Post on 02-Aug-2020
Andrea Goldsmith
Stanford University
Stanford Networking gSeminar
May 28, 2009
Future Wireless NetworksFuture Wireless NetworksUbiquitous Communication Among People and Devices
Next‐generation CellularWireless Internet AccessWireless MultimediaSensor Networks Smart Homes/SpacesAutomated HighwaysIn‐Body NetworksAll this and more …
F t C ll PhFuture Cell PhonesSan Francisco
Everything wireless in one deviceBurden for this performance is on the backbone network
BSBS
San Francisco
BSBS
Internet
PhoneSystem
New YorkNth-GenCellular
Nth-GenCellular
BS
Much better performance and reliability than today‐ Gbps rates, low latency, 99% coverage indoors and out
Future Wifi:Future Wifi:Multimedia Everywhere, Without WiresPerformance burden also on the (mesh) network
802.11n++
• Streaming video• Gbps data rates
Wireless HDTVand Gaming
• Gbps data rates• High reliability• Coverage in every room
D i Ch llDevice ChallengesBT
Size and CostCellular
GPS
FM/XM
Multiband AntennasMultiradio Coexistance Apps WLAN
DVB-H
Multiradio CoexistanceIntegration
ppProcessor
MediaProcessor
WLAN
Wimax
Software Defined Radio:Software‐Defined Radio: Is this the solution to the device challenges?
Cellular
BT
GPS
FM/XM A/D
A/D
AppsProcessor
M di
WLAN
DVB-HA/D
DSPA/D
Wideband antennas and A/Ds span BW of desired signals
MediaProcessor Wimax A/D
Wideband antennas and A/Ds span BW of desired signalsDSP is programmed to process the desired signal based on carrier frequency, signal shape, channel characteristics, etc.Avoids specialized hardware
Today, this is not cost, size, or power efficient
D i S l tiDevice Solutions Silicon evolution will reduce size and powerp
130nm→95nm→65nm→45nm→ 32nm→…
Circuit design not anticipatedCircuit design not anticipatedCMOS PA efficiency and power will improveA/D technology will improveWideband antenna design will improveWideband antenna design will improveTools for digital design will improve
Room for innovation at the RF/baseband interfaceRoom for innovation at the RF/baseband interface
Dedicated silicon will remain faster, cheaper, and lower power than processor‐based designs
But less flexible and with more costly development
S t Ch llSystem Challenges
Managing interferenceManaging interferenceReliabilityHigh‐bandwidth applicationsHigh‐bandwidth applicationsScarce spectrumReal‐time constraintsReal time constraintsUbiquitous coverage indoors and out
System SolutionsSystem 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)Adaptive techniques (in time, space, and frequency)
Better MAC and networking techniquesMore efficient use of wireless spectrumMore efficient use of wireless spectrum
RelayingPicocells and FemtocellsPicocells and FemtocellsCooperation and Cognition
Cross‐Layer DesignCross Layer Design
Much room for improvement and innovation
MIMO in Wireless NetworksMIMO in Wireless Networks
How should MIMO be fully exploited?How should MIMO be fully exploited?At a base station or Wifi access point
MIMO Broadcasting and Multiple AccessMIMO Broadcasting and Multiple Access
Network MIMO: Form virtual antenna arraysDownlink is a MIMO BC, uplink is a MIMO MACCan treat “interference” as a known signal or noiseCan cluster cells and cooperate between clusters
Multiplexing/diversity/interferenceMultiplexing/diversity/interference cancellation tradeoffs in MIMO networks
Stream 2
Interference
Stream 1
Spatial multiplexing provides for multiple data streamsSpatial multiplexing provides for multiple data streamsTX beamforming and RX diversity provide robustness to fadingTX beamforming and RX nulling cancel interferenceg g
Optimal use of antennas in wireless networks unknown
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
C∝NI ⋅ NT⋅2log2(M)xNNI : No. RX IterationsNT: No. OFDM TonesM: Constellation Size
Reduced complexity receiver options:(Iterative) MMSE Spherical‐decoders M‐Algorithm etc
N: No. Antennas
(Iterative) MMSE, Spherical‐decoders, M‐Algorithm, etc.Performance/complexity tradeoffs depend on N and M
For 64QAM, 211tones, 6 antennas: C NI×211×236
Key area for innovation
Coverage Indoors and Out:Coverage Indoors and Out:Cellular versus Mesh
FemtocellOutdoors Indoors
Femtocell
Wifi MeshCellular has good coverage outdoors
Relaying increases reliability and range
Wifi mesh has a nichemarket o tdoors
Cellular cannot provide reliable indoor coverageWifi t k l dWifi mesh has a nichemarket outdoors
Hotspots/picocells enhance coverage, reliability, and data rates.
Wifi networks already ubiquitous in the homeAlternative is a consumer‐installed Femtocell
Multiple frequencies can be leveraged to avoid interference
installed FemtocellWinning solution will depend on many factors
S Wi l S tScarce Wireless Spectrum
$$$$$$
and Expensive
S t l RSpectral ReuseDue to its scarcity, spectrum is reusedy, p
In licensed bands and unlicensed bands
BS
Cellular, Wimax Wifi, BT, UWB,…
I t f F i d F ?Interference: Friend or Foe?If treated as noise: FoeIf treated as noise: Foe
PSNR =IN
SNR+
Increases BER, reduces capacity
If decodable: Neither friend nor foe
Increases BER, reduces capacity
If decodable: Neither friend nor foe
Multiuser detection can completely remove interference
Ideal Multiuser DetectionSignal 1
Signal 1
- =Signal 1
Demod
IterativeMultiuserDetection
Signal 2Demod
Signal 2
- =
Why Not Ubiquitous Today? Power and A/D Precision
I t f F i d F ?Interference: Friend or Foe?
If exploited via cooperation and cognitioncooperation and cognition
Friend
Especially in a network settingEspecially in a network setting
Cooperation in Wireless NetworksCooperation in Wireless Networks
Many possible cooperation strategies:Virtual MIMO , generalized relaying, interferenceVirtual MIMO , generalized relaying, interference forwarding, and one‐shot/iterative conferencing
Many theoretical and practice issues:Many theoretical and practice issues:Overhead, forming groups, dynamics, synch, …
C i i h C i MIMOCapacity with Cooperative MIMO
2 2 MIMO B dTX1
G
RX12x2 MIMO Bound
2x1 Broadcast BoundTX Coop Only
GG
TX2 RX2
TX Coop OnlyTX/RX Coop
RX Coop OnlyTime
Division
TX cooperation needs large cooperativeTX cooperation needs large cooperative channel gain to approach broadcast bound2x2 MIMO bound unapproachable
General Relay StrategiesTX1 RX1TX1
relay
X1
Y3=X1+X2+Z3
Y4=X1+X2+X3+Z4
X3= f(Y3)
TX2 RX2X2
Y5=X1+X2+X3+Z5
Can forward message and/or interferenceR l f d ll f h
RX2
Relay can forward all or part of the messages Much room for innovation
Relay can forward interferenceRelay can forward interference To help subtract it out
B fi i l t f d b thBeneficial to forward bothinterference and message
Intelligence beyond Cooperation:Intelligence beyond Cooperation: CognitionC i i di i l iCognitive radios can support new wireless users in existing crowded spectrum
Without degrading performance of existing usersWithout degrading performance of existing users
Utilize advanced communication and signal processing techniquestechniques
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 ParadigmsUnderlay
Cognitive radios constrained to cause minimal interference to noncognitive radiosinterference to noncognitive radios
InterweaveCognitive radios find and exploit spectral holes toCognitive radios find and exploit spectral holes to avoid interfering with noncognitive radios
OverlayOverlayCognitive radios overhear and enhance noncognitive radio transmissions
Knowledgeand
Complexity
Underlay SystemsCognitive radios determine the interference theirCognitive radios determine the interference their transmission causes to noncognitive nodes
Transmit if interference below a given threshold
IPNCR
The interference constraint may be met
NCRNCR
CR CR
The interference constraint may be metVia wideband signalling to maintain interference below the noise floor (spread spectrum or UWB)the noise floor (spread spectrum or UWB)
Via multiple antennas and beamforming
I t S tInterweave SystemsMeasurements indicate that even crowded spectrum is
d ll i d f inot used across all time, space, and frequenciesOriginal 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 location must be agreed upon between TX and RXHole is then used for opportunistic communication with minimal interference to noncognitive users
Overlay Cognitive Systemsh k l d f hCognitive user has knowledge of other
user’s message and/or encoding strategyUsed to help noncognitive transmission
Used to presubtract noncognitiveUsed to presubtract noncognitiveinterference
RX1CRCR
RX2NCR
P f G iPerformance Gains from Cognitive Encoding
outer bound
our scheme
prior schemes
Only the CRtransmits
Crosslayer Protocol DesignCrosslayer Protocol Design
Application
NetworkNetwork
Access
Link
dHardware
Substantial gains in throughput efficiency andSubstantial gains in throughput, efficiency, and end-to-end performance from cross-layer design
Di it /M lti l i i MIMODiversity/Multiplexing in MIMO
Use antennas for multiplexing:Use antennas for multiplexing:
ST C d
f d
High-RateQuantizer
ST CodeHigh Rate Decoder
Error Prone
Use antennas for diversity
Low-RateQuantizer
ST CodeHigh
DiversityDecoder
Low Pe
How should antennas be used? Depends on end-to-end metric.
DMT i MIMOWi l N kDMT in MIMO Wireless Networks
3.5
4DMT of (4,1,3) half-duplex relay channel
d4,1(r)
2
2.5
3
y ga
in, d
(r)
,
d1,3
(r)
1
1.5
Div
ersi
ty
dDDF(r)
dvDF(r)
dfDF
(r), a=0.5
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.5
Multiplexing gain, (r)
vDF( )
Wh b I f C ll i ?What about Interference Cancellation?
b d f l l d• Antennas can be used for multiplexing, diversity, or interference cancellation
•Cancel M‐1 interferers with M antennas
• What metric should be optimized?p
Cross-Layer Design
Delay/Throughput/RobustnessDelay/Throughput/Robustness across Multiple Protocol Layers
BA
B
Multiple routes through the network can be used for l l d d d l /lmultiplexing or reduced delay/lossSpatial dimension of MIMO adds new degree of freedom
A li ti i l d i ti lti lApplication can use single‐description or multiple description codes
Can optimize optimal operating point for theseCan optimize optimal operating point for these tradeoffs to minimize distortion
Network Fundamental Limits
Network Metrics
Capacity Delay
Network Fundamental Limits
Fundamental Limitsof Wireless Systems
(DARPA Challenge Program)
Outage
(DARPA Challenge Program)
BC
Cross‐layer Design andE d t d P f
A
DCapacity
Delay
End‐to‐end Performance
(C*,D*,O*)
Research Areas‐ Fundamental performance limits and tradeoffs
D
Robustness
and tradeoffs ‐ Node cooperation and cognition‐ Adaptive techniques‐ Layering and Cross‐layer design
Application Metrics
‐ Network/application interface‐ End‐to‐end performance optimization and guarantees
Wireless Sensor Networks
• Smart homes/buildingsSmart homes/buildings• Smart structures• Search and rescue• Homeland securityy• Event detection• Battlefield surveillance
Energy (transmit and processing) is the driving constraintData flows to centralized location (joint compression)Data 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 TradeoffsCross‐Layer Tradeoffs under Energy Constraints
HardwareAll nodes have transmit, sleep, and transient modesEach node can only send a finite number of bitsy
LinkHigh‐level modulation costs transmit energy but saves circuit energy (shorter transmission time)Coding costs circuit energy but saves transmit energy
AccessAccessPower control impacts connectivity and interferenceAdaptive modulation adds another degree of freedom
Routing:Circuit energy costs can preclude multihop routing
Mi i E R iMinimum Energy Routing
0 085
0.1 Red: hub nodeGreen: relay/source
4 3 2 10.1150.185
0.085 Green: relay/source
ppsRppsR
8060
2
1
==
(0,0) (5,0) (10,0) (15,0)
0.515 ppsRppsR
2080
3
2
=
• Optimal routing uses single and multiple hops
• Link adaptation yields additional 70% energy savings• Link adaptation yields additional 70% energy savings
Cooperative Compression
Source data correlated in space and time
Nodes should cooperate in compression as well as communication and routing
Joint source/channel/network coding
What is optimal: virtual MIMO vs. relaying
Distributed Control over WirelessDistributed Control over Wireless Automated Vehicles‐ Cars‐ Airplanes/UAVs‐ Insect flyers
Interdisciplinary design approach• Control requires fast, accurate, and reliable feedback.• Wireless networks introduce delay and loss• Wireless networks introduce delay and loss• Need reliable networks and robust controllers • Mostly open problems: Many design challenges
Wireless Biomedical SystemsWireless Biomedical SystemsWireless
TelemedicineTelemedicine
WirelessNetwork
fIn‐ Body Wireless Devices‐Sensors/monitoring devices
Recovery fromNerve Damage
‐Drug delivery systems‐Medical robots‐Neural implants Challenges being defined
Tech Transfer ChallengesTech Transfer Challenges
• Communication and network theory can be implemented in a real system in 3‐12 months
f / h h l fl
with sufficent $$$
• Information/Communication Theory heavily influence next‐gen. wireless systems (mainly at the PHY & MAC layers)
• Idealized assumptions have been liberating• Idealized assumptions have been liberating
• Above PHY/MAC, there is little fundamental theory, which has prevented real breakthroughs
Industry people read our papers and implement our ideas
• Launching a startup is the best way to do tech transferLaunching a startup is the best way to do tech transfer
• We need more/better ways to exploit academic innovation
SummaryTh t i i l t h l iThe next wave in wireless technology is upon us
Thi t h l ill bl li ti th t illThis 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 systemsrequirements of these next generation systems
A systems view and interdisciplinary design approach y p y g ppholds the key to these innovations