5GTNF WORKSHOP

5G-FORCE introduction and Network technology enablers R&D

Jose Costa-Requena/Aalto

27/01/2021

5G FORCE PARTNERS

• Academic partners

• Industry partners

• Mobile operators and regulators

5G FORCE

• 5G infrastructure fills the gap between laboratory-based 5G testing environments and commercial network deployments in real deployment across the campus.

• 5G platform built together with industry vendors offer trials tailored infrastructure configurations for telecom and vertical industries and scientific community

• Facilitate the deployment of new use cases for verticals to support industry players to monetize investment in 5G mobile networks

• Engage SME, start-ups and industry players to test the benefits of 5G features with open platform for experimentation (e.g. hackathons)

5G FORCE- Finnish Research infrastructure

ODC/Oulu

Disributed 5GTNEPC components

VTTOulu/5GTN+

Disributed 5GTNEPC components

University

Oulu/5GTN+

Helsinki UniversityKumpula TAKE5

CSC/ESpoo

Nokia EPCOulu/Rusko

CORNETCentria UAS/Ylivieska

Trial network

Tampere

TUAS/TurkuCORNET

AALTO Espoo/TAKE-5

VTT Espoo/TAKE-5

Nokia Karaportti/TAKE-5

CWDMMux

CWDMMux

FUNETDWDM

FUNETDWDM

NetAct/iOMS

Nokia SNRDistributed 5GTNEPC Components

OpenEPCEPC simulator

Willab

1Gb1Gb

1Gb

FUNET IP router

FUNET IP router

Nokia /TRe

FUNET IP router

1Gb

1Gb(10Gb)

10Gb

Router at Aalto/Nokia

Managed&Owned

IP router (L3)VPNTBD

Dedicated lightpath/VLAN (L2)

WP1:5GTNF/phase3 Ecosystem Coordination

WP2 URLLC and Industrial Internet

- E2E network slicing

- Service based architecture, cloudification and Multi-access Edge computing

- Industrial Internet

- Massive scale autonomous IoT networks

- E2E Reliability for URLLC

- URLLC standardisation

WP3: Radio Technology enablers

WP4: Artificial Intelligence

WP5: Service security privacy and data management-

WP6: Validation and Impact

5G Research Areas

End-to-End 5G slicing. Solution for IEC 104 communications

using P4-NETFPGA-based accelerators

End to End Slicing. Dynamic Edge firewalling using cloud-

based security management service

• Enhancement to SBA:• Adoption of service mesh architecture for the deployment of SBA-based 5GC.• Service mesh architecture adds support for load balancing and robustness to the

basic SBA.• Service mesh architecture provide high level of observability and tracing on the NFs.

• Enhancement to Edge computing:• Introduction of the concept of Ultra Far Edge (UFE).• In addition to traditional Edge nodes, UFE nodes are hosted in the UE domain.• UFE nodes contribute computational resources to the Edge Cloud platform.• UFE can be orchestrated from centralized Data Center or can provide Edge services

autonomously to the end users.

• Enhancement to Network slicing:• Introduction of the concept of network slice mobility.• Network slice mobility addresses service continuity for highly mobile UEs (e.g.,

autonomous vehicles, UAVs, and high-speed trains).• A network slice follows its mobile users, and it is relocated from one Data Center to

another by means of live-service migration.

Service Based Architecture. Improvements to the Key Enablers of 5G (D2.2.3)

Massive scale autonomous IoT networks. Dimensioning of compute resources for

the sensing use case in 5G (D2.1.1)

• The sensing platform is deployed as part of a virtual network slice.

• The VNF that runs the sensing platform has limited resource in terms of CPU and Memory.

• Results shows that a VNF with 4 CPUs and 4 GB of memory is able to efficiently collect data from up to 10k sensors.

• One instance can cover an area of 25 Km2 when considering dense sensor network (400 sensor/Km2) and 1 Km2 when considering ultra dense sensor network (10k sensor/Km2).• Example: In the dense network scenario, 9 instances are required to collect data

from Helsinki area (213.8 Km2).

• Connect very large numbers of IoT devices into a wireless mesh with scalable, reliable, and cost-efficient operation

Low latency backhaul mesh network

Example nodes from one floor

• The objective is to minimize the number of handovers in a use-case consisting of a robot equipped with an accurate positioning device, moving along predetermined routes, in an indoor environment.

• The indoor environment is equipped with eight pico-cells.

• The coverage map of each pico-cells is described by a function that maps position coordinates to RSRPs.

• A Neural Network (NN) is used to predict the next position of the robot.

• The predicted position is used as input to the radio map functions to take optimized handover decisions.

• The obtained results show that positioning based handover using NN can reduce the number of handovers from 64% to 2% in an environment free of radio link failures, and from 200% to 17% in an environment free of outage events.

E2E Reliability for URLLC. Positioning based intra-frequency handover in indoor

cellular network for ultra reliable communications assisted by radio maps (D2.5.1)

• User plane latency:• Introduction of TTI in 5G NR yield a lower latency compared to LTE (5G NR RTT = 0.9 VS LTE RTT = 11-28 ms).• Subcarrier spacing impact the latency of 5G NR, for example, the two-way latency is equal to 5 ms when the

subcarrier spacing is set to 15khz, and equal to 1.2 ms when subcarrier spacing is set to 120khz.

• Control plane latency:• Defined as the time required to move from a battery efficient mode e.g. IDLE to the start of a continuous data

transfer e.g. ACTIVE mode.• The transition from IDLE state to ACTIVE state requires 50-100 ms in LTE and less than 10 ms in 5G NR (thanks to

the RRC connected inactive functionality).• Radio resources allocation takes 10-20 ms in LTE and requires no latency in 5G NR (thanks to the grant free

access).• The total control plane latency in 5G varies between 31.5 ms and 9.1 ms, depending on the value of TTI.

• Reliability:• 5G NR adopts several techniques for increasing the reliability: Antenna diversity, Macro diversity and multi-

connectivity, interference cancellation and hybrid automatic repeat request (HARQ).• These methods have the potential to have the probability of block error rate (BLER) equal up to 10-5.• The achieved reliability depends on subcarrier spacing, bandwidth, and different carrier aggregation.

E2E Reliability for URLLC. Achievable performance in latency and reliability in 5G NR (D2.3.5)

• Change request against 3GPP TS 23.003 regarding communication reliability via service bindings and NF Set concept.

• Change request against 3GPP TS 23.501 regarding the User Data Repository (UDR).

• Three pseudo change requests against 3GPP TR 29.893 regarding the evaluation of HTTP3/QUIC as a transport protocol for 5GC Service Based Interfaces.

• Pseudo change request against 3GPP TR 23.758 regarding the relocation of application context in edge cloud environments.

• Contribution to network slicing work in IETF regarding the alignment of IETF slicing and 3GPP/SA6 slicing approaches.

• Contribution to ETSI ZSM regarding Ultra Far Edge (UFE) concept.

URLLC Standardisation

5G FORCE Outcome

State of the art 5G system with both commercial and prototype solutions to support verticals and companies to deploy services

2 x 5G RRH

5G 3.5GHz

FPRB = Power for 4G and

5G RRH

LTE 2600

Antenna

Väre roof RKK2419 1:2 → OIH-talo 13:14Väre roof RKK2419 3:4 → OIH-talo 17:18

5G FORCE OutcomeProvide an ecosystem for local companies to experiment and explore 5G benefits

Finland

AMF gNB

APP Server

Intercontinental Internet Line (Seoul-Helsinki)

230~240 msec RTT, 1~10Gbps

UPF

South Korea

SMF

Signalling

User plane

gNBUPF

The 5G Force ecosystem also provides international visibility for international projects and cooperation

THANK YOU!!

Jose Costa-Requena (jose.costa@aalto.fi)

Raimo Kantola (raimo.kantola@aalto.fi)

Riku Jäntti (riku.jantti@aalto.fi)

27/01/2021

mailto:jose.costa@aalto.fimailto:raimo.kantola@aalto.fi