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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.
MIT Lincoln LaboratorymimoASAP-2
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TopicsMIMO Communication
• Introduction– Military wireless communication– MIMO definition– Ad hoc antenna networks
• MIMO Theory• Phenomenology• Receiver
MIT Lincoln LaboratorymimoASAP-3
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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
MIT Lincoln LaboratorymimoASAP-4
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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
MIT Lincoln LaboratorymimoASAP-5
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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
MIT Lincoln LaboratorymimoASAP-6
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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
MIT Lincoln LaboratorymimoASAP-7
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TopicsMIMO Communication
• Introduction• MIMO Theory
– Capacity– Phenomenology– Interference Mitigation– Space-Time Coding
• Phenomenology• Receiver
MIT Lincoln LaboratorymimoASAP-8
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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
MIT Lincoln LaboratorymimoASAP-9
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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
MIT Lincoln LaboratorymimoASAP-10
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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
MIT Lincoln LaboratorymimoASAP-11
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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
MIT Lincoln LaboratorymimoASAP-12
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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
MIT Lincoln LaboratorymimoASAP-13
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TopicsMIMO Communication
• Introduction• MIMO Theory• Phenomenology
– Experimental setup– Phenomenology
• Receiver
MIT Lincoln LaboratorymimoASAP-14
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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
MIT Lincoln LaboratorymimoASAP-15
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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
RandomMeasuredLine-of-sight
2 3 4Mode #
10
0
-10
Rel
ativ
e P
ow
er (d
B)
1
RandomMeasuredLine-of-sight
2 3 4Mode #
10
0
-10
Rel
ativ
e P
ow
er (d
B)
1
TransmitArray
MIT Lincoln LaboratorymimoASAP-16
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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
MIT Lincoln LaboratorymimoASAP-17
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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
MIT Lincoln LaboratorymimoASAP-18
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TopicsMIMO Communication
• Introduction• MIMO Theory• Phenomenology• Receiver
– Space-time-frequency adaptive processing
– Multiuser detection– MCMUD– Experimental performance
MIT Lincoln LaboratorymimoASAP-19
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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
MIT Lincoln LaboratorymimoASAP-20
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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
MIT Lincoln LaboratorymimoASAP-21
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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
MIT Lincoln LaboratorymimoASAP-22
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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
MIT Lincoln LaboratorymimoASAP-23
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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
I
Q
MIT Lincoln LaboratorymimoASAP-24
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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)
MIT Lincoln LaboratorymimoASAP-25
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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
MIT Lincoln LaboratorymimoASAP-26
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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
MIT Lincoln LaboratorymimoASAP-27
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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)
MIT Lincoln LaboratorymimoASAP-28
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Backup SlidesAdvanced Shoe-Phone Technology
MIT Lincoln LaboratorymimoASAP-29
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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
MIT Lincoln LaboratorymimoASAP-30
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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)
MIT Lincoln LaboratorymimoASAP-31
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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
MIT Lincoln LaboratorymimoASAP-32
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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
MIT Lincoln LaboratorymimoASAP-33
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Important Antenna Array Concepts
Diversity
Adaptive Spatial BeamformingUser of
Interest
InterferingUser
Null inBeam Pattern
TransmitterReceive
Array
AdaptiveSpatialFilter
MIT Lincoln LaboratorymimoASAP-34
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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
MIT Lincoln LaboratorymimoASAP-35
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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
MIT Lincoln LaboratorymimoASAP-36
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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 †
MIT Lincoln LaboratorymimoASAP-37
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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