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AndreaGoldsmith
StanfordUniversityIEEECommunicationTheoryWorkshop
Maui,HI
May14,
2012
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FutureWireless
Networks
UbiquitousCommunicationAmongPeopleandDevices
NextgenerationCellular
WirelessInternetAccess
WirelessMultimedia
SensorNetworksSmartHomes/Spaces
AutomatedHighways
SmartGrid
BodyArea
Networks
Allthisandmore
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FutureCell
Phones
Much
better
performance
and
reliability
than
today
Gbpsrates,lowlatency,99%coverageindoorsandout
Everythingwirelessinonedevice
Burdenforthisperformanceisonthebackbonenetwork
BSBS
PhoneSystem
BS
San Francisco
ParisNth-GenCellular
Nth-GenCellular
Internet
LTEbackbone
is
the
Internet
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Careful what you wish for
Growth
in
mobile
data,
massive
spectrum
deficit
and
stagnant
revenues
requiretechnicalandpoliticalbreakthroughsforongoingsuccessofcellular
Source:UnstrungPyramidResearch2010Source:FCC
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Canweincreasecellularsystemcapacityto
compensate
for
a
300MHz
spectrum
deficit?
Withoutincreasingcost?
orpowerconsumption?
WhatwouldShannonsay?
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RethinkingCells
in
Cellular
Traditionalcellulardesigninterferencelimited MIMO/multiuser
detection
can
remove
interference
CooperatingBSsformaMIMOarray:whatisacell? Relayschangecellshapeandboundaries DistributedantennasmoveBStowardscellboundary
Smallcells
create
acell
within
acell
Mobilecooperationviarelaying,virtualMIMO,analognetworkcoding.
Small
Cell
Relay
DAS
Coop
MIMO
Howshouldcellularsystemsbedesigned?
Willgainsinpracticebebigorincremental;incapacity orcoverage?
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Aresmall
cells
the
solution
to
increasecellularsystemcapacity?
Yes,with
reuse
one
and
adaptive
techniques(Alouini/Goldsmith1999)
A=D2AreaSpectralEfficiency
S/Iincreaseswithreusedistance(increaseslinkcapacity).
Tradeoffbetweenreusedistanceandlinkspectralefficiency(bps/Hz).
AreaSpectralEfficiency:Ae=Ri/(D2)bps/Hz/Km2.
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TheFuture
Cellular
Network:
Hierarchical
ArchitectureMACRO:solving
initialcoverage
issue,existing
network
FEMTO:solving
enterprise &
home
coverage/capacity
issue
PICO:solving
street,enterprise
&home
coverage/capacity
issue
FuturesystemsrequireSelfOrganization(SON)(andWiFiOffload)
10xLowerCOST/Mbps
10xCAPACITYImprovement
Near100%COVERAGE
(morewithWiFiOffload)
Macrocell Picocell Femtocell
Todaysarchitecture3MMacrocellsserving5billionusersAnticipated
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SON Premise and Architecture
NodeInstallation
InitialMeasurements
SelfOptimization
SelfHealing
SelfConfiguration
Measurement
SON
Server
SoNServer
Macrocell BS
Mobile GatewayOr Cloud
Small cell BS
X2
X2X2
X2
IP Network
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Algorithmic Challenge: Complexity
Optimal channel allocation was NP hard in2nd-generation (voice) IS-54 systems
Now we have MIMO, multiple frequencybands, hierarchical networks,
But convex optimization has advanced a lotin the last 20 years
Innovationneededtotamethecomplexity
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GreenCellular
Networks
Minimizeenergyatboththemobileandbasestationvia
NewInfrastuctures:
cell
size,
BS
placement,
DAS,
Picos,
relays
NewProtocols: CellZooming, CoopMIMO,RRM,Scheduling,Sleeping, Relaying
Low
Power
(Green)
Radios:
Radio
Architectures,
Modulation,
coding,MIMO
Pico/Femto
Relay
DAS
Coop
MIMO
Howshouldcellularsystemsberedesignedforminimumenergy?Researchindicatesthat
significantsavingsispossible
GerhardFettweistalk
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AntennaPlacement
in
DAS
OptimizedistributedBSantennalocation
Primal/dualoptimizationframework
Convex;standard
solutions
apply
For4+ports,onemovestothecenter
Upto23dBpowergainindownlink
Gainhigher
when
CSIT
not
available
3Ports
6Ports
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Codingfor
minimum
total
power
IsShannon
capacity
still
agood
metric
for
system
design?
B1
B2
B3
B4
X5
X6
X7
X8
ExtendsearlyworkofElGamalet.al.84andThompson80
Computational
Nodes Onchip
interconnects
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Fundamentalarea
time
performance
tradeoffs
Forencoding/decodinggoodcodes,
Stayawayfromcapacity!
Closeto
capacity
Largechiparea
Moretime
Morepower
Areaoccupiedbywires Encoding/decodingclockcycles
B1
B2
B3
B4
X5
X6
X7
X8
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Sparsity:where
art
thou?
Sparse state space: e.g reduced-dimension network
control
Sparse users: e.g. reduced-dimension multiuser detection
To exploit sparsity, we need to find
communication systems where it exists
Sparse samples: e.g. sub-Nyquist sampling
Sparse signals: e.g. white-space detection
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CompressedSensing
BasicpremiseisthatsignalswithsomesparsestructurecanbesampledbelowtheirNyquistrate
Signalcanbeperfectlyreconstructedfromthesesamplesbyexploitingsignalsparsity
Thissignificantly
reduces
the
burden
on
the
front
end
A/Dconverter,aswellastheDSPandstorage
Enablerforwhitespace,SDandlowenergyradios? Onlyforincomingsignalssparseintime,freq.,space,etc.
RobCalderbanks
Talk
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SoftwareDefined
(SD)
Radio:
WidebandantennasandA/DsspanBWofdesiredsignals
DSPprogrammedtoprocessdesiredsignal:nospecializedHW
Cellular
AppsProcessor
BT
MediaProcessor
GPS
WLAN
Wimax
DVB-H
FM/XM A/D
A/D
DSPA/D
A/D
Today,thisisnotcost,size,orpowerefficient
Compressed sensing reduces A/D and DSP burden
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SparseSamples
Foragivensamplingmechanism(i.e.anewchannel) Whatistheoptimalinputsignal? Whatisthetradeoffbetweencapacityandsamplingrate? Whatknownsamplingmethodsleadtohighestcapacity?
Whatistheoptimalsamplingmechanism? Amongallpossible(knownandunknown)samplingschemes
Sampling
Mechanism(rate fs)
New Channel
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Capacityunder
Sub
Nyquist
Sampling
Theorem1:
Theorem2: BankofModulator+FilterSingleBranchFilterBank
Theorem3:
Optimalamong
all
time
preservingnonuniform
sampling
techniquesofratefs(ISIT12;Arxiv)
zzzzzzzzzz)(ts ][ny
q(t)
p(t)
)(1 ts
)(tsi
)(tsm
)(s
mTnt
)(s
mTnt
)(s
mTnt
][1 ny
][nyi
][nym
equalszzzzzzzz
zz
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SelectsthembrancheswithmhighestSNR Example(Bankof2branches)
JointOptimization
of
Input
and
Filter
Bank
highest
SNR
2nd highest
SNR
low SNR
s
kffX 2
fX
skffX
skffX
)( skffH
)( fH
)( skffH
)( skffN
)( fN
)( skffN
)( skffS
)( fS
)( skffS
)2( skffH
)2( skffN )2( skffS
low
SNR
fY1
fY2
Capacity monotonic in fs
Howdoesthistranslatetopracticalmodulationandcodingschemes
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IdealMultiuser
Detection
WhyNot
Ubiquitous
Today?
Power
and
A/D
Precision
Signal 1Demod
IterativeMultiuserDetection
Signal 2Demod
- =Signal 1
- =
Signal 2
A/D
A/D
A/D
A/D
MUDproposedforLTE
(closeslinkat 7dBSNR)
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ReducedDimension
MUD
ExploitsthatnumberofactiveusersGisrandomandmuchsmallerthantotalusers(alacompressedsensing)
Usingcompressed
sensing
ideas,
can
correlate
with
M~log(G)waveforms
Reducedcomplexity,size,andpowerconsumption
r(t)
g1 t 2g4 t n(t)
h1(t)
1
Tb
0
Tb
1
Tb
0
Tb
1
Tb
0
Tb
h2(t)
hM(t)
Decision
Decision
Decision
Linear
Transformation
bi
aijj1
M
cj
c1
c2
cM
b1
b2
bN
10%Performance
Degradation
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ReducedDimension
Network
Design
Random Network State
Sampling
and
Learning
ApproximateStochastic Controland Optimization
Reduced-Dimension
State-Space Representation
Utility estimation
Sparse
Sampling
Projection
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Feedbackin
Communications
Memorylesspointtopointchannels: Capacityunchangedwithperfectfeedback
Feedbackdrastically
increases
error
exponent
(L
fold
exponential)
Feedbackreducesenergyconsumption
Why?
Outputfeedback
CSI
Acknowledgements
Something
else?
Capacityofchannelswithfeedbacklargelyunknown
Forchannels
with
memory
and
perfect
feedback
Underfiniterateand/ornoisyfeedback
Formultiuserchannels
Formultihopnetworks
ARQis
ubiquitious
in
practice:
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CognitiveRadios
Cognitiveradiossupportnewwirelessusersinexistingcrowdedspectrumwithoutdegradinglicensedusers Utilize
advanced
communication
and
DSP
techniques
Coupledwithnovelspectrumallocationpolicies
Technologycould Revolutionize
the
way
spectrum
is
allocated
worldwide
Providemorebandwidthfornewapplications/services
Multipleparadigms
Underlay
(exploiting
unused
spatial
dimensions)
and
Overlay
(exploitingrelayingandinterferencecancellation)promising
PTx
IP
PRxCRTx CRRx
CRRx
PRx
PTx
CRTx
MIMOCognitiveUnderlay CognitiveOverlay
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TheSmart
Grid:
FusionofSensing,Control,Communications
carbonmetrics.eu
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Wirelessand
Health,
Biomedicine
and
Neuroscience
Doctoron
achip
Cellphoneinforepository
Monitoring,remote
interventionandservices
Cloud
ThebrainasawirelessnetworkEKGsignalreception/modelingInformationflow(directedMI)SignalencodinganddecodingNervenetwork(re)configuration
BodyArea
Networks
UbliMitras
talk
Todd
Colemans
Talk
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Summary
Communicationsresearchaliveandwell
Communicationstechnologywillenablenew
applicationsthatwillchangepeopleslivesworldwide
Designinnovationwillbeneededtomeetthe
requirementsofnextgenerationwirelessnetworks
Asystemsviewandinterdisciplinarydesignapproach
holdsthekeytotheseinnovations
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