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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