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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
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