Download - Electrical Engineering and Computer ScienceT N H A E G U N I V E R S I F T Y O I C H I 1 8 1 7 ElectricalEngineeringandComputerScience Example Scenario (Application Layer) Mobiles

T� H� E� U� N� I� V� E� R� S I T� Y� O F� M� I� C� H

I�G�A�N�1� 8� 1� 7� ElectricalEngineeringandComputerScience

Low Power Wir elessCommunication Network

DesignMethodologies

WayneE. StarkHuaWang,Andrew Worthen,PaulLiang,

RobbyGupta,JackEast,Al Hero,StephaneLafortune,DemosthenisTeneketzis

Universityof MichiganAnn Arbor, MI 48109

e-mail: [email protected]

1



Objectives� Determinethetrade-off betweenperformanceand

energy in awirelesscommunicationnetwork

(TASK I).

Energy

Performance

2



Observations

� Theperformanceandenergy consumptionof a

wirelessnetwork is determinedby network

protocol,physicallayersdesign(codingand

modulation)aswell aspropagationanddevice

characterisitics

3



Observations

� Withoutaconstraintonenergy eachof these

subsystemscanbeoptimizedindividually.

� By imposinganenergy constrainttheoverall

systemsoptimizationrequiresacouplingbetween

thedifferentlayersin thesystemhierarchy.

4



Objectives(cont.)

� Provideamethodologyfor integrateddesign,

optimizationandsimulationof acommunication

systemincludingtheapplicationlayer, network

protocol,datalink layerprotocols,physicallayer

anddevice layer.

5



Example Scenario(Application Layer)

� Mobilesareinitially locatedin a smallgeographicalregionanddesireto keeptrackof thepositionof othernodesasnodesmove towarda destination(situationawareness).

� Eachnodemovestowardthedestination(1 m/s)but withrandomvariations(uniformover a1 m2 area)andknows itsown position(throughGPS).

� Eachnodehasafinite amountof energy for transmissionofmessagesandprocessingof receivedmessages.

6



Destination

6km

1km

Nodes

7



Destination

6km

1km

Nodes

8



PropagationEffects� Propagationdecreasesthereceivedpower

proportionalto d4.

10−1

100

101

102

103

−120

−100

−80

−60

−40

−20

0

20

distance (m)

Lo

ss (

dB

)

9



Amplifier Effects� Theamplifierhaslow efficiency at low drive levels

andhighefficiency athigh levels.

0 2 4 6 8 100

10

20

30

40

50

60

70

80

Pin

(mW)

P out (m

W)

0 2 4 6 8 100

10

20

30

40

50

60

70

Pin

(mW)

Powe

r add

ed e

fficie

ncy

(%)

10



Observations� Thepropagationeffectwouldseemto imply thatroutingthe

messageswouldbepower efficientwith low power for eachlink.

� Thepower efficiency curvewouldsuggestthatasinglebroadcaststrategy wouldbepower efficientsincetheamplifieris not efficientat low drive levels.

� Higheramplifierintermodulationproductsathighdrivelevelscauseincreasedinterference.

� Whatis theroutingalgorithm/amplifierdrive level for

optimumperformancefor a givenenergy constraint?

11



Methodology� To determinetradeoff betweenperformanceand

energy for complex systemsanappropriate

combinationof simulationandoptimizationis

necessary. Thesystemsaregenerallytoocomplex

for niceanalyticformulationandthusrequire

simulation.

� Thenumberof parametersis very largesoefficient

optimizationandsimulationis necessary.

12



Methodology (cont.)

� Webreaktheprobleminto layersandare

investigatingthesimulation/optimizationproblem

andthecouplingbetweenlayers.

13



PerformanceOptimization Methodology

Network

SimulationOpt

.

Processing

SimulationOpt

.

Device

Simulation

Opt

imiz

atio

n

Opt

.

Alg

orith

ms

OptimizationCriteria

Scenarios/Requirements

BatteryCapacity

ProtocolCompressionType

CodingType

ModulationType

Frequency

AntennaModelAmplifier Models

Parameters Objective Function

14



Optimization

� How oftenshouldnodestransmittheir location

packets?

� How muchenergy shouldbeallocatedto the

receiver for processingpackets?

� At whatdrive level shouldtheamplifieroperate?

15



Objective Function

Let Gi � j= mean-squareerrorfor user j of theposition

of useri,

G � E � � minEct ,Ecr

Ect Ecr � E

minT � q

1N � N 1 �

∑i

∑j !#" i

Gi � j

E � total energy $ Ect � energy for transmitter$

Ecr � energy for receiver

16



Protocolat Network Layer� Eachnodecarriesa batterywith a certaincapacityand

broadcastsits positionawarenesspacketeveryT seconds.

� Themobilenetwork usestimedivision multiple access(TDMA), whereeachnodein thenetwork hasits owntransmissiontimeslot.

� After a transmission,thenoderetransmitsthesamepacketwith probabilityq if thereis enoughenergy left in thebatteryandenoughtime left in its transmissiontimeslot for onepacket transmission.

17



ProcessingLayer Block Diagram

Channel

EncoderInterleaver Modulate Spread PA

Channel

DespreadDemodulateDeinterleaverChannel

Decoder

Pcc PInt Pmod Pspread Pamp

PdespreadPdemodPDeintPcd

18



Goal for ProcessingLayer

Computethepacketerrorprobabilitiesasa function

of thetransmitterandreceiverenergy andreceived

SNRoptimizedover otherparameters.

Pe � fP � Ect $ Ecr $ SNR�

� minNM � ND

gP � Ect $ Ecr $ SNR$ NM $ ND �

NM % bits of quantizationfor demodulator

ND % bits of quantizationfor decoder&

19



Initial Models for ProcessingLayer� Convolutional,Turbo,(Golayor Hadamard,otherBlock)

ChannelCode

� BPSKModulationwith RaisedCosineFiltering

� Frequency-HoppedSpread-Spectrum

� AWGN,(Flat(Ideal)Rayleigh,PineStreet,AmericanLegionDrive,Measurements)

� TappedDelayLine MatchedFilter (Equalization)

� CoherentDemodulation

� Viterbi Decoding,Iterative Decoding,(Hadamard/Golay)Decoding

20



Parametersfor Optimization� Block Lengthof Packet

� PreambleSize(for synchronization)

� Numberof Bits/Hop

� SampleRateat Input to Equalizer/MatchedFilter

� Numberof Bits of Quantizationat Input toEqualizer/MatchedFilter

� Numberof Bits of Quantizationfor CoefficientsinEqualizer/MatchedFilter

21



Parametersfor Optimization (cont.)

� Numberof Tapsin Equalizer/MatchedFilter

� Bits of QuantizationatOutputof Equalizer/MatchedFilter(Input to Decoder)

� Numberof Bits Quantizationin DecoderMetric (e.g.Turboor Convolutionalor Golayor Hadamard)

� Numberof Iterationsfor Iterative Decoder

22



ProcessingEnergy Estimation

� Energy dissipationof digital circuitry is estimated

basedon0.6µmstandardcell technology.

� Energy for thecomputationintensive algorithmis

estimatedby summingtheenergy of the

operations.Theenergy for eachindividual

operationis individually synthesizedusingEpoch

CAD tool.

23



� Power/Energy consumptionfor thealgorithm

containingintensivememoryaccessor highly

interconnectedstructureareestimatedfrom the

actualcircuit layoutsynthesis(e.g.turbodecoding,

convolutionaldecoding).

� Thesupplyvoltageof thecircuit usedfor the

algorithmimplementationarescaledto handlethe

rangeof operatingspeedsto getthelowestpossible

energy.

24



Matched Filter Structur e

w0

xk z-1

Qcw1Qc

dQ xk-1( )dQ (xk )z-1

Qcwp-1

dQ (xk-p+1 )

dQ yk^( )

dQ

dQ dQdQ

25



MSE Performancefor Matched Filter Alone

2000 3000 4000 5000 6000 7000 8000 9000 10000

0

0.2

0.4

0.6

0.8

1

1.2

Energy per iteration (pJ)

Mea

n S

qu

are

Err

or

Finite Precision Matched Filter Perf. vs. Energy

26



Power PerformanceTradeoffs for Codes

0 50 100 150 200 250 3002

2.5

3

3.5

4

4.5

5

5.5

6

6.5

convolutional K=7 bit=hard,3,4,5,6

turbo 1 iteration

N=256 bit=2,3,4

N=512 bit=3,4

N=1024 bit=3,4

turbo 2 iterations

turbo 3 iterations

turbo 4 iterations

Decoder Performance vs PowerS

NR

(E

b/N0)

at B

ER

= 1

0−5

Power Dissipation (mW)

27



Initial SystemAssumptions� Packet lengthof 224(252)informationbitsor 460

(512)channelbits

� Constraintlength7 convolutionalcode(Turbo

code)

� BPSKmodulationwith squareroot raisedcosine

filter (rolloff=0.3)

� Transmitterfilter implementedwith 19 taps

(infinite quantization)

28



� Symbolduration20µs.

� Frequency-hoppedwith 23 (32)bits/hop,20(16)

hopsperpacket

� Nonlinearamplification(modelfrom device layer)

� AdditivewhiteGaussiannoisechannel

29



Initial SystemAssumptions(cont.)� Variablesignalenergy-to-noisedensityratio (SNR)

� Idealdown conversion(frequency dehopping)

� Matchedfilter implementedwith a tapdelayline

modelwith 19 tapsandND bitsof quantizationfor

thecoefficientsandtheinput sampledat four times

thedatarate.

� Convolutionaldecoderwith NE bits of quantization

� Variablereceiver processingenergy constraint

30



Packet Err or Probability vs. Ect, Ecr

4

6

8

10

x 10−4 1.922.12.22.32.42.52.62.7

x 10−4

10−5

10−4

10−3

10−2

10−1

100

Ecr (J)

Performance vs. Energies

Ect (J)

Pac

ket e

rror

rat

e

PacketErrorProbabilityfor SNR=1,2,3,4,5dB

31



Notes

This plot shows thebit errorrateasa functionof the

receiverprocessingenergy constraintandthe

transmitteramplifierenergy constraint.Thesurfaces

are,from topdown, SNR=1dB, 2 dB, 3dB,4 dB, and

5 dB. ThegridsareinterpolatedandtheSNR=5dB

datais extrapolatedfrom thesimulationresults.

32



Objective Function

Next we renormalizeour resultsby relatingthe

receivedSNRto Ect , N0 � 174dBm/Hzandanoise

figureof 3 dB.

SNR � PoutTsGtGrh2t h2

r ηrηt

d4N0

wherePout � f1 � Ect � .

33



Objective Function

distances=3162m,3320m,3558m,3953m

34



46

810 x 10

−4

1.8 2 2.2 2.4 2.6 2.8

x 10−4

10−4

10−3

10−2

10−1

100

Ect (J)


Ecr (J)

Pac

ket e

rror

rat

e

35



Objective Function

46

810 x 10

−4

1.8 2 2.2 2.4 2.6 2.8

x 10−4

10−5

10−4

10−3

10−2

10−1

100

Ect (J)


Ecr (J)

Bit

erro

r ra

te

36



Flat RayleighFading (SNR=8,12,15dB)

4

6

8

10

x 10−4

22.12.22.32.42.52.62.7

x 10−4

10−3

10−2

10−1

Ect (J)Ecr (J)

Performance vs. Energies: Flat Rayleigh channelB

it er

ror

rate

37



Fading with Interleaving (SNR=3,6,9)

4

6

8

10

x 10−4

22.12.22.32.42.52.62.7

x 10−4

10−5

10−4

10−3

10−2

10−1

100

Ect (J)

Performance vs. Energies: Flat Rayleigh channel, SNR=3,6,9 dB

Ecr (J)

Bit

erro

r ra

te

38



Conclusions

Error ratesignificantlylargerwith Rayleighfading

without interleaving.

39



Objective Function

Considerthecasewhereeachnodeis transmittinga

fractionα of thetime andreceiving packetsa fraction

1 α of thetime. Weoptimizethedesignto getthe

lowestpacketerrorratefor agivenaverageenergy per

packet. In otherwords,for E given,find theoptimal

performancesubjectto

αEct � 1 α � Ecr ' E

This givesthesolutionto theabove optimization

40



problemusingtheprocessinglayermodelandchannel

propagationmodelgivenabove.

41



Objective Function (d=3320m)

2 3 4 5 6 7 8 9

x 10−4

10−3

10−2

10−1

100

Average packet energy (J)

pack

et e

rror

rat

e

Performance vs. Weighted Total Energy

α=0.125 α=0.5 α=0.8

42



Thefloor in performanceoccursbecausethe

transmitterhasamaximumoutputpower (becauseof

theamplifier)

43



Objective Function for Turbo Codes

distance=3200m

2 3 4 5 6 7 8 9 10 11

x 10−4

10−4

10−3

10−2

10−1

100


pack

et e

rror

rat

e

α=0.5

α=0.8

α=0.125

44



Conclusion

� For low power levelsthetransmittedpowergain in

usingturbocodesdoescompensatefor the

additionalcomplexity at thereceiver.

� For largerpower levelsweexpectthis to reverse.

45



Optimal Parameters(α ( 0 ) 125)

2.2 2.4 2.6 2.8 3 3.2 3.4 3.6

x 10−4

0.5

1

1.5

2

2.5x 10

−4

aver

age

com

pone

nt e

nerg

y (J

)

2.2 2.4 2.6 2.8 3 3.2 3.4 3.6

x 10−4

10−3

10−2

10−1

100


pack

et e

rror

rat

e

Uppercurve is receiverpower andlowercurve is

transmitterpower.

46



Optimal Parameters(α ( 0 ) 5)

3 3.5 4 4.5 5 5.5 6 6.5

x 10−4

0

1

2

3

4

5x 10

−4

aver

age

com

pone

nt e

nerg

y (J

)

3 3.5 4 4.5 5 5.5 6 6.5

x 10−4

10−3

10−2

10−1

100


pack

et e

rror

rat

e

Uppercurve is transmitterpower andlowercurve is

receiverpower.

47



Amplifier Effects

Considerdifferentbiasvoltagesasoptimization

parameterratherthanbackoff.

48



0 5 10 15 20 25 30 350

20

40

60

80

100

120

140

160

180

Pin (mW)

Pout

(mW)

vd=34 5 6 7

0 5 10 15 20 25 30 350

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Pin (mW)

Powe

r added

efficie

ncy (%

)

vd=34 5 6 7

49



Objective Function

0 0.5 1 1.5 2 2.5

x 10−3

10−7

10−6

10−5

10−4

10−3

10−2

10−1

100


pack

et er

ror r

atePerformance vs. Weighted Total Energy

α =0.125α =0.5 α =0.8

IBO=1 dB

50



Objective Function

2.5 3 3.5 4 4.5 5 5.5

x 10−4

10−8

10−6

10−4

10−2

100


pack

et e

rror r

ate

Performance vs. Weighted Total Energy (α = 0.125)

IBO=2 dB IBO=1 dB IBO=0.5 dB

51

T* H+ E, U- N. I/ V0 E1 R2 S3 I4 T5 Y6 O7 F8 M9 I: C; H

I<G=A>N?1@ 8A 1B 7C ElectricalEngineeringandComputerScience

PerformanceEvaluation

1

2

3

x 10−3

1.922.12.22.32.42.5

x 10−4

10−5

10−4

10−3

10−2

10−1

100

Ect (J)


Ecr (J)

Pack

et e

rror r

ate

IBO=2 dB

52



PerformanceEvaluation

01

23

x 10−31.922.12.22.32.42.5

x 10−4

10−5

10−4

10−3

10−2

10−1

100

Ect (J)


Ecr (J)

Pack

et e

rror r

ate

IBO=1 dB

53



SystemEvaluation

0

1

2

3

x 10−3

1.922.12.22.32.42.5

x 10−4

10−5

10−4

10−3

10−2

10−1

100

Ect (J)


Ecr (J)

Pack

et e

rror r

ate

IBO=0.5dB

54



Simulation Times

In orderto producethesimulation5 differentPin drive

levelswerechosen,and5 differentSNR’s were

chosenandthenoptimizedover theparameters(bits

of quanitzationfor matchedfilter anddecoder).This

resultedin about250differentsimulationswhich

takesabout3 daysrunningon20SparcUltra 10s.

55



Overall Optimization and Simulation

PoutEct

q, T, Ect

E , Ect cr

PePout

Optimization

ProcessingLayer

SNR

Position Error

Device Layer

DistributedSystemLayer

56




Step1: Optimizationprogramdeterminesparameters

Ect D Ecr D T D q.

Step2: Deviceandprocessinglayerdetermines

Pout D Eat D Ear amplifieroutputpower.

Pout E f1 F Ect G

Eat E f2 F Ect GEar E f3 F Ecr G

57



This is actualenergy perpacket consumed(which

might belessthantheenergy constraint)

58




Step3: Distributedsystem(network) layer

determinesSNR.

SNR E PoutTsGtGrh2t h2

r ηrηt

d4N0

wherePoutH amplifieroutputpower TsH channelsymbolduration

GtH transmitterantennagain GrH receiver antennagain

htH transmitterantennaheight hrH receiver antennaheight

ηtH transmitterantennaefficiency ηrH receiver antennaefficiency

dH distance N0H thermalnoisepowerdensity

59




Step4: ProcessinglayerdeterminesPe, packeterror

probability. This is actuallydoneoff line resulting

in a function fI 4 usedby thenetwork layer:

Pe E fI 4 F Ect D Ecr D SNRG

Step5: Network layerusesPe to determineif the

simulatedpacket transmissionleadsto acorrect

receptionor not; it thenupdatesthepositionerror.

60



Overall Simulation and Optimization

J For agivenparametersetrun 40parallel

simulationsof network performance(mean-square

error).Usethefirst 30 thatfinish.

J Usesimulatedannealingwith thehide-and-seek

algorithmto determineparametersvaluesfor each

iteration

61



MSE vs. Energy

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 11

2

3

4

5

6

7

8

9

Battery Capacity (Joule)

Ave

rag

e P

osi

tio

n E

stim

atio

n E

rro

r (m

eter

)Average Position Estimation Error As a Function of Battery Capacity

one−hop broadcastingmulti−hop routing

Drop Zone 1km x 1km50mW AmplifierConvolutional CodePacket Length 224 bits

62



MSE vs. Energy

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 11

2

3

4

5

6

7

8

9


Ave

rag

e P

osi

tio

n E

stim

atio

n E

rro

r (m

eter


50mW Amplifier 120mW Amplifier

Drop Zone 2km x 2kmConvolutional CodePacket Length 224 bitsOne−hop Broadcasting

63



MSE vs. Energy

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 11

2

3

4

5

6

7

8

9


Ave

rag

e P

osi

tio

n E

stim

atio

n E

rro

r (m

eter


one−hop broadcastingmulti−hop routing

Drop Zone 2km x 2km 50mW Amplifier Convolutional Code Packet Length 224 bits

64



MSE vs. Energy

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 11

2

3

4

5

6

7

8

9

10

11


Ave

rag

e P

osi

tio

n E

stim

atio

n E

rro

r (m

eter


turbo code convolutional code

Drop Zone 1km x 1km50 mW AmplifierPacket Length 224 bitsOne−hop Broadcasting

65



MSE vs. Energy

0 1 2 3 4 5 6 7 82.5

3

3.5

4

4.5

5

5.5

6


Ave

rag

e P

osi

tio

n E

stim

atio

n E

rro

r (m

eter



Drop Zone 2km x 2km 50mW Amplifier Packet Length 1024 bitsOne−hop Broadcasting

66



MSE vs. Energy

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 11

2

3

4

5

6

7

8

9

10

11


Ave

rag

e P

osi

tio

n E

stim

atio

n E

rro

r (m

eter



Drop Zone 2km x 2km 50 mW Amplifier Packet Length 224 bitsOne−hop Broadcasting

67



Conclusions

J A methodologyfor optimizingandsimulatinga

wirelesscommunicationnetwork including,

applicationlayer, network protocol,processing

layeranddevice layerwith significantlylower

computationthanabruteforceapproachhasbeen

achieved.

68



J Themethodologyhasbeenverifiedfor anexample

scenario.Themethodologyallowsusto compare

differenttypesof routingalgorithmstakinginto

accountdeviceeffects(amplifiernonlinearity, for

example),propagationeffects,modulationand

codingeffectsaswell aspower consumptionof

digital. circuitry.

69



Next Steps:Network Layer

K Comparedifferentroutingalgorithms

K Examinemethodologiesfor differentcontrolfunctionsandtheireffecton thecommunicationalgorithms

K Investigateoptimizationof OFDM for highspeedcommunications.

K Determineamethodologyfor couplingincrementalredundancy transmissionwith theoverall simulation.

70



Next Steps:ProcessingLayer

J Incorporateadaptiveequalizationinto simulation

(with preamble).

J Incorporateothercodes(e.g.turbocodes)into the

simulation.

J Incorporateadjacentchannelinterferenceinto the

simulation.

71



Next Steps:ProcessingLayer (cont.)

J Investigatedifferentmodulationtechniquesandthe

interactionwith theamplifier

simulation/optimization

J Investigateoptimizationof OFDM for highspeed

communications.

72



Next Steps:DeviceLevel

J Incorporateanalogdecodingsimulationfor

iterative receiversinto simulation

J Incorporatefadingchannelmodelsinto simualtion

J Incorporatedirectionalantennasinto

simulation/optimization

73



Next Steps:DeviceLevel (cont.)J Incorporatetherecentresultsfor nonlinear

amplifiersbelow into thesimulation(JackEast)

1. Discovereda new techniquefor nonlinearsimulationusingharmonicbalnanceandfixedpoint techniques

2. Extendedconventionalnonlinearmicrowavesimulationtoolsfor widebanddigitally modulatedsignalsusingareducedcomplexity approach

3. Discovereda new optimizationalgorithmthateliminatestehneedfor gradientcalculationwhichsignificantlyreducesthecomputationalcomplexity

74



Publication

[1] W. Stark,H. Wang,A. Worthen,P. Liang,

A. Hero,S.Lafortune,andD. Teneketzis,“Low

energy wirelesscommunicationnetwork

design,” 2000AllertonConferenceon

Communication,Control andComputing,

October2000.

75



Outside Opinion

Date: Fri, 10 Nov 2000 17:38:43 -0800 (PST)

From: Andrea Goldsmith <[email protected]>

To: [email protected]

Subject: thanks for the talk slides and paper

Wayne,

Thanks much for the slides and paper from your

Allerton talk. It’s really great stuff.

Andrea

76