Future of MIMO SPL - University of Texas at...

What is the Role of MIMO in Future Cellular Networks:

Massive? Coordinated? mmWave?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

(c) Robert W. Heath Jr. 2013

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�3

Dr. Robert W. Heath, University of Texas at Austin

DIGITAL COMMUNICATIONS

PHYSICAL LAYER EXPLORATION LAB USING THE NI USRP™ PLATFORM

front.pdf 1 9/12/11 4:46 PM

http://sine.ni.com/nips/cds/view/p/lang/en/nid/210087

http://sine.ni.com/nips/cds/view/p/lang/en/nid/210087

OutlineMIMO in cellular networks

Coordinated Multipoint a.k.a. network MIMO

Massive MIMO

Millimeter wave MIMO

Comparison between technologies

Parting thoughts

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The MIMO Concept

MIMO (multiple-input multiple-output) communication channel Leverages multiple antennas at the transmitter and receiver

MIMO is broadly incorporated into wireless systems Cellular communication, wireless local area networks, ad hoc networks

Provides capacity, quality, robustness, resilience to interference

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MIMO communication exploits matrix propagation channels

Transmitter Receiver

multiple inputs

multiple outputs

Hpropagation

channelmultiple

TX antennasmultiple

RX antennas


Point-to-Point MIMO

Single user MIMO communication Send multiple data streams a.k.a. spatial multiplexing

Highest performance requires rich scattering environment

Incorporated into several commercial wireless systems

Ex: 8 antennas at the base station and 4 antennas at the mobile station

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Multiplexing gains are limited by number of antennas at users

# of parallel data streams is the multiplexing gain

2 data streams


Point-to-Multipoint MIMO

Multiuser MIMO (MU-MIMO) Simultaneously send independent data streams to several users

Multiplexing gains are obtained even with less ideal propagation

Main obstacles: near-far problem & channel correlation

Flexibility in user scheduling may overcome the obstacles

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Multiplexing gains are limited by number of antennas at base stations

2 data streams

2 data streams

4 streams total


Where is MIMO Headed?

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Massive MIMOB

mmWave MIMO

Coordinated MIMO

Candidate architectures for 5G



Massive MIMO



Parting thoughts

�9


What is Network MIMO?

Coordinated transmission from multiple base stations Known as CoMP or Cooperative MIMO or base station coordination

Interference 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


Network MIMO Architectures

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Coordinate over backhaul

eNodeB eNodeBeNodeB

Like DAS uses local coordination

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

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SNR (dB)Su

m-ra

tes

(bits

/sec

/Hz)

K=1 K=3K=9

−1500 −1000 −500 0 500 1000 1500−1500

−1000

−500

0

500

1000

1500

Linear increase

BSMSISD=500m

19 cells and 3 sectors per cellBS-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

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SNR (dB)

Sum

-rate

s (b

its/s

ec/H

z)

K=1K=3K=9

Sum-rates saturation

−1500 −1000 −500 0 500 1000 1500−1500

−1000

−500

0

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BSMSISD=500m

19 cells and 3 sectors per cellBS-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


Network MIMO Conclusions

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Observations Network MIMO promises a way to get rid of interference

...Yet uncoordinated interference still limits high SNR performance

Backhaul constraints and system overheads further reduce performance gains

General disconnect between academia and industry on the potential

Forecast Already incorporated into 4G, coordination will be part of 5G as well

Architectures will evolve to support network MIMO-like coordination

• Distributed antenna systems are a good starting point

• Distributed radio access networks are a likely evolution point

• Cloud radio access networks are a possible end objective



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 simultaneously

An 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�18

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.

pilot contamination

Uplink trainingDownlink channel

reciprocity

asymptotic orthogonality

interference


Centralized vs. Distributed

Fixed number of base station antennas per cell

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7 cells without sectorization 12 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.


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

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35

40

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Number of base station antennas6X

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DownlinkUplink

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


Critical Issues in Massive MIMOGains 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 users

A 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 antennas

Role of asynchronism in pilot contamination and resulting performance

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Massive MIMO ConclusionsObservations

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

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



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

Huge amount of spectrum available in mmWave bands* Cellular systems live with limited microwave spectrum ~ 600MHz

29GHz possibly available in 23GHz, LMDS, 38, 40, 46, 47, 49, and E-band

Technology advances make mmWave possible Silicon-based technology enables low-cost highly-packed mmWave RFIC**

Commercial products already available (or soon) for PAN and LAN

Already deployed for backhaul in commercial products

1G-4G cellular 5G cellular

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

* Z. Pi,, and F. Khan. "An introduction to millimeter-wave mobile broadband systems." IEEE Communications Magazine, vol. 49, no. 6, pp.101-107, Jun. 2011. ** T.S. Rappaport, J. N. Murdock, and F. Gutierrez. "State of the art in 60-GHz integrated circuits and systems for wireless communications." Proceedings of the IEEE, vol. 99, no. 8, pp:1390-1436, 2011


Smaller wavelength means smaller captured energy at antenna 3GHz->30GHz gives 20dB extra path loss due to aperture

Larger bandwidth means higher noise power and lower SNR 50MHz -> 500MHz bandwidth gives 10dB extra noise power

The Need for Gain

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Solution: Exploit array gain from large antenna arrays

microwave noise bandwidth

mmWave noise bandwidth

microwave aperture

mmWave aperture

TXRX

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4⇡


Antenna Arrays are Important

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Narrow beams are a new feature of mmWave Reduces fading, multi-path, and interference

Implemented in analog due to hardware constraints

Baseband Processing

~100 antennas

Baseband Processing

antennas are small (mm)

highly directional MIMO transmission

used at TX and RX

Arrays will change system design principles


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

1

Summary of Cellular Millimeter Wave Channels

I. MEASUREMENT RESULTS

−10 −8 −6 −4 −2 0 2 4 6 8 100.2

0.3

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SINR Threshold in dB

Cov

erag

e Pr

obab

ility

Omni DirectionalDirectional: HPBW=40o

Directional: HPBW=10o

Fig. 1.

−10 −8 −6 −4 −2 0 2 4 6 8 100

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SINR threshold in dB

Cov

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Omni−DirectionalNt=3, HPBW=0.2π

Nt=4, HPBW=0.15π

Nt=5, HPBW=0.12π

Nt=10, HPBW=0.06π

MicrowaveOut of Range: Nt=10

Fig. 2.

Gain from directional antenna array


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 ConclusionsObservations

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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Massive MIMO



Parting thoughts

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Stochastic Geometry for Cellular

Stochastic geometry is a tool for analyzing microwave cellular 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 a

Poisson point process (PPP)performance analyzed for a typical user

Need to incorporate features of each technology

Baccelli


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.!Tianyag Bai and R. W. Heath Jr., ``Performance Analysis of mmWave Cellular Systems,’’ under preparation.

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Fig. 8. .


SINR Coverage Comparison

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5

−10 −5 0 5 10 15 200.1

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SINR Threhold in dB

SIN

R C

ov

era

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Pro

ba

bil

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

Massive MIMO: Nt=∞

mmWave: Nt=64, R

c=100m

SU MIMO: 4X4

Fig. 1.

Gain from directional antennas and blockages

in mmWave

Gain from larger number of antennas

SINR CCDF


6

0 5 10 150

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Spectrum Efficiency (bps/Hz)

Ra

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mmWave: Nt=64, R

c=100m

SU MIMO: 4X4

Fig. 2.

Coverage Comparison

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Gain from directional antennas and blockages in mmWave

Gain from massive number of antennas

Spectrum Efficiency CCDF


Rate Comparison

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mmWave: Nt=64, R

c=100m

SU MIMO: 4X4

Fig. 3.

Gain from larger bandwidth

Gain from serving multiple users

Rate CCDF


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



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

mmWave MIMO

large potential for peak rates, more hardware challenges, requires more spectrum, more radical system design

potential

Questions?Robert W. Heath Jr.

The University of Texas at Austin

www.profheath.org

http://www.profheath.org

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