task include the development of the 5G-MiEdge AP and we feel like

there is a lot of interest on the effect of light to heavy rain on mmWave communication.

These two and all other subtasks are detailed in Deliverable D4.2 [D4.2].

Development of MiEdge AP

To combine the two technologies that define the MiEdge AP, we carefully evaluated the

overall requirements, as shown in Table 2-9.

Table 2-9 - Overall requirements for MiEdge AP

Feature Requirement

Computation power High computation capabilities

Scalable to match use case

Wired connectivity multiple Gbps

Access connectivity mmWave

multiple Gbps

Backhaul connectivity

(multiple interfaces)

mmWave, multiple Gbps,

200 m range

With high computation power to run edge cloud applications, multiple Gbps of throughput

and a possible link distance of 200 m for efficient outdoor deployments, we chose the

hardware detailed in Table 2-10. A mini pc as a solid base, with connectivity for high speed

wired and wireless interfaces and a specifically designed reflect array to increase the backhaul

link distance, we can cover those requirements.

Table 2-10 - Selected hardware for MiEdge AP

Feature Selected Hardware

Computation power Intel i7:

- Intel NUC

- Gigabyte Brix

Wired connectivity 10 Gbps PCIe cards

Access connectivity IEEE 802.11ad router:

- Netgear Talon AD7200

Backhaul connectivity

(multiple interfaces)

IEEE 802.11ad AP prototype:

- Panasonic WiGig/IEEE 802.11ad module

- Reflect array with 26 dBi gain

The next steps were to implement the required functionality in software:

1. Control of mmWave access

2. Controlling of edge cloud containers for instantiation, migration, clean up

3. mmWave link configuration

4. Controlling of antenna alignment

5. Power management

And assemble the components into a functional device, like the one shown in Figure 2-23,

consisting of the computation device, two mmWave backhaul links, mmWave and sub 6 GHz

access.

Figure 2-23 - 5G-MiEdge AP

The entire functionality was integrated with the control channel to allow central orchestration

by our selected SDN controller. We then ran careful evaluation of the entire setup to verify the

interaction of the components, as well as the long-term stability and reliability of the network.

All details on this were published in Deliverables D4.2 [D4.2] and D4.3 [D4.3].

Development of mmWave MiEdge Shower with integrated mmWave massive antennas

The 32x32 element array antenna that has been designed and realized within the project for the

gate information shower (Tokyo Olympic use case) as described in Deliverable D2.3 [D2.3]

has been fully characterized performing also measurements in far field conditions (analysis and

measurements in near field have been done previously). Even if the in the use-case the antenna

is used in near-field the radiation measurement have been done at far-field for the following

reasons:

Actual antenna loss is an important parameter for the link budget estimation. It's very

difficult to measure the antenna losses at near-field distance, it is more accurate the losses

evaluation based on measured realized gain at far-field distance compared to the computed

antenna directivity;

Comparison between computed and measured far-field antenna patterns allows verifying

the complete manufacturing process and the antenna array simulation model.

The far-field measurements have been performed in the CATR (Compact Antenna Test

Range) at the TIM premises located in Torino, as shown in Figure 2-24. Details are reported

in deliverable D2.3 [D2.3].

Figure 2-24 - The antenna array installed in the CATR and the pattern cut reference system

The measurements performed are the realized gain and the radiation pattern cuts in the

frequency bands 56-67 GHz.

The realized gain has been measured by using the gain transfer also known as gain comparison

method. A calibrate standard gain horn: Flann model 25240-25 has been used as reference. The

results of the antenna on axis (=0) measurements, for two different prototypes, carried out in

the 56-67 GHz range with 0.1 GHz step, are shown in Figure 2-25.

Figure 2-25 - Measured realized gain

Figure 2-26 shows an example of measured radiation pattern on an antenna prototype. The total

power pattern is obtained as √|𝐸|2 + |𝐸|2

where 𝐸 and 𝐸 are the measured field

components.

Figure 2-26 - Total power pattern cuts at 62.64 GHz

For more details about these measurements, please see deliverable D4.2 [D4.2].

Evaluation of Rainfall Effects

For the evaluation of the rainfall effects on mmWave communication, we based our

algorithms on two recommendation of the ITU-R, P.676 “Attenuation by atmospheric gases”

[ITU-R-676] and P838, “Specific attenuation model for rain for use in prediction methods”

[ITU-R-838]. With these recommendations, we created figures to illustrate the attenuation for

different frequencies and different link distances.

We focused on the 28 GHz and 60 GHz frequency bands and a link distance of 0 to 250 m to

characterize typical scenarios.

Figure 2-27 shows the attenuation in the 28 GHz band, with rain intensities of 10 mm/h for

very light to 80 mm/h for very heavy rain in the Asia region. The attenuation on a link

distance of 250 m reaches approx. 2.5 dB in heavy Asian rain.

Figure 2-27 - Rain attenuation for 28 GHz over distance

In Figure 2-28 -, the same link distance of 0 to 250 m and rain intensities of 10 mm/h to

80 mm/h are used, but for the 60 GHz frequency band. At this frequency, the attenuation

reaches a maximum of approx. 5.5 dB at 250 m link distance.

Figure 2-28 - Rain attenuation for 60 GHz over distance

Both attenuation levels can be compensated by either reducing the MCS level or using

sophisticated antenna arrays that are reasonably small for these frequencies and become more

and more common.


Task 4.2 is the actual implementation and realization of the designs developed in

WP2.

2.4.3 Task T4.3: Field trials toward Tokyo Olympic

Contributors: FHG, TTech, KLAB, PANA



Task 4.3 targets field trials with the developed MiEdge network elements in the real world

scenario of the 5G Berlin Testbed. Selected 5G-MiEdge scenarios and use cases were evaluated

under real-world constraints in order to prove feasibility and gain insight into optimization

potential of SDN based orchestration.

The field trials and final demonstration at EuCNC2019 created global visibility and convincing

feasibility arguments for standardization contributions.

Output

This task is focusing on field trials toward Tokyo Olympic and all the work was building up

to this goal. Therefore, the output consists of the following topics.

Testbed architecture and Selected Scenarios

All the field tests run on two testbeds with almost identical hardware and features in Berlin

and Tokyo. As a first step for the field trials, we identified the necessary architecture to satisfy

all requirements.

To conduct evaluations that efficiently cover all the developed KPIs, stress them and verify

the functionality of the entire setup, we selected three scenarios and finally, we defined which

KPI is best evaluated in which scenario.

Deployment of Testbed and Operational Testing

With the first phase of architecture and selecting scenarios and KPIs done, the testbeds were

deployed and each component was individually tested before verifying the functionality of the

entire setup.

Measurement and Evaluation Results

Finally the evaluations and measurements are conducted, showing the performance of the

setup, interworking of individual components and novelty when integrating central

orchestration, mmWave meshed backhaul, mmWave access and edge cloud functionality in

four sections:

Dynamic Backhaul Reconfiguration

Dynamic Crowd

Autonomous Driving

Integration with 5G!Pagoda

Final Demo at EuCNC 2019

As the project is now finished, we did a final demonstration at the EuCNC 2019 in Valencia,

presenting three prototype nodes and selected features.

Deliverable



4.3 5G-MiEdge field trials integrated in 5G-Berlin

Testbed toward Tokyo Olympic 2020

T4.3 M36

Highlights

Dynamic Backhaul Reconfiguration

The first series of tests, we evaluated the dynamic routing configuration and load balancing in

the mmWave backhaul network. Beginning with an indoor testbed architecture, shown in

Figure 2-29, close to the general architecture developed for Deliverable D4.3 [D4.3].

Figure 2-29 - Architecture for evaluation of Dynamic Backhaul Reconfiguration

Our test environment consisted of four mmWave mesh nodes, with one sender and two

connected receivers. Allowing several paths when transmitting data between the sender and

the receivers.

Then we defined the KPIs for evaluation and conducted several tests:

Baseline Backhaul Link Reconfiguration

To evaluate the baseline performance when reconfiguring the routes, as well as the entire

topology with the movable mmWave interfaces. In this setup, node N1 is just using a single

mmWave interface. As shown in Figure 2-30, we start with one defined path between sender

and receiver, Sender – N1 – N2 – N4 – Receiver 1.

Figure 2-30 - Initial configuration: N1 mmWave interface aligned with N2

Now we rotate the interface in node N1 for 70° to face node N3 and re-establish the links,

configure the forwarding rules and continue the data transmission on the path

Sender – N1 – N3 – N4 – Receiver 1.

The measurement shows a flawless functionality, but due to the single mmWave interface in

node N1, there is unavoidable link interruption during the reconfiguration. The following tests

are designed to avoid this, by using multiple mmWave interfaces on each node and utilizing

the redundant paths in the mesh topology.

Figure 2-31 - Final configuration: N1 mmWave interface aligned with N3

Optimal Steerable mmWave Mesh Backhaul Reconfiguration

Building on the baseline performance, this test series included more complex reconfiguration

scenarios with a mesh backhaul topology.

Adaptive On/Off Mesh Backhaul Operation

The third series of tests in the dynamic backhaul configuration was conducted to evaluate the

performance and power savings when controlling the power state of individual nodes, turning

them off when idle and back on when necessary.

All details on the evaluation are presented in Deliverable D4.3 [D4.3].

Autonomous Driving

We developed a testbed, displayed in Figure 2-32, to demonstrate the performances of the

scenario and conducted outdoor measurement to confirm the capability of cooperative

perception via LiDAR HD-map exchange among OBU (vehicles) and RSU (infrastructures).

Figure 2-32 - PoC system architecture

Figure 2-33 - Cooperative perception performed by mmWave V2X

The experiment was conducted at YRP Center, Yokosuka city, Japan as shown in Figure 2-33.

The system includes one OBU, two RSUs and a SDN controller on a remote server. WiMAX

was used as the control channel to exchange control message between the SDN controller and

other network entities. mmWave backhaul was set up between the two RSUs for exchanging

their taken LiDAR maps. These LiDAR maps are transferred and merged to the LiDAR map

taken by the OBU itself when the vehicle comes to the vicinity of RSUs via 802.11ad based

WiGig access channel. Owing to the map exchange, OBU can extend its vision even to the

hidden area e.g. the road segment between point 4 and 5 in Figure 2-33 even when the vehicle

is still at point 2. SDN is employed to dynamically construct route for sharing dynamic maps.

Owing to this system, the vehicle can realize hidden obstacles in advance and avoid unexpected

collision for the sake of safety. Details can found be in Deliverable D4.3 [D4.3].




Table 2-11 - Technical Risks in WP4

These two risks from the table were identified very early in the project, so we could carefully

plan our mitigation measurements to avoid them. Nevertheless, working with early prototypes

and bleeding edge products left a constant worry about shortage of components. To further

decrease the chance of this risk actually materializing, we did exchange hardware between the

two testbeds in Berlin and Tokyo for our experiments.

Compared to unavailable hardware, development of software was easier to plan and most of the

time we were able to stick to our schedule. Same as with the hardware, we synchronized the

development between Berlin and Tokyo to increase the efficiency and reduce the risk of

components being unavailable when required. We also travelled between the two locations

frequently, staying for several weeks and joining our efforts to finish certain aspects of the

testbed.

Overall, the communication between the partners helped a lot to avoid these risks.

WP4 Unavailable hardware

resources

Medium This project was carefully planned and drafted. WP1 and WP2

laid a solid foundation for the tasks in WP4 and we share

similar resources on both testbeds, in Berlin and Tokyo. This

allows us to have frequent discussions on which hardware to

use.

WP4 Serious delays on

implementation of

planned features

Medium The shared hardware on both testbeds allows us to work on the

implementation more efficiently, we can detect possible

problems early and either avoid them entirely, or fix them

without losing precious time or resources.

2.5 Work package 5: Standardization, spectrum regulation, dissemination and

exploitation

Contributors: CEA, FHG, Intel, TI, URom, TTech, KLAB, PANA

The goal of this work package is to create awareness about the 5G-MiEdge project and its

specific objectives and technical results. The WP has undertaken several activities to ensure

that the project results were spread across various communication channels. Thereby the

consortium has addressed European and international 5G research activities (e.g., other

collaborative project in the framework of the 5GPP association), research societies, industry

forums, and standardization and regulation bodies (3GPP, IEEE, and ETSI). Finally, in order

to maximize the impact of 5G-MiEdge to the scientific community we have published the main

results in high quality international journals and magazines.

2.5.1 Task 5.1: Dissemination and exploitation of project results

Contributors: CEA, FHG, Intel, TI, URom, TTech, KLAB, PANA



This task focused on planning the dissemination activities of the project. These include: inputs

to the project’s public website; the active collaboration with other consortia, with networks of

experts and other EU-, national- and local-funded projects; the organization of scientific and

industrial workshops and special sessions at conferences; the publications of scientific papers

in magazines, journals, and conferences. Moreover, this task promotes the exploitation of the

project’s results and of the scientific knowledge and expertise acquired by the partners.

Output

In summary, the main dissemination activities carried out during the third year of the project

are the following:

The submission of nine journal papers for publication: three were published, one was

accepted and will be published soon, and five are still under review.

Twelve papers by 5G-MiEdge partners were accepted and presented at major

international conferences.

The co-organization of three workshops and three panels, hosted by the conferences

IEEE WCNC 2019 and EuCNC 2019, in close collaboration with other research

projects.

The attendance to three industrial events, where the project’s results were presented.

The regular update of the project web site.

Moreover, as mentioned in Section 3.2.1, during the third year of the project, four patents were

filed by PANA and KDDI, whereas two further patent proposals were submitted by CEA.

The following sub-sections contain a further description of the abovementioned activities,

which are reported in detail in [D5.3].

Deliverables



5.1 First report on dissemination, standards,

regulation and exploitation plan

T5.1,

T5.2

M12

5.2 Second report on dissemination, standards,


T5.1,

T5.2

M24

5.3 Final report on dissemination, standards,


T5.1,

T5.2

M36

Highlights

5G-MiEdge has given a particular importance to dissemination activities, in order to ensure that all

main results of the project achieved the broadest possible impact on the global telecommunication

eco-system, both at the academic and at the industrial level. In the third year of the project, the

dissemination activities reported in [D5.3] mainly addressed the following four target groups:

General public

Dissemination to the general public has been realized mainly through the project website and press

releases. The project web site has been regularly updated, in order to reflect the most recent

project progress, announce important milestones and key achievements, as well as upcoming

events. Furthermore, the 5G-MiEdge partner Tokyo Institute of Technology advertised

important obtained results on mmWave communications for real-time high-quality video

transmission from drones, in a press release [TTech Press].

Scientific community

5G-MiEdge targeted to disseminate its results in international journals and the presentations of

papers to the most important conferences, as well as via the participation to workshops and

panels in major IEEE and ACM events. In the third project year, we submitted nine journal

papers for publication. Of these, three were published (in IEEE Proceedings, IEEE Transactions

on Signal Processing, and Hindawi Wireless Communications and Mobile Computing), one has

been accepted (in IEEE Vehicular Technology Magazine), and five are under review (and were

submitted to IEEE Transactions on Mobile Computing, IEEE Access, IEEE Transactions on

Communications, and IEEE Transactions on Signal Processing).

We have also presented twelve papers to notable international and domestic conferences,

namely IEEE VTC Fall 2018, EUSIPCO 2018, IEEE WCNC 2019, IEEE ICASSP 2019, IEEE

INFOCOM 2019, and EUCNC 2019.

5G-MiEdge partners have also been invited to present the results of our project in several

international conferences, workshops, and industrial panels, such as IEEE PIMRC 2018, IEICE

MWP Technical Workshop, IEEE WCNC 2019, IEEE and EURASIP Summer School on

Network- and Data-Driven Learning, and EUCNC 2019.

In addition, the following events have been co-organized:

At the IEEE Wireless Communications and Networking Conference (WCNC), April

2019 in Marrakesh, 5G-MiEdge co-organized two workshops in collaboration with

other research projects focusing on millimeter wave technologies (5GENESIS,

ULTRAWAVE, and 5G-CHAMPION):

o The “12th International Workshop on Evolutional Technologies & Ecosystems

for Beyond 5G (WDN-5G)”,

o The “2nd Workshop on Economics and Adoption of Millimeter Wave

Technology in Future Networks”.

Moreover, we co-organized the following panels:

o The final panel discussion of the WDN-5G workshop,

o The panel “Smart Spectrum Exploitation in Current and Future Wireless

Communication Systems” together with other research projects (5GENESIS,

5GENHANCE), during the Workshop “5th IEEE International Workshop on

Smart Spectrum (IWSS 2019)”.

At the European Conference on Networks and Communications (EUCNC), June 2019

in Valencia, 5G-MiEdge co-organized the Workshop “Emerging 5G business models:

Opportunities for SMEs and large companies – lessons from the 5G PPP (5G-EBM)”

with several other research projects (5G City, 5G-CORAL, 5G-EVE, 5GENESIS, 5G-

Transformer, CARMEN, Sat5G, and 5G TANGO).

Finally, the conference EuCNC 2019, held in Valencia (Spain) from June 18 to 21st, was the

chosen event for our final demonstration, showing a mmWave meshed backhaul with edge

computing, central orchestration and mmWave access. A detailed presentation of the performed

demo can be found in [D4.3].

Industrial community

5G-MiEdge targeted a constant attendance to the most important industrial events, participating

through the creation of stands, showroom and booths. During the third year of the project, we

have disseminated our activities during three notable industrial events:

5G Italy, Rome, Italy, December 4-6 2018, which was an event where politics,

regulatory authorities, research, businesses, economy and public administrations met,

addressing the challenges and opportunities of the upcoming 5G;

CEATEC JAPAN 2018, Makuhari Messe, Chiba, Japan, October 16-19 2018, where we

had a joint booth exhibition with the research project 5G!Pagoda;

and Wireless Technology Park (WTP), Tokyo, Japan, May 29-31 2019.

More details on these events can be found in the 5G-MiEdge public Deliverable D1.4 Section

2.3 “Ecosystem impact” [D1.4].

Other collaboration activities

The interactions of the 5G-MiEdge project with other research projects and in general with the

5G and beyond ecosystem has been elaborated in details in the 5G-MiEdge public Deliverable

D1.4 Section 2.3 ”Ecosystem impact” [D1.4], where we highlight the tight collaborations with

the sibling projects 5G-MiEdge+, 5G!Pagoda, and with the 5G PPP association. As mentioned

above, 5G-MiEdge, in the third and last year, co-organized several events at the most important

international venues with the following other research projects: 5G Champion, 5GENESIS,

ULTRAWAVE, 5GENHANCE, 5G City, 5G-CORAL, 5G-EVE, 5G-Transformer, CARMEN,

Sat5G, and 5G TANGO. These projects have been accurately selected in the 5G ecosystem,

since their objectives are interconnected with the 5G-MiEdge ones.


Task 5.1 received inputs from all the task of WP1, WP2, WP3, and WP4.

2.5.2 Task 5.2: Standards and regulatory bodies

Contributors: Intel, FHG, CEA, TI, URom, TTech, KLAB, PANA



This task focused on standardization activities from individual consortium partners as well as

joint contributions. It also aimed at promoting and exposing the results coming out of the project

regulators and to industrial fora, in order to facilitate authorities and public acceptance of both

edge computing and mmWave communications.

Output

5G-MiEdge is an international research project that, if compared to other similar endeavors, has

a relatively small number of partners. Nevertheless, it managed to interact with regulatory

bodies and impact standards bodies also during the third year of project activities, as described

in this sub-section. In particular, 5G-Miedge contributed to both 3GPP and IEEE with the

contributions described below. In addition to standards, another important aspect for achieving

an effective deployment of a new mobile access technology is the alignment with and

potentially the impact on regulatory bodies. Therefore, 5G-MiEdge has been tightly monitoring

and supporting the work that is ongoing in regulatory bodies in preparation of the ITU World

Radiocommunication Conference (WRC) 2019. Moreover, during the third and last year of the

project, we have met with one of the most important European regulator, Ofcom UK, to present

and discuss the major 5G-MiEdge’s results.

Deliverable



5.1 First report on dissemination, standards, regulation

and exploitation plan

T5.1,

T5.2

M12

5.2 Second report on dissemination, standards,


T5.1,

T5.2

M24

5.3 Final report on dissemination, standards, regulation

and exploitation plan

T5.1,

T5.2

M36

Highlights

Standardization impact

In the third year, the project has presented a technical document to the 3GPP RAN plenary #84

in Newport Beach (CA), USA, under the work done on the 3GPP Release 17: “RP-191341 -

Multi-TRP for Enhanced Mobility,” 3GPP RAN #84, Rel-17 Workshop, June 4th, 2019.

5G-MiEdge has been also participating in the IEEE works and meetings, continuously

monitoring the progress of IEEE 802.11ay, which aims to develop the next generation of the

60 GHz wireless communication. In January 2019, IEEE 802.11 officially launched

IEEE802.11bd as a task group to discuss the next-generation V2X communications. Currently,

the task group is discussing the proposed 5G-MiEdge use cases. The following project

contribution was presented at the IEEE 802.11 July meeting: IEEE 802.11-19-0840, "Use cases

for 11bd using high data rate," PANA, July 2019.

Regulatory bodies impact

Our main interactions with regulatory bodies in the last twelve project months were:

Ofcom UK meeting, December 2018: a representative of the 5G-MiEdge consortium,

Dr. Valerio Frascolla, met a group of Ofcom UK personnel at their premises in London

in December 2018. The meeting was attended by around half a dozen of people, and an

open and interesting discussion started on the potential of the 5G-MiEdge project

technologies.

Ofcom CH meeting, January 2019: during the ETSI RRS meeting held at Ofcom

Switzerland in Biel, a 5G-MiEdge representative, within the scheduled presentation of

another research project called FUTEBOL, verbally mentioned also the Omotenashi use

case as elaborated by 5G-MiEdge. Some questions were posed and a short discussion

was held on the planned benefits of the newly introduced 5G-MiEdge technologies.

ITU-R: in ITU-R, discussions on frequency allocations are progressing well. The wireless group

of Asia-Pacific Telecommunity (APT) approved a work plan for preparing a report on “the

mmWave ITS applications in APT countries” at AWG-24. This was proposed by Japan with

the approval of the MIC through PANA’s input to the ITS Information-Communications Forum.

In addition, the mmWave sub-working group was established under the Forum, in order to

discuss mmWave ITS applications. Currently, PANA and Forum partners are working on

preparing a draft for AWG-25. The knowledge gained through 5G-MiEdge can be utilized for

V2X related projects, such as the study of ITS system using mmWave toward a connected car

society funded by MIC, which has been led by PANA working with TTech.


Task 5.2 received inputs from all the task of WP1, WP2, WP3, and WP4.


There have been no specific threads identified for WP5, except the general ones from Section

2.6.2.4.

2.6 Work package 6: Project management

Lead beneficiary: FHG (EU) / TTech (JP)

Work package 6 includes all the specific functions assigned to the Project Coordinator and the

Technical Manager to ensure that the project successfully achieves its stated objectives on time,

within budget, and with the expected high level of quality of the technology developed. FHG

is the project leader in the European consortium, whereas TTech takes this role in the Japanese

consortium. FHG and TTech co-coordinate the project for both the administrative and technical

parts.

2.6.1 Task 6.1: Administrative project management

Contributors: FHG, all



The basic purpose for project management is to ensure the correct level of coordination and

cooperation amongst the project consortium members. In this context, the successful

administrative coordination of a project is an important side of the project management, which

includes also relevant legal issues and policies for proper management of IPR. The project

administrative management covers the following activities:

Preparation and coordination of General Assembly meetings (one kick-off meeting at

the beginning of the project, at least two general assembly meetings per year, project

technical committee teleconference on a monthly basis, WP meetings as necessary) and

of technical reviews.

Setup of partners’ communication means, internal rules, common document templates;

setup of the project quality framework allowing deliverables quality supervision, risk

analysis and contingency planning, scientific outputs monitoring.

Budget administration in order to match the plans approved by the General Assembly;

management of legal and contractual obligations.

Communication and reporting with the Commissions (European Commission (EC) for

Europe and Ministry of Internal Affairs and Communications (MIC) in Japan), interface

with related projects and other parties.

Reporting technical and financial status of the project to Commissions within 60 days

after the end of each 12-month period. A final report will include all outputs over whole

period of the project.

Secure resources for administrative and financial procedures.

Output

In the third period, Task 6.2 coordinated the cooperation between the project consortium

members and managed the meetings.

Deliverables

Task 6.1 contributed to this annual report AR6.3.


AR6.1 First annual status report of 5G-MiEdge

project

T6.1,

T6.2

M12

AR6.2 Second annual status report of 5G-MiEdge

project

T6.1,

T6.2

M24

AR6.3 Final annual status report of 5G-MiEdge

project

T6.1,

T6.2

M36

Highlights

Fifth General Assembly: November 2018 in Bangkok, Thailand

In November 2018, the fifth general assembly took place in Bangkok, Thailand. The GA was

organized in a country not belonging to the consortium since many consortium members were

invited to deliver talks at an international conference called SmartCom2018, host by IEICE

Smart Radio Technical Committee and technically co-sponsored by 5G-MiEdge. After the two-

day conference held at Mandarin Hotel, Bangkok, Thailand, the general assembly was

organized at the Sukosol Bangkok Hotel, which provided meeting package reserved via

Fraunhofer HHI. Figure 2-34 shows a group photo of the consortium in the meeting room.

Figure 2-34 - Group picture at 5th General Assembly, November 2018 in Bangkok

Presentations and discussions about our plans for the project in the 3rd year were conducted for

two days and strategies/actions to response to reviewers’ comments at the second review

meeting were decided.

Sixth General Assembly: April 2019 in Marrakech, Morocco

The sixth general assembly, again took place in a country not belonging to the consortium. The

GA was organized at Kenzi Menara Palace, Marrakech, Morocco, where meeting package for

5G-MiEdge reserved by Fraunhofer HHI, for two days, in conjunction to an international

conference called WCNC2019 organized at the same city. At this conference, 5G-MiEdge

consortium organized a workshop in cooperation with ULTRAWAVE and 5Genesis. The final

GA was characterized by two intense days of interactive discussions especially on PoC

development and field trials at the end of the project. On the first evening, we had a nice social

dinner and enjoyed the local food. There is a group picture in Figure 2-35.

Figure 2-35 - Consortium dinner at 6th General Assembly, April 2910 in Marrakech

The General Assembly was particularly useful to coordinate our efforts, strengthen the bond

among the partners, and optimize the approach to meet our goals at the end of the project and

toward to final review meeting.


Task 6.1 oversees all other work packages and the general progress of the whole project

2.6.2 Task 6.2: Technical coordination

Task leader: TTech



The success of a medium-scale focused research project strongly depends on the quality of its

consortium. The stance (history of past experiences and international relevance) of the partners

is an early indication of such quality, but excellence can only be materialized by ensuring that

each partner is fully dedicated to the project’s objectives, which in turn requires constant

oversight. This task is designed to provide a framework within which such quality oversight

will be exercised. The leader of this task promoted two technical directors each from EU and

Japan i.e. Prof. Sergio Barbarossa (URom) and Prof. Makoto Ando (Tokyo Tech). The two

directors supervise the following activities:

Supervise the content and quality of deliverables and quality of technical and scientific

output.

Perform quality checks by reviewing and pre-approving deliverables and defining best

practices in order to ensure that the planned objectives are achieved at the highest of the

partners’ abilities.

Report and discuss quality issues and risks at project meetings and whenever needed.

Risks will be early identified and will be immediately reported to the coordinators for

management and mitigation.

Management of the technical activity, coordinating WP leader activity to guarantee

coherent research, deliveries and full achievement of the project technical objectives.

This includes cross-WP work organization, the definition of topics to address in

meetings and conference calls, technical progress monitoring. Activities also involve

the technical coordination related to studies and developed technologies, ensuring

coherent research under common working assumptions, hypothesis, parameters, and

implementation of proper measures to ensure integration of the WP results in the proof

of concept.

Output

In the third period, Task 6.2 coordinated the technical consensus of the project consortium

members in all submitted deliverables.

Deliverable

Task 6.2 contributed to this annual report AR6.3.


AR6.1 First annual status report of 5G-MiEdge

project

T6.1,

T6.2

M12

AR6.2 Second annual status report of 5G-MiEdge

project

T6.1,

T6.2

M24

AR6.3 Final annual status report of 5G-MiEdge

project

T6.1,

T6.2

M36

Highlights

Monthly teleconferences among all partners are regularly established to discuss about the

project’s progress. In addition, ad-hoc meetings per WP or deliverable were done on a

regular basis to keep the project on schedule.

T6.2 also invited external advisors, who are experts of the field to supervise the project.

Especially, external experts were invited to present and share their views at our general

assembly:

o At the fifth general assembly:

o Dr. Yoshiaki Kiriha (Tokyo University, Japan)

Softwarized LTE in FLARE Network Slices

o At the sixth general assembly (in SmartCom2018):

o Dr. Haesik Kim (VTT, Finland)

5G Enhanced Mobile Broadband Access Networks in Crowded

Environments

o Prof. Noriharu Suematsu (Tohoku Univ., Japan)

Direct digital RF transceiver technology for high-SHF fully digital Massive

MIMO

o Dr. Doohwan Lee (NTT Corp., Japan）

An Experiment of 100 Gbps Wireless Transmission Using OAM-MIMO

Multiplexing in 28 GHz

Risk Assessment

The identified risks are shown in Table 2-12. We are continuously monitoring the possible risks;

the work package leader of each work package is responsible for bringing newly identified risks

up for discussions in our monthly meetings. Those risks are identified by monitoring the

scientific publications, conferences and technical innovations related to the work packages. The

period for those risks does vary with the identified risks. Some risks exist throughout the entire

project, they were identified early and constantly monitored, while some risks, like

implementation of certain features, only manifest once the corresponding task has started.

Each work package in Section 2 has a separate section explaining the corresponding risks and

mitigation actions.

In our monthly meetings, we discuss possible plans either to avoid those risks entirely or to

utilize them to our advantage.

Table 2-12 - Risk Assessment: Technical Risks


Task 6.2 oversees all other work packages and the general progress of the whole project.

WP Risk description Impact/

Probability

Comment

ALL Cultural conflicts

between EU and

Japanese partners

Low Many of the partners have already cooperated in other projects

and activities.

ALL Specific expertise or

resource is missing

Medium The 5G-MiEdge consortium brings together leading European

and Japanese organizations with complementary skills for the

successfully achievements of the technical objectives.

Advisory boards of experts from both sides were also

established to supervise the project. All partners have

committed to mobilize all necessary resources for achieving

the project objectives and experimental facilities have been

identified based on their accessibility.

WP1 Requirements and use

cases not identified or

detailed insufficiently

to design the 5G-

MiEdge architecture

Low Use cases and preliminary requirements have been already

identified D1.1 and D1.2. The first architecture was defined

very early in the 2nd year of the project, and by adapting to

outputs of WP2 and WP3 activities.

WP2

WP3


finished in time or

includes design

mistakes







WP2

WP3

Emergence of

competitive

technologies




WP4 Unavailable hardware

resources

Medium This project was carefully planned and drafted. WP1 and WP2

laid a solid foundation for the tasks in WP4 and we share

similar resources on both testbeds, in Berlin and Tokyo. This

allows us to have frequent discussions on which hardware to

use.

WP4 Serious delays on

implementation of

planned features

Medium The shared hardware on both testbeds allows us to work on the

implementation more efficiently, we can detect possible

problems early and either avoid them entirely, or fix them

without losing precious time or resources.

Impact

3.1 Impact on Academia and Research Centers

Contributors: FHG, CEA, URom, TTech

To maximize its impact to Academia and Research Centers, 5G-MiEdge has targeted to

disseminate its results in international journals and the presentations of papers to the most

important conferences, as well as via the participation to workshops and panels in major IEEE

and ACM events. In addition, in order to guarantee our impact in the 5G ecosystem, and in

particular to the academia, the project has co-organized (together with other collaborative

research projects) twelve workshops. It is worth to highlight that most of those workshops have

been hosted in well-known international IEEE conferences like ICC, WCNC, and Globecom.

Finally, we have attempted to strengthen the EU-JP collaboration by co-organizing workshops

at EuCNC (2017 and 2018), which is a major EU event, and participating to the preparation of

the 2017/2018 SmartCom, which is historically one of the most important conference in the

Japanese ecosystem.

We have continuously made available on our website public deliverables, scientific articles,

and have been advertising the project participation to international events related to the mobile

community.

At the current stage, we have nine accepted journals/magazines and one book chapter. Five

additional manuscripts have been submitted in the last months of the projects. Amongst all

those publications, it is worth to highlight out contributions to IEEE Wireless Communications,

IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing,

Proceedings of the IEEE, IEEE Transactions on Signal Processing, and IEEE Vehicular

Technology Magazine. We have also participated to the most relevant technical conferences

such as IEEE Globecom, IEEE INFOCOM, IEEE ICASSP, IEEE WCNC, and IEEE ICC, to

present the main results of the 5G-MiEdge project. The number of summited and accepted

manuscripts has increased throughout the project lifetime, finally reaching up to around fifty

conference papers.

3.2 Impact on Industry and ecosystem

Contributors: TI, PANA, KLAB, TTech, Intel

Vehicle-to-Everything (V2X) is a key technology for automated driving, which is one of the

most promising growth markets. The technologies developed in 5G-MiEdge are essential for

enabling massive data upload, 3D map download, and cooperative sensor sharing. On top of

the technology development of key enablers for suture telecommunication systems, 5G-MiEdge

has been contributing to global standardization activities. In January 2019, IEEE 802.11

officially launched IEEE802.11bd as a task group to discuss the next-generation V2X, where

the use of 60 GHz band has been approved as its scope. Currently, the task group is under the

discussion of use cases and PANA presented a contribution related to mmWave V2X use cases,

which have been discussed in the 5G-MiEdge project. Also, the knowledge gained through 5G-

MiEdge will be utilized for other V2X related projects, such as “the study of ITS system using

mmWave toward a connected car society” funded by MIC, which has been led by PANA

working with TTech as one of the committee members. These efforts will maximize impact on

industry and ecosystem.

The several results obtained on edge computing, mainly jointly done by Intel and Sapienza

University in a now fully established and very fruitful collaboration, which will continue also

after the project end, will guarantee that Intel will be at the forefront of the international

companies that can propose appealing edge-related products in the forthcoming wireless

systems of the future. Not only the knowledge learnt, but also the results brought by Intel to

standardization bodies, like 3GPP, and the discussions held with regulatory bodies, like Ofcom,

increased the visibility and the impact of Intel in the broadest possible ecosystem. Finally it is

worth mentioning that Intel will benefit from the synergies with the other project partners as

well, especially the Japanese ones, as the project allowed Intel to have a much better

understanding of the Japanese ecosystem, knowledge that Intel will exploit in future products

launch in that geographical area.

In order for mobile operators to utilize the project results widely and generally, it is necessary

that the results will be implemented in future wireless system as standardized technologies.

Interesting technical outcomes of control signalling and algorithms for optimizations have

emerged from the project. In particular, KLAB proposed novel control signalling and

algorithms to allocate appropriate resources on 5G platforms in terms of user’s mobility at

international academic conferences, among the main outcome from WP3 studies.

3.2.1 List of submitted patents

The following Table 1-1 shows the patents that were submitted during the lifetime of the

project, including the country, authors, current application status and patent number.

Table 3-1 - List of submitted patents

# Date Country Title Authors Status WP Patent No.

1 Mar.

2017

Japan User equipment,

base station

apparatus and

wireless

communication

system

M.

Kobayashi,

K.

Takinami,

S. Okasaka

Filed WP2 P2018-148363A

2 Oct.

2017

Japan Wireless

communication

apparatus and

wireless

communication

method

K.

Takinami,

M.

Kobayashi,

T.

Urushihara

Filed WP2 2017-197597

(application

number)

3 Feb.

2018

China User equipment,

base station

apparatus and

wireless

communication

system

M.

Kobayashi,

K.

Takinami,

S. Okasaka

Filed WP2 201810116022.2

(application

number)

4 Feb.

2018

US User equipment,

base station

apparatus and

wireless

communication

system

M.

Kobayashi,

K.

Takinami,

S. Okasaka

Filed WP2 15/908536

(application

number)

5 Sept.

2017

France Méthode de

répartition de

charge dans un

réseau

hétérogène à

multitechnologie

d'accès radio

G. Ghatak

A. De

Domenico

Filed WP2 US20190075570A1

6 Dec.

2017

Japan Wireless

Communications

Scheme

G. K. Tran

，K.

Sakaguchi

Filed WP1,

WP3

No. 2017-253912

7 Jul.

2018

Europe Connected cache

empowered edge

cloud computing

offloading

E.

Calvanese

Strinati, N.

di Pietro

Application

submitted

WP3 N/A

8 Jul.

2018

Japan Communication

service system,

user terminal

equipment,

control

equipment,

service

management

equipment, edge

host server,

communication

control method

and computer

program

K. Yunoki,

H. Shinbo

Filed WP3 No. 2018-130844

9 Feb.

2019

France Méthode d'accès

par liaison

multiple à un

réseau

E.

Calvanese

Strinati, N.

di Pietro

Proposal

submitted

WP2/WP3

3.3 Cooperation with other Projects

5G-MiEdge has been tightly working with its sibling project called MiEdge+, funded by MIC,

Japan, to impact the industry and ecosystem via jointly developing testbeds (especially focused

on V2X scenarios) and demonstrating the collaborative research results via joint field

measurements and demonstrations during the final year of the two projects. In addition, 5G-

MiEdge has also been strongly collaborating with the network consortium called 5G!Pagoda

via joint technical discussions and demonstrations at big exhibition shows, e.g., CEATEC2018,

to further increase the penetration of MEC technologies in both wireless and wired networks.

3.3.1 Cooperation with 5G!Pagoda

5G! Pagoda (Federating Japanese and European 5G Testbeds to Explore Relevant Standards

and align Views on 5G Mobile Network Structure Supporting Dynamic Creation and

Management of Network Slices for Different Mobile Services) and 5G-MiEdge are so called

‘twin projects’. They were selected by the same EU-Japan joint call, under the same topic “5G:

Next Generation Communication Networks (EUJ-01-2016)”, funded by the European

Commission (EC) under the Horizon 2020 research and innovation programme, and by the

Japanese MIC as Strategic Information and Communications R&D Promotion Programme

(SCOPE).

The two projects have been collaborating tightly with each other to create synergies, with

5G!Pagoda mainly focusing on 5G networks, and 5G-MiEdge on 5G access. The overall

objective of 5G!Pagoda is standardization and verification of End-to-End (E2E) network slicing

technologies through EU/Japan collaborative R&D efforts. On the other hand, 5G-MiEdge

project has developed application centric RANs by combining mmWave access and edge

computing, to satisfy extreme requirements on high data rate and low latency, needed in specific

scenarios such as the 2020 Tokyo Olympic Games and automated driving.

The two projects, especially during their third year, complemented very well each other, for

instance allowing experts of one project to join the general assembly of the other one. Based on

the discussions held, we concluded that the technologies of the two projects could synergize

with each other. E.g., 5G-MiEdge can provide 5G!Pagoda with MiEdge enabled RANs, so to

meet the requirements set by the chosen applications, whereas 5G!Pagoda is able to provide

5G-MiEdge with E2E networks to realize E2E slice for specific applications including inter-

continental MEH migration (for more details see [D4.3]).

One symbol of the collaboration between both projects was that we co-organized a joint

exhibition booth at the Combined Exhibition of Advanced Technologies (CEATEC) JAPAN

2018 in the ARIB area, to display both novel wireless and wired network technologies, and the

possibility for future further collaboration among the partners of the two consortia, as shown in

Figure 3-1 below.

Figure 3-1 - 5G-MiEdge and 5G!Pagoda joint booth at CEATEC2018

3.3.2 Cooperation with MiEdge+

MiEdge+ is the sibling project of 5G-MiEdge, funded by the Japanese Ministry of Internal

Affairs and Communications (MIC), and for that it was expected that a very tight interaction

with the 5G-MiEdge project would have taken place. NICT, Panasonic and Tokyo Tech are

among the members of MiEdge+, which targets to virtually construct location specific small

area access networks operated by a micro operator, especially under the consideration of

roaming mobile terminals, which might join this micro operator’s network. In addition to 5G

access technology, MiEdge+ plans to introduce edge cloud to simultaneously realize high data

rate and low latency communications, so to support location specific applications, e.g., (foreign)

audiences at event sites such as at the Tokyo 2020 Games Olympic stadium.

Differently from 5G-MiEdge, MiEdge+ covers all kinds of 5G access technologies and takes

into account the interoperation of different micro operators, which might share the same

network infrastructure. Since MiEdge+ is a sibling project of 5G-MiEdge, both funded by MIC

from Japan side, both projects have been working together, mostly in parallel, and a tight

cooperation has been achieved, especially on disseminating the research outcomes and in

impacting relevant standardization bodies.

For that reason, the two projects did work tightly to co-develop testbeds (for more details see

[D4.3]), especially focusing on V2X scenario and conducted joint outdoor experiment at the

YRP center (the office of NICT, the leader of MiEdge+) to demonstrate the effectiveness of the

constructed prototype. The same system was exhibited at the Wireless Technology Park (WTP)

2019 event to disseminate their joint activities in the MIC area in a shared booth, where the

joint testbed developed by the both projects was showcased. The booth, shown in Figure 3-2

also displayed posters, summarizing common research achievements throughout the 3-years

lifetime of both projects.

Figure 3-2 - 5G-MiEdge and MiEdge+ joint booth in WTP2019

Update of the plan for exploitation and dissemination of results

The diversity in expertise of the 5G-MiEdge consortium partners increases the probability of a

successful impact on the global market of the technologies proposed, devised, and developed

within the project lifetime.

In the following, we provide an update w.r.t. the exploitation plans of all the consortium

partners.

4.2.1 Equipment vendors (Intel, PANA)

During the whole lifetime of the project, the main achieved results have been constantly

disseminated in internal meetings within the consortium participants R&D divisions, especially

focusing on the system architects, the regulatory and standardization bodies internal

communities.

With the end of the 5G-MiEdge project in June 2019 also the work in the 3GPP standard bodies

groups has ended on the 5G Phase II definition, and the first commercial networks start to

appear in some key areas of the world. The work done during the project on use cases, scenarios

and system architecture will be beneficial for the still forthcoming deployment of mmWave

access, which is supposed to happen not before 2020. The equipment vendors in the consortium

have therefore time to absorb the project results and leverage on them in the forthcoming

planned new products targeting the diverse 5G vertical markets.

With regard to the individual exploitation plans, as defined at the beginning of the project, one

can say that they have not changed much at the end of the third year of the project. Industrial

partners have learnt lots of useful information on edge computing, mmWave access and 5G

verticals, that will be instrumental to better shape their future products that are supposed to hit

the market after 2020.

Finally, the technological expertise build-up in the project will provide a competitive advantage

versus other companies and potential or real competitors, not involved in the project. Indeed

the possibility of filing new patents to protect the newly developed technologies is an additional

key aspect in the exploitation of the project results in the mid and long term.

4.2.2 Telecom operators (TI, KLAB)

TI participated to this project with its Innovation Department whose main task is to investigate

new technologies and services transferring then the results to the other company departments

(engineering, purchasing, marketing, etc.). Each department will exploit the results with respect

to its responsibilities. Hereunder are reported some of the way of exploitation of the 5G-MiEdge

results. The project results could be reflected as a guidance of their future deployment plan

easing the introduction of new network technologies in their infrastructures and enabling then

new services to be provided in new operational scenarios. In addition to this general way of

exploitation, TI gained specific expertise and knowledge testing for the first time a real

prototype of a millimeter wave antenna array designed for an innovative service like the

information shower. The gained experience in this area clarified the adherence of the theoretical

model used in the design with the actual measurable performance providing useful hints to be

taken into account for the design preparatory to potential future introduction of mm-wave

segments in the network. Very valuable for TI are also the results in the area of automated

driving due to the involvement of the company in several related activities. Finally, TI will

exploit the project results in standardization and regulation bodies dealing with the topics

investigated by 5G-MiEdge. It is obviously essential for the operator the definition of good and

interoperable standardized solutions prior to consider the introduction of new technologies in

the network.

In this context, that is more targeting future releases of 3GPP specifications, during the third

year of the project, there has been an important shift from 5G trials to the first 5G commercial

networks.

In particular, at beginning of July 2019, TI started the 5G commercial service in the cities of

Rome, Turin and Naples. Within the year 2019, it will arrive at another six major cities – Milan,

Bologna, Verona, Florence, Matera and Bari – 30 tourist destinations, 50 industrial districts and 30

specific projects for big businesses, with speeds of up to 2 Gbps. By 2021, there will be coverage for

120 major cities, 200 tourist destinations, 245 industrial districts and 200 specific projects for big

businesses, with speeds increasing progressively up to 10 Gbps and overall coverage of about

22% of the population. Many municipalities will be able to make use of 5G, including superfast

connections through FWA (Fixed Wireless Access).

Moving from the trials to the commercial service will allow the operators to face for the first

time the real challenges to be solved by an operational 5G network better highlighting at the

same time the areas (both business and technical related) where improvements are needed in

order to meet all the ambitious service requirements that are expected to be satisfied by 5G. In

addition, the results are disseminated broadly in academic conferences, papers and panels for

better understanding.

KDDI Research (KLAB) participated to this project as the research center of KDDI, which is

the second largest mobile operator in Japan. KLAB gained specific knowledge on resource

allocations and control for edge computing integrated in the mobile network in terms of user’s

mobility in this project. Services which will utilize edge computing are still under

considerations for real services. However, the project results would be taken into account for

the future deployment.

KDDI will launch 5G service in some limited areas in September 2019, especially at the

stadiums of Rugby World Cup Japan 2019. KDDI will demonstrate some 5G showcases, e.g.

video distribution, security surveillance by drones. In 2020, large-scale 5G service will be

planned to launch mainly in major cities, Tokyo, Osaka, Nagoya, etc.

In order to utilize full expected 5G features, it is essential to develop new kinds of services,

which will work on the 5G platform. KDDI opened a development base called “KDDI

DIGITAL GATE” in September 2018 for creating 5G and IoT business solutions with partner

companies. It can propose KDDI’s available assets like solutions of wireless connectivity, data

center, big data, AI, machine learning, agile software development, etc. Through various

developments of “Proof of Concept” (PoC) or service trials with partners, KDDI is going to

realize varieties of 5G services. To implement the services on the 5G platform including edge-

computing system, the project results will also be considered.

4.2.3 Research institutes (FHG, CEA)

The research institutes Fraunhofer Heinrich-Hertz-Institute and CEA-LETI, the Laboratory for

Electronics & Information Technology, has worked in 5G-MiEdge to increase their knowledge

on the future network design, for better understanding of current industry needs, and to identify

new ideas, challenges and focus for future research activities, specifically in the domains of

millimeter wave technologies, edge computing, and optimization. The obtained knowledge will

be used to invent solutions for future challenges, transfer new technological solutions to their

industrial partners, in particular network vendors, telecom operators, service providers and

others. CEA and FHG have already produced notable scientific papers targeting both

international conferences and journals/magazines. Thanks to the results obtained in 5G-

MiEdge, CEA is starting new research topics for enabling ultra- broadband communications in

frequency bands higher than those considered by 5G-MiEdge and he is working with industrial

partners, which are not part of 5G-MiEdge consortium, for the optimization of multi-RAT

networks. To maximize the exploitation of the results achieved in 5G-MiEdge, CEA has filed

a patent related to the studies carried out in 5G-MiEdge in WP2 and two related to the studies

on WP3. CEA has showcased the results originated in the framework of 5G-MiEdge in the CEA

LETI Innovation Days yearly event. In addition, CEA is exploiting the knowledge and skills

acquired during the project in its activities in the framework of the ETSI ENI ISG.

4.2.4 Universities (URom, TTech)

The role of universities was to continuously develop new theoretical methods and technologies

to improve the efficiency of 5G networks. The acquired knowledge has been disseminated to

industry and society through major international peer-reviewed conferences, workshops and

journals, PhD schools and seminars. Working in 5G-MiEdge gave the opportunity to the

university partners to significantly improve their competences in many aspects of 5G, from both

theoretical, architectural, and technological sides. The plan for the future is to invest on these

competences to apply to new projects and to enroll more and more students to get involved in

the 5G deployment. During the project, URom improved also its skills on machine learning

methods and then it plans to use this expertise in a plethora of new applications. TTech

considerably improves its expertise on automated driving, communications among vehicles and

drones. The plan of TTech is to build on this added expertise to play a key role in the future

deployment of new services. TTech also plays the role of the coordinator between 5G-MiEdge

and other projects in Japan i.e. MiEdge+ and 5G!Pagoda to further impacting the industry and

ecosystem via such collaboration.

Update of the data management plan (if applicable)

This does not apply.

Follow-up of recommendations and comments from previous review(s) (if

applicable)

This section shows the remarks and recommendations we received from our second review

meeting in September 2018 in Brussels. The remarks were carefully considered during our

second year and below are our specific replies to each remark and recommendation.

6.1 Recommendations from second Review Report

R1: Clearly document milestones in the PPR

We added a list of milestones with details on the content of each one and its date of completion.

R2: Update the PPR with the description of the way recommendations from the

previous review have been taken into consideration.

In the deliverables of the third period, we took care of the previous recommendations. This

section provides an overview.

R3: Title and authors of the submitted patents are required, provided that this does not

violate confidentiality constraints.

The list of patents in Table 3-1 contains the necessary details on submitted patents.

R4: It should be clarified which of the developed algorithms will be actually

implemented in the testbed.

In the deliverables D4.2 [D4.2] and D4.3 [D4.3], we tried to connect the previously developed

algorithms to specific testbed functions. Most developed algorithms in WP3 are too large in

scale to be tested thoroughly on a testbed with a few nodes, but we tried to give a glimpse on

their impact. The most used novel algorithm relates to the liquid RAN C-plane, which acts as a

backbone of the entire setup.

R5: The amended description of risks is missing and should be provided.

We have been constantly monitoring risks and proposing mitigation issues in our monthly

online meetings. The identified risks have been handled immediately, when possible, or

discussed during the project general assemblies, which happen on average every 6 months. In

this final periodic report, we added information in the WP6 section, containing general

descriptions on each risk and details for each WP.

R6: The relation with other projects should be clearly written in detail. Also the plan

about collaboration with other projects should be written in detail.

We described the most important collaborations of the 5G-MiEdge project, i.e. the synergies

and joint activities with 5G-MiEdge+ and 5G!Pagoda, in Section 3.3.

R7: The concept of “efficient beamforming protocol” should be clarified.

This was explained in Section 2.4 of Deliverable D2.1 [D2.1]. To minimize the overhead of

beam alignment in high user density environment, a beacon frame is utilized to estimate the

Access Point (AP) beam direction. In addition, if the UE successfully receives the ACK frame

from the AP, beamforming training is skipped. Simulation shows that the proposed method

improves the total throughput by 18% for 10 UEs and 60% for 50 UEs.

R8: Some figures in deliverables should be revised: label each axis, enlarge font sizes,

avoid overlap with caption, and so on.

During the third year, we carefully checked the readability of each one of the figures

contained in all the deliverables.

R9: Novelty and contributions should be clearly written.

In the third year, we added more information about novelties, especially in WP3 deliverables,

and tried to highlight from where the contributions originated.

6.2 Recommendations concerning future work

R1: Strengthen activities in standardization bodies.

5G-MiEdge is an international research project that, if compared to other similar endeavors, has

a relatively small number of partners. Never the less it managed to interact with regulatory

bodies and impact standards bodies throughout the complete duration of the project. In the third

year, we have increased the interactions with regulatory and standards bodies. Specifically,

beside 3GPP, we have interacted and influenced the work of IEEE 802.11ay, Ofcom, ITU-

R/APT, and ETSI ISG. Specific details on these interactions are described in D5.3 [D5.3].

R2: Increase the project technical impact by targeting technical journal publications.

Following the reviewers’ recommendation, we have increased the number of manuscripts

submitted to high quality, international journals. Overall, we have produced fifteen manuscripts,

out of which ten have been already accepted or published. We have carefully selected the proper

venues, and the accepted papers have been published in journals such as IEEE Wireless

Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on

Mobile Computing, Proceedings of the IEEE, IEEE Transactions on Signal Processing, and

IEEE Vehicular Technology Magazine. The overall detail of this activity is described in D5.3

[D5.3].

R3: Carefully plan performance evaluation activities in field trials.

For the field trials, as explained in deliverable D4.3 [D4.3], the testbeds in Berlin and Tokyo

have carefully evaluated which concept to showcase on which testbed, and how to conduct

those evaluations in the most beneficial and effective way. Besides numerous emails and

telephone conferences, we actually sent two colleagues from Fraunhofer HHI to Japan for three

weeks in March 2019, to help with the evaluations.

R4: Provide an assessment of the rainfall effect on the performance of mmWave links.

In the deliverable D4.2 [D4.2], we carefully evaluated two recommendations from ITU-R to

provide the necessary insight on the impact of light to very heavy rain on mmWave links.

R5: Evaluate, at least with qualitative considerations, the amount of signalling burden

required by the coordination among the access points and MEHs to implement the

resource allocation strategies developed in WP3.

WP3 evaluated in its studies the amount of signalling involved in the novel proposed system

architecture.

For example, an autonomous decentralized control was considered for workload placement in

the hierarchical edge cloud, instead of a centralized control. It exchanges information of

resource usage status among neighboring MEC hosts to determine workload placements. These

information exchanges among MEC hosts can be periodic, so that the amount of signalling does

not occupy the bandwidth of the backhaul network. [D3.2]

Concerning the computation caching algorithms developed in WP3, it is worth noticing that the

task popularity and the input/output data sizes can be measured (or estimated or inferred)

directly by the serving small cell, and in general do not need to be exchanged with or retrieved

from other nodes of the network. Hence, at a single-small-cell level, the implementation of

computation caching techniques does not require an increase of exchanged control signalling

with respect to the state-of-the-art protocols and algorithms for task offloading in MEC

networks. [D3.3]

R6: Strengthen the collaboration with other related projects, beyond workshop.

In the third year of the project, we have strengthen our collaboration with other projects, beyond

workshops, co-organizing panels and producing joint publications.

In D1.4 Section 2.3 ”Ecosystem impact” [D1.4], we have described our tight collaborations

with the sibling projects 5G-MiEdge+, 5G!Pagoda, and with the 5G PPP association.

5G-MiEdge in the third and last year co-organized several events at the most important

international venues with the following other research projects: 5GENESIS, ULTRAWAVE,

5G Enhance, 5G City, 5G-CORAL, 5G-EVE, 5G-Transformer, CARMEN, Sat5G, 5G TANGO.

R7: Currently there are more and more related works lead by operators and academic

institutes. Should clearly check those activities and revise the plan, if needed.

We have been continuously checking the activities performed by other projects, as well as the

work done by 3GPP groups, in order to make sure that we are up to date on the latest

developments happing outside of the 5G-MiEdge project reach.

Deviations from Annex 1 and Annex 2 (if applicable)

During the third period, there was no deviation from Annex 1 and 2.

7.1 Tasks

All tasks were implemented as planned.

7.2 Use of resources

The following three tables contain the actual and planned use of resources for each partner,

including differences and a sum over all WPs. Table 7-1 shows the revised data for the first

period, Table 7-2 for the second one and Table 7-3 for the third period. Finally, Table 7-4

contains the numbers for the combined entire project duration.

Table 7-1 - First period: Revised actual and planned use of resources per partner [PM]

Partner WP1 WP2 WP3 WP4 WP5 WP6 Total

Fraunhofer

Actual 2.00 3.76 0.00 0.00 1.00 2.00 8.76

Planned 1.71 3.42 0.00 1.67 1.09 1.33 9.22

Difference 0.29 0.34 0.00 -1.67 -0.09 0.67 -0.46

CEA

Actual 2.28 4.15 4.64 1.65 2.33 0.00 14.97

Planned 2.14 3.96 4.03 0.44 1.36 0.00 11.95

Difference 0.09 0.14 0.56 1.16 0.94 0.00 3.02

Intel Actual 3.36 0.00 2.12 0.00 1.16 0.00 6.64

Planned 3.86 0.00 2.48 1.00 1.36 0.00 8.70

Difference -0.50 0.00 -0.36 -1.00 -0.20 0.00 -2.06

TI

Actual 2.00 2.29 0.00 0.00 1.00 0.00 5.29

Planned 0.86 3.42 0.00 0.11 0.55 0.00 4.94

Difference 1.14 -1.13 0.00 -0.11 0.45 0.00 0.35

URom

Actual 1.70 2.20 6.20 1.00 0.80 0.00 11.90

Planned 1.71 2.28 6.21 0.33 0.82 0.00 11.35

Difference -0.01 -0.08 -0.01 0.67 -0.02 0.00 0.55

Table 7-2 - Second period: Actual and planned use of resources per partner [PM]


Fraunhofer

Actual 1.07 2.44 0.00 6.06 0.75 1.07 11.39

Planned 1.43 4.56 0.00 8.33 1.45 1.33 17.10

Difference -0.36 -2.12 0.00 -2.27 -0.70 -0.26 -5.71

CEA

Actual 2.22 4.09 4.58 1.59 2.0 0.00 14.48

Planned 1.79 5.28 5.38 2.22 1.82 0.00 16.49

Difference 0.44 -1.18 -0.79 -0.62 0.19 0.00 -2.01

Intel Actual 3.29 0.00 3.52 2.00 1.97 0.00 10.78

Planned 3.21 0.00 3.31 1.00 1.82 0.00 9.34

Difference 0.08 0.00 0.21 1.00 0.15 0.00 1.44

TI

Actual 1.50 2.62 0.00 0.00 0.50 0.00 4.62

Planned 0.71 5.14 0.00 0.67 0.73 0.00 7.25

Difference 0.79 -2.52 0.00 -0.67 -0.23 0.00 -2.63

URom

Actual 1.43 2.88 8.27 2.00 1.10 0.00 15.68

Planned 1.43 2.88 8.28 2.00 1.09 0.00 15.68

Difference 0.00 0.00 -0.01 0.00 0.01 0.00 0.00

Table 7-3 - Third period: Actual and planned use of resources per partner [PM]


Fraunhofer

Actual 1.04 3.2 0.00 9.98 2.55 1.15 17.92

Planned 0.86 1.01 0.00 5 1.45 1.33 9.66

Difference 0.18 2.19 0.00 4.98 1.10 -0.18 8.26

CEA

Actual 1.75 2.46 4.22 1.9 2.45 0.00 12,78

Planned 1.07 1.76 3.59 1.33 1.82 0.00 9.57

Difference 0.68 0.7 0.63 0.57 0.63 0.00 3.21

Intel Actual 3.12 0.00 1.60 0.91 1.90 0.00 7.53

Planned 1.93 0.00 2.21 1.00 1.82 0.00 6.95

Difference 1.19 0.00 -0.61 -0.09 0.00 0.00 0.57

TI

Actual 0.5 -0.00 0.00 3 0.66 0.00 4.16

Planned 0.43 0.00 0.00 0.33 0.73 0.00 1.49

Difference 0.07 0.00 0.00 2.67 -0.07 0.00 2.67

URom

Actual 0.89 0.96 5.60 0.00 1.12 0.00 8.57

Planned 0.86 0.96 5.52 0.00 1.09 0.00 8.43

Difference 0.03 0.00 0.08 0.00 0.03 0.00 0.14

Table 7-4 - Overall: Actual and planned use of resources per partner [PM]


Fraunhofer

Actual 4.11 9.40 0.00 16.04 4.30 4.22 38.07

Planned 4.00 9.00 0.00 15.00 4.00 4.00 36.00

Difference 0.11 0.40 0.00 1.04 0.30 0.22 2.07

CEA

Actual 6.25 10.70 13.44 5.14 6.78 0.00 42.31

Planned 5.00 11.00 13.00 4.00 5.00 0.00 38.00

Difference 1.25 -0.3 0.44 1.14 1.78 0.00 4.31

Intel Actual 9.77 0.00 7.24 2.91 5.03 0.00 24.95

Planned 9.00 0.00 8.00 3.00 5.00 0.00 25.00

Difference 0.77 0.00 -0.76 -0.09 0.03 0.00 -0.05

TI

Actual 4.00 4.91 0.00 3.00 2.16 0.00 14.07

Planned 2.00 9.00 0.00 1.00 2.00 0.00 14.00

Difference 2.00 -4.09 0.00 2.00 0.16 0.00 0.07

URom

Actual 4.02 6.04 20.07 3.00 3.02 0.00 36.15

Planned 4.00 6.00 20.00 3.00 3.00 0.00 36.00

Difference 0.02 0.04 0.07 0.00 0.02 0.00 0.15

7.2.1 Explanations for resource deviation

Deviations from the planned resources are explained in the following.

Fraunhofer Heinrich Hertz Institute

In the second period, we had to make an adjustment in three categories:

Personnel costs

The amount of 627.37 Euro reflects the difference between the pre- and the post-calculation of

the personnel costs.

Other goods and services

Our costs for travel to Valencia and travel and registration fee for Barcelona of 2737.86 Euro

have not been claimed after period two, as they have not yet been available in the accounting

system.

LRI

The adjustments reflects the post-calculated LRI cost of -1268.90 Euro.

The third period contained many tasks for the Fraunhofer HHI. We planned two general

assemblies outside of Europe, in Bangkok and Marrakech. All the evaluations and experiments

of the hardware and testbed were scheduled in this period. In addition, we conducted the final

demonstration on the EuCNC 2019 and most recently the orchestration of our final review in

Japan in September 2019.

Therefore, we have overspent in WP2 for 2 PM, 5 PM in WP4 and 1 PM in WP5. When taking

the underspending of the second period into account, this amount is reduced to slight

overspending in all WPs, most notable 1.04 PM in WP4 and resulting in a sum of 2.07 PM.

However, all project goals were successfully reached and we believe the overspending was

necessary.

CEA

Regarding CEA effort overspending as compared to the plan, this is mainly related to the large

participation of non-permanent staff (PhD and Postdocs) to WP1 and WP4 activities and the

dissemination effort produced by CEA as WP5 leader. The larger participation of non-

permanent staff with respect to the original plan has been required by temporal lack of a

permanent researcher, due to a sabbatical leave. It is worth to highlight that the effort

overspending has not increased the overall costs at CEA side.

Intel

Adjustments provided for the first reporting period results from the small errors in the

personnel costs calculation and the travel calculation which were found during the check

while preparing the final version of financial statement.

Considering the three years of duration of the project there are no major differences (-0.05 PM)

between the overall actual spent hours and the original PM budget planned at the beginning of

the project.

The slight more effort spent on WP1 (+0.77 PM) was balanced by the slight less effort spent on

WP3 (-0.76 PM). The small shift of focus stems from the need to ensure a smooth synergy

between the two teams (the European and the Japanese one) at the expenses of a smaller than

planned involvement with the technical work in WP3.

TI

The effort spent by TI in Year 3 (4.16 PM) led to an overall spent effort over the three years

project of 14.07 PM, nearly in perfect agreement with the planned resources.

WP1: No deviations with respect to the planned effort modified according to AR6.2.

WP2: The actual effort was lower with respect to the plan reported in AR6.2 (-2.09 PM). This

was due to some unexpected issues (customs problems) arising from the shipment of the

antenna prototypes provided by TTECH and to be tested by TI in its Turin CATR laboratory.

Consequently, the antenna prototypes have been available for the measurements in Turin only

at the end of July 2018, which was already Year 3. Since the last WP2 deliverable was not

dedicated to experimentations, it was decided to perform such an activity within the more

experimental oriented WP4 reporting the results in deliverable D4.2. For this reason, 2 PM

were moved from WP2 to WP4 increasing the WP4 effort from 1 to 3 PM.

WP4: The spent effort in Year 3 was 3 PM, i.e., 2 PM higher than the AR6.2 plan (see the

above text related to WP2 for the explanation of the deviation).

WP5: The effort spent in year 3 was 0.66 PM leading to an overall effort over the three years

of 2.16 PM, nearly in line with the original resource plan.

URom

There was no deviation in the third period.

7.2.1 Unforeseen subcontracting


7.2.2 Unforeseen use of in kind contribution from third party against payment or free

of charges (if applicable)


Acronyms and Abbreviations

PoC Proof of Concept

AWG-24 APT Wireless Group

APT Asia-Pacific Telecommunity

ITS International Telecommunications Society

MIC Ministry of Internal Affairs and Communications (of Japan)

HetNet Heterogeneous Network

ETSI RRS European Telecommunications Standards Institute, Reconfigurable Radio

Systems

ITU International Telecommunication Union

ITU-R ITU Radio Communication Sector

V2X Vehicle-to-Everything

QoS Quality of Service

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Documents

Project1 Number: EUJ-01-2016-723171 Project Acronym: 5G … · 2019-09-26 · dissemination activities. In the third year of the project, one deliverable has been submitted. D5.3