Channel Models for Massive MIMO

Massive MIMO and Channel Modeling for Millimeter Wave

Gustavo Fraidenraich Engenharia Elétrica

Departamento de Comunicações Unicamp

1

Achieving 10000x capacity

Source: IEEE Spectrum, July 2004, n. 72

10x Performance

20x Spectrum

50x Base Stations = 10000x

Performance

Massive MIMO mmWave Densification

What is Massive MIMO?

BS

User 1

User 2

User K 3

MM-1

12

T. L. Marzetta, “The case for MANY (greater than 16) antennas as the base station,” in Proc. ITA, San Diego, CA, USA, Jan. 2007.

Thomas L. Marzetta , "Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas ,” IEEE Trans. Commun. 2010.

4

Antenna Array Gain

1 Element

10 Elements 20 Elements

20 Elements

-1.0 -0.5 0.0 0.5 1.0

-1.0

-0.5

0.0

0.5

1.0

N=1

-1.0 -0.5 0.0 0.5 1.0-1.0

-0.5

0.0

0.5

1.010

-1.0 -0.5 0.0 0.5 1.0-1.0

-0.5

0.0

0.5

1.020

-1.0 -0.5 0.0 0.5 1.0-1.0

-0.5

0.0

0.5

1.05

2 Elements

Antenna Aperture λ /D

D

5

What is Massive MIMO

Hundreds of BS antennasTens of Users

A very large antenna array at each base station A large number of users are served simultaneously An excess of base station (BS) antennas

Essentially multiuser MIMO with lots of base station antennas

6

BS

User

M

12

Maximal Ratio CombiningUplink

h1*

h2*

hM*

∑

h1

h2

hM

7

BS

User

M

12

Maximal Ratio TransmissionDownlink

Knowledge of the Channel at the transmitter side. Reciprocity!

h1

h2

hM

h1*

h2*

hM*

8

Bit Error Probability Maximal Ratio Combiningy = x + z

Pb =Q2Eb

N0

⎛

⎝⎜⎞

⎠⎟

y = [h1 h2 h3!hM ]x + zy = hx + zh†yM

MRC

Pb =121− γ b

γ b +M⎛

⎝⎜⎞

⎠⎟M −1+ k

k⎛⎝⎜

⎞⎠⎟k=0

M−1∑ 12+ 12

γ b

γ b +M⎛

⎝⎜⎞

⎠⎟

k

AWGN Channel

AWGN Channel +Fading with Diversityγ b =

Eb

N0

9

0 5 10 15 2010-6

10-5

10-4

0.001

0.01

0.1

1

Maximal Ratio CombiningBit Error Probability

M=1

M=2

M=8M=50

Only Gaussian Noise

17 dB

10

Averaging the Fast FadingN=1 N=2

N=4N=200

0 1 2 3 4 5 6 7 8 9 10x 104

−120

−100

−80

−60

−40

−20

0

20

0 1 2 3 4 5 6 7 8 9 10x 104

−60

−50

−40

−30

−20

−10

0

10

20

0 1 2 3 4 5 6 7 8 9 10x 104

−120

−100

−80

−60

−40

−20

0

20

0 1 2 3 4 5 6 7 8 9 10x 104

−120

−100

−80

−60

−40

−20

0

20

Powe

r (dBm

)

distance distance

Powe

r (dBm

)

Powe

r (dBm

)Po

wer (dBm

)

distance distance

11

Maximal Ratio Combining

h1

h2

h3

h4 h5

|h1|2 |h2|2 |h3|2 |h4|2 |h5|2

Geometrical Interpretation

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System Model

h1

h2

hKx1

x2

xK

Processing for user i

y = xihii=1

K

∑ + z

hi*yM1Mhihi

* →1

1Mhih j

* → 0

13

MRT Precoding

MASSIVE MIMO FOR NEXT GENERATION WIRELESS SYSTEMS

Erik G. Larsson, ISY, Linköping University, Sweden Ove Edfors, Lund University, Sweden Fredrik Tufvesson, Lund University, Sweden Thomas L. Marzetta, Bell Labs, Alcatel-Lucent, USA

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L Cells1 2

L

System Model

15

S3 Multipath

x

h n

15

Slow Fading +Shadowing

Fast Fading

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Signal-to-interference-plus-noise Ratio

• Fading and noise vanish as M grows to infinity! • SIR expression is independent of the transmitted powers. • For an arbitrarily small transmitted energy- per-bit, the SIR can be approached arbitrarily closely by employing a sufficient number of antennas.

SIR = β jkl2

β jkl2 +Gv

l≠ j∑

M→∞⎯ →⎯⎯ β jkl2

β jkl2

l≠ j∑

M

β jkl2

l≠ j∑

Gv

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Pilot ContaminationUplink Training

Pilot Contamination

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Pilot Contamination

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Experimental Results for Massive MIMOLund University - Sweden 128 antennas freq. 1.2 ~ 6 GHz 10 users National Instrument Plataform - USRP

1,2 meters

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Experimental Results for Massive MIMO Lund University - Sweden

10 mobile uses stream HD video on uplink to basestation

Basestation streams 10 HD videos on downlink to users.

High speed data streaming for multiple users

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Experimental Results for Massive MIMOLund University

128 Antennas 128 Virtual Antenna Array

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γ = λmax − λmin

γ

4 Terminals, M=4,32, and 128 - H (4 x M)

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LOS scenario with four users co-located

NLOS scenario with four users co-located

LOS scenario where the four users are well separated.

Experimental Results for Massive MIMOAngle of Arrival

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Experimental Results for Massive MIMOArgos: Practical Many-Antenna Base Stations Rice University, Bells Labs and Yale University

64 Antennas WARP Plataform freq. 2.4 GHz

Argos: Practical Many-Antenna Base Stations

Clayton Shepard, Hang Yu, Narendra Anand, Lin Zhong1

Li Erran Li, Thomas Marzetta2,

Richard Yang3

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Experimental Results for Massive MIMO

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!!"

#$$%

&=

2221

1211

hhhh

H

11h

22h

21h

12h

MIMO Model

Mt Mr

Capacity scales with the number of users

C = min Mt ,Mr( )log2 1+ SNR( )if Mt ≫ Mr

C = Mr log2 1+ SNR( )

Angular Spread

Source: David Tse –Fundamentals of Wireless Communications

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Experimental Results for Massive MIMO

Total transmission power is scaled by 1/M.

K=15 terminals

5,7x Gain

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M=64 Antennas at BS

Power per terminal scaled by 1/K.

Experimental Cell Capacity

Ccell = log2 1+ SINR( )k=1

K∑K

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Millimeter-Wave communicationAtmospheric Absorption is not a major problem

Channel Modeling for millimeter Wave

• Parameters – Free Space Attenuation – Path Loss Exponent – AOA (Angle of Arrival) and AOD (Angle of

Departure) – Penetration loss

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The equation often leads to an erroneous belief that free space attenuates an electromagnetic wave according to its frequency.The expression for FSPL actually encapsulates two effects:

Free Space Attenuation

Distance dependency Frequency dependency of Antenna

Attenuation = PTPR

= 4πd 2 4π f2

c21G

Antenna Gain=1

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10 100 1000 104d HmetersL

60

80

100

120

140

AttenuationHdBL


3 GHz

60 GHz

26 dB

d=150 m

d=3000m

A dB( ) = 20 log104πcdf⎛

⎝⎜⎞⎠⎟

= 20 log10 d( )+ 20 log10 f( )−147.55

f - Hz d - meters

Antenna Gain = 1

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• For a fixed antenna area, the beamforming gain grows with ;

• The increase in path loss can be entirely compensated by applying beam forming;

• In fact, the path loss can be more than compensated relative to today’s cellular systems, with beamforming applied at both ends.

• We conclude that maintaining the same physical antenna size, mmW propagation does not lead to any reduction in path loss relative to current cellular frequencies.

λ −2


Path Loss ExponentL=10nlog10 d( )

0

45

90

135

180

1 10 100 1000

n=2 - Free Space

n=6 - Indoor Environments

n=4 - Two Ray Model

n=1,5 Waveguide

d (meters)

L (dB)

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Frequency LOS NLOS Distance Reference900 MHz 5.3 30-400 [7] 1800 MHz 5.5 30-400 [7] 2 GHz 1,56 1-20 [4]2,3 GHz 6 30-400 [7]5 GHz 1,87 1-20 [4]17 GHz 1,98 1-20 [4]28 GHz 2 2,92 30 — 200 [1]28 GHZ 2,6 3,4 1—100 [2]28 GHz 5,52 1-100 [9]38 GHz 2.3 3.86 [10]60 GHz 1,52 0,5 — 3 [5]73 GHz 2 2,57 30 — 200 [1]73 GHz 2 3,4 1—100 [2]

Path Loss Exponent

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Path L

oss Ex

pone

nt

0

0,8

1,6

2,4

3,2

4

Frequency (GHz)0 20 40 60 80

y = 0,0007x + 1,9583

1,561,87 1,98 2

2,62,3

1,52

2

Path Loss ExponentLine of Sight

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Path L

oss Ex

pone

nt

0

1,2

2,4

3,6

4,8

6

Frequency (GHz)0 20 40 60 80

y = -0,0363x + 5,3757

5,35,56

2,923,4

5,52

3,86

2,57

3,4

Path Loss ExponentNon-Line of Sight

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Frequency Loss (dB) Material Reference

800 MHz 7 Wall [6]

900 MHz 14,2 Wall [7]

1.8 GHz 13,5 Wall [3]

1,8 GHz 13,4 Wall [7]

2,3 GHz 12,8 Wall [7]

28 GHz 35,5 Wall [8]

Penetration Loss

40

0

10

20

30

40

0 7,5 15 22,5 30

Penetration Loss

Frequency (GHz)

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Path Loss Exponent

25 dB

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AOA - Angle of Arrival

0

15 °

30 °

45 °

60 °75 °90 °105 °

120 °

135 °

150 °

165 °

180 °

195 °

210 °

225 °

240 °255 ° 270 ° 285 °

300 °

315 °

330 °

345 °

The perfect Angle of Arrival

D

θ ~ λD

# Resolvable Paths

Nr =Ωr

θr

1) As the frequency increases, decreases and the therefore the resolvability of the antenna array increases.

2) As the frequency increases the angular spread decreases.

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θ ~ λD


Source: David Tse book

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George R. MacCartney Jr and Theodore Rappaport, "Millimeter Wave Propagation Measurements for Outdoor Urban Mobile and Backhaul Communications in New York City,”IEEE ICC 2014.

28 GHz 6 main Lobes

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George R. MacCartney Jr and Theodore Rappaport, "Millimeter Wave Propagation Measurements for Outdoor Urban Mobile and Backhaul Communications in New York City,”IEEE ICC 2014.


73 GHz 3 main Lobes

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AOA - Angle of ArrivalIn order to overcome the loss in the degrees of freedom, we must use 2D antennas.

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Delay Spread

The RMS delay spread is independent of frequency in the LOS scenario

Source: Dajana Cassioli, Luca Alfredo Annoni and Stefano Piersanti, “Characterization of Path Loss and Delay Spread of 60-GHz UWB Channels vs. Frequency, “ IEEE ICC 2013 - Wireless Communications Symposium.

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Delay Spread

For NLOS, delay spread increases with the frequency and then saturates.

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Set of measurements at 10 GHz - Penetration loss - AOA - Knife edge diffraction - Delay Spread

Prof. Matti Latva-Aho

PhD. Student Claudio F. Dias

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Virtual Antenna Array 20x20

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Virtual MIMO channel Measurement system

Schneider LMDCE572 Stepper motors

R&S ZNB20 4-port VNA

10 GHz dual-polarized pach antennas

RX TX

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• Distance between antennas was 4.9 meters measured between antenna array origins

• 4 cases:

Test measurements in Anechoic chamber (2)

Tx array Rx array3x3 3x31x1 20x2020x20 1x11x1 20x2 *

(*) RX unit rotated clockwise 18.8 degrees

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Corner diffraction measurement

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Knife-edge diffraction

ν = 2Hb

H

55


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• Simple penetration loss measurements with few antenna locations

• Idea was to measure the penetration by moving antennas only fractions of wavelength between the measurements

Wall Penetration Loss Measurements

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Conclusions Benefits from the (many) excess antennas

Simplified multiuser processing (MRC and MRT) Reduced transmit power Thermal noise and fast fading vanish

mmW Communication Narrow-beam communication is new to cellular communications and poses difficulties. Free space does not increase as frequency increases (keeping the same effective antenna area). Penetration loss is the new problem (on-off behavior of the channel). The loss of degrees of freedom, as frequency increases, may be compensated using 2D antennas. We need 3D channel modeling to better understand all the physical phenomena.

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References[1] - Mustafa Riza Akdeniz, Yuanpeng Liu, Mathew K. Samimi, Shu Sun, Student Member, IEEE, Sundeep Rangan, Theodore S. Rappaport, and Elza Erkip, "Millimeter Wave Channel Modeling and Cellular Capacity Evaluation,”, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 32, NO. 6, JUNE 2014. [2] - Millimeter Wave Cellular Ultra-Wideband Statistical Channel Model for NonLine of Sight Millimeter-Wave Urban Channels Communications: Channel Models, Capacity Limits, Challenges and Opportunities Prof. Ted Rappaport NYU WIRELESS, NYU Polytechnic School of Engineering, Joint work with Sundeep Rangan and Elza Erkip. [3] - A. F. Toledo, D. GJ Lewis, and A.M.D. Turkmani, "Radio Propagation into Buildings at 1.8 GHz” [4] P. Nobles, and F. Halsall, "Delay Spread and Received Power Measurements within a Building at 2GHz, 5 GHz and 17 Ghz,” [5] - Maria-Teresa Martinez-Ingles, Davy P. Gaillot, Juan Pascual-Garcia, Jose-Maria Molina-Garcia-Pardo, Martine Lienard, and José-Víctor Rodríguez, “Deterministic and Experimental Indoor mmW Channel Modeling, “IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 13, 2014 1047. [6] -D. Cox, "Measurements of 800 MHz Radio Transmission Into Buildings with Metallic Walls”, The Bell System Technical Journal 1983 [7] - A. F. Toledo, , Adel Turlmani, and David Parsons, "Estimating Coverage of Radio Transmission into and within Buildings at 900, 1800, and 2300 MHz,” IEEE Personal Communications April 1998. [8] - Hao Xu, Member, IEEE, Vikas Kukshya, Member, IEEE, and Theodore S. Rappaport, Fellow, IEEE , “Spatial and Temporal Characteristics of 60-GHz Indoor Channels, “IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 20, NO. 3, APRIL 2002. [9] - Mathew Samimi, Kevin Wang, Yaniv Azar, George N. Wong, Rimma Mayzus, Hang Zhao, Jocelyn K. Schulz, Shu Sun, Felix Gutierrez, Jr., and Theodore S. Rappaport , 28 GHz Angle of Arrival and Angle of Departure Analysis for Outdoor Cellular Communications using Steerable Beam Antennas in New York City, VTC 2013. [10] - Theodore S. Rappaport, Yijun Qiao, Jonathan I. Tamir, James N. Murdock, Eshar Ben-Dor , “Cellular Broadband Millimeter Wave Propagation and Angle of Arrival for Adaptive Beam Steering Systems (Invited Paper),”RWS 2012.[11] - Dajana Cassioli, Luca Alfredo Annoni and Stefano Piersanti, “Characterization of Path Loss and Delay Spread of 60-GHz UWB Channels vs. Frequency, “ IEEE ICC 2013 - Wireless Communications Symposium.

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Technology

Channel Models for Massive MIMO