analysis of urban millimeter wave microcellular...

Slides © Robert W. Heath Jr. (2016)

Analysis of Urban Millimeter Wave Microcellular NetworksYuyang Wang†, KiranVenugopal†, Andreas F. Molisch§,

and Robert W. Heath Jr. †

†The University of Texas at Austin §University of Southern California

The UT authors are funded by U.S. Department of Transportation through D-STOP Tier 1 University Transportation Center and Texas Department of Transportation project CAR-STOP. Dr. Molisch’s work is supported by NSF and Samsung.


Manhattan type urban vehicular network

2http://energyfuse.org/videos/mit-study-shows-big-benefits-of-autonomous-cars-no-traffic-lights-and-congestion-at-intersections

Dense skyscrapers:severe signal blockage and attenuation

Clustered users at intersections: generate heavy load for BS

Diverse set of terminals: vehicles, pedestrians, bicyclists…

Various applications: vehicular safety, infotainment…

Most challenging environment for mmWave communication


3

Stochastic geometry network models

[1] T. Bai and R. W. Heath Jr., “Coverage and rate analysis for millimeter wave cellular networks,” IEEE Trans. Wireless Comm., 2014.[2] M. Kulkarni, S. Singh, and J. G. Andrews. "Coverage and rate trends in dense urban mmwave cellular networks.” IEEE GlobeCom, 2014.[3] F. Baccelli, and X. Zhang. "A correlated shadowing model for urban wireless networks.” IEEE INFOCOM, 2015.[4] M. Farooq, H. ElSawy, and M. Alouini. "Modeling inter-vehicle communication in multi-lane highways: A stochastic geometry approach.” IEEE VTC fall, 2015.

Realistic urban microcell PL model and tractable V2I analysis framework

MmWave w/ blockage [1-2] V2V no mmWave [4]Manhattan no mmWave [3]

blockedunblocked

blocked

unblocked


4

Exploit new urban pathlossmodel for mmWave microcells

Analysis framework for outdoor mmWave using the

Manhattan line processesQuantify & compare

interference

Contributions


System model

5


Manhattan distance based pathloss model

6A. F. Molisch, A. Karttunen, S. Hur, J. Park and J. Zhang, "Spatially consistent pathloss modeling for millimeter-wave channels in urban environments,” EUCAP, Davos, 2016, pp. 1-5.

PLdB(dL,DN) = 10↵L log10 dL + 10

X

d̃2DN

↵N log10˜d+M�

# corners

LoS segment: 1st segment of link

LoS PL exponent NLoS segments set: all segments except 1st

corner loss

NLoS PL exponent

Manhattan distance based PL modelEuclidean distance is NOT the right way to characterize pathloss


7

1D-PPPs and stretch out to form

MPLP streets

Vert/hori streets intensity are: λsv and λsh

Street width not considered

BSs are randomly dropped on streets as

PPP

BS 1D intensity on vert/hori streets are: λtv

and λth

Manhattan poisson line proces (MPLP)

Analyze performance of the typical user on

horizontal street


Coverage analysis

8


Coverage analysis: a new approach (1/3)

9

SINRo

=Ptho

u

I�L + I

�V + I�N +N0

Three interference Gaussian noise

Transmit power Small-scale fading

Associated link path gain u

CDF of associated link path gain u

coverage probability Pc(T, u) conditioned on u

coverage probability Pc(T) deconditioned on uStep 3:

Step 2:

Step 1:

Assumption: vehicle is associated with the strongest BS (smallest PL)

I�V

interference @ LoS street

interference @ NLoS vert/cross streets

interference @ NLoS hori/parallel streets

I�L

I�H

Typical receiver o

Coverage analysis


Coverage analysis: a new approach (2/3)

2. Conditioned coverage probability Pc(T, u) conditioned on u

3. Deconditioned coverage probability Pc(T)

10

pc(u, T ) = exp(�C1u�1

) exp(�C2u� 1

↵L) exp

⇣�C3u

� 1↵N

⌘,

and

PDF of u

C1 = TN0, C2 = 2�th%,

C3 = 2�sv�

✓1� ↵L

↵N

◆⇣2�tvc

1↵L

%

⌘ ↵L↵N

% =

Z 1

1

1

1 + T

�1µ

↵Ldx

Pc(T ) =

Z 1

0fU (u)pc(u, T )du

1. CDF of associated link path gain u

F (u) = exp

⇣�2�thu

� 1↵L

⌘exp

✓�2�sv�

✓1� ↵L

↵N

◆⇣2�tvc

1↵L

⌘ ↵L↵N u� 1

↵N

◆


LI�H(Tu�1) ⇡ 2�sv

r2

�svK1

⇣2p

2�sv

⌘

Coverage analysis: a new approach (3/3)Jensen’s inequality on interference Laplace transform

11

NLoS horizontal LoS interference

NLoS vertical Modified Bessel function of 1st order

very small

K1(µ) ⇠ µ�1 LI�H(Tu�1) ⇡ 1.

NLoS-H is negligible

LI�L(Tr↵L

) � exp (�!Er [2�thr])

= exp(�!)

LI�V(Tr↵L

) � exp

⇣�#�svEr

h(2�tvr)

↵L↵N

i⌘

exp

�#�sv

✓�tv

�th

◆ ↵L↵N

�

✓1 +

↵L

↵N

◆!

1. NLoS-V contributes little to interference2. LoS interference is still dominant


Numerical results

12


Fitting parameters for Euclidean pathloss modelsEuclidean model 1

Euclidean model 2

Blockage prob in MPLP

Fitting parameters Valuesin model 1 11.8

in model 1 -19dB

in model 2 2.5

in model 2 0dB

in model 2 11

in model 2 5dB

13

PLdB(d) = 10↵̃ log10 d+�1

PLdB(d) = (1� I(pB(d)))�10↵̃L log10 d+�

L2

�

+ I(pB(d))�10↵̃N log10 d+�

N2

�

PL exponents and offset to fit

Parameter fitting are divided into LoS and NLoS

Bernoulli RV with parameter of blockage prob. ↵̃

�1

↵̃L

↵̃N

�L2

�N2

Parameter fitting: linear regression results

pB(d) = 1� 1� exp(�2d(�sh + �sv))

2d(�sh + �sv).


SINR Threshold (dB)-5 0 5 10

Cove

rage

Pro

babi

lity

0

0.05

0.1

0.15

0.2

0.25Our ModelEuc Model 1Euc Model 2

14

SNR at BS (dB)10 15 20 25 30 35 40 45 50

Ergo

dic

Capa

city

(bps

/Hz)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Our ModelEuc Model 1Euc Model 2

Comparing pathloss models

Significant differences with Euclidean PL modelsin ergodic capacity/coverage!


Association link path gain distribution

Associated Link Channel Gain Threshold(dB)-50 -45 -40 -35 -30 -25 -20 -15 -10

CD

F o

f A

ssoci

ate

d L

ink

Channel G

ain

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

Simu LOS+V/H-NLOSSimu LOS+V-NLOSSimu LOSAna LOS+V-NLOSAna LOS

-36 -34 -32

0.94

0.95

0.96

15

LoS association dominates

Small gap of CDF w/ and w/o NLoS-V or H

Analysis and simulation overlap


Coverage probability

16SINR Threshold (dB)

-20 -15 -10 -5 0 5 10 15 20

Cove

rage P

robabili

ty

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Simu LOS+V-NLOSSimu LOS+V/H-NLOSAna LOS+V-NLOS

New coverage analysis givesexact/concise results

Only consider interferers located on the same street


Conclusions & future work

17


18

Vehicular mobilityMore realistic street modeling, e.g., street

width

Multiple vehicles and analysis of system

throughput

Tractable and realistic MPLP model &

Manhattan PL model

LoS dominates association & interference


Questions?

19

analysis of urban millimeter wave microcellular...

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