1

Andrea Goldsmith

Wireless Systems LaboratoryStanford University

Comsoc Distinguished LectureGothenburg, Sweden

March 17, 2010 Sweden Chapter

Future Wireless NetworksUbiquitous Communication Among People and Devices

Next‐generation CellularNext generation CellularWireless Internet AccessWireless MultimediaSensor Networks Smart Homes/SpacesAutomated HighwaysIn‐Body NetworksAll this and more …

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Future Cell PhonesSan Francisco

Everything wireless in one deviceBurden for this performance is on the backbone network

BSBS

PhoneSystem

New YorkNth-GenCellular

Nth-GenCellular

Internet

Much better performance and reliability than today‐ Gbps rates, low latency, 99% coverage indoors and out 

y

BS

Future Wifi:Multimedia Everywhere, Without WiresPerformance burden also on the (mesh) network

802.11n++

Wireless HDTVand Gaming

• Streaming video• Gbps data rates• High reliability• Coverage in every room

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Device Challenges

Size and CostBT

FM/XM

Multiband AntennasMultiradio CoexistanceIntegration

Cellular

AppsProcessor

Media

GPS

WLAN

DVB-H

g MediaProcessor Wimax

Software‐Defined Radio: 

BTFM/XM A/D

Is this the solution to the device challenges?

Cellular

AppsProcessor

MediaProcessor

GPS

WLAN

Wimax

DVB-H

FM/XM A/D

A/DDSP

A/D

A/D

Wideband antennas and A/Ds span BW of desired signalsDSP programmed to process desired signal: no specialized HW

Today, this is not cost, size, or power efficient

Compressed sensing may be a solution in underused spectrum

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System Challenges

Managing interferenceReliabilityyHigh‐bandwidth applicationsScarce spectrumReal‐time constraintsUbiquitous coverage indoors and out

System SolutionsBetter link layer design

Low‐complexity OFDM and MIMO (PHY wars are over)High‐performance modulation and coding Adaptive techniques (in time, space, and frequency)

Better access and networking techniquesMore efficient use of wireless spectrum

RelayingPicocells and FemtocellsCooperation and Cognition

Cross‐Layer Design

Much room for improvement and innovation

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Multicarrier Modulation (OFDM)Delay=T

Ts

T>>Ts

xcos(2πf0t)

xΣ

R bps R/N bps

R/N bps

Modulator

Modulator

Serial To

ParallelConverter

s(t)

Used in Wimax 4G 802 11

0

Breaks data into N substreams with Bandwidth B/NLong symbols (T<<Ts)  removes interference between symbols

Substream modulated onto separate carriersEfficient DSP implementation using IFFTs/FFTs

cos(2πfNt)

/ p Used in Wimax, 4G, 802.11

Multiple Input Multiple Output SystemsMIMO systems have multiple (M) transmit and receiver antennasand receiver antennas

With perfect channel estimates at TX and RX, decomposes to M indep. channelsM f ld it i SISO tM‐fold capacity increase over SISO systemWithout increasing bandwidth or power!

Demodulation complexity reduction when channel known at the transmitter and receiverCan also use antennas for diversity

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Diversity/Multiplexing in MIMOUse antennas for multiplexing:

Use antennas for diversity 

High-RateQuantizer

ST CodeHigh Rate Decoder

Error Prone

Low Pe

Low-RateQuantizer

ST CodeHigh

DiversityDecoder

How should antennas be used? Depends on end-to-end metric.

MIMO Receiver ComplexityReceiver Complexity is a problem

It affects design time, size, cost, battery life, etc. Complexity Exponential in Constellation Size/Antenna No.

For a full MAP RX

Reduced complexity receiver options:

C∝NI ⋅ NT⋅2log2(M)xNNI : No. RX IterationsNT: No. OFDM TonesM: Constellation SizeN: No. Antennas

Reduced complexity receiver options:(Iterative) MMSE, Spherical‐decoders, M‐Algorithm, etc.Performance/complexity tradeoffs depend on N and MReceivers must be robust to imperfect CSI at TX and RX

We have developed low‐complexity algorithms that are robust to imperfect CSI.

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Algorithm Performance

Cooperative Techniques in Cellular

Network MIMO: Cooperating BSs form a MIMO arrayDownlink is a MIMO BC uplink is a MIMOMAC

Many open problemsfor next‐gen systems

Downlink is a MIMO BC, uplink is a MIMO MACCan treat “interference” as known signal (DPC) or noiseCan cluster cells and cooperate between clustersCan also install low‐complexity relays

Mobiles  can cooperate via relaying, virtual MIMO, conferencing, analog network coding, …

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Capacity Gain with Virtual MIMO (2x2)

x1

x2

GG

TX cooperation needs large cooperative channel gain to approach broadcast channel boundMIMO bound unapproachable

Multicasting with a Relay

Develop structured codes that exploit network topology

Relay decodes and multicasts modulo sum of messagesRelay decodes and multicasts modulo sum of messages

Linear codes achieve the capacity region for finite-field modulo additive channels

Nested-lattice codes approach upper bound for Gaussian channels and surpass standard random coding schemes

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Multiplexing/diversity/interference cancellation tradeoffs in cellular

Interference

Stream 1

Stream 2

Interference

Spatial multiplexing provides for multiple data streamsTX beamforming and RX diversity provide robustness to fadingTX beamforming and RX nulling cancel interference

Optimal use of antennas in wireless networks unknown

Coverage Indoors and Out:

Outdoors

Cellular (Wimax) versus Mesh

IndoorsFemtocell

Cellular has good coverage outdoors

Relaying increases reliability and range

Outdoors

Cellular cannot provide reliable indoor coverage

Indoors

Wifi Mesh

Relaying increases reliability and range 

Wifi mesh has a nichemarket outdoors

Hotspots/picocells enhance coverage, reliability, and data rates. 

Multiple frequencies can be leveraged to avoid interference

reliable indoor coverageWifi networks already ubiquitous in the homeAlternative is a consumer‐installed FemtocellWinning solution will depend on many factors

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Scarce Wireless Spectrum

$$$

and Expensive

Spectral ReuseDue to its scarcity, spectrum is reused

BS

In licensed bands and unlicensed bands

Cellular, Wimax Wifi, BT, UWB,…

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Interference: Friend or Foe?If treated as noise: Foe

INPSNR+

=

Increases BER, reduces capacity

If decodable: Neither friend nor foe

Multiuser detection can completely remove interference

Ideal Multiuser Detection- =Signal 1

Signal 1 Demod

IterativeMultiuserDetection

Signal 2Signal 2

Signal 2Demod

- =

Why Not Ubiquitous Today?  Power and A/D Precision 

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If exploited via

Interference: Friend or Foe?

If exploited via cooperation and cognition

FriendFriend

Especially in a network setting

Cooperation in Wireless Networks

Many possible cooperation strategies:Virtual MIMO , generalized relaying, interference forwarding, and one‐shot/iterative conferencing

Many theoretical and practice issues:Overhead, forming groups, dynamics, synch, …

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General Relay StrategiesTX1 RX1

X1 Y4=X1+X2+X3+Z4

Can forward message and/or interference

TX2

relay

RX2X2

Y3=X1+X2+Z3

4 1 2 3 4

Y5=X1+X2+X3+Z5

X3= f(Y3)

Can forward message and/or interferenceRelay can forward all or part of the messages 

Much room for innovation

Relay can forward interference To help subtract it out

Beneficial to forward bothinterference and message

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Intelligence beyond Cooperation: CognitionCognitive radios can support new wireless users in 

d dexisting crowded spectrumWithout degrading performance of existing users

Utilize advanced communication and signal processing techniques

Coupled with novel spectrum allocation policies

Technology could Revolutionize the way spectrum is allocated worldwide 

Provide sufficient bandwidth to support higher quality and higher data rate products and services

Cognitive Radio ParadigmsUnderlayy

Cognitive radios constrained to cause minimal interference to noncognitive radios 

InterweaveCognitive radios find and exploit spectral holes to avoid interfering with noncognitive radios

OverlayCognitive radios overhear and enhance noncognitive radio transmissions

Knowledgeand

Complexity

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Underlay SystemsCognitive radios determine the interference their transmission causes to noncognitive nodestransmission causes to noncognitive nodes

Transmit if interference below a given threshold

NCR

IPNCR

CR CR

The interference constraint may be metVia wideband signalling to maintain interference below the noise floor (spread spectrum or UWB)

Via multiple antennas and beamforming

Interweave SystemsMeasurements indicate that even crowded spectrum is not used across all time, space, and frequencies

Original motivation for “cognitive” radios (Mitola’00)

These holes can be used for communicationInterweave CRs periodically monitor spectrum for holesHole location must be agreed upon between TX and RXHole is then used for opportunistic communication

Compressed sensing reduces A/D and processing requirements 

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Overlay Cognitive SystemsCognitive user has knowledge of other user’s message and/or encoding strategyuser s message and/or encoding strategyCan help noncognitive transmission

Can presubtract noncognitive interference

RX1CR

RX2NCR

Similar ideas apply to cellular overlays and cognitive relays

Performance Gains from Cognitive Encoding

outer bound

our scheme

prior schemes

Only the CRtransmits

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Cognitive Relay 1

Cellular Systems with Cognitive Relays

data

Source

Cognitive Relay 2

Enhance robustness and capacity via cognitive relaysCognitive relays overhear the source messagesCognitive relays then cooperate with the transmitter in the transmission of the source messagesCan relay the message even if transmitter fails due to congestion, etc.

Crosslayer Protocol Design

ApplicationApplication

Network

Access

Link

Hardware

Substantial gains in throughput, efficiency, and end-to-end performance from cross-layer design

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Multiple Antennas in Multihop Networks

•Antennas can be used for multiplexing, diversity, or interference cancellationor interference cancellation

•Cancel M‐1 interferers with M antennas•Errors occur due to fading, interference, and delay•DMT of worst‐case hop dominates

• What metric should be optimized? Cross-Layer Design

Delay/Throughput/Robustness across Multiple Protocol Layers

B

Multiple routes through the network can be used for multiplexing or reduced delay/loss

Spatial dimension of MIMO adds new degree of freedom

A

p g

Application can use single‐description or multiple description codes

Can optimize optimal operating point for these tradeoffs to minimize distortion

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Application layer

Cross‐layer design for video Loss-resilientsource coding

and packetization

Network layer

Congestion-distortionoptimized

routing

Congestion-distortionoptimized

scheduling

Traffic flows

Transport layer

Rate-distortion preamble

MAC layer

Link layer

Capacity assignment

for multiple service classes

Adaptivelink layer

techniques

Link capacities

Link state information

Video streaming performance 

5 dB

3-fold increase

100 (logarithmic scale)1000

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Wireless Sensor Networks

• Smart homes/buildings• Smart structuresSmart structures• Search and rescue• Homeland security• Event detection• Battlefield surveillance

Energy (transmit and processing) is the driving constraintData flows to centralized location (joint compression)Low per‐node rates but tens to thousands of nodesIntelligence is in the network rather than in the devices

Cross‐Layer Tradeoffs under Energy Constraints

HardwareAll nodes have transmit, sleep, and transient modesEach node can only send a finite number of bits

LinkHigh‐level modulation costs transmit energy but saves circuit energy (shorter transmission time)Coding costs circuit energy but saves transmit energy

AccessPower control impacts connectivity and interferenceAdaptive modulation adds another degree of freedom

Routing:Circuit energy costs can preclude multihop routing

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Total Energy (MQAM)  

Adaptive Coded MQAMReference system has log2(M)=3 (coded) or 2 (uncoded)

90% savingsat 1 meterat 1 meter.

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Minimum Energy Routing

4 3 2 10.115

0.515

0.185

0.085

0.1 Red: hub nodeGreen: relay/source

ppsRppsRppsR

208060

3

2

1

===

(0,0) (5,0) (10,0) (15,0)

• Optimal routing uses single and multiple hops

• Link adaptation yields additional 70% energy savings

Cooperative Compression

S d t l t d i d tiSource data correlated in space and timeNodes should cooperate in compression as well as communication, routing, and multicast

Joint source/channel/network codingWhat is optimal: virtual MIMO vs. relaying

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“Green” Cellular Networks

How should cellular systems be designed to conserve energy at both the mobile and base station

The infrastructure and protocols should be redesigned based on miminum energy consumption, including

Base station placement and cell size

Cooperation and cognition 

MIMO and virtual MIMO techniques

Modulation, coding, relaying, routing, and multicast

Distributed Control over Wireless Automated Vehicles‐ Cars‐ Airplanes/UAVsAirplanes/UAVs‐ Insect flyers

Interdisciplinary design approach• Control requires fast, accurate, and reliable feedback.• Wireless networks introduce delay and loss• Need reliable networks and  robust controllers • Mostly open problems: Many design challenges

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Apps in Health, Biomedicine and NeuroscienceDoctor‐on‐a‐chip‐Cell phone as repositoryof medical information‐Monitoring, remote intervention and services 

WirelessNetwork

Neuro/Bioscience applications‐ EKG signal reception/modeling‐ Information science‐ Nerve network (re)configuration‐ Implants to monitor/generate signals‐In‐body sensor networks

Recovery fromNerve Damage

SummaryThe next wave in wireless technology is upon us

This technology will enable new applications that will change people’s lives worldwide

Design innovation will be needed to meet the requirements of these next‐generation systems

A systems view and interdisciplinary design approach holds the key to these innovations