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Capacity of Wireless Networks. MAGNÚS MÁR HALLDÓRSSON, PROFESSOR SCHOOL OF COMPUTER SCIENCE | RU LECTURE MARATHON. Current topic: Wireless Communication. How much communication can you have in a wireless network ? How long does it take to meet a given communication demand?. - PowerPoint PPT Presentation
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MAGNÚS MÁR HALLDÓRSSON, PROFESSORSCHOOL OF COMPUTER SCIENCE | RU LECTURE MARATHON
Capacity of Wireless Networks
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Current topic: Wireless Communication
• How much communication can you have in a wireless network?
• How long does it take to meet a given communication demand?
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Capacity: How much communication can you have in a wireless network?
• Not a new problem...
• Studied empirically, in EE• Studied analytically (EE)
– Assumptions about input distribution– Only existential
• Studied algorithmically, in CS:– But, in simplistic models
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The Algorithmic Capacity of Wireless Networks
• We want:
• -- General properties– that holds for all inputs and all situations
• -- Algorithms– to create efficient protocols
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InterferenceRange
CS Models: e.g. Disk Model (Protocol Model)
ReceptionRange
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Example: Protocol vs. Physical Model
1m
Assume a single frequency (and no fancy decoding techniques!)
Let =3, =3, and N=10nWTransmission powers: PB= -15 dBm and PA= 1 dBm
SINR of A at D:
SINR of B at C:
4m 2m
A B C D
Is spatial reuse possible? NO Protocol Model
YES With power control
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Possible Application – Hotspots in WLAN
Traditionally: clients assigned to (more or less) closest access point far-terminal problem hotspots have less throughput
XY
Z
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Possible Application – Hotspots in WLAN
Potentially better: create hotspots with very high throughputEvery client outside a hotspot is served by one base station Better overall throughput – increase in capacity!
XY
Z
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Some of our results
• First algorithm for capacity maximization with provable performance [Goussievskaia, H, Wattenhofer, Welzl, INFOCOM ‘09]
• Algorithmic results for capacity with power control[H, ESA ‘09]
• Generalizations: metrics, power assignments etc.[H, Mitra, SODA ‘11]
• Distributed algorithms[H, Mitra, submitted]
• More to come...
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Future work
• Treating obstacles, walls, etc.
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Attenuation by objects
• Shadowing (3-30 dB): – textile (3 dB)– concrete walls (13-20 dB)– floors (20-30 dB)
• reflection at large obstacles• scattering at small obstacles• diffraction at edges• fading (frequency dependent)
reflection scattering diffractionshadowing
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Future work
• Treating obstacles, walls, etc.• Coding techniques• Spectrum management and cognitive radio• Communication structures• Basic questions: Weighted capacity & scheduling
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Thanks!
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Signal-To-Interference-Plus-Noise Ratio (SINR) Formula
Minimum signal-to-interference
ratio
Power level of sender u Path-loss exponent
Noise
Distance betweentwo nodes
Received signal power from sender
Received signal power from all other nodes (=interference)
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Network Topology?
• All these capacity studies make very strong assumptions on node deployment, topologies– randomly, uniformly distributed nodes– nodes placed on a grid – etc.
What if a network
looks differently…?
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EE Models: e.g. SINR Model (Physical Model)
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