system level modeling of v2i communication in ...€¦ · system level modeling of v2i...

System Level Modeling of V2I Communication in MicrocellularUrban Networks

CDLab Open day (Module 2, A1)

Blanca Ramos ElbalNovember 15, 2018

Dependable Wireless Connectivity for the Society in Motion

V2X Communication

• Vehicle-to-vehicle communication

• Vehicle-to-infrastructure communication

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V2X Communication

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V2X Communication

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Contents

Scenario

Relay Association

Simulation results

Next steps: relay position

Conclusions and Outlook on Planned Future Work

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Contents

Scenario

Relay Association

Simulation results

Slide 3 / 16

Scenario: Relays and transmitters

• The V2V communication takes place in uplink.• Assumption: Bs does orthogonal scheduling in uplink.

Slide 4 / 16

Scenario

• Manhattan grid ∼Poisson line process λs .

• Micro cells λb.

• Bs-UE communication.

• UE distributed as a PPP ineach lane of density λu .

• UEs: relays (λr ) and transmitters (λt).

B. Ramos Elbal, M.K. Müller,S. Schwarz, and M. Rupp,”Coverage-Improvement of V2I Communication Through Car-Relays in MicrocellularUrban Networks” in 2018 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, Sept 2018

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Scenario

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Scenario

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Scenario

BS

UE

Reference UE

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Scenario

BS

UE

Reference UE

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Scenario

BS

UE

Reference UE

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Scenario

BS

UE

Reference UE

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Scenario

BS

UE

Reference UE

Slide 5 / 16

Pathloss Model

L(xm) = 10αL log10 x1 +M∑

m=2

10αN log10 xm

x1

αL pathloss exponent in LOSαN pathloss exponent in NLOS

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Pathloss Model

m=2

10αN log10 xm

x1

x2

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Pathloss Model

m=2

10αN log10 xm

x2

x3

x1

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Relay association: largest channel gain

Largest channel gain for BSs in LOS:

FLOSBS (u) = exp

(−2λbu

− 1αL

)

Largest channel gain for BSs in NLOS (perpendicular):

FNLOSBS (u) = exp

(−2λs(2λb)

αLαN u

− 1αL Γ

(1−

αL

αN

))

Parallel streets signal negligible.Largest channel gain:

FBS (u) = FLOSBS (u)FNLOSBS (u)

-160 -140 -120 -100 -80 -60 -40 -20 0 20

u (dB)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

FLOS

(u)

FNLOS

(u)

F(u)

FLOS

(u) sim.

FNLOS

(u) sim.

F(u) simulation

b= 1 BS/km,

s= 1 st/100m

b= 2 BS/km,

s= 2 st/100m

CD

F

Y. Wang, K. Venugopal, A. F. Molisch, and R. W. Heath,”Analysis of Urban Millimeter Wave Microcellular Networks” in 2016 IEEE 84thVehicular Technology Conference (VTCFall), Montreal, QC, Canada, Sept 2016

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FLOSBS (u) = exp

(−2λbu

− 1αL

)Largest channel gain for BSs in NLOS (perpendicular):

FNLOSBS (u) = exp

(−2λs(2λb)

αLαN u

− 1αL Γ

(1−

αL

αN

))

-160 -140 -120 -100 -80 -60 -40 -20 0 20

u (dB)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

FLOS

(u)

FNLOS

(u)

F(u)

FLOS

(u) sim.

FNLOS

(u) sim.

F(u) simulation

b= 1 BS/km,

s= 1 st/100m

b= 2 BS/km,

s= 2 st/100m

CD

F

Slide 7 / 16

FLOSBS (u) = exp

(−2λbu

− 1αL

)Largest channel gain for BSs in NLOS (perpendicular):

FNLOSBS (u) = exp

(−2λs(2λb)

αLαN u

− 1αL Γ

(1−

αL

αN

))

-160 -140 -120 -100 -80 -60 -40 -20 0 20

u (dB)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

FLOS

(u)

FNLOS

(u)

F(u)

FLOS

(u) sim.

FNLOS

(u) sim.

F(u) simulation

b= 1 BS/km,

s= 1 st/100m

b= 2 BS/km,

s= 2 st/100m

CD

F

Slide 7 / 16

LOS/NLOS Probability

pLOSBS =

∫ ∞0

FNLOSBS (u)fLOSBS (u)du

pNLOSBS =

∫ ∞0

FLOSBS (u)fNLOSBS (u)du

XXXXXXLinkλs 1 1.5 2

LOS 0.9692||0.9844 0.9543||0.9801 0.9396||0.9688NLOS 0.0307||0.0156 0.0457||0.0184 0.0603||0.0312

XXXXXXLinkλb 1 1.5 2

LOS 0.9396||0.9688 0.9396||0.9607 0.9396||0.9751NLOS 0.0603||0.0312 0.0603||0.0328 0.0603||0.0210

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pLOSBS =

∫ ∞0

pNLOSBS =

∫ ∞0

LOS 0.9692||0.9844 0.9543||0.9801 0.9396||0.9688NLOS 0.0307||0.0156 0.0457||0.0184 0.0603||0.0312

LOS 0.9396||0.9688 0.9396||0.9607 0.9396||0.9751NLOS 0.0603||0.0312 0.0603||0.0328 0.0603||0.0210

1 1.5 2

s (st/100m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

pLOS

pNLOS

theory

sim.

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pLOSBS =

∫ ∞0

pNLOSBS =

∫ ∞0

LOS 0.9692||0.9844 0.9543||0.9801 0.9396||0.9688NLOS 0.0307||0.0156 0.0457||0.0184 0.0603||0.0312

LOS 0.9396||0.9688 0.9396||0.9607 0.9396||0.9751NLOS 0.0603||0.0312 0.0603||0.0328 0.0603||0.0210

1 1.5 2

s (st/100m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

pLOS

pNLOS

theory

sim.

1 1.5 2

b (BS/km)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1p

LOS

pNLOS

theory

sim.

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Contents

Scenario

Relay Association

Simulation results

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Relay association: cell edge

→Relay should be within the cell→The chosen relay is not always the closest one

• Assumption: user attached to a LOS Bs.• Cell edge in LOS.

pno−relayLOS =

∫ ucell−LOS0

fLOSrelay (u)du

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ucell-LOS

d1st

d2nd

0 200 400 600 800 1000 1200 1400 1600 1800

distance(m)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

pdf

10-3

fLOS

BS

fLOS

BS-2

pno−relayLOS =

∫ ucell−LOS0

fLOSrelay (u)du

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ucell-LOS

d1st

d2nd

-100 -90 -80 -70 -60 -50 -40 -30 -20

u (dB)

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

pdf

fLOS

BS

fLOS

BS-2

pno−relayLOS =

∫ ucell−LOS0

fLOSrelay (u)du

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ucell-LOS

d1st

d2nd

-100 -90 -80 -70 -60 -50 -40 -30 -20

u (dB)

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

pdf

fLOS

BS

fLOS

BS-2

pno−relayLOS =

∫ ucell−LOS0

fLOSrelay (u)du

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Relay association: empty cells

• Cell edge in NLOS.

ucell-LOS

d1st

d2nd

fLOSBS (u) = fNLOSBS (u)|u=ucell−NLOS

pno−relayNLOS =∫ ucell−NLOS

0 fNLOSrelay (u)du

• Probability to have an empty cell

pno−relay = pno−relayLOS pno−relayNLOS

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ucell-LOS

ucell-NLOS

d1st

d2nd

0 fNLOSrelay (u)du

pno−relay = pno−relayLOS pno−relayNLOS

-200 -180 -160 -140 -120 -100 -80 -60 -40 -20 0

u (dB)

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

pdf

fLOS

BS

fNLOS

BS

Slide 10 / 16

ucell-LOS

ucell-NLOS

d1st

d2nd

0 fNLOSrelay (u)du

pno−relay = pno−relayLOS pno−relayNLOS0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

u (users/100m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Pn

o-r

ela

y

b= 1 BS/km,

s= 1 street/100m

b= 1.5 BS/km,

s= 1.5 street/100m

b= 2 BS/km,

s= 2 street/100m

Simulation results

Theory

Slide 10 / 16

ucell-LOS

ucell-NLOS

d1st

d2nd

0 fNLOSrelay (u)du

u (users/100m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Pn

o-r

ela

y

b= 1 BS/km,

s= 1 street/100m

b= 1.5 BS/km,

s= 1.5 street/100m

b= 2 BS/km,

s= 2 street/100m

Simulation results

Theory

Slide 10 / 16

ucell-LOS

ucell-NLOS

d1st

d2nd

0 fNLOSrelay (u)du

u (users/100m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Pn

o-r

ela

y

b= 1 BS/km,

s= 1 street/100m

b= 1.5 BS/km,

s= 1.5 street/100m

b= 2 BS/km,

s= 2 street/100m

Simulation results

Theory

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Relay association

BS

Relay

Reference UE

max(min(SINRBS-relay,SINRrelay-user)) > SINRBS-user

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Relay association

BS

Relay

Reference UE

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Relay association

d

r1

r2

BS

Relay

Reference UE

Slide 11 / 16

Contents

Scenario

Relay Association

Simulation results

Slide 11 / 16

Simulation results: Parameters

Parameter ValueαL 2αN 4

PTXb 10 WPTXu 0.2 W

N0 −174 dBm/HzBW 10MHz{µ, σ} {0, 1}

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Simulation results

Performance improvement: SINR and coverage, considering T = 0dB

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

u(user/100m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Pim

pro

ve

me

nt

s= 1 street/100m

s= 1.5 street/100m

s= 2 street/100m

SINR improvement

Coverage Improvement

Slide 13 / 16

Simulation results

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

u(user/100m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Pim

pro

ve

me

nt

s= 1 street/100m

s= 1.5 street/100m

s= 2 street/100m

SINR improvement

Slide 13 / 16

Simulation results

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

u(user/100m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Pim

pro

ve

me

nt

s= 1 street/100m

s= 1.5 street/100m

s= 2 street/100m

SINR improvement

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

u(user/100m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Pim

pro

ve

me

nt

SINR improvement

b= 1 BS/km

b

= 1.5 BS/km

Slide 13 / 16

Simulation results

Coverage probability. T = 5dB

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

u(user/100m)

0.75

0.8

0.85

0.9

0.95

1

Pco

ve

rag

e

s= 1 street/100m

s= 1.5 street/100m

s= 2 street/100m

Assisted link

Direct link

Slide 14 / 16

Simulation results

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

u(user/100m)

0.75

0.8

0.85

0.9

0.95

1

Pco

ve

rag

e

s= 1 street/100m

s= 1.5 street/100m

s= 2 street/100m

Assisted link

Direct link

Slide 14 / 16

Simulation results

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

u(user/100m)

0.75

0.8

0.85

0.9

0.95

1

Pco

ve

rag

e

s= 1 street/100m

s= 1.5 street/100m

s= 2 street/100m

Assisted link

Direct link

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

u(user/100m)

0.7

0.75

0.8

0.85

0.9

0.95

1

Pco

ve

rag

e

b= 1 BS/km

b

= 1.5 BS/km

Assisted link

Direct link

Slide 14 / 16

Contents

Scenario

Relay Association

Simulation results

Slide 14 / 16

Relay positiond

r1

r2

Challenges:

• Coverage probability of relay assisted link• Identify cell boundaries

Slide 15 / 16

Relay positiond

r1

r2

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

u (users/100m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1b= 2bs/km,

s = 1.5streets /100m

r2/d

r1/d

Challenges:

Slide 15 / 16

Relay positiond

r1

r2

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

u (users/100m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1b= 2bs/km,

s = 1.5streets /100m

r2/d

r1/d

Challenges:

Slide 15 / 16

Contents

Scenario

Relay Association

Simulation results

Slide 15 / 16

Conclusions• Probability for a user to be attached to a BS in LOS is significantly larger than to be in NLOS.• Probability of improving SINR and coverage by utilizing relays largely depends on the relay density.• Probability to have an empty cells of relays.• Coverage probability values, we showed that they can be significantly improved when the relay density is

large enough.

Future work• Find analytical expression for the MLP scenario coverage probability.• Extend the analysis to 2-D PPPs scenarios.• Include throughput and capacity.• Use V2X communication to extend the robustness of the network.• Introduce age of information1 metric.

Thank you for your [email protected]

1S. Kaul , R.Yates , M. Gruteser,”Real-time status: How often should one update?” in 2012 Proceedings IEEE INFOCOM, March 202, pp.

2731-2735

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large enough.

2731-2735

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large enough.

2731-2735

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large enough.

2731-2735

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large enough.

2731-2735

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large enough.

2731-2735

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large enough.

2731-2735

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Side Projects

• Evaluating the throughput performance at 2 GHz and 3.5 GHz in a massive MIMO systemRamos Elbal, B. and Ademaj, F. and Schwarz, S.and Rupp, M., WSA 2017

• The Impact of Carrier Frequency at 800 MHz and 3.5 GHz in Urban and Rural Environments Using LargeAntenna ArraysRamos Elbal, B. and Ademaj, F. and Schwarz, S.and Rupp, M., TELFOR 2018

Slide 1 / 3

Relay position: signal

d

r1

r2

BS

Relay

Reference UE

min(SINRBS-relay, SINRrelay-user) > SINRBS-user

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

BS-user distance (m)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

pdf

10-3

Slide 2 / 3

d

r1

r2

BS

Relay

Reference UE

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

pdf

10-3

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

distance to the n-th neighbour (m)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

pd

f

10 -3

1st

2nd

3rd

4th

5th

6th

Slide 2 / 3

d

r1

r2

BS

Relay

Reference UE

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

pdf

10-3

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

distance to the n-th neighbour (m)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

pd

f

10 -3

1st

2nd

3rd

4th

5th

6th

0 1000 2000 3000 4000 5000 6000

BS-relay distance (m)

0

0.5

1

1.5

2

2.5

pd

f

10 -3

1st

2nd

3rd

4th

5th

6th

Slide 2 / 3

Relay position: Interference

• Scenario

• LOS interference

• NLOS interference: mapping

• Simplified scenario

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• Scenario

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• Scenario

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• Scenario

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• Scenario

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• Scenario

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• Scenario

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ScenarioRelay AssociationSimulation resultsNext steps: relay positionConclusions and Outlook on Planned Future WorkAppendix

system level modeling of v2i communication in ...€¦ · system level modeling of v2i...

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