Robust MIMO Wireless Communication in the Presence · PDF fileRobust MIMO Wireless...

MIT Lincoln LaboratorymimoASAP-1

bliss

Robust MIMO Wireless Communication in the Presence

of Interference Using Ad Hoc Antenna Arrays

Dr. Daniel W. Bliss& Amanda M. Chan

MIT Lincoln [email protected]

This work was sponsored by the U.S. Air Force under Air Force contract F19628000-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed

by the United States Government.


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TopicsMIMO Communication

• Introduction– Military wireless communication– MIMO definition– Ad hoc antenna networks

• MIMO Theory• Phenomenology• Receiver


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Advanced Military Wireless Communications

High Data RateApplications

Non-Line-of-SightComplicated Multipath

Non-Line-of-SightComplicated Multipath

UrbanEnvironment

Real-time tactical information for

war-fighters

Ad Hoc DistributedShort Range Network

Reach-backLink

RemoteControlled Vehicles

ForestedEnvironment

High PowerJammer

Low CostClose Approach

Jammers


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Complicated MultipathEnvironment

MIMO CommunicationMultiple-Input Multiple-Output

• Single transmitted data stream• Single received data stream• Employ array of antennas at both

transmitter and receiver• Employ multiple modes through

environment – Not just point-to-point beamforming

Data…01110111001…

Data…01110111001…

TransmitAntenna

ArraySpace-Time

Coding

MultipleInput

ReceiveAntenna

Array

bounce offbuilding

Space-TimeReceiver

MultipleOutput


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Advantages of MIMO Communication

• Coherent receive beamforming– Gain– Jammer mitigation

• Transmit spatial diversity– Fading mitigation– Shadowing mitigation– Jammer avoidance

• Enables high spectral efficiency– Enables high data rates given

limited bandwidths– Low duty cycle communication

TransmitterReceiver

MIMO CommunicationMultiple-Input Multiple-Output

TransmitArray

ReceiveArray

SISO CommunicationSingle-Input Single-Output

bounce offbuilding


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Distributed Ad Hoc Antenna ArraysMultiple-Input Multiple-Output

• Single transmit data stream• Single received data stream• Employ users as antenna array

– Coherently process received signal

• Use local network to move distributed data to/from interested user

Inter-GroupLink

HasInformation

NeedsInformation

Intra-GroupLink

Issues• Local networking• Relative local

oscillator errors

Issues• Local networking• Relative local

oscillator errors


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• Introduction• MIMO Theory

– Capacity– Phenomenology– Interference Mitigation– Space-Time Coding

• Phenomenology• Receiver


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MIMO Capacity Bound

• MIMO bound follows different theoretical limit• Divide total energy amongst transmitters

avoiding compressive regime of SISO Shannon limit

Spectral Efficiency(b/s/Hz)

Eb/N

0(d

B)

Shannon Limit

SISO

4x4 MIMO

8x8 MIMO TransmitPowerMatrixChannel

Matrix

Determinant

ChannelLoss

BandwidthNormalized

CapacityTransmit Power

(noise normalized)

Assuming FlatMIMO Channel

Assuming FlatMIMO Channel


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MIMO Channel Knowledge

Channel knowledge affects MIMO capacity and coding

Informed Transmitter Uninformed Transmitter

Determinant

CIT ? max

tr{P}? Po

log2 I ? HP H†ChannelCapacity(b/s/Hz)

TransmitPower Matrix

(noise-normalized)

ChannelMatrix

CUT = log2 I+

Po

nTx

H H†

TransmitterChannel

Knowledge

Total Power(noise-normalized)

NumberOf Transmitters


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Channel Matrix8 x 8 MIMO Example

Line-of-Sight

Random ScatteringC

apac

ity

(b/s

/Hz)

Line-of-Sight

RandomScattering

Channel Capacity

Mean SISO SNR (dB)

UninformedTransmitter

Line-of-Sight

RandomScattering

Eigenvalues of HH†

Rel

ativ

e P

ow

er (

dB

)

Eigenvalue #

Channel matrix, H, contains complex attenuation between each

transmit and receive antenna


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Jammer Mitigation & AvoidanceSINR Loss

Transmitter

TransmitArray

InterferenceInterference

ReceiveArray

SIMO MIMO

95% Outage

Capacity

• Adaptive performance in the presence of Jammer

• MIMO has better outage capacity performance

• Assumptions– Single high power jammer– I.I.D. random Gaussian

channel– MIMO uninformed

transmitter


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Space-Time Coding

• Space-time coding converts information bits to waveform distributed amongst antennas

• Space-time coding analogous to conventional (SISO) coding approaches– Trellis– Low density parity check– Turbo

···Coding

& Modulation

Information…0110…

Space-Time Turbo Code Example • Total data rate 2 b/s/Hz• 4 transmit antennas• 4096 bit interleavers• QPSK constellations• Uninformed transmitter

Space-Time Turbo Code Example • Total data rate 2 b/s/Hz• 4 transmit antennas• 4096 bit interleavers• QPSK constellations• Uninformed transmitter

DifferentSignals


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• Introduction• MIMO Theory• Phenomenology

– Experimental setup– Phenomenology

• Receiver


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MIMO ExperimentSummer 2002

2 Groups of 4, or8 Coherent

TransmittersNear PCS band

2 Groups of 4, or8 Coherent

TransmittersNear PCS band

16-ChannelHi-Fidelity

Data RecordingSystem

16-ChannelHi-Fidelity

Data RecordingSystem

4 TransmitAntennas

• Investigate channel phenomenology

• Study space-time coding • Explore transmitter

coherence requirements• Demonstrate robustness to

– Jamming– Cochannel interference


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Channel ModesExperimental Results

ReceiveArray

MIT

Cambridge

Rel

ativ

e P

ow

er (d

B)

Mode #

RandomMeasuredLine-of-sight

10

0

2 3 4

-10

1


2 3 4Mode #

10

0

-10

Rel

ativ

e P

ow

er (d

B)

1


2 3 4Mode #

10

0

-10

Rel

ativ

e P

ow

er (d

B)

1

TransmitArray


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CDF of

Channel StationarityCDF’s of Power Weighted Mean cos2? n

Stationary Transmitter

Moving Transmitter (5-10 m/s)

0.1

0.9

Indoor

Outdoor

0.10.9


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Delay-Frequency CorrelationsExperimental Data

#1 #2

Delay (resolution cells, 8? s)

Fre

qu

ency

Off

set

(res

olu

tio

n c

ells

, 60H

z)

Time-Frequency Pulse Response

ReceiveArray

MovingTransmitter Delayed and

Doppler ShiftedSignal

#1 #2

0

-10

-20

-30

-40 Rel

ativ

e P

ow

er (

dB

)

Offset In Delay

AndFrequency

Offset In Delay

AndFrequency


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• Introduction• MIMO Theory• Phenomenology• Receiver

– Space-time-frequency adaptive processing

– Multiuser detection– MCMUD– Experimental performance


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ReceiveArray

MovingTransmitter

Delayed andDoppler

Shifted Signal

Adaptive Beamforming in Multipath

Space-Time-Frequency Adaptive Processing

···? ?

? ? ?

?

? f ? f

? f ? f ? f···

? f

? f ? f? f

Ada

pted

Co

effi

cien

ts···

?

Delay TapsFrequency Taps

··· Filters weights Jointly take into accountspace-time-frequency

correlations

Space-Time-FrequencyFilter Cube

Space4 Antennas

Tim

e±4

µs

Freq

uenc

y±2

00 Hz


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Notional Multiuser Detection

Signal 1 + Signal 2

DemodulateSignal 1

DemodulateSignal 1

RemodulateSignal 1

RemodulateSignal 1

??-

+

Bits

Signal 1

Signal 2

Signal 1

Signal 2

+Generic Stage of Successive Decoding


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Tem

pora

lS

ubtr

actio

n

Turb

oE

ncod

er

ChannelEstimation

Block for Each Transmitter

Spa

ce-T

ime

Fre

quen

cyA

dapt

ive

Bea

mfo

rmer

InfoBits

MCMUD for Space-Time Turbo Code

• Multichannel Multiuser Detector (MCMUD, pat. pending)

• Iterative decoder

• Channel estimate– Training-based– Data-directed

• Estimation subtraction (multiuser detection)

• Space-time-frequency

adaptive beamformers

Spa

ce-T

ime

Mul

tiple

xer

Spa

ce-T

ime

Dem

ultip

lexe

r

Turb

oD

ecod

er


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Tem

pora

lS

ubtr

actio

n

Turb

oE

ncod

er

ChannelEstimation


Spa

ce-T

ime

Fre

quen

cyA

dapt

ive

Bea

mfo

rmer

InfoBits








Spa

ce-T

ime

Mul

tiple

xer

-

=

Spa

ce-T

ime

Dem

ultip

lexe

r

Turb

oD

ecod

er


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Tem

pora

lS

ubtr

actio

n

Turb

oE

ncod

er

ChannelEstimation


Spa

ce-T

ime

Fre

quen

cyA

dapt

ive

Bea

mfo

rmer

InfoBits








Spa

ce-T

ime

Mul

tiple

xer

Spa

ce-T

ime

Dem

ultip

lexe

r

Turb

oD

ecod

er

I

Q


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Adaptive Spatial Processing

Iteration #1

Iteration #2 Iteration #3

Experimental ResultsSuccessive MCMUD Iterations

Tx #1 Tx #2

Tx #3 Tx #4

Tran

ing

-bas

ed

Sp

ace-

Fre

qu

ency

Filt

er

Iteration #1

Tx #1 Tx #2

Tx #3 Tx #4

Dat

a-D

irec

ted

Sp

ace-

Tim

e-Fr

eque

ncy

Filte

r

Iteration #2

Tx #1 Tx #2

Tx #3 Tx #4

Sp

ace-

Tim

e-Fr

eque

ncy

Filte

rW

ith M

ulti

use

r D

etec

tion

Iteration #3

Receiver Bit Error Rate

Mean SISO SNR (dB)


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4x4 MIMO PerformanceMotion, Jammers, and LO Errors

• 2 Noise Jammers (25 dB JNR)• Moving transmitter (25 mph)• Artificial relative local

oscillator error (? 80 Hz)

Jammer SpatialMode Distribution

Rel

ativ

e P

ow

er (

dB

)

Mode #

ReceiveArray

25mph

Jammers25 dB JNR

Experimental MIMO Performance

• Error-free 2b/s/Hz data-link • Near performance of

jammer-free environment!

20 dB Better ThanSISO Theoretic Limit 20 dB Better Than

SISO Theoretic Limit


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Summary

• MIMO provides robust communication links

• New receiver design concepts (MCMUD) enable communication in complicated environments

• Demonstrated dramatic performance advantages using experimental data

• MCMUD enables coherent use of ad hoc distributed networks for MIMO communication


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Acknowledgements

• MIT Linconln Laboratory New Technology Initiative Board

• Experiment team– Sean Tobin, Jeff Nowak, Lee Duter, John Mann,

Bob Downing, Peter Priestner, Bob Devine, Tony Tavilla, Andy McKellips, Gary Hatke

• Code, algorithm and experiment design– Keith Forsythe, Peter Wu, Ali Yegulalp

• Analysis support– Amanda Chan

• Students– Nick Chang (U. Mich),

Naveen Sunkavally (MIT)


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Backup SlidesAdvanced Shoe-Phone Technology


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MIMO Ground-to-Ground ExampleNon-Line-Of-Sight

ReceiveArray

MIT

TransmitArray

Cambridge

BostonUniversity

• 4 x 4 MIMO performance dramatically better than SISO

– 6 times bit rate– Fading resistance

• Significant Doppler introduced by fast vehicles on Storrow Dr.

MIMO vs. SISO

Mean SISO SNR

Simulated SISOBlock Fading

1/3 b/s/HzDiversity 1

Experimental4x4 MIMO2 b/s/Hz

CharlesRiver

Simulated


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Space-Time Codes Used in Experiment

4 Transmitters

• Alamouti (2 Tx) , ? = 2• Block, ? = 3• Turbo, ? = 2• Turbo, ? = 4• CDMA, ? = 12/256 • LDPC, ? = 1 • LDPC, ? = 2• Trellis (Chen) , ? = 2

8 Transmitters

• Channel probe• 2+2+2+2 Trellis, ? = 6• Block, ? = 3• Turbo, ? = 4• Turbo, ? = 8• CDMA, ? = 18/256• CDMA, ? = 20/256• LDPC, ? = 2

Space-Time Code Source• New Designs• Provided by campus• Literature

? – Spectral Efficiency (b/s/Hz)


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MCMUD Detector Progressive Complexity

• Joint channel and data estimation• First iteration access to limited training data or channel

estimate from previous frame • Increase detector complexity with iteration• Increase number of turbo iterations with number of

detector iterations

TrainingBasedCoarseSpace

FrequencyBeamformerP

erfo

rman

ce

DecisionDirectedSpaceTime

FrequencyBeamformer

DecisionDirectedSpaceTime

FrequencyBeamformer

MultiuserDetection

…

Detector Iteration


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History of Wireless Communication

Year

1865

1887

HertzConfirms

E&M

MaxwellElectro-

Magnetics

Berrou, et alTurbo Codes

1993

1996

FoschiniMIMO

1998

TarokhSpace-Time

Trellis Codes

Liu, et alSpace-TimeTurbo Codes

1958

Price&Green(Lincoln)

Rake Receiver

QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture.

Cooper, et al(Motorola)

“Handheld”Cell Phone

1973

1982

GSMDigital CellularDevelopment

Begins

1987

SINCGARSProduction

QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture.

Bell-LabCar Radio

Phone

1901

MarconiPracticalWirelessSystem

1924

1942

First USMilitary

HandheldBC-611

ShannonInformation

Theory

1948

HammingError-

CorrectingCodes

1950


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Important Antenna Array Concepts

Diversity

Adaptive Spatial BeamformingUser of

Interest

InterferingUser

Null inBeam Pattern

TransmitterReceive

Array

AdaptiveSpatialFilter


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The Channel MatrixA Toy Model

• Toy MIMO channel model– 2x2 – line of sight

• Resolving individual antennas increases eigenvalue

• MIMO systems in real environments employ scatterers to increase effective aperture

• Toy MIMO channel model– 2x2 – line of sight

• Resolving individual antennas increases eigenvalue

• MIMO systems in real environments employ scatterers to increase effective aperture Channel Matrix,

Eigenvalues of Channel Matrix2x2 MIMO System

Var

y A

per

ture

??

b ?2?

arccos

??h 1

†??h 2

??h 1

??h 2

??

H ???h 1

??h 2? ?

? 2 a ??v 1??v 2? ?

Unit normsteering vector


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Space-Time Turbo Code

• Block diagram for space-time turbo code

• Rate 2 b/s/Hz• 123 kChip/s• 4 Tx antennas• 4096 bit interleavers• QPSK constellation• Optional training data

InfoBits

ConvolutionalEncoder

ConvolutionalEncoder

Interleaver#1

Interleaver#2

AlternatingInterleaver

Interleaver#1

Interleaver#2

?

Space-TimeTurbo Code

i

i

?

Blo

ckW

ise


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Uncooperative External InterferenceEffective Loss of Complexity

• Uncooperative interference is equivalent to spatially correlated noise

• Covariance of interference plus noise

• Maximize capacity by “decorrelating” channel matrix with respect to interference

• Estimate using new • Modes near interference energy

become less useful• Effectively reduces the

environmental complexity

• Uncooperative interference is equivalent to spatially correlated noise

• Covariance of interference plus noise

• Maximize capacity by “decorrelating” channel matrix with respect to interference

• Estimate using new • Modes near interference energy

become less useful• Effectively reduces the

environmental complexity

Channel Capacity in Interference

Noise-NormalizedTransmit

CovarianceMatrix

Interference WhitenedChannel Matrix

Informed Transmitter (IT)

Uninformed Transmitter (UT)

R

˜ H ? R? 1/ 2 H

˜ P ˜ H

˜ C IT ? maxtr{ ˜ P }? Po

log2 I ? ˜ H ̃ P ˜ H †

˜ C UT ? log2 I ?Po

nTx

˜ H ˜ H †


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The Channel Matrix

• Channel matrix, H, contains complex attenuation between each transmit and receive antenna

• Large channel eigenvalues of HH† are useful

Rel

ativ

e P

ower

Channel Eigenvalues

HighCapacity

EnvironmentLowCapacity

Environment

Sorted by Eigenvalue Strength

Few Useful Modes

Many Useful Modes

Many Useful Modes

Scatterers

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