Future of MIMO Plenary Heath

8/18/2019 Future of MIMO Plenary Heath

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Robert W. Heath Jr.The University of Texas at Austin

Wireless Networking and Communications Group

www.profheath.orgPresentation (c) Robert W. Heath Jr. 2013

http://www.profheath.org/http://www.profheath.org/http://www.profheath.org/


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Outline

MIMO in cellular networks

Coordinated Multipoint a.k.a. network MIMO

Massive MIMO

Millimeter wave MIMO

Comparison between technologies

Parting thoughts

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(c) Robert W. Heath Jr. 2013

MIMO in Cellular Systems

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# of parallel data streams is the

multiplexing gain2 data streams

Point-to-point MIMO

2 data streams

2 data streams

sum of the # of parallel datastreams is the multiplexing gain

Multiuser MIMO

limited by # user antennas

limited by # BS antennas


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Where is MIMO Headed?

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Massive MIMO

mmWave MIMO

Coordinated MIMO

Candidate architectures for 5G cellular


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Outline



Massive MIMO



Parting thoughts

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What is Network MIMO?

Coordinated transmission from multiple base stations

Known as CoMP or Cooperative MIMO or base station coordinationInterference turned from foe to friend

Exploits presence of a good backhaul connection

Used to improve area spectral efficiency, system capacity

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Out-of-cell

interference

cellular backhaul network


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Network MIMO Architectures

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Coordinate over backhaul

eNodeB eNodeBeNodeB

Like DAS uses localcoordination

Cloud RAN

Processing for many base

stations using cloud

Coordination clusters Dynamic coordination



Throughput gains when out-of-cluster interference is ignored

More cooperation leads to higher gains

Cell edge pushed further out, no uncoordinated interference in the cell

Potential Gains from Coordination

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0 5 10 15 20 25 300

10

20

30

40

50

60

70

80

90

SNR (dB)

S u m

- r a t e s ( b i t s / s e c / H z )

K=1

K=3

K=9

!1500 !1000 !500 0 500 1000 15001500

1000

!500

0

500

1000

1500

Linear increase

BS

MSISD=500m

19 cells and 3 sectors per cell

BS-MS distance= 192 m

Grid model for a cellular network

Unbounded gains

sum rates per cell



Performance saturates with out-of-cluster interference

30% performance gains observed in industrial settings

Addressing Out-of-Cell Interference

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0 5 10 15 20 25 300

5

10

15

20

25

SNR (dB)

S

u m - r a t e s ( b i t s / s e c / H z )

K=1

K=3K=9

Sum-rates saturation

!1500 !1000 !500 0 500 1000 1500!1500

!1000

!500

0

500

1000

1500

BS

MSISD=500m

19 cells and 3 sectors per cell

BS-MS distance= 192 m

Grid model for a cellular network

Bounded gains

Be mindful of the saturation point

A. Lozano, R. W. Heath, Jr., and J. G. Andrews, ̀ `Fundamental Limits of Cooperation" to appear in

the IEEE Trans. on Info. Theory. Available on ArXiv.



Critical Issues with Network MIMOOut-of-cell interference

When included, results are not as good

Feedback overhead

Need channel state information

Performance seriously degrades

Control channel overhead

Increased reference signal overhead

Backhaul link constraints

Backhaul link latency (delayed CSI and data sharing)

Cluster edge effects

Coordination still has a cell edge with fixed clusters

Dynamic clustering solves the problem, but more more implementation overhead

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Backhaul delay

eNodeB eNodeB

channel state feedback overhead

Data payloadReference signal


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Outline



Massive MIMO



Parting thoughts

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What is Massive MIMO?

A very large antenna array at each base station

An order of magnitude more antenna elements in conventional systems

A large number of users are served simultaneouslyAn excess of base station (BS) antennas

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Essentially multiuser MIMO with lots of base station antennas

hundreds of BS antennas

tens of users

T. L. Marzetta, “Noncooperative cellular wireless with unlimited numbers of base station antennas,” IEEE Trans. Wireless Commun., vol. 9, no. 11, pp.3590–3600, Nov. 2010.



Massive MIMO Key Features

Benefits from the (many) excess antennas

Simplified multiuser processing

Reduced transmit power

Thermal noise and fast fading vanish

Differences with MU MIMO in conventional cellular systems

Time division duplexing used to enable channel estimation

Pilot contamination limits performance

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F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson, “Scaling up MIMO: Opportunities and challenges with very

large arrays,” IEEE Signal Processing Mag., vol. 30, no. 1, pp. 40–60, Jan. 2013.

pilotcontamination

Uplinktraining



Massive MIMO Key Features

Benefits from the (many) excess antennas

Simplified multiuser processing

Reduced transmit power

Thermal noise and fast fading vanish

Differences with MU MIMO in conventional cellular systems

Time division duplexing used to enable channel estimation

Pilot contamination limits performance

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F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson, “Scaling up MIMO: Opportunities and challenges with very

large arrays,” IEEE Signal Processing Mag., vol. 30, no. 1, pp. 40–60, Jan. 2013.

Downlinkchannel

reciprocity

asymptoticorthogonality

interference


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Centralized vs. Distributed

Fixed number of base station antennas per cell

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BS

BS antennaclusters

7 cells without sectorization12 users uniformly distributed in each cell

ISD = 500mCentralized Distributed

* K. T. Truong and R. W. Heath, Jr., “Impact of Spatial Correlation and Distributed Antennas for Massive MIMO systems,” to

appear in the Proceedings of the Asilomar Conference on Signals, Systems, and Computers, Nov. 3-6, 2013.


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Potential Gains from Massive MIMO

Distributing antennas achieves higher gains

Saturation is not observed without huge # of antennas

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24 48 72 96 120 144 16815

20

25

30

35

40

45

50

55

60

Number of base station antennas

number of

24 48 72 96 120 144 16810

15

20

25

30

35

40

45

Number of base station antennas

number of

Downlink Uplink

7 cells without sectorization, 12 users uniformly distributed in each cell, ISD = 500m


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Critical Issues in Massive MIMO

Gains are not that big with not-so-many antennas

Require many antennas to remove interference

Need more coordination to remove effects of pilot contamination

Massive MIMO seems to be more “uplink driven”

Certain important roles are reserved between base stations and usersA different layout of control and data channels may be required

Practical effects are not well investigated

Channel aging affects energy-focusing ability of narrow beams

Spatial correlation reduces effective DoFs as increasing number of antennasRole of asynchronism in pilot contamination and resulting performance

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Massive MIMO Conclusions

Observations

Pilot contamination is a big deal, but possibly overcome by coordination

Performance is sensitive to channel aging effects *

Good performance can be achieved with distributed antennas *

Not clear how to pack so many microwave antennas on a base station

Needs more extensive simulation study with realistic system parameters

Forecast

Massive MIMO will probably not be used in isolation

Will be combined with distributed antennas or base station coordination

• Reduces the effects of pilot contamination

• Work with smaller numbers of antennas

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* K. T. Truong and R. W. Heath, Jr., “Effects of Channel Aging in Massive MIMO Systems,” to appear in the Journal ofCommunications and Networks, Special Issue on Massive MIMO, February 2013.


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Outline



Massive MIMO



Parting thoughts

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Why mmWave for Cellular?

Microwave

3 GHz 300 GHz

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28 GHz 38-49 GHz 70-90 GHz300 MHz

Cellular systems live with a little microwave spectrum600MHz total (best case) in the US

Spectrum efficiency is king (MIMO, MU MIMO, HetNets)

Huge amount of spectrum available in mmWave bands [KhanPi]29GHz possibly available in 23GHz, LMDS, 38, 40, 46, 47, 49, and E-band

mmWave already used in LAN, PAN, and VANET

mmWave links are used for backhaul in cellular networks

1G-4G cellular 5G cellular

m i l l i m e t e r w a v e


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Coverage Gains from Large Arrays

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Serving BS

Typical User

PPP Interfering BSs

Buildings

LOS & blocked path loss model from measurements.A Poisson layout of mmWave BSs.Average cell radius Rc=100m.

A Boolean scheme building model.

mmWave networks can provide acceptable coverage

Directional array gain compensates severe path loss

Smaller beamwidth reduces the effect of interference

!10 !8 !6 !4 !2 0 2 4 6 8 100.2

0.3

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0.5

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0.7

0.8

0.9

1

SINR Threshold in dB

C o v e r a g e P r o b a b i l i t y

Omni Directional

Directional: HPBW=40o

Directional: HPBW=10o

Gain from directional antenna array


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Critical Issues in mmWave MIMO

Dealing with hardware constraints

Need a combination of analog and digital beamforming

Array geometry may be unknown, may change

Performance in complex propagation environments

Evaluate performance with line-of-sight and blocked signal paths

Must adapt to frequent blockages and support mobility

Entire system must support directionality

Need approval to employ the spectrum

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mmWave Conclusions

Observations

Coverage may be acceptable with the right system configuration

Strong candidate for higher per-link data rates

Hardware can leverage insights from 60GHz LAN and PAN

Highly directional antennas may radically change system design

Supporting mobility may be a challenge

Forecast

Will be part of 5G if access to new spectrum becomes viable

Most likely will co-exist with microwave cellular systems

Will remain useful for niche applications like backhaul

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Outline



Massive MIMO



Parting thoughts

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Stochastic Geometry for Cellular

Stochastic geometry is a useful tool for analyzing cellular nets

Reasonable fit with real deployments

Closed form solutions for coverage probability available

Provides a system-wide performance characterization

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J. G. Andrews, F. Baccelli, and R. K. Ganti, "A Tractable Approach to Coverage and Rate in Cellular Networks", IEEE Transactions on Communications, November 2011.T. X. Brown, "Cellular performance bounds via shotgun cellular systems," IEEE JSAC, vol.18, no.11, pp.2443,2455, Nov. 2000.

base station locations

distributed (usually) as aPoisson point process (PPP)performanceanalyzed for atypical user


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Comparing Different Approaches

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CoMP model

Sectored cooperation model

Typical user can be edge or center user of the cluster

Several assumptions made to permit calculation

mmWave model

Directional antennas are incorporated as marks of the base station PPP

Blockages due to buildings incorporated via random shape theory

Massive MIMO model

Analyze asymptotic case with infinite number of antennas at the base station

No spatial correlation, includes estimation error, pilot contamination

New expressions derived for each case

Tianyang Bai and R. W. Heath, Jr., ̀ ` Asymptotic Coverage Probability and Rate in Massive MIMO Networks ,'' submitted to IEEE Wireless Communications Letters,May 2013. Available on ArXiv.


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SINR Coverage Comparison

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−10 −5 0 5 10 15 200.1

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1

SINR Threhold in dB

S I N R C

o v e r a g e P

r o b a b i l i t y

CoMP: Nt=4, 2 Users, 3 BSs/ Cluster

Massive MIMO: Nt

=∞

mmWave: Nt=64, R

c=100m

SU MIMO: 4X4

Gain from directionalantennas and blockages

in mmWave

Gain from larger numberof antennas


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Rate Comparison

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0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.1

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Cell Throughput in Mbps

R a t e C o v e r a g e P r o

b a b i l i t y

CoMP: Nt=4, 2 Users, 3 BSs/ Cluster

Massive MIMO: Nt=∞

mmWave: Nt=64, R

c=100m

SU MIMO: 4X4

Gain from larger bandwidth

Gain fromserving multiple users


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Rate Comparison

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Massive

MIMO MMwave CoMP SU MIMO

Signal BW 50 MHz 500 MHz 50 MHz 50 MHz

User/ Cell 8 1 2 1

bps/Hz per user 5.47 bps/Hz 8.00 bps/Hz 4.34 bps/Hz 4.95 bps/Hz

Rate/Cell 21.88 bps/Hz 8.00 bps/Hz 8.68 bps/Hz 4.95 bps/Hz

Capacity/ Cell 1.10 Gbps 4.00 Gbps 434 Mbps 248 Mbps

mmWave outperforms due to more available BW

* of course various parameters can be further optimized** SU MIMO is 4x4 w/ zero-forcing receiver


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Outline



Massive MIMO



Parting thoughts

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Parting Thoughts

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Conclusions

cooperative

MIMO

cooperation will be used in some form, more powerful

with better infrastructure, need to be mindful of overheads

in system design

massive

MIMO

some potential for system rates, need large base station

arrays, can be used with cooperation

mmWaveMIMO

large potential for peak rates, more hardware challenges,

requires more spectrum, more radical system design

potential


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Robert W. Heath Jr.The University of Texas at Austin

fh th
http://www.profheath.org/http://www.profheath.org/

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Future of MIMO Plenary Heath