The road ahead for wireless technology: Dreams and Challenges
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Transcript of The road ahead for wireless technology: Dreams and Challenges
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The road ahead for wireless technology:Dreams and Challenges
Andrea Goldsmith
Wireless Systems LaboratoryStanford University
SWL Xi Dian UniversityXi’an, China
August 19, 2011
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Future Wireless NetworksUbiquitous Communication Among People and Devices
Next-generation CellularWireless Internet AccessWireless MultimediaSensor Networks Smart Homes/SpacesAutomated HighwaysSmart GridBody-Area NetworksAll this and more …
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Future Cell Phones
Much better performance and reliability than today- Gbps rates, low latency, 99% coverage indoors and out
BSBS
PhoneSystem
BS
San Francisco
New YorkNth-Gen
Cellular
Nth-Gen
Cellular
Internet
Everything wireless in one deviceBurden for this performance is on the backbone network
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Future Wifi:Multimedia Everywhere, Without Wires
802.11n++
Wireless HDTVand Gaming
• Streaming video• Gbps data rates• High reliability• Coverage in every room
Performance burden also on the (mesh) network
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Device ChallengesSize and CostPower and HeatMultiband AntennasMultiradio CoexistanceIntegration
Cellular
AppsProcessor
BT
MediaProcessor
GPS
WLAN
Wimax
DVB-H
FM/XM
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Software-Defined (SD) Radio:
Wideband antennas and A/Ds span BW of desired signalsDSP programmed to process desired signal: no specialized HW
Cellular
AppsProcessor
BT
MediaProcessor
GPS
WLAN
Wimax
DVB-H
FM/XM A/D
A/DDSP
A/D
A/D
Is this the solution to the device challenges?
Today, this is not cost, size, or power efficientCompressed sensing may be a solution for sparse signals
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Compressed SensingBasic premise is that signals with some sparse structure can be
sampled below their Nyquist rate
Signal can be perfectly reconstructed from these samples by exploiting signal sparsity
This significantly reduces the burden on the front-end A/D converter, as well as the DSP and storage
Might be key enabler for SD and low-energy radiosOnly for incoming signals “sparse” in time, freq., space, etc.
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Scarce Wireless Spectrum
Hence regulated, and expensive
$$$
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Spectral ReuseDue to its scarcity, spectrum is reused
BS
In licensed bands
Cellular, Wimax
Wifi, BT, UWB,…
and unlicensed bands
Reuse introduces interference
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Interference: Friend or Foe?If treated as noise: Foe
If decodable: Neither friend nor foe
INPSNR
Increases BER, reduces capacity
Multiuser detection can completely remove interference
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Ideal Multiuser Detection
Signal 1 Demod
IterativeMultiuserDetection
Signal 2Demod
- =Signal 1
- =
Signal 2
Why Not Ubiquitous Today? Power and A/D Precision
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Reduced-Dimension MUDExploits that number of active users G is random and
much smaller than total users (ala compressed sensing)Using compressed sensing ideas, can correlate with
M~log(G) waveformsReduced complexity, size, and power consumption
r(t)
g1 t 2g4 t n(t)
h1(t)
1Tb
0
Tb
1Tb
0
Tb
1Tb
0
Tb
h2(t)
hM (t)
Decision
Decision
Decision
Linear Transform
ation
˜ b i
aijj1
M
c j
c1
c2
cM
˜ b 1
˜ b 2
˜ b N
10% Performance Degradation
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If exploited via cooperation and cognition
Friend
Interference: Friend or Foe?
Especially in a network setting
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Rethinking “Cells” in Cellular
Traditional cellular design “interference-limited”MIMO/multiuser detection can remove interferenceCooperating BSs form a MIMO array: what is a cell?Relays change cell shape and boundariesDistributed antennas move BS towards cell boundaryFemtocells create a cell within a cellMobile cooperation via relaying, virtual MIMO, analog network
coding.
Femto
Relay
DAS
Coop MIMO
How should cellularsystems be designed?
Will gains in practice bebig or incremental; incapacity or coverage?
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Gains from Distributed Antennas10x power efficiency gain with 3 distributed antennas3-4x gain in area spectral efficiency
Small cells yield another 3-4x gain
---- Optimal Placement---- Random Placement---- Central Placement
DAS
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Cooperation in Ad-Hoc Networks
Similar to mobile cooperation in cellular:Virtual MIMO , generalized relaying, interference
forwarding, and one-shot/iterative conferencingMany theoretical and practice issues:
Overhead, half-duplex, grouping, dynamics, synch, …
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Generalized Relaying
Can forward message and/or interference Relay can forward all or part of the messages
Much room for innovation Relay can forward interference
To help subtract it out
TX1
TX2
relay
RX2
RX1X1
X2
Y3=X1+X2+Z3
Y4=X1+X2+X3+Z4
Y5=X1+X2+X3+Z5
X3= f(Y3) Analog network coding
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Beneficial to forward bothinterference and message
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In fact, it can achieve capacity
S DPs
P1
P2
P3
P4
• For large powers Ps, P1, P2, analog network coding approaches capacity
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Intelligence beyond Cooperation: CognitionCognitive radios can support new wireless users in
existing crowded spectrumWithout degrading performance of existing users
Utilize advanced communication and signal processing techniquesCoupled 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
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Cognitive Radio ParadigmsUnderlay
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 nodesTransmit if interference below a given threshold
The interference constraint may be metVia wideband signalling to maintain interference below
the noise floor (spread spectrum or UWB)Via multiple antennas and beamforming
NCR
IP
NCRCR CR
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Interweave SystemsMeasurements indicate that even crowded spectrum is not 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 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 strategyCan help noncognitive transmissionCan presubtract noncognitive interference
RX1
RX2NCR
CR
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Performance Gains from Cognitive Encoding
Only the CRtransmits
outer boundour
schemeprior schemes
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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.
data
Source
Cognitive Relay 1
Cognitive Relay 2
Cellular Systems with Cognitive Relays
Can extend these ideas to MIMO systems
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Wireless Sensor Networks
Energy (transmit and processing) is the driving constraint Data flows to centralized location (joint compression) Low per-node rates but tens to thousands of nodes Intelligence is in the network rather than in the devices
• Smart homes/buildings• Smart grid• Search and rescue• Homeland security• Event detection• Battlefield surveillance
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Cross-Layer Tradeoffs under Energy Constraints
Hardware All nodes have transmit, sleep, and transient modes Each node can only send a finite number of bits
Link High-level modulation costs transmit energy but saves
circuit energy (shorter transmission time) Coding costs circuit energy but saves transmit energy
Access Power control impacts connectivity and interference Adaptive modulation adds another degree of freedom
Routing: Circuit energy costs can preclude multihop routing
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Total Energy (MQAM)
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Green” Cellular Networks
Minimize energy at both the mobile and base station viaNew Infrastuctures: cell size, BS placement, DAS, Picos, relaysNew Protocols: Cell Zooming, Coop MIMO, RRM, Scheduling,
Sleeping, RelayingLow-Power (Green) Radios: Radio Architectures, Modulation,
coding, MIMO
Pico/Femto
Relay
DAS
Coop MIMO
How should cellularsystems be redesignedfor minimum energy?
Research indicates thatsignicant savings is possible
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Antenna Placement in DASOptimize distributed BS antenna locationPrimal/dual optimization frameworkConvex; standard solutions applyFor 4+ ports, one moves to the centerUp to 23 dB power gain in downlink
Gain higher when CSIT not available
3 Ports
6 Ports
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Distributed Control over Wireless
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
Automated Vehicles- Cars/planes/UAVs- Insect flyers
: Many design challenges
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Wireless and Health, Biomedicine and Neuroscience
Doctor-on-a-chip- Cell phone info repository- Monitoring, remote intervention and services
Cloud
The brain as a wireless network- EKG signal reception/modeling- Signal encoding and decoding- Nerve network (re)configuration
Body-AreaNetworks
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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