(some) Device Localization,

Mobility Management and

5G RAN Perspectives

Mikko Valkama

Tampere University of TechnologyFinland

mikko.e.valkama@tut.fi

+358408490756

December 16th, 2016

TAKE-5 and TUT, shortly

• Our work will primarily focus on WP3 and WP4

• WP3

– energy efficiency, mobility and localization related topics

– primarily with 5G RAN focus

• WP4

– massive MTC, ultra-reliable MTC,

– multi-radio connectivity

– front-haul, back-haul, self-backhauling technologies

– all with 5G RAN focus

• TUT based PIs: Mikko Valkama, Sergey Andreev, Yevgeni

Koucheryavy

• Contact: mikko.e.valkama@tut.fi

2

WP3: 5G Radio Networks and Localization

• 5G networks, substantially enhanced positioning accuracy (3GPP TR 38.913 & TR 22.862, 5GPP vision and white papers, NGMN 5G white paper, 5G Forum white papers, etc.)

E.g., 1 meter accuracy outdoors

substantially better compared to current networks (e.g. LTE OTDOA, UTDOA)

• Lots of new verticals calling for this, e.g.

self driving cars, robots, drones, intelligent traffic systems, traffic management, …

• Technical enablers

dense networks, antenna arrays, wide bandwidth, multicarrier waveforms, short radio frames/sub-frames, frequent UL pilots

www.tut.fi/5G/positioning/

WP3: 5G Radio Networks and Localization

• Developed novel cascaded Extended Kalman Filter based solutions for

High-efficiency ToA and DoA estimation and tracking in individual access nodes, based on UL reference signals

Corresponding position estimation and tracking, as well as UE clock offset estimation and tracking in a central node

Facilitates also mutual synchronization of the access nodes with mutual clock offsets

www.tut.fi/5G/positioning/

Example 1: localizing and

tracking a moving car

5

• UN in LoS with two closest ANs

• Random route, v = 20…50 km/h

• Assume ANs are mutually

unsynchronized, UNs have clock

offsets

• ANs have 2D concentric circular

antenna arrays consisting of 9

cross-dipoles

• Uplink reference signal TX power =

+3 dBm

• OFDM waveform, B = 100MHz, fc =

3.5GHz

• Detailed ray-tracing based

propagation modeling

See demo at: www.tut.fi/5G/TWC16

50 m

Fig. Madrid grid

Access nodes:

density ~ 50m*

Streets

Housing block

Example 1: localizing and

tracking a moving car

6

See video at: www.tut.fi/5G/TWC16

UL reference signals processed and EKFs updated only once per 100ms

Example 2: localizing and

tracking a flying drone

7

See demo at: www.tut.fi/5G/GLOBECOM16

• A flying drone in LoS with two closest ANs

• Random flying route with max velocity of 50 km/h, including also landings

and take-offs

• Antenna models, radio frame, etc. similar to the previous example

• Detailed ray-tracing based propagation modeling

Example 2: localizing and

tracking a flying drone

8

See video at: www.tut.fi/5G/GLOBECOM16

UL reference signals processed and EKFs updated once per 100ms

WP4: wireless self-backhauling

• We have been studying three different options

for performing the self-backhauling while serving

the UEs in the uplink (UL) and downlink (DL)

– Half-duplex scheme (classical, for refence)

– Full-duplex scheme

– Relay-type scheme

• Next, these different schemes are shortly

described

12.4.2017 9

Half-duplex backhauling

• The basic scheme, where transmission and

reception are divided in time

– No interference between UEs, and essentially no

interference at all assuming good beamforming

12.4.2017 10

Time slot 1 Time slot 2

Access nodeBackhaul node

UE

UE

UE

UE

UEUE

Access nodeBackhaul node

UE

UE

UE

UE

UEUE

Inband full-duplex backhauling

• In the full-duplex scheme, all transmission and

receptions are done simultaneously

– High efficiency, but also complex interference

mechanisms

12.4.2017 11

Access nodeBackhaul node

UE

UE

UE

UE

UEUE

Relay-type backhauling

• A relay-type scheme combines the good sides of

half-duplex and full-duplex schemes

– less interference sources, while the full-duplex

capability is still leveraged in the access node

12.4.2017 12

Time slot 1 Time slot 2

Access nodeBackhaul node

UE

UE

UE

UE

UEUE

Access nodeBackhaul node

UE

UE

UE

UE

UEUE

Resource allocation:

Optimizing the transmit powers

• Careful allocation of the different transmit powers is

crucial to control the interference, and to improve the

energy-efficiency

• This can be done in different ways, such as by

mazimizing the sum-rate or ensuring a minimum

Quality-of-Service (QoS)

• Here, the results are provided for a case where the

transmit powers are minimized subject to a QoS

requirement in the form of minimum per UE spectral

efficiency

– Minimum data rate requirements for UL and DL13

Example

results

12.4.2017 14

• The optimized

TX powers

are calculated

for large amounts of randomly dropped UEs in

the network

– The cumulative distribution functions of the transmit

powers can be compared between the different

schemes

• This shows which of the schemes is capable of

fulfilling the same QoS requirements with the

best energy-efficiency (lowest transmit powers)

Parameter Value

Number of access node TX/RX antennas 200/100

Number of DL/UL UEs 10/10

Number of DL/UL backhaul streams 12/6

DL/UL rate requirement, per UE 8/2 bps/Hz

Cell radius 50 m

Distance to the backhaul node 75 m

Center-frequency 3.5 GHz

Example result 1

12.4.2017 15

• The full-duplex (FD) scheme achieves significantly lower

transmit powers both in the AN and in the UEs than the relay-

type (RL) or half-duplex (HD) schemes

– However, for some of the UE positions, it cannot fulfill the QoS

requirements with any finite transmit power (since the CDF

saturates to a value less than 1)

Example result 2

12.4.2017 16

• When investigating the total TX power usage (UL+DL), the FD

scheme also outperforms the other solutions

– However, with less SI cancellation, the probability of not fulfilling the

QoS requirements with any finite transmit power is larger

• Also, the relay scheme starts to perform rather poorly when the

amount of SI cancellation is 110 dB or less

Inhouse prototype device for

inband full-duplex

12.4.2017 17

• Fully operational full-duplex

demonstrator developed

and up and running at TUT

• Operates at 2.4 GHz ISM

band, supports up to

200 MHz instantaneous BW

• Contains advanced RF self-

interference cancellation as

well as novel digital self-

interference cancellation

solutions, all in real time

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www.tut.fi/full-duplex

Example RF measurement results

12.4.2017 18

• Live measurements incorporating also Aalto-based back-to-back relay

antenna*, 80 MHz instantaneous BW at 2.4 GHz

• More than 100 dBs of measured TX-RX isolation

* D. Korpi, M. Heino, C. Icheln, K. Haneda, and M. Valkama, "Compact inband full-duplex relays with beyond

100 dB self-interference suppression: Enabling techniques and field measurements," IEEE Transactions on

Antennas and Propagation, accepted, to appear, 2016.

19

WP4: Massive and Ultra-reliable MTC

Objectives

• New MAC, RRM and protocol solutions to enable novel 5G use cases related to both massive and critical MTC.

• Support of ultra-reliable MTC applications and services.

Challenges

• Understanding channel behaviour (propagation) to support critical and massive connectivity communications in 5G-grade MTC scenarios.

• Propose models and solutions able to enable ultra-reliable low latency communications.

12.4.2017

20

5G-grade IoT research on factory automation

Office areaMachinery area

Transmitter 1 Transmitter 2

150 m

75 m

• Since statistical channel models are not suitable for factory environments, a comparison between “real” and statistical path loss measurements has been investigated by using our in-built Ray-based (RL) and system-level (SLS) tools.

• The conclusion is the impossibility to generalize a path loss formula for ”any” indoor scenario. Thus, a proper one has to be achieved in accordance to the environment considered.

12.4.2017

21

SNR DL Heatmaps

System-level

simulator (SLS)

Ray-based

simulator

12.4.2017

22

Pathloss assessment • Statistical channel models are only

suitable for scenarios in which theywere made.

• The gap between deterministic and“real” models is very important.

• TUT Ray-based tool is useful tocharacterize practical channelpropagation.

• A deterministic path loss formulacan be obtained through extensivesimulations.

In order to conduct a comprehensive system-level analysis, propagationbehaviour needs to be understood correctly. In doing this, our RL tool providespowerful instruments to have a complete understanding on wireless channelpropagation in factories of the future (i.e., Industry 4.0).

16.12.16

WP4: Multi-RAT Integration Reliability in Multi-RAT networks research track

Objectives:

Study options for Multi-RAT integration on different

layers.

Improve reliability in Multi-RAT networks by using

simultaneous connections to various RATs.

Challenges:

Finding application independent solutions

Balancing energy efficiency with reliability/performance

gains

16.12.16

TUT testbed setup

TUT testbed with Ericsson pico base stations, which are

connected to the Aalto EPC:

UE has both LTE and WiFi interfaces on at the same time,

and can send the same data duplicated over both links.

Duplication currently works only on application layer.

16.12.16

Example scenario concept

Application used is simple echo server.

User is moving out of WiFi coverage, so UE starts

sending data over LTE as well.

16.12.16

Future work

Consider additional scenarios:

Mission critical: Constantly send over all interfaces for maximum

reliability.

IoT: Balance energy efficiency with reliability gains.

Test with “real” applications

Prototype demo