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MAGNÚS MÁR HALLDÓRSSON, PROFESSOR SCHOOL OF COMPUTER SCIENCE | RU LECTURE MARATHON

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?.

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MAGNÚS MÁR HALLDÓRSSON, PROFESSOR SCHOOL OF COMPUTER SCIENCE | RU LECTURE MARATHON

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  1. Capacity of Wireless Networks MAGNÚS MÁR HALLDÓRSSON, PROFESSOR SCHOOL OF COMPUTER SCIENCE | RU LECTURE MARATHON

  2. 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?

  3. 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

  4. The Algorithmic Capacity of Wireless Networks • We want: • -- General properties • that holds for all inputs and all situations • -- Algorithms • to create efficient protocols

  5. CS Models: e.g. Disk Model (Protocol Model) ReceptionRange InterferenceRange

  6. Example: Protocol vs. Physical Model Assume a single frequency (and no fancy decoding techniques!) Let =3, =3, and N=10nW Transmission powers: PB= -15 dBm and PA= 1 dBm SINR of A at D: SINR of B at C: C D B A 4m 1m 2m NO Protocol Model Is spatial reuse possible? YES With power control

  7. Possible Application – Hotspots in WLAN Traditionally: clients assigned to (more or less) closest access point  far-terminal problem  hotspots have less throughput Y X Z

  8. Possible Application – Hotspots in WLAN Potentially better: create hotspots with very high throughput Every client outside a hotspot is served by one base station  Better overall throughput – increase in capacity! Y X Z

  9. 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...

  10. Future work • Treating obstacles, walls, etc.

  11. 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) shadowing reflection scattering diffraction

  12. Future work • Treating obstacles, walls, etc. • Coding techniques • Spectrum management and cognitive radio • Communication structures • Basic questions: Weighted capacity & scheduling

  13. Thanks!

  14. Signal-To-Interference-Plus-Noise Ratio (SINR) Formula Received signal power from sender Power level of sender u Path-loss exponent Minimum signal-to-interference ratio Noise Distance between two nodes Received signal power from all other nodes (=interference)

  15. 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…?

  16. EE Models: e.g. SINR Model (Physical Model)

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